Southern corn leaf blight (SCLB) is caused by the fungal pathogen Bipolaris maydis (syn. Cochliobolus heterostrophus Drechsler) and is a common disease of fall crops of sweet corn. Phenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait. As an alternative, we integrated computer vision (CV)-based phenotyping, genome-wide association studies (GWASs), and predictive breeding approaches to dissect the genetic basis of SCLB resistance. We utilized a sweet corn diversity panel with 693 genotypes, for which whole-genome resequencing produced a high-density single-nucleotide polymorphism (SNP) dataset. Broad-sense heritability for visual scoring ranged from 0.44 to 0.73, while CV-based phenotyping produced estimates ranging from 0.56 to 0.73 in multi-environment resistance trials conducted across 5 years and three locations. We performed GWAS using 16,755,210 SNPs and identified 41 associated SNPs. Genomic selection (GS) models on visual scoring phenotypes achieved moderate prediction accuracies under cross-validation of untested genotypes across characterized environments (0.22-0.47) and high prediction accuracies when predicting tested genotypes in uncharacterized environments (0.49-0.68). Using CV-based phenotypes for GS, we observed prediction accuracies of 0.45-0.47 under the untested genotypes in the characterized environments cross-validation scheme and 0.59-0.62 under the tested genotypes in the uncharacterized environments scheme. GS demonstrated reliability for ranking the individuals across a gradient of environments. These findings identify candidate loci and predictive breeding strategies to accelerate the development of resistant sweet corn cultivars.
Why it matches plant phenotyping methodsCVベースの病害抵抗性表現型測定を視覚評定と比較し、多環境・多年次試験で妥当性を評価しており、フェノタイピング手法が研究の中心である。
abstractPhenotyping for SCLB resistance is performed through visual scoring, which is subjective and may limit genetic gain for this quantitative trait.
Reproduction assets foundThe authors state that all datasets (phenotype data) and analysis code (CV phenotyping script, customized GAPIT script) are publicly available in their GitHub repository, which is listed in allowed_urls.Code · publiche images taken for each plot were saved in JPG format and analyzed using a CV method. Here, we refer to the CV method as a custom Python script written using the OpenCV library version 4.5.0, a set of tools for CV (Bradski, 2000 ). The Python script used for leaf CV image analysis is available in our public GitHub repository ( https://github.com/Resende‐Lab/SCLB‐Disease ).
FIGURE 1
Leaf imaging set up with QR‐coded plot IDs (bottom right) and color checker for computer vision phenotyping of southern corn leaf blight disease severity in sweet corn.
In the CT19 environment, a black cloth attached to a wooden board was used as the background. A wooden frame was used to clamp the leaves down toOpen asset ↗Resende‐Lab/SCLB‐Diseaselines:199-209Dataset · publicBLUP and BayesB model implemented in BGLR.
ACKNOWLEDGMENTS
This work was supported by the National Institute of Food and Agriculture USDA‐NIFA2018‐51181‐28419, USDA‐NIFA2019–05410, and USDA‐NIFA 2022–51181‐38333.
DATA AVAILABILITY STATEMENT
All the datasets and codes used in this study are available in the following repository: https://github.com/Resende‐Lab/SCLB‐Disease
REFERENCES
Amadeu , R. R.
,
Cellon , C.
,
Olmstead , J. W.
,
Garcia , A. A. F.
,
Resende , M. F. R.
, &
Muñoz , P. R.
( 2016 ).Open asset ↗Resende‐Lab/SCLB‐Diseaselines:566-596Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Why it matches plant phenotyping methods植物表面の三次元形状を測定するオープンな構造化光プラットフォームと再構成・解析ワークフローを開発し、葉で投影バイアスを評価しており、表現型取得手法が中心である。
abstractHere we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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rendered surfaces, the underlying reconstructions are in the same record.
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1Open asset ↗Zenodo · 10.5281/zenodo.22167250pdf-raw-page:35 lines:1-52Code · public1151
Source data for all graph panels are provided with this paper; for panels showing
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rendered surfaces, the underlying reconstructions are in the same record.
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source data are available at Zenodo under
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https://doi.org/10.5281/zenodo.22167598.55
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are available at https://doi.org/10.5281/zenodo.22167471.56
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The software and analysis notebooks are released under the MIT licence and the
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hardware design files under CERN-OHL-P v2. The visible-light platform described
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hereOpen asset ↗Zenodo · 10.5281/zenodo.22167598pdf-raw-page:35 lines:1-52Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically composites spectral observations over fixed calendar windows, implicitly assuming phenological synchrony across fields. We present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction, for a quality trait whose physiology, senescence-linked nitrogen remobilization, contrasts with the season-integrating behavior of yield. Integrating Sentinel-2 imagery (31 vegetation indices, 10 spectral bands), ERA5-Land reanalysis, gSSURGO soil properties, and USGS 3DEP topography across 228 commercial winter wheat fields in western Kansas (2024–2025), we compared six temporal strategies (peakrelative vs. calendar × monthly, biweekly, growth-stage) using three ensemble tree models under nested cross-validation with Boruta feature selection. A single 30-day post-peak window (peak + [16,45] days) was the top-performing and most consistently selected window, chosen in 4 of 5 outer folds, reproducing prior accuracy under random cross-validation (R2 ≈ 0.28); though its advantage over the best calendar window was not statistically significant (paired bootstrap p = 0.08). Under leave-county spatial cross-validation, however, this skill did not transfer across counties (Sentinel-2–only R2 ≈ 0.01; per-county median R 2 = −0.23), indicating the satellite signal supports within-region interpolation but not spatial extrapolation to unseen counties; ablation shows that neither the spectral nor the static features transfer across counties on their own, and the residual crosscounty skill emerges only from their combination. A near-real-time application at ∼3 weeks before harvest retains most within-region skill at a modest accuracy cost. The results delineate where spectral-peak-relative alignment helps, concentrating a senescence-linked signal within region, and where it does not, providing an honest operational baseline for satellite-based grain-quality monitoring.
Why it matches plant phenotyping methods小麦の穀粒タンパク質濃度という植物形質を対象に、衛星時系列のスペクトルピーク相対アラインメントを開発・比較評価し、交差検証で性能と空間移 transfer 性を検証しているため、方法が中心的である。
abstractWe present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction
Reproduction assets foundThe preprint explicitly releases the authors' analysis code (data-acquisition pipeline, feature engineering, cross-validation/modeling, figure scripts) at a public GitHub repository, and a de-identified field-level GPC dataset released alongside the code repository. Both are paper-specific, public, and actionable. The Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗Ciampitti-Labpdf-page:48 lines:1-55Dataset · publica de-identified version of the dataset is released alongside the code repositoryOpen asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published18 Aug 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 0 · OpenAlex ↗
Key message Our scalable two-step method estimates genotypic performance and genetic parameters from ranking data, producing reliable results comparable to quantitative analyses, enabling the integration of ranking data into breeding pipelines. Plant breeding research has chiefly relied on on-station experiments to evaluate varietal performance. Nevertheless, these trials often fail to represent on-farm growing conditions and farmers' preferences, potentially leading to poorly defined breeding targets. Recent work has demonstrated the potential of using on-farm verification trials combined with ranking data to support farmers in evaluating varieties while providing information that is representative of farmers' needs. Despite this potential, scalable methods for quantifying genetic differences and assessing the strength of the genetic signal in such trials remain limited. Here, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters. The approach follows a common strategy in quantitative genetics, in which parameters are estimated from tables of genotypic means and their variances. In our framework, these estimates are obtained from Thurstonian and/or Plackett-Luce models, which treat rankings as observations of an underlying continuous trait associated with genotypic performance. Using simulated data, we showed that genotypic mean estimates derived from ranking analyses are linearly related to those obtained from quantitative trait analyses and that their variances adequately capture estimation uncertainty. We further demonstrated that incorporating these estimates and their variances into a second-step mixed-effects model yields accurate estimates of variance components. Analyses of groundnut, maize, and sweetpotato datasets confirmed the applicability of the approach and showed that ranking data can provide reliable estimates of genetic parameters. We argue that this framework can be scaled to obtain genotypic performance estimates from multi-trial on-farm data.
Why it matches plant phenotyping methods作物品種の遺伝型性能をランキングデータから推定する統計的方法そのものが研究の中心であり、育種に再利用可能な植物性能の推定手法を開発・検証している。
abstractHere, we present a two-step procedure for analyzing trials based on ranking data, allowing the estimation of genetic parameters.
Reproduction assets foundThe paper's Data availability statement provides public access to the observed groundnut and sweetpotato ranking/trial datasets (Zenodo 17112492), the authors' R functions and simulation workflow (GitHub hdorado/tricot-ranking-analysis, archived Zenodo 17942919), and supplementary material with methods and figures (ZenDataset · publicThe observed data for groundnut and sweetpotato used in this study are publicly available and can be accessed at: Global multi-crop agricultural trial data supported by citizen science, Zenodo [ https://doi.org/10.5281/zenodo.17112492 ]Open asset ↗Zenodo · 10.5281/zenodo.17112492lines:205-225Code · publicThe R functions and simulation workflow used in this study are publicly available at: - Source code available from: [ https://github.com/hdorado/tricot-ranking-analysis ]Open asset ↗GitHub · hdorado/tricot-ranking-analysislines:205-225Code · public- Archived software available from: [ https://doi.org/10.5281/zenodo.17942919 ] - License: [MIT License]Open asset ↗Zenodo · 10.5281/zenodo.17942919lines:205-225Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.
Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。
abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves
Reproduction assets foundThe paper's data availability statement explicitly makes the 3,454-image A. senegal leaf imaging dataset public on Zenodo and the ML implementation source code public on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe plant leaf imaging data used in the work is publicly available at https://doi.org/10.5281/zenodo.16531486.Open asset ↗zenodo · 10.5281/zenodo.16531486html-lines:480-497Code · publicThe source code of the implementation is available at https://github.com/softwareinnovationslabBITS/CDRF_ASenegal_MLImagingOpen asset ↗github · softwareinnovationslabBITS/CDRF_ASenegal_MLImaginghtml-lines:480-497Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects. Methods We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions. Results Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana . Discussion Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.
Why it matches plant phenotyping methods葉の形態形質を自動測定し、種予測まで行うImageJプラグインとワークフローの開発・評価が中心であり、植物フェノタイピング手法として適格です。
abstractTo streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions.
Reproduction assets foundThe paper's authors publicly released the Garryanalyzer ImageJ plug-in source code on GitHub, all original and modified leaf images used in the morphometric analyses on Zenodo, and the full leaf morphometric measurement dataset plus R Lasso analysis code in a second Zenodo repository. All are paper-specific, public,可直接Code · publicThe source code and installation instructions for Garryanalyzer can be accessed on GitHub at https://github.com/zxie8561/Garryanalyzer.Open asset ↗https://github.com/zxie8561/Garryanalyzer · zxie8561/Garryanalyzerhtml-lines:210-274Dataset · publicAll images used in the morphometric analyses, both original and modified, are available on Zenodo (https://doi.org/10.5281/zenodo.17462266).Open asset ↗https://doi.org/10.5281/zenodo.17462266 · 10.5281/zenodo.17462266html-lines:210-274Dataset · publicThe full dataset of leaf morphometric measurements of both GBIF and Portland samples, R code for Lasso analysis, and other miscellaneous files are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.17546152).Open asset ↗https://doi.org/10.5281/zenodo.17546152 · 10.5281/zenodo.17546152html-lines:210-274Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Accurate identification of individual plants from unmanned aerial vehicle (UAV) imagery is essential for high-throughput phenotyping and data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction. The pipeline integrates UAV image processing, user-guided annotation of selected undistorted images, convolutional neural network–based object-detection training, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. This workflow preserves native image geometry during detection while maintaining coordinate traceability from source imagery to georeferenced outputs. Across five independent training runs using early-season maize imagery, MatchPlant achieved source-image-level detection performance of AP@0.5 = 90.3 ± 1.1% and mAP@0.5:0.95 = 43.4 ± 2.9%. Orthomosaic-level evaluation after forward projection showed AP@0.5 = 89.7 ± 0.8% and recall = 93.7 ± 1.2%, demonstrating the workflow’s ability to transfer plant detections into georeferenced outputs. Plant-level traits, including plant height derived from canopy height models and NDVI derived from vegetation index rasters, showed strong agreement with manual annotations ( r = 0.87–0.97). Detection outputs were reused across time points with minimal additional annotation, supporting temporal phenotyping during early growth. The framework was validated using maize imagery from a single site and growing season, where plant separation remained clear. By combining modular design, reproducibility, and coordinate traceability, MatchPlant provides an open-source workflow for UAV-based plant-level analysis, with broader applications requiring validation across additional crops, sensors, growth stages, GSDs, and field conditions.
Why it matches plant phenotyping methodsUAV画像から個体検出と植物形質（草高・NDVI）を抽出する、オープンソースの再利用可能なワークフローを開発・検証しており、植物フェノタイピング手法が中心である。
abstractThis study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction.
Reproduction assets foundThe paper's MatchPlant analysis pipeline is publicly available on GitHub, and the maize case-study training dataset and pre-trained model are publicly available on Zenodo; both are paper-specific, public, and actionable.Dataset · publicThe public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Model / weights · publicThe training dataset and pre-trained model used in the maize case study presented in Section 3 are also publicly available via Zenodo ( Sangjan et al., 2025a ) at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:70-82Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
With climate change and global population growth, accelerating the breeding of superior crop varieties is essential for food security. Genomic prediction, which uses genome-wide genetic markers to predict crop traits, plays an important role in intelligent crop breeding. However, existing methods often lack stable and accurate performance across crops and traits. Here, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm. Compared with the best-performing single base learners, GEG2P improves prediction accuracy by 4.02% on average across maize, wheat, rice, chickpea, and soybean. We use SHAP to quantify the contribution of SNPs to phenotype prediction and find that SNPs with large effects captured by different base learners are functionally complementary. This study provides a robust and accurate genomic prediction method for crop breeding.
Why it matches plant phenotyping methods作物形質の遺伝子型から表現型を予測するアンサンブル計算法を開発し、複数作物で精度比較・検証しており、表現型推定手法が研究の中心である。
abstractHere, we propose GEG2P, a genetic algorithm-based ensemble learning method for genotype-to-phenotype prediction, integrates 20 base learners, dynamically selects their combinations through an iterative optimization strategy, and optimizes their weights using the genetic algorithm.
Reproduction assets foundThe paper provides public author code (GitHub GEG2P repository and Docker Hub image), a Zenodo deposit of significant SNP interaction pairs generated in this study, and a Figshare link with the wheat genotypic and phenotypic data used in the analyses. These are paper-specific, publicly available, and actionable.Code · publicScripts used in this study are available at GitHub [ https://github.com/Deep-Breeding/GEG2P ] 89 .Open asset ↗GitHub · Deep-Breeding/GEG2Plines:236-266Dataset · publicThe genotypic and phenotypic data of wheat are available at Figshare [ https://figshare.com/s/287c2c7f1623008487a5 ] 68 .Open asset ↗Figsharelines:236-266Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant diseases significantly affect agricultural productivity and global food security, while accurate disease identification remains challenging because of uncertain and overlapping visual symptoms in leaf images. Existing deep learning approaches often require large annotated datasets and suffer from limited interpretability in practical agricultural environments. This study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset. The objective is to develop an interpretable and reliable classification model capable of handling uncertainty in plant disease patterns through feature-driven fuzzy similarity analysis. The methodology integrates image preprocessing, color and texture feature extraction, variance-based feature weighting, prototype generation using K-means clustering, and fuzzy similarity computation using Mahalanobis distance and Gaussian membership functions. RGB, HSV, and Gray-Level Co-occurrence Matrix (GLCM) features are extracted from standardized leaf images and evaluated within an improved fuzzy soft classification framework. Performance comparison is carried out using machine learning models including Support Vector Machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) implemented in Python using Scikit-learn libraries. Experimental simulation results demonstrate that the proposed framework achieves competitive classification performance while preserving interpretability and robustness under uncertain feature distributions. Performance evaluation is conducted through accuracy analysis, ROC-AUC curves, confusion matrices, ablation studies, and Wilcoxon Signed-Rank statistical testing. The proposed Improved Fuzzy Soft model achieved an accuracy of 88.57% which is less than LDA (94.92%), Random Forest (97.78%) and SVM (97.94%) classifiers. However, in the cross data set validation, the proposed Improved Fuzzy Soft model achieved an accuracy of 67.35% which is greater than LDA (51.02%), Random Forest (51.02%) and SVM (55.10%) classifiers. Statistical validation using the Wilcoxon Signed-Rank Test produced a p-value of [Formula: see text], confirming that the performance difference between the Improved Fuzzy Soft framework and the Random Forest classifier is statistically significant under the current experimental setting.
Why it matches plant phenotyping methodsトマト葉画像から植物病害状態を推定する解釈可能な画像解析・分類フレームワークを開発し、複数モデル、交差データセット検証、アブレーション、統計検定で評価しており、病害表現型の取得・抽出手法が中心である。
abstractThis study presents a multi-parameter improved fuzzy soft set-based framework for plant disease classification using tomato leaf images from the PlantVillage dataset.
Reproduction assets foundThe paper uses public tomato leaf image datasets (PlantVillage and PlantDoc from Kaggle) as phenotyping inputs and states the authors' Improved Fuzzy Soft Framework implementation is publicly available on Zenodo with source code and reproduction instructions.Dataset · publicThe dataset analyzed during the current study are available in the repository:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗kaggle.com/datasets/abdallahalidev/plantvillage-datasetpdf-page:24 lines:1-75Code · publicThe implementation of the proposed Improved Fuzzy Soft Framework is publicly available through the Zenodo repository:
https://doi.org/10.5281/zenodo.20570546
The repository contains the source code, documentation, and instructions required to reproduce the experiments reported in
this study.Open asset ↗zenodo · 10.5281/zenodo.20570546pdf-page:25 lines:1-74Code / dataset availability confirmedEurope PMC · bioRxiv · checked 11 Sept 2026
Solitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. We developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. Over a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904). Detected foraging trips correlated strongly with brood cell counts (R2 = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820) identified solar radiation as the dominant driver of foraging activity, followed by temperature. BeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts.
Why it matches plant phenotyping methods植物ではなく昆虫を対象とするが、映像から採餌・営巣行動を抽出する技術開発として中心的であり、指定スコープの植物表現型ではないため除外。
abstractWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video
Reproduction assets foundThe paper explicitly states that source code, 3D STL files, and validation datasets/code are publicly available on the authors' GitHub repository and ScholarSphere. These directly support reproducing the paper's behavioral-event detection pipeline and its evaluation (annotated videos, classifier training/LOVO cross-vaCode · publicSource code for
software and 3D stl files can be found on the official GitHub repository here
https://github.com/Team-Insect-Net/BeeMonitor.Open asset ↗Team-Insect-Net/BeeMonitorpdf-page:2 lines:1-57Dataset · publicValidation datasets and code are available on
Scholars Sphere here
https://scholarsphere.psu.edu/resources/55f1f34b-959f-4c60-8dd3-9b33fb09357f.Open asset ↗Scholars Sphere · 55f1f34b-959f-4c60-8dd3-9b33fb09357fpdf-page:2 lines:1-57Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.
Why it matches plant phenotyping methods綿花葉の病斑・病害状態を画像から検出する手法を開発し、複数データセットとベースラインで性能検証しているため、植物表現型取得が中心である。
abstractWe present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages.
Reproduction assets foundThe authors explicitly state that their source code, trained models, and implementation details are publicly available, and the data availability statement points to the same repository, which hosts the self-constructed CCLD cotton leaf disease dataset (6,856 images, 6 classes) used for the paper's phenotyping/disease-Code · publicFurthermore, to facilitate future research, our source code, trained models, and implementation details have been made publicly available at https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:331-343Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:1278-1317Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Eggplant (Solanum melongena) is a major cash crop, yet field detection of its pests and diseases remains difficult because disease evidence is simultaneously occluded by foliage, blurred at lesion boundaries, and highly variable in scale. Fruit rot is especially challenging: the waxy epidermis and purple anthocyanin-rich surface reduce chromatic contrast, while infection spreads gradually from water-soaked tissue to necrotic tissue, producing diffuse borders between diseased and healthy regions. In this work, we reframe eggplant disease detection through a context-boundary-scale coupling principle, which states that accurate field detection should jointly model incomplete contextual cues, ambiguous lesion boundaries, and scale-varying symptom morphology rather than optimize these cues independently. ISA-YOLO is proposed as an implementation of this principle on top of YOLOv13 through coordinated context modeling, boundary-aware aggregation, and progressive multi-scale fusion. Experiments on two public datasets show that ISA-YOLO achieves 78.1 and 77.7% mAP at 30.66 and 31.74 FPS, outperforming mainstream detectors in overall trade-off between accuracy and speed. After pruning and quantization, inference speed increases to about 75 FPS while maintaining strong accuracy. These results indicate that the proposed principle provides an effective pathway for accurate and deployable eggplant pest and disease detection in smart agriculture.
Why it matches plant phenotyping methodsナスの病害・害虫を画像から検出するISA-YOLOモデルの開発と性能評価が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
titleA high-performance detection model ISA-YOLO for eggplant pests and diseases.
Reproduction assets foundThe paper uses four public Roboflow image datasets (BISU, UTM, Papaya, Tomato) and states that supporting data and code are publicly available on Zenodo, all with explicit URLs in the Data availability section.Dataset · publicwas supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No newOpen asset ↗eggplant-disease-detectionlines:1509-1560Dataset · publicty
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and international guidelines and legislation when collecting the data. No new data collection was performed for this research. The authors confirm that the use of these datasets in thOpen asset ↗eggplant-disease-detection-5fuqvlines:1509-1560Dataset · publicof the outcomes of the Provincial Undergraduate Training Program on Innovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these publicOpen asset ↗papaya-eswlk-r2ydylines:1509-1560Dataset · publicnovation and Entrepreneurship (Number: S202510108100). This work was supported by National Natural Science Foundation of China grants (62472269, 62072291).
Data availability
This research relies entirely on four publicly available detection datasets: Papaya ( https://universe.roboflow.com/hm-xsfz9/papaya-eswlk-r2ydy ), Tomato ( https://universe.roboflow.com/test-fqgof/tomato-rotten ), BISU ( https://universe.roboflow.com/bohol-island-state-university-vgjlb/eggplant-disease-detection ), and UTM ( https://universe.roboflow.com/utm-xpfqs/eggplant-disease-detection-5fuqv ). The authors believe that the creators of these public datasets complied with relevant institutional, national, and internatOpen asset ↗tomato-rottenlines:1509-1560Code · publicarch. The authors confirm that the use of these datasets in this study is fully compliant with their original licenses and ethical guidelines. The final images presented in the article accurately reflect the original data and meet community standards. The data and code supporting the conclusions of this article are available at https://zenodo.org/records/19425300 .
Declarations
Ethics approval and consent to participateOpen asset ↗Zenodo · 19425300lines:1509-1560Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Agricultural leaf disease detection is crucial for early intervention and yield protection in precision agriculture. Among representative economic crops, such as apples, leaf lesions are typically small and appear in complex backgrounds, making accurate detection performed on resource-constrained embedded devices challenging. To address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n. A novel Dual-Aspect Feature Complementary Mapping (DAFCM) module type is embedded in their backbone to recover lost semantic and spatial information, while the original YOLO11n's neck is replaced by an Efficient Enhanced Cross-Scale Feature Fusion (EE-CSFF) module, which incorporates Gated Differential Convolutional Fusion (GDCF) modules to strengthen cross-scale information flow and small-object representation. Experimental results obtained on the ALDSOD dataset show that, compared with the YOLO11n baseline, DCL-YOLO improves recall from 81.9% to 84.6%, mAP50 from 86.8% to 88.4%, and mAP50:95 from 47.0% to 47.8%, while also reducing the parameter count from 2.58 M to 1.91 M and Giga Floating-Point Operations (GFLOPs) from 6.3 to 5.5. After applying Layer-Adaptive Magnitude-based Pruning (LAMP), the parameter count and GFLOPs are further reduced to 0.75 M and 2.7, respectively, with mAP50 and mAP50:95 still exceeding the baseline by 1.2 and 0.5 percentage points, respectively. When deployed on an embedded device, the pruned model achieved 15.2 FPS and 139 msec per image, confirming its applicability in real-time scenarios. Furthermore, cross-domain validation, performed on the Global Wheat Head Detection (GWHD) dataset, indicates the stable generalization capabilities of the proposed models across environmental domain shifts. The DCL-YOLO's source code is publicly available at: https://github.com/q123-code/dcl-yolo.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する軽量モデルを開発し、データセットで性能比較・クロスドメイン検証・組込み機器での実装評価を行っており、植物の病害状態推定手法が研究の中心です。
abstractTo address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n.
Reproduction assets foundThe paper's constructed ALDSOD apple-leaf disease detection dataset is publicly available via Zenodo DOI, and the authors' DCL-YOLO source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData Availability: The constructed ALDSOD dataset used in this study is available for download from the following DOI: https://doi.org/10.5281/zenodo.17198053 .Open asset ↗zenodo · 10.5281/zenodo.17198053lines:148-159Code · publicThe DCL-YOLO’s source code is publicly available at: https://github.com/q123-code/dcl-yolo .Open asset ↗github · q123-code/dcl-yololines:148-159Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Plant diseases affecting leaves and fruits cause substantial yield and economic losses worldwide, particularly in horticultural crops cultivated under diverse agro-climatic conditions. Early and accurate disease diagnosis is essential for effective crop management; however, manual inspection is time-consuming, subjective, and often infeasible at large scale. In this work, we present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition. The dataset comprises labeled RGB images of healthy and diseased leaves and fruits from three economically important crops—tomato, chilli, and papaya—captured under real-field and semi-controlled environments, reflecting significant variability in illumination, background complexity, and disease severity. To demonstrate the applicability of the dataset, several commonly used convolutional neural network (CNN) architectures, including VGG, ResNet, DenseNet, MobileNet, and EfficientNet models, were trained and evaluated on the TCP dataset using transfer learning. Experimental results show that deep CNN models can effectively learn discriminative visual features corresponding to disease-specific patterns such as leaf spots, lesions, discoloration, curling, and fruit surface abnormalities. Lightweight models such as MobileNet achieve competitive performance with reduced computational cost, while deeper architectures provide improved accuracy at the expense of higher complexity. The results highlight the importance of dataset diversity for robust model generalization across multiple crops and plant organs. The TCP dataset provides a challenging benchmark for single-crop and multi-crop disease classification and supports the development of advanced deep learning, attention-based, and explainable AI models for precision agriculture. By enabling reproducible research and realistic performance evaluation, this dataset contributes toward scalable and practical AI-driven plant disease diagnosis systems aimed at reducing yield losses and supporting sustainable agriculture.
Why it matches plant phenotyping methods植物の葉・果実の病徴を画像から評価する大規模データセットとベンチマークを中心に扱っており、植物病害状態の画像ベース表現型解析に該当する。
abstractwe present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition.
Reproduction assets foundThe paper introduces the TCP (Tomato-Chilli-Papaya) fruit and leaf disease image dataset and reports CNN experiments on it. The dataset is publicly deposited on Mendeley Data, and the authors state that analysis code is available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:27 lines:1-51Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:27 lines:1-51Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Agriculture is significant in world food production and global economic stability, but leaf disease and pest infection can cause a threat to crop quantity and quality. Thus, it became crucial to have timely and accurate identification of plant leaf disease to prevent loss in agriculture on a large scale and to have sustainable crop management. This paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases. This application uses U-Net segmentation to precisely find leaf regions and MobileNetV3-Large to quickly classify diseases with less computation on the computer. The application performs end-to-end processing, from image acquisition to segmentation and disease prediction on mobile devices. An experiment was performed on publicly available crop disease datasets containing various leaf images having different disease types. The model obtained an accuracy of 98.85% showing improved generalization with minimal latency. The design of the model was such that it was suitable for inference on-device while still being robust enough despite changes in lighting conditions, background noise, and camera resolution. LeafLiteX is a low-cost, easy to use, offline-capable, and in-the-moment decision-making supportive diagnostic application that supports farmers and agrarians who require early detection. This paper demonstrates the capabilities that can be achieved using edge-optimized machine learning and computer vision to support the development of smart agriculture technologies. While traditional methods rely solely on classification, this research focuses more on practical implementation by incorporating segmentation, lightweight classification, and explainability to develop a mobile-friendly model.
Why it matches plant phenotyping methods葉画像から病害状態をセグメンテーション・分類する手法とモバイルアプリ自体が研究の中心であり、植物病害の表現型推定に該当する。
abstractThis paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases.
Reproduction assets foundThe paper's Data availability statement explicitly links the public PlantVillage (Mendeley) and PlantDoc (GitHub) leaf-image datasets used for its experiments, and provides the authors' LeafLiteX source code on GitHub.Dataset · publicThe dataset used in this study is publicly available from the repository:
https://data.mendeley.com/datasets/tywbtsjrjv/1Open asset ↗data.mendeley.com · tywbtsjrjv/1pdf-page:29 lines:1-74Code · publicThe source code is available on the following link: https://github.com/phdpawan/LeafLiteX.Open asset ↗github.com/phdpawan/LeafLiteXpdf-page:29 lines:1-74Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Fruit size, shape, color, and percent fruit rot are important quality traits for breeding cranberry ( Vaccinium macrocarpon Ait.). Image analysis can be used to measure these traits, but affordable hardware for standardized image capture and integrated user‐friendly software pipelines are lacking. Additionally, no image‐based method exists to estimate percent fruit rot, an otherwise tediously and subjectively measured trait. We created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images. Trained deep neural network models were highly accurate for segmenting sound fruit (F1 score: 99.4%) and detecting rotten fruit (F1 score: 98.5%). We applied the BerryBox to images of cranberries harvested across 3 years from a 156‐clone breeding population. Narrow‐sense heritability estimates of image‐based fruit color, shape, size, and percent fruit rot ranged from 0.37 to 0.95. Random subsampling showed that 25–30 berries per genotype were sufficient to describe the variation in the full dataset. We demonstrated the utility of BerryBox traits in a small‐scale genetic linkage mapping analysis, detecting significant marker–trait associations that coincided with those of traditionally measured traits. The BerryBox software was able to accurately segment fruit from images of blueberries without model retraining, showing its applicability to other similarly shaped fruits. The software pipeline and BerryBox materials and assembly instructions are publicly available for others to adopt for low‐cost image‐based phenotyping.
Why it matches plant phenotyping methodsクランベリー等の果実形質と腐敗率を画像から抽出する低コスト撮像装置・ソフトウェアパイプラインを開発し、精度検証と他果実への適用性評価を行った、中心的な植物フェノタイピング手法研究である。
abstractWe created the BerryBox, a simple and inexpensive lightbox, camera mount, and accompanying software pipeline to standardize the capture and analysis of postharvest fruit images.
Reproduction assets foundThe paper explicitly states public availability of the annotated image datasets (USDA Ag Data Commons DOI), R analysis scripts, the BerryBox Python software package with pre-trained models, and the model training code, all with author-provided public URLs.Dataset · publics (LOD)
score at a particular marker exceeded that computed at the
α = 0.05 level under null models generated via 1000 random
permutations.
2.8 Data, software, and equipment
instruction availability
The image datasets, along with annotations, are publicly
available through the USDA National Agricultural Library
Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v.
4.5.0; R Core Team, 2025). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis Open asset ↗10.15482/USDA.ADC/29853332pdf-raw-page:8 lines:1-125Code · publicable through the USDA National Agricultural Library
Ag Data Commons (https://doi.org/10.15482/USDA.ADC/29853332). All analyses in this study were performed in R (v.
4.5.0; R Core Team, 2025). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NOpen asset ↗github.com/neyhartj/BerryBox_FruitPhenotypingpdf-raw-page:8 lines:1-125Code · public). Scripts to replicate the analyses,
along with a list of materials for recreating the Berry-
Box, are available from the GitHub repository https://github.com/neyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a
custom model using high-performance computing resources
or the widely available Google Colab environment (Rippner
et al., 2022).
3 RESULTOpen asset ↗github.com/NeyhartLab/berryboxaipdf-raw-page:8 lines:1-125Code · publiceyhartj/BerryBox_FruitPhenotyping. Software for run-
ning the image capture and analysis software pipeline is
available as a Python package from the GitHub reposi-
tory https://github.com/NeyhartLab/berryboxai. The package
includes pre-trained models for berry segmentation and fruit
rot detection, and the code is available from https://github.com/NeyhartLab/berryboxai_training_public for training a
custom model using high-performance computing resources
or the widely available Google Colab environment (Rippner
et al., 2022).
3 RESULTS
3.1 Deep learning model training
The trained berry segmentation model achieved an overall
accuracy of 98.9% and an F1 score of 99.4%. The fruit rot
detection mOpen asset ↗github.com/NeyhartLab/berryboxai_training_publicpdf-raw-page:8 lines:1-125Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.
Why it matches plant phenotyping methodsバラ花弁画像データセットの構築と、花弁の健康状態・色分類による植物状態評価が研究の中心であり、画像ベースの表現型計測データセットに該当する。
abstractThe dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals.
Reproduction assets foundThe paper's own rose petal image dataset is publicly deposited on Mendeley Data, and the authors' validation/metadata scripts are publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe RoseVisuals dataset is publicly available on Mendeley Data at Direct URL to data: https://data.mendeley.com/datasets/f44jwtbfjg/5. Data Identification Number: 10.17632/f44jwtbfjg.5. Repository Name: RoseVisuals.Open asset ↗Mendeley Data · 10.17632/f44jwtbfjg.5html-lines:246-284Code · publicThe RoseVisuals codebase, comprising all validation scripts, is publicly available on GitHub Repository at https://github.com/Arya-S14/RoseVisuals-Validation-Doc.Open asset ↗GitHubhtml-lines:246-284Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate and timely identification of plant diseases along with their severity is critical for effective crop management and minimizing agricultural losses. While recent advances in deep learning have demonstrated high performance in plant disease classification, limited attention has been given to quantifying disease severity, which is essential for informed agronomic decision-making. To address this gap, this study proposes TomatoMTL, a unified multi-task learning framework for simultaneous disease classification and severity estimation of tomato leaf diseases from a single image. The proposed architecture employs a shared ResNet50-based convolutional backbone augmented with CBAM-based feature refinement, followed by task-specific branches for disease classification and severity prediction. Furthermore, a cross-task attention mechanism is introduced to enable interaction between disease-specific and severity-related features, thereby enhancing the robustness of severity estimation. To effectively leverage partially labeled data, a masking strategy is incorporated during training. Experimental evaluation on a publicly available tomato leaf disease severity dataset demonstrates that the proposed model achieves 97.85% disease classification accuracy and 77.66% severity prediction accuracy, outperforming state-of-the-art single-task classifiers including EfficientNetV2-S, ViT-B/16, and ConvNeXt-Tiny as well as existing multi-task learning baselines including Cross-Stitch Networks and MTAN. Comprehensive ablation studies confirm the individual contributions of CBAM, MixUp and CutMix augmentation, and the cross-task attention mechanism. Statistical significance analysis across five independent runs yields p-values less than 0.001 and Cohen's d greater than 14, establishing the reliability of the reported improvements. Quantitative localization analysis reveals that the model achieves 89.4% Pointing Game accuracy, confirming that attention maps focus on biologically meaningful disease regions. The proposed framework represents a complete and effective approach for integrated plant disease analysis with strong potential for real-world precision agriculture applications.
Why it matches plant phenotyping methodsトマト葉画像から病害の重症度という植物状態を推定するマルチタスク画像解析手法を開発・評価しており、表現型取得・推定が研究の中心である。
abstractthis study proposes TomatoMTL, a unified multi-task learning framework for simultaneous disease classification and severity estimation of tomato leaf diseases from a single image.
Reproduction assets foundThe paper's Data availability and Code availability statements explicitly link a public Kaggle tomato leaf disease severity dataset (the phenotyping image data used) and the authors' public GitHub repository containing the TomatoMTL implementation scripts and documentation.Dataset · publicThe datasets analysed during the current study are publicly available in the kaggle Data repository at: https://www.kaggle.com/datasets/janiruwalisingha/tomato-leaf-disease-severity-dataset .Open asset ↗kaggle Data repository · tomato-leaf-disease-severity-datasetlines:354-386Code · publicThe implementation, along with relevant scripts and documentation, can be accessed through the following GitHub repository: https://github.com/Parnika798/tomato_leaf_disease .Open asset ↗GitHub repository · Parnika798/tomato_leaf_diseaselines:354-386Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotClassificationDisease symptoms / severity
This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique's parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process's accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task's MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system's predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.
Why it matches plant phenotyping methods植物画像に基づく病害分類モデルの開発・評価が研究の中心的技術貢献であり、感染植物の状態を直接推定しているため含める。
abstractThe study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics
Reproduction assets foundThe paper explicitly states that implementation code, trained models, and experimental configurations are publicly available in an authors' GitHub repository, and that the PlantifyDr plant disease dataset and IP02 pest detection dataset used in the study are available on Kaggle. These are paper-specific, public, and可直接Code · publicThe implementation code, trained models, and experimental configurations used are publicly available in: “ https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigation ”. The repository contains all necessary instructions and dependencies required to reproduce the reported experimental results.Open asset ↗https://github.com/sumanthvmani/-Pest-Detection-and-Smart-Irrigationlines:253-302Dataset · publicThe datasets generated and/or analyzed during the current study are available in the [PlantifyDr Dataset and Pest detection dataset] repository“ https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset ”Open asset ↗https://www.kaggle.com/datasets/lavaman151/plantifydr-datasetlines:400-436Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 × 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.
Why it matches plant phenotyping methodsリンゴ果実の硬度とBrixという植物器官形質を、ハイパースペクトル画像と深層学習で非破壊推定するデータセット・モデル・汎化性能評価が研究の中心である。
abstractThis study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness.
Reproduction assets foundThe paper explicitly states that the hyperspectral apple dataset (5756 apples, firmness/Brix/starch measurements) is deposited in the University of Essex research data repository and that the data cleaning, model training, and analysis code is on GitHub, both with public URLs.Dataset · publicThe datasets generated during and analysed during the current study are available in the University of Essex repository ( https://researchdata.essex.ac.uk/228/ )Open asset ↗researchdata.essex.ac.uk · 228lines:192-220Code · publicthe code used for data cleaning, model training and analysis are available on GitHub: ( https://github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaging.git )Open asset ↗github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaginglines:192-220Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Single-molecule fluorescence in situ hybridization (smFISH) has emerged as a powerful tool for studying gene expression dynamics with unparalleled precision and spatial resolution in a variety of biological systems. Recent advancements have expanded its application to encompass plant studies, yet there remains a need for a simple and robust smFISH method adapted to plant tissue sections. Here, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections. This method exhibits remarkable sensitivity, enabling the detection of low-expression transcripts, including long non-coding RNAs. By integrating a deep learning-based algorithm into our image analysis pipeline, our method enables precise assignment of RNA abundance in nuclear and cytoplasmic compartments. The method also enables robust integration with immunofluorescence, as cryosectioning enhances antibody penetration. This allows for the sequential visualization and quantification of both RNAs and endogenous proteins within the same cells. Finally, this study demonstrates the use of smFISH to validate single-cell RNA sequencing (scRNA-seq) expression patterns in plant tissues. By extending smFISH to plant cryosections, plant scientists will be able to exploit the full potential of quantitative transcript analysis at cellular and subcellular resolution.
Why it matches plant phenotyping methods植物組織向けcryo-smFISHプロトコルと画像解析法を開発し、RNA量を細胞・細胞内区画で定量する手法が研究の中心である。分子測定ではあるが、植物組織の状態を定量する方法として技術的貢献が明確。
abstractHere, we present an optimized smFISH protocol, termed cryo-smFISH, for visualizing and quantifying single mRNA molecules in plant tissue cryosections.
Reproduction assets foundThe authors deposit all data underlying graphs/heatmaps plus custom R/Python scripts and Cellpose segmentation models in a public GitHub repository specific to this paper. Third-party tools (FISH-quant, DeconvolutionLab2, Stellaris Designer) are generic and excluded.Code · publicAll custom code, including R/Python scripts and Cellpose segmentation models, is available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .
Funding
This work was supported by Vetenskapsrådet (2023-03895), the Novo Nordisk Foundation (NFF24OC0093553 and NNF25OC0100533), and the Carl Tryggers Stiftelse (CTS 18- 325).
Acknowledgments
We thank A. Menkis for initial technical support with cryostat operation and Alexandre Berr for scientific feedback. We also thank memOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:122-152Dataset · publictic ( Bolger et al., 2014 ). The raw gene-count matrix was obtained using the pseudoalignment software Kallisto ( Bray et al., 2016 ). RNA-seq reads were normalized as transcripts per million (TPM).
Data and code availability
The supplemental information and all data underlying the graphs and heatmaps presented are available at https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections .Open asset ↗zhang_et_al_smFISH_cyrosectionslines:106-121Code · publicech.com/stellaris-designer . For mRNA detection, the coding sequence of the target gene was entered into the program, which automatically generated a set of probes complementary to the target mRNA.
The sequences of the probes were then subjected to quality control using an automated local blast R script, available on GitHub at: https://github.com/xuezhang911/zhang_et_al_smFISH_cyrosections/tree/main/smFISHprobes . The smFISH probes used in this study and their respective fluorophores are shown in Supplemental Table 3 . The probes were diluted in Tris-EDTA buffer to a final stock concentration of 25 μM.
Cryo-smFISH
Sample preparationOpen asset ↗zhang_et_al_smFISH_cyrosectionslines:75-85Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Why it matches plant phenotyping methods植物の3D形態表現型抽出を目的としたデータセットで、画像・点群・器官アノテーション・グラフ・形質値を提供し、再現可能なフェノタイピング手法の開発と評価を直接支援している。
abstractThis article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tomato diseases pose a significant threat to global agricultural production, often leading to substantial yield loss and major economic damage. Traditional disease detection methods rely on manual inspection, which is not only time-consuming and labor-intensive but also difficult to implement for real-time monitoring. While deep learning-based object detection techniques offer a potential alternative to manual inspection, existing models still face challenges in extracting subtle disease features, suppressing complex background interference, and in handling multi-scale disease representations in complex agricultural environments, limiting detection performance. To address these limitations, this paper proposes a novel TDD-YOLO model for precise tomato-disease detection (TDD) in complex agricultural settings. The proposed model is based on YOLOv11 with the following three main improvements: (1) a feature enhancement module is added to improve the backbone's ability to extract disease spot textures; (2) a joint attention mechanism is introduced to explicitly model cross-dimensional dependencies, effectively suppressing background interference; and (3) a feature fusion module is added to retain disease information across different scales while reducing computational costs. Experimental results, obtained on the Tomato-Village dataset (containing field-acquired images of tomato leaves with six diseases, collected in real agricultural environments, featuring complex backgrounds and varying illumination conditions) and Tomato-Disease dataset (emphasizing a greater diversity in tomato disease types along with healthy leaf samples), demonstrate that the proposed TDD-YOLO model outperforms the baseline in detection of tomato diseases (e.g., by improving mAP@50 and mAP@50:95, averaged across disease categories, by 4.1% and 6.0% on Tomato-Village and by 3.6% and 3.9% on Tomato-Disease, respectively) and state-of-the-art models (e.g., by improving the average mAP@50 and mAP@50:95, compared to the first runner-up, by 3.2% and 4.7% on Tomato-Village and by 2.4% and 2.1% on Tomato-Disease, respectively), while maintaining good parameter count and computational complexity, confirming its effectiveness and potential for practical usage in complex agricultural environments. The author-generated code and weight files are publicly available at https://github.com/LingShaQ/TDD-YOLOCode.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するYOLOモデルを開発し、複数データセットでベースラインおよび既存モデルと比較検証しており、植物病害フェノタイピング手法が中心である。
abstractExperimental results, obtained on the Tomato-Village dataset
Reproduction assets foundThe paper's tomato-disease detection experiments rely on two public image/annotation datasets (Tomato-Village on GitHub, Tomato-Disease on Zenodo), and the authors explicitly state their generated code and weight files are publicly available on GitHub. The Ultralytics YOLO repositories are generic third-party librariesCode · publicThe author-generated code and weight files are publicly available at https://github.com/LingShaQ/TDD-YOLOCode.Open asset ↗LingShaQ/TDD-YOLOCodehtml-lines:110-113Dataset · publicAll data used in this article are obtained from the publicly available Tomato-Village dataset (https://github.com/mamta-joshi-gehlot/Tomato-Village)Open asset ↗mamta-joshi-gehlot/Tomato-Villagehtml-lines:1159-1171Dataset · publicthe publicly available Tomato-Disease dataset (https://zenodo.org/records/15868289).Open asset ↗html-lines:1159-1171Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationCountingGrowth / development / phenology
Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画データセットであり、自動フェノロジー検出と空間検証のためのベンチマーク基盤が中心である。
abstractThe Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions.
Reproduction assets foundThe paper describes a public multi-modal Actinidia chinensis phenology dataset (annotated images, georeferenced videos, ground-truth counts) deposited on Zenodo, plus authors' MIT-licensed preprocessing scripts on GitHub. CVAT and FiftyOne are generic third-party tools and excluded.Dataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:12 lines:1-92Code · publicCustom scripts for dataset preparation are publicly available under the MIT License at https://github.com/Open asset ↗GitHubpdf-page:12 lines:1-92Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitively labor-intensive. We present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters, providing a more comprehensive representation of the plant’s architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we propose a method that generates token sequences containing a procedural definition of the plant architecture. This work uses only synthetic images for training and testing, where “exact” architectural parameters were known, which allowed for testing of the hypothesis that organ-level architectural parameters could be extracted from imagery data using a vision language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. Then, a VLM was trained to predict plant architecture token sequences from images. Our results demonstrate that the model can predict plant architecture tokens with an F1 score of 0.73 in a teacher-forcing method. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. Our model achieves lower MAPE than feature regression-based methods in estimating bulk plant-level traits that require understanding of the occluded 3D structure of the plant, such as leaf count and leaf area. We conclude that generating plant architecture and parameter extraction from synthetic imagery are feasible using a VLM approach, supporting future extension to real imagery.
Why it matches plant phenotyping methods画像から器官レベルの植物構造と形態形質を抽出するVLM手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractWe present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters
Reproduction assets foundThe paper's footnotes explicitly state that the authors' code is available on GitHub and the synthetic cowpea image/XML dataset is available on Hugging Face, both paper-specific and publicly actionable. The Helios URL is a generic third-party simulator library, not a paper-specific asset.Code · public1. ^ Code is available at: https://github.com/GEMINI-Breeding/Image2PlantArchitecture .Open asset ↗GEMINI-Breeding/Image2PlantArchitecturelines:600-676Dataset · public2. ^ Dataset is available at: https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML .Open asset ↗heesup/Cowpea-Architecture-XMLlines:600-676Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2. The dataset, sourced from Kaggle's Plant Village repository, includes 152 images of healthy potato leaves and 1000 images each of early and late blight. The methodology covers data preparation, model architecture design, training, evaluation, and deployment. During data preparation, the data set was split into training sets (80%) and testing sets (20%), with images resized to 128x128 pixels. The Deep Learning (DL) models built using CNN with 4 different optimizers (ADAM, SGD, RMSPROP, and ADAMAX) and trained using a sparse categorical cross-entropy loss function, include multiple convolutional and pooling layers for feature extraction, and fully connected layers for classification. Early stopping was used to prevent overfitting. Model performance was assessed using accuracy, loss curves, confusion matrix, ROC curve, precision recall curve, classification report, and F1 score. In addition, we have used data augmentation to balance the dataset by increasing healthy potato leaves 6 times and the use of Ensemble Deep Learning (EDL). EDL10 which contains DL1 (CNN + ADAM), DL2 (CNN + SGD), DL3 (CNN + RMSPROP) and DL4 (CNN + ADAMX) performs best with a accuracy score of 97.0%. This highlights the importance of data balancing and the use of the ensemble classification approach for the detection of blight in Potato Leaves.
Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類するCNN・アンサンブル手法の設計、評価、データ拡張が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis paper explores the use of optimized convolutional neural networks (CNNs) to classify diseases affecting potato leaves using TensorFlow-2.
Reproduction assets foundThe paper uses the public Kaggle PlantVillage potato leaf image dataset and archives its complete analysis source code on Zenodo with explicit availability statements and URLs.Code · publicThe complete source code is hosted in a DOI-minting repository and has been archived on Zenodo to ensure long-term accessibility and reproducibility. The code is released under an open-source license. The archived version corresponding to this publication is available at : https://doi.org/10.5281/zenodo.19624017Open asset ↗Zenodo · 10.5281/zenodo.19624017lines:252-314Dataset · publicThe datasets generated and/or analysed during the current study are available at : PlantVillage Dataset, accessed from https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset.Open asset ↗Kaggle · plantvillage-datasetlines:252-314Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tea (Camellia sinensis) is the world's second most consumed beverage, enjoyed daily by more than two billion people. In Bangladesh, it serves as a cornerstone agricultural export and a major sector of the domestic economy. However, commercial tea cultivation remains highly vulnerable to fungal and pest-related diseases such as Blight, Red Rust, and Helopeltis which severely reduce crop yield and compromise leaf quality. While early detection is critical to preventing widespread outbreaks, traditional manual inspection is slow, subjective, and highly error-prone. Deep learning provides a scalable alternative, yet single-branch networks often struggle to capture both minute disease lesions and broader structural degradation simultaneously. To address this, we propose a Hybrid Feature Fusion architecture that runs two highly efficient feature extractors in parallel: EfficientNetV2-Small to isolate fine-grained local textures, and MobileNetV3-Small to capture the global structural context of the leaf. The models were trained and evaluated on a real-world dataset of 2,000 annotated images, evenly distributed across the four target classes (Blight, Red Rust, Helopeltis, and Healthy). Before training, the images underwent a standardized preprocessing pipeline including resizing to 224 × 224 pixels and normalization, supplemented by a dynamic augmentation strategy featuring random rotations, horizontal flips, and brightness adjustments to improve model robustness. The proposed hybrid framework achieved an outstanding peak classification accuracy of 96.80% alongside a macro Area Under the Curve (AUC) of 0.9980. To rigorously validate its performance, the hybrid model was benchmarked against six diverse architectures: a Vision Transformer (ViT-B16 at 76.40%), a Custom CNN (89.60%), MobileNetV3 (94.40%), ResNet50 (95.60%), DenseNet121 (96.40%), and EfficientNetV2-B3 (97.60%). Although EfficientNetV2-B3 achieved a marginally higher raw accuracy, the proposed dual-branch framework delivered a superior precision-recall balance and faster convergence stability. These findings demonstrate that the proposed hybrid methodology is highly reliable and computationally balanced, making it an ideal candidate for integration into Internet of Things (IoT) edge devices for real-time disease monitoring in precision agriculture.
Why it matches plant phenotyping methods茶葉の病徴を画像から分類する深層学習手法の開発と、注釈付きデータセットおよび複数モデルとのベンチマーク検証が中心であり、植物病害状態の表現型推定に該当する。
abstractwe propose a Hybrid Feature Fusion architecture
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the curated 2000-image tea leaf dataset on Mendeley Data and the analysis code on GitHub, both with public URLs matching allowed_urls.Dataset · publicThe dataset comprising 2000 annotated tea leaf images was curated under real-world field conditions. It has been made available at https://data.mendeley.com/datasets/3x42rbj8yv/1.Open asset ↗3x42rbj8yv/1html-lines:465-480Code · publicThe computational code supporting the findings of this study is publicly accessible on GitHub: https://github.com/rayhankhan2192/Tea_Leaf_Disease_Model.Open asset ↗GitHub · rayhankhan2192/Tea_Leaf_Disease_Modelhtml-lines:465-480Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP 50-95 , mAP 50 , and mAP 75 , respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP 50 over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するモデルを開発し、複数データセットで比較検証、エッジデバイス実装まで評価しており、植物病害表現型の取得手法が中心である。
abstractThis paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection.
Reproduction assets foundThe paper's M-TLD tomato leaf disease dataset (2212 images, 6581 annotations) and the GBR-DETR implementation/training code are explicitly stated to be publicly available at the authors' GitHub repository.Dataset · publicThe M-TLD dataset and all annotation files are publicly available at https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026) to facilitate reproducibility and future research.Open asset ↗zhuojiaxiong6/DETRlines:38-108Code · publicThe code and dataset used in this study are publicly available at the following GitHub repository: https://github.com/zhuojiaxiong6/DETR (accessed on 29 April 2026). This repository contains the implementation of GBR-DETR, a Detection Transformer variant developed for detecting tomato leaf diseases and pests. All relevant training scripts, configuration files, and instructions for dataset usage are provided in the repository.Open asset ↗zhuojiaxiong6/DETRlines:673-675Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.
Why it matches plant phenotyping methodsイネ葉の病徴領域を画像から分類・局在化する深層学習ワークフローが研究の中心であり、GrabCut・輪郭検出と複数モデルの比較評価を含むため、植物病害状態の画像ベース表現型計測として採用。
abstractA deep learning approach is used as the core method to identify diseases in rice leaves.
Reproduction assets foundThe paper explicitly states that the complete implementation code and the rice leaf disease image dataset (1914 samples) used in this study are publicly available: code on the authors' GitHub repository and the dataset on Zenodo (DOI 10.5281/zenodo.15817084). Both are paper-specific, public, and actionable.Code · publicThe complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.Open asset ↗ummershakeel03/Rice-Leaf-Diseases-Classificationhtml-lines:1357-1368Dataset · publicThe dataset for this research study is available at: https://doi.org/10.5281/zenodo.15817084.Open asset ↗10.5281/zenodo.15817084html-lines:1357-1368Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study proposes RoViT-KAN, a multi-task deep learning framework for plant disease classification, severity estimation, and uncertainty quantification. The architecture integrates a DeiT-Tiny Vision Transformer backbone with task-specific heads for disease classification, ordinal severity prediction, and heteroscedastic uncertainty estimation. To enhance interpretability, a Kolmogorov–Arnold Network (KAN) module is introduced to model continuous disease severity through learnable spline-based transformations. A four-stage curriculum learning strategy is employed to stabilize multi-task optimization by progressively activating prediction objectives. The model is evaluated on a rose leaf disease dataset comprising 3,113 original images and 10,000 augmented samples across four classes: healthy leaf, leaf holes, black spot disease, and dry leaf condition. Experimental results demonstrate a classification accuracy of 99.70%, with calibrated uncertainty estimates (Brier score = 0.0914) and reliable severity prediction. Ablation studies validate the contribution of each architectural component. The model highlights the potential of combining transformer-based architectures with uncertainty-aware learning and interpretable neural representations for robust plant disease analysis.
Why it matches plant phenotyping methodsバラ葉の画像から病害分類と病害重症度を推定する手法を開発・評価しており、植物の病態を対象とした画像ベース表現型解析が研究の中心である。
abstractThis study proposes RoViT-KAN, a multi-task deep learning framework for plant disease classification, severity estimation, and uncertainty quantification.
Reproduction assets foundThe paper's rose leaf disease dataset (RoseLeafSet) is publicly deposited on Mendeley Data, and the authors' RoViT-KAN implementation code is publicly available on GitHub, both with explicit availability statements and URLs matching allowed entries.Dataset · publicThe datasets analyzed during the current study are publicly available in the Mendeley Data repository at: https://data.mendeley.com/datasets/9g668bfhy5/3.Open asset ↗Mendeley Datahtml-lines:716-742Code · publicThe code used to develop and evaluate the model in this study is publicly available to support transparency and reproducibility of the research. The implementation, along with relevant scripts and documentation, can be accessed through the following GitHub repository: https://github.com/nishitbohra/RoViT-KAN-Interpretable-Vision-Transformer-for-Rose-Disease-Severity-Estimation.Open asset ↗GitHubhtml-lines:716-742Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
RootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Root segmentation is a fundamental yet challenging task in image-based plant phenotyping. Accurate segmentation is a prerequisite for extracting root traits relevant to plant physiology, breeding, and agronomy. While U-Net and other convolutional neural network (ConvNet) architectures have been applied to root segmentation, no systematic comparison of multiple Transformer and ConvNet architectures has been conducted across diverse root imaging conditions. RESULTS: We evaluated 21 segmentation architectures across nine diverse root image datasets, training 1511 models to assess all combinations of architecture, dataset, pre-training strategy, and learning rate, producing over 3 million segmentations for evaluation. Transformer-based models significantly outperformed ConvNets for Dice (mean Dice 0.679 vs 0.659; [Formula: see text]). Root-diameter and root-length correlation were also higher for Transformers, but the differences were not statistically significant ([Formula: see text] and [Formula: see text] respectively). Pre-training significantly improved mean Dice from 0.623 to 0.666 ([Formula: see text]), with Transformers benefiting more from pre-training than ConvNets (Dice improvement + 0.072 vs + 0.021; [Formula: see text]), supporting the hypothesis that fine-tuned Transformers transfer more effectively across large domain gaps. MobileSAM achieved the highest Dice score (0.693) while maintaining computational efficiency. Both architecture families underestimated thin root length compared to manual annotations. Dataset choice explained 70.9% of performance variance, far exceeding model architecture (6.7%). PURPOSE: Transformer architectures significantly outperform ConvNets for root segmentation accuracy, and pre-training significantly improves performance, particularly for Transformers. Pre-trained MobileSAM offers the best accuracy at competitive computational cost. Dataset choice dominates performance variance, suggesting practitioners should prioritize data curation over architecture selection.
Why it matches plant phenotyping methods根の画像セグメンテーション手法を複数データセットで体系的に比較・検証し、根長・根径などの形質抽出性能も評価しているため、植物フェノタイピング手法が中心である。
abstractRoot segmentation is a fundamental yet challenging task in image-based plant phenotyping.
Reproduction assets foundThe paper's root image datasets (DeepRootLab, Grassland, Chicory, PRMI) are publicly available, and the authors' training code and modified RhizoVision Explorer trait-extraction fork are on GitHub with explicit availability statements.Dataset · publicImages are available from https://zenodo.org/records/15213661 .Open asset ↗Zenodo · 15213661lines:872-982Dataset · publicImages are available from https://figshare.com/ndownloader/articles/20440497/versions/2 .Open asset ↗Figshare · 20440497lines:872-982Dataset · publicImages are available from https://zenodo.org/records/3527713 .Open asset ↗Zenodo · 3527713lines:872-982Dataset · publicImages are available from https://gatorsense.github.io/PRMI/ .Open asset ↗lines:872-982Code · publicTraining code is available at https://github.com/sotlampr/seg .Open asset ↗GitHub · sotlampr/seglines:1183-1225Code · publicAll nine root image datasets used in this study are publicly available. DeepRootLab images are available from Zenodo (https://zenodo.org/records/15213661). Grassland images are available from Figshare (https://figshare.com/ndownloader/articles/20440497/versions/2). Chicory images are available from Zenodo (https://zenodo.org/records/3527713). The six PRMI datasets (Papaya, Peanut, Sesame, Sunflower, Cotton, Switchgrass) are available from https://gatorsense.github.io/PRMI/. Training code is available at https://github.com/sotlampr/seg. The modified RhizoVision Explorer fork used for trait extraction is available at https://github.com/sotlampr/RhizoVisionExplorer.Open asset ↗GitHub · sotlampr/RhizoVisionExplorerlines:1294-1347Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Accurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping, as breeding programs rely on reliable severity estimates to identify genotypes with improved resistance. However, most deep learning approaches have been developed as pathogen-specific models, which limits scalability in field-grown barley where multiple diseases naturally co-occur and exhibit substantial visual similarity. Results We evaluated whether a multiclass segmentation model can simultaneously detect and distinguish two fungal diseases of barley, Puccinia hordei and Ramularia collo-cygni, and compared its performance with two disease-specific binary models. Using 336 high-resolution leaf scans collected in the field with naturally occurring co-infections, the multiclass model achieved higher Dice scores for brown rust (0.59 vs 0.40; +47.5% relative improvement) and ramularia (0.60 vs 0.53; +13.2% relative improvement). It also captured a greater proportion of individual lesions across both classes. At the genotype level, the model-predicted disease area percentages were highly consistent with those from ground truth annotations ([Formula: see text]). Conclusions A unified multiclass framework can more effectively segment visually similar foliar diseases than separate binary models, while simplifying the computational workflow. This provides a scalable basis for automated resistance assessment within breeding pipelines. Code and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.
Why it matches plant phenotyping methods圃場画像から葉面病害面積をセグメンテーションし、遺伝子型レベルの病害重症度を推定する手法を開発・比較・検証しており、植物フェノタイピングが中心です。
abstractAccurate segmentation of foliar diseases under field conditions is essential for large-scale phenotyping
Reproduction assets foundThe paper's annotated barley leaf disease segmentation dataset (Mendeley Data DOI 10.17632/4ny92p2r8f.1) and the authors' analysis/segmentation code (GitHub grimmlab/BarleyDiseaseSegmentation) are explicitly declared publicly available, directly reproducing this paper's phenotyping measurements and computational modelsDataset · publicThe annotated dataset and the code implementing our machine learning–based model are publicly available on Mendeley Data (https://doi.org/10.17632/4ny92p2r8f.1) and GitHub (https://github.com/grimmlab/BarleyDiseaseSegmentation).Open asset ↗Mendeley Data · 10.17632/4ny92p2r8f.1lines:133-140Code · publicCode and data are publicly available at https://github.com/grimmlab/BarleyDiseaseSegmentation, with Mendeley Data dataset DOI 10.17632/4ny92p2r8f.1.Open asset ↗GitHub · grimmlab/BarleyDiseaseSegmentationlines:1-70Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
Why it matches plant phenotyping methods植物形質をハイパースペクトル画像から推定する深層学習について、OOD条件での不確実性推定手法Dis_UNを開発・評価しており、表現型取得・推定手法が中心である。
abstractwe propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty
Reproduction assets foundThe paper's authors publicly released their uncertainty-analysis code (two GitHub repositories) and the study data (Hugging Face dataset) with explicit availability statements and URLs. The EnMAP and NEON hyperspectral scenes are third-party public data sources, not paper-specific deposits, and the supplement is not anCode · publicThe code for this study is available at: https://github.com/echerif18/Multi_trait_Uncertainty/ (last access: 8 March 2026).Open asset ↗echerif18/Multi_trait_Uncertaintylines:449-456Dataset · publicThe data used in this study are available on Hugging Face: https://doi.org/10.57967/hf/7838 (Cherif et al., 2026).Open asset ↗Hugging Face · 10.57967/hf/7838lines:457-483Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.
Why it matches plant phenotyping methods植物表現型抽出を自然言語で自動化するAIシステムの開発であり、ツールとワークフローが研究の中心です。代表的ケーススタディと評価タスクによる検証も行っています。
abstractwe introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction.
Reproduction assets foundThe paper deposits its PhenoAssistant analysis code (with chat logs and generated outputs) on GitHub, and uses public phenotyping datasets: the CVPPP2017 leaf segmentation challenge data (case study 1 training/evaluation) and the CVPPA@ICCV'23 WW2020 winter wheat nutrient-deficiency dataset (case study 3), both on CodaCode · publicThe code for this research, as well as the chat logs and generated outputs of the case studies and evaluations, are available at Github [ https://github.com/vios-s/PhenoAssistant/ ] 78 .Open asset ↗vios-s/PhenoAssistantlines:224-268Dataset · publicThe data used for training and evaluating the computer vision model used in case study 1 are publicly available from the CVPPP2017 Leaf Segmentation Challenge dataset (A1 and A4 subsets) at CodaLab [ https://codalab.lisn.upsaclay.fr/competitions/8970 ].Open asset ↗CodaLab · CVPPP2017lines:224-268Dataset · publicThe winter wheat data used in case study 3 are publicly available from the CVPPA@ICCV'23: image classification of nutrient deficiencies in winter wheat and winter rye dataset (WW2020 subset) at CodaLab [ https://codalab.lisn.upsaclay.fr/competitions/13833 ].Open asset ↗CodaLab · WW2020lines:224-268Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Proper detection and treatment of plant leaf diseases are essential factors for achieving good crop yields and ensuring food security. Convolutional Neural Networks (CNNs) have shown significant potential for classifying diseases from leaf images. Instead, most current work focuses on image-level prediction and ignores the relationship between infected leaves. This limitation somewhat constrains their use in modelling disease spread. Also, it makes them less efficient in typical field situations where disease is transmitted from plant to plant by physical contact. Moreover, existing CNN architectures do not access inter-lobar contextual information, an essential factor for early detection and control. To tackle this, we propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass. The method uses the enhanced CNN model (LDDNet), with Atrous Spatial Pyramid Pooling (ASPP) and a Channel-Spatial Attention Module (CSAM), to achieve a more precise representation across multiple scales. These embeddings are then used to construct a similarity graph, enabling a GNN to infer likely disease transmission paths among leaves. We evaluate the PlantVillage dataset on the proposed model, and the results show that it outperforms state-of-the-art CNN-based methods, achieving 98.88% classification accuracy and 98.71% F1 Score. Additionally, we were able to predict disease spread with a high AUC-ROC of 0.942 and an MCC of 0.884 using our framework. These results confirm that AdjLeafGNN can accurately model both local and relational patterns. The approach we propose is scalable and interpretable, facilitating real-time monitoring and control of diseases in precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害を分類し、隣接葉間の病害拡大を推定する深層学習・GNN手法を提案・評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractwe propose AdjLeafGNN, an innovative hybrid deep learning and graph neural network model that performs multi-class leaf disease classification and probabilistic prediction of adjacent-leaf disease spread in a single pass.
Reproduction assets foundThe paper uses the public Kaggle PlantVillage leaf-image dataset as its phenotyping input and releases the complete AdjLeafGNN implementation (model, preprocessing, training, evaluation) on GitHub with a Zenodo-archived DOI.Dataset · publicthe dataset was obtained from the publicly available Kaggle distribution of the PlantVillage dataset: https://www.kaggle.com/datasets/mohitsingh1804/plantvillageTheOpen asset ↗html-lines:584-607Code · publicThe complete source code of the proposed AdjLeafGNN framework, including model implementation, training scripts, and evaluation pipeline, is publicly available. GitHub repository: https://github.com/surekhareddy123/AdjLeafGNN. A permanent archived version of the repository has been deposited in Zenodo and assigned the following DOI: 10.5281/zenodo.18862439.Open asset ↗https://github.com/surekhareddy123/AdjLeafGNN · 10.5281/zenodo.18862439html-lines:584-607Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Abstract. Accurate and timely identi cation of plant diseases is essential for improving crop productivity and ensuring sustainable agricultural practices. This paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops: Apple, Banana, Citrus, Guava, Mango, and Papaya. The dataset comprises high-quality RGB images representing both healthy and diseased samples, with disease symptoms including spots, lesions, discoloration, blight, rot, and fungal and bacterial infections captured under diverse real-world conditions. Variations in illumination, background complexity, viewing angles, growth stages, and symptom severity are intentionally included to enhance the robustness and generalizability of learning models developed using this data. The dataset is structured in a class-wise manner and preprocessed to support direct integration with deep learning frameworks. It is extensively used to train, validate, and evaluate deep learning based plant disease classi cation models, enabling automatic feature learning from raw images without manual intervention. Experimental usage demonstrates that the dataset is well suited for convolutional neural networks and attentionbased architectures, facilitating e ective discrimination between multiple disease categories across di erent crops and plant organs. By providing a uni ed multi-crop, multi-disease benchmark, this dataset aims to accelerate research in automated crop disease diagnosis, precision agriculture, and intelligent decision-support systems for sustainable farming.
Why it matches plant phenotyping methods植物の葉・果実の病徴画像を収録したデータセット／ベンチマークであり、病害状態の画像ベース推定を中心的に扱うため。
abstractThis paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops
Reproduction assets foundThe paper's core asset is the ABCGMP fruit and leaf disease image dataset, publicly deposited on Mendeley Data, with author analysis code also stated to be available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:33 lines:1-56Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:33 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model's decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .
Why it matches plant phenotyping methods果樹葉の病斑領域を画像から分割し、複数病害の重症度を定量推定する深層学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractthis research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves.
Reproduction assets foundThe paper's authors publicly release source code and trained models on GitHub, and the study analyzes two public Kaggle plant-image datasets (DiaMOS Plant and PlantVillage) used directly for the multi-disease severity phenotyping experiments.Code · publicThe source code and trained models are publicly available at: https://github.com/mqasim0787/Multi-Disease-Severity .Open asset ↗https://github.com/mqasim0787/Multi-Disease-Severity · mqasim0787/Multi-Disease-Severitylines:1-23Dataset · publicThe datasets analyzed during the current study are available publicly in the Kaggle repository, DiaMOS dataset (1) and PlantVillage Dataset (2) 0.1. [https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset]Open asset ↗https://www.kaggle.com/datasets/alexandraneagu101/diamos-plant-dataset · diamos-plant-datasetlines:964-977Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Early detection of maize leaf diseases is essential to prevent yield losses. Existing vision-based models face challenges in real-world environments due to data imbalance, lighting variations, and interpretability. This study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings. MaizeFormerX employs multi-scale patch embeddings and a Cross-Scale Attention Fusion (CSAF) module to capture both detailed lesion textures and larger disease patterns. The CSAF output is processed through a transformer encoder stack using multi-head self-attention to model long-range dependencies. Robust preprocessing and dataset-specific augmentations were applied to improve feature extraction and address class imbalances in the Dataverse, Tanzania, and Plagues Maiz datasets. For interpretability, Grad-CAM was used for pixel-level saliency mapping in an efficient web application. When benchmarked against MobileViT, EfficientFormer, TinyViT, and Swin Transformer, MaizeFormerX achieved 97.8% accuracy on Dataverse, 97.5% on Tanzania, and 96.9% on Plagues Maiz, outperforming Swin Transformer V2 by 2–3%. Cross-domain testing yielded 88.9% accuracy when trained on Dataverse and tested on Tanzania, surpassing baseline performance by 3–6%. Class-wise analysis revealed F1 scores over 98% for Healthy and MLB classes with 6× augmentation, and over 97% for MSV. Ablation studies highlighted the significance of the cross-scale attention module for high MCC during domain shifts. This study introduces a precise, explainable, and efficient image-based method for classifying maize diseases, which could aid in more targeted crop management, reduce unnecessary agrochemical use, and promote sustainable maize production in future decision-support environments.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から分類する手法の開発・ベンチマーク・交差ドメイン検証が中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThis study presents MaizeFormerX, a lightweight Vision Transformer designed for cross-domain, explainable maize disease detection on resource-limited settings.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public maize leaf image datasets used for its phenotyping/disease-classification experiments (Dataverse, Tanzania/Mendeley, Plagues Maiz/figshare) and an authors' GitHub repository containing all code, preprocessing pipelines, and experimental configs. All四Dataset · publicThe datasets used in this study are publicly available and sourced from Dataverse (https://doi.org/10.7910/DVN/LPGHKK)Open asset ↗Dataverse · 10.7910/DVN/LPGHKKhtml-lines:2304-2339Code · publicAll code, preprocessing pipelines, and experimental configurations used in this work are available at: https://github.com/rezaul-h/MaizeFormerX/.Open asset ↗github · rezaul-h/MaizeFormerXhtml-lines:2304-2339Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Premise Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets.
Why it matches plant phenotyping methods植物のストレス応答として形態形質や収量変化を予測するANNベースの計算ツールを開発し、イネで予測を検証しており、表現型推定が中心である。
abstracta composite artificial neural network (ANN)-based multi-target classification and regression deep learning model was developed using machine learning algorithms
Reproduction assets foundThe paper's ANN model code (scripts, Jupyter Notebooks, example datasets) is publicly available on GitHub, and the underlying morphological combined-stress phenotype dataset is publicly downloadable from SCIPDb. Supporting Information appendices contain raw/processed training data and validation data but no explicit作者-Code · publicnteraction in plants. Applications in Plant Sciences
14(2): e70047. 10.1002/aps3.70047
Piyush Priya, Prachi Pandey, Rubi Jain, and Manu Kandpal contributed equally to this work.
DATA AVAILABILITY STATEMENT
The scripts, Jupyter Notebooks, quick start guide, and example datasets used in this study are freely available at GitHub ( https://github.com/scipdatabase/Prediction_model ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), availablOpen asset ↗scipdatabase/Prediction_modellines:392-432Dataset · public), Python package scikit‐learn v1.4.2 ( https://scikit-learn.org/stable/ ), and Google Tensorflow version 2.17.0 ( https://www.tensorflow.org/ ) were used to implement the deep learning model in this study.
Data mining
The SCIPDb FTP server was utilized to download the morphological dataset for 41 distinct stress combinations ( https://db.nipgr.ac.in/plant_complete/downloads.php ; accessed on December 2021) (Priya et al., 2023 ). The dataset integrated into SCIPDb has been obtained through literature mining performed by employing relevant and carefully designed keywords (Appendix S1 ). The major search engines (Appendix S2 ) and the inclusion of various keyword variants ensured comprehensiveOpen asset ↗lines:41-51Dataset · publicdel ). The literature sources used for data extraction and for training the ANN model are provided in the Supporting Information. For details on various stress combinations and input data features, readers may refer to the Stress Combinations and their Interactions in Plants Database (SCIPDb) (Priya et al., 2023 ), available at https://db.nipgr.ac.in/plant_complete/index_orangesunset.php .
REFERENCES
Ahuja, I.
, De Vos R. C. H., Bones A. M., and Hall R. D.. 2010. Plant molecular stress responses face climate change. Trends in Plant Science
15: 664–674.
Atkinson, N. J.
, Lilley C. J., and Urwin P. E.. 2013. Identification of genes involved in the response of Arabidopsis to simultaneous bioticOpen asset ↗lines:392-432Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modeling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9 M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7 M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴画像を対象に、病害分類のための新規Vision Transformerモデルを開発・比較しており、植物病害状態の画像ベース表現型推定が中心である。
abstractwe propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks.
Reproduction assets foundThe authors publicly release their analysis code and dataset splits (including their field-collected primary maize leaf image dataset) via a GitHub repository, and the paper's classification experiments use the public Kaggle Corn or Maize Leaf Disease Dataset (COMLDD). Both are paper-specific, public, and actionable.Code · publicThe program code and dataset splits for the three datasets used in this study, including our primary data, can be found at https://github.com/DamarisWaema/ConvDeiT-Tiny (accessed on 18 March 2026).Open asset ↗DamarisWaema/ConvDeiT-Tinylines:121-289Dataset · publicGhose S.
Corn or Maize Leaf Disease Dataset
Available online: https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on 10 July 2025)Open asset ↗lines:474-623Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deploying rice disease detectors in the field remains challenging because models that are accurate in the lab are often poorly calibrated and provide limited uncertainty estimates, raising the risk of costly misclassification. This paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria: classification error, calibration quality, uncertainty estimation, model size, inference latency, and energy consumption. The proposed framework presents conditional temperature scaling, an adaptive scheme that mitigates over-calibration and preserves reliability. The framework is implemented in Python on a lightweight two-headed classifier and evaluated on the Paddy Doctor dataset, MO-BBBC base framework achieves 90.6% disease accuracy and 97.9% variety accuracy; improves calibration to [Formula: see text] ([Formula: see text]% better than strong post-hoc baselines); achieves micro-AUC of 0.994/0.999 and micro-AP of 0.961/0.994 (disease/variety); delivers robust OOD detection (AUROC = 0.887/0.886); and supports real-time inference at [Formula: see text] ms and [Formula: see text] ms per 64-sample batch on CPU/GPU with Monte Carlo Dropout uncertainty. The resulting Pareto set enables practitioners to trade accuracy for efficiency and reliability, narrowing the gap between prototype validation and field deployment in precision agriculture.
Why it matches plant phenotyping methods植物病害状態を画像から検出する分類・校正・不確実性推定フレームワークが研究の中心であり、植物の病害表現型を対象とした手法開発と評価に該当する。
abstractThis paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria
Reproduction assets foundThe paper publicly releases its authors' analysis code (MO-BBBC framework, calibration, leakage audits, evaluation scripts) on GitHub, and a Zenodo deposit containing the paper-specific split indices, metadata, and reproducibility notebook. The underlying PaddyDoctor plant image dataset used for all phenotyping/class- Code · publicaddyDoctor images and to reproduce the results reported in the manuscript.
All code used to implement the MO–BBBC framework, multitask classifier, uncertainty-aware curricula, calibration routines, and evaluation scripts (including leakage audits, OOD evaluation, and generation of all tables and figures) is freely available at: https://github.com/manhas82/MO-BBBC-Rice.git .
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethics statement
This study uses only publicly available plant imagery and does not involve human participants, animalOpen asset ↗https://github.com/manhas82/MO-BBBC-Rice.gitlines:361-386Dataset · publicAll data underlying the findings in this study are available without restriction. The minimal dataset underlying the reported analyses (including the exact group-aware train/validation/test split indices, supporting metadata, and a complete reproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classOpen asset ↗Zenodo · 10.5281/zenodo.18471419lines:361-386Dataset · publicvalidation/test split indices, supporting metadata, and a complete reproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classification-and-benchmarking . The Zenodo record contains the files needed to reconstruct our exact experimental partitions from the original PaddyDoctor images and to reproduce the results reportedOpen asset ↗lines:361-386Dataset · publicproducibility notebook) is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18471419 .
The underlying images and labels used in this work come from the public PaddyDoctor image dataset [ 18 ], which can be accessed from the official project page https://paddydoc.github.io/dataset/ and via its IEEE DataPort record https://ieee-dataport.org/documents/paddy-doctor-visual-image-dataset-automated-paddy-disease-classification-and-benchmarking . The Zenodo record contains the files needed to reconstruct our exact experimental partitions from the original PaddyDoctor images and to reproduce the results reported in the manuscript.
All code used to implement the MO–BBBC framework, multiOpen asset ↗IEEE DataPortlines:411-413Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Dimorphic species exhibited higher discrimination capacity (average accuracy of 80–83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed significant overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral integration showed a strong correlation (R² = 0.72; P = 0.003), and both the combined datasets (spectra and outline) and the individual datasets of spectral and shape features revealed a high phylogenetic signal (λ = 1–0.84), indicating partial coevolution between frond shape, chemical composition, and the evolutionary history of the group. Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Why it matches plant phenotyping methodsフロンド形状をElliptical Fourier Analysisで定量化し、赤外分光との統合を用いて系統識別性能を評価しており、植物器官形質の取得・解析手法が研究の中心です。
abstractusing Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA).
Reproduction assets foundThe authors state that raw data, processed data, and R analysis code for the frond outline morphometrics are publicly available on GitHub (Microgramma-Outline), and the FT-NIR spectral data repository (Microgramma-FTNIR) is referenced in the methods. Both are paper-specific, public, and actionable.Code · publicSciELO Preprints - Este documento é um preprint e sua situação atual está disponível em: https://doi.org/10.1590/SciELOPreprints.15500
573 The raw data, processed data, and R analysis code are publicly available on GitHub:
574 https://github.com/labevofern/Microgramma-Outline.git.
575
576 REFERENCES
577 Ackerly D.D. (2004) Adaptation, Niche Conservatism, and Convergence: Comparative
578 Studies of Leaf Evolution in the California Chaparral. The American Naturalist, 163, 654–
579 671.
580 Adams D.C., Collyer M.L. (2018) Multivariate Phylogenetic Comparative Methods:
581 Evaluations, Comparisons, and RecoOpen asset ↗labevofern/Microgramma-Outline · Microgramma-Outlinepdf-layout-page:25 lines:1-48Dataset · publicbiting the highest
157 perpendicular distance from the line connecting the first and last bands in the R² × ranking
158 plot (Fig. S2). Following the methods described in Mendonça et al. (2026), spectral data were
159 acquired using a PerkinElmer Frontier™ near-infrared Fourier transform spectrometer (FT-
160 NIR) available at (https://github.com/labevofern/Microgramma-FTNIR).
161 Phylogenetic comparative analyses
162 To provide a phylogenetic framework for comparative morphometric and spectral analyses,
163 we used the pruned version of the Microgramma chloroplast phylogenetic inference from
164 Mendonça et al. (2026). This tree was based on the Bayesian phylogenetic tree published by
165 AOpen asset ↗labevofern/Microgramma-FTNIR · Microgramma-FTNIRpdf-layout-page:8 lines:1-55Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate flower-load assessment is critical for informed thinning strategies in orchard management. UAV-based deep learning automated counting offers efficiency advantages, yet precise counting is heavily dependent on abundant annotated data, which is scarce and costly to obtain in agricultural settings. While semi-supervised learning alleviates dependency on manual annotation, its application to UAV-based orchard imagery faces challenges: complex backgrounds and small target sizes, which undermine pseudo-label reliability. To address these challenges, this study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages. First, a color-SAM flower extractor (CSAM-FE) is proposed to preprocess images using a strategy combining color thresholding with the Segment Anything Model (SAM), suppressing background noise and extracting high-quality flower clusters, thereby providing purified inputs for the subsequent counting network. Second, an uncertainty-guided semi-supervised flower counting network (USCount-Net) is proposed for accurate stage-specific flower counting with limited labeled data. The USCount-Net incorporates two key components: an adaptive pseudo-label filtering (PLF) mechanism based on frequent forward uncertainty estimation (FFUE) is designed to dynamically suppress noisy gradient backpropagation, mitigating error propagation from unreliable pseudo-labels; and a noise-sensitive adaptive gated fusion (AGF) module is introduced to fuse cross-scale features without redundancy, addressing significant scale variations across phenological stages and observation angles. Comparative experiments on a self-built apple flower counting dataset demonstrate that USCount-Net achieves lower MAE and RMSE than state-of-the-art methods at 10%, 30%, and 50% labeling ratios. The results demonstrate that the proposed methodology serves as methodological support for rapid and precise apple flower counting in low-annotation agricultural scenarios.
Why it matches plant phenotyping methodsリンゴ花の画像抽出・計数手法と半教師あり解析ネットワークを開発し、データセット上で比較評価しているため、植物表現型取得が中心である。
abstractthis study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages.
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' USCount-Net source code on GitHub and the self-built apple flower counting dataset (UAV images, annotations, flower cluster images) on Google Drive.Code · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗USCount-Netlines:681-780Dataset · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗lines:681-780Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In agricultural automation, precise cotton segmentation is a key step for tasks such as intelligent harvesting and yield estimation. However, in complex field environments, factors such as background interference and irregular target shapes severely affect segmentation accuracy. Existing deep learning methods offer certain advantages but still generally suffer from limitations including insufficient accuracy, over-segmentation, and misidentification. To address these challenges, this study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet), which optimizes the ParaTransCNN architecture by incorporating the 2D Selective Scan (SS2D) module to replace the original Transformer branch, effectively balancing the extraction of local details and global semantic information while reducing computational burden. To enhance the model's perception of irregularly shaped cotton, a Deformable Convolutional Networks v1 (DCNv1) module is integrated into the Vision Mamba (VMamba) branch, further improving the delineation of target boundaries. Additionally, an Atrous Spatial Pyramid Pooling (ASPP) module is introduced at the end of the Convolutional Neural Network (CNN) branch to strengthen multi-scale feature representation. To optimize the fusion of channel and spatial information, the Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanism replaces the original module, enhancing feature modeling capability. Experimental results on an in-field cotton image dataset demonstrate that CMNet outperforms existing mainstream methods, achieving Dice, mIoU, and Accuracy of 91.06%, 84.18%, and 98.10%, respectively, while reducing parameter count and computational complexity, thus exhibiting excellent performance. Furthermore, generalization experiments on multiple other plant datasets also achieved outstanding results, validating the model's adaptability and potential for broader applications in multi-crop segmentation tasks, providing valuable insights for smart agriculture segmentation research. The source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.
Why it matches plant phenotyping methods綿花画像から植物領域を抽出する新規セグメンテーション手法を中心に開発・検証しており、植物表現型の画像取得・抽出ワークフローに該当する。
abstractthis study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet)
Reproduction assets foundThe authors explicitly state that the source code and dataset for CMNet are publicly available on GitHub. The paper also uses several public Roboflow plant image datasets in its generalization experiments, cited with public URLs in the references.Code · publicThe source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.Open asset ↗halidanmu/CMNethtml-lines:106-109Dataset · publicELTE (2023). Assignment 2 dataset. Available online at: https://universe.roboflow.com/elte-msgqy/assignment_2-mjhau (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLaola (2024). Defect banana dataset. Available online at: https://universe.roboflow.com/laola/defect-banana-qf4f6 (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLuffy24312 (2023). Cnn dataset. Available online at: https://universe.roboflow.com/luffy24312/cnn-myqtl.Open asset ↗html-lines:835-919Dataset · publicVyuha T. (2025). Rose dataset. Available online at: https://universe.roboflow.com/tech-vyuha/rose-kfpuf (Accessed November 4, 2025).Open asset ↗html-lines:835-919Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modelling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から分類する手法を新規に開発し、複数データセットで性能比較・検証しており、病害フェノタイピング手法が中心である。
abstractwe propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the authors' program code and dataset splits (including their field-collected primary dataset). The paper also evaluates on the public Kaggle Corn or Maize Leaf Disease Dataset, a public plant-image dataset directly used for the论文'sCode · publicData Availability Statement: The program code and dataset splits for the three datasets used in this study,
including our primary data, can be found at https://github.com/DamarisWaema/ConvDeiT-Tiny.Open asset ↗DamarisWaema/ConvDeiT-Tinypdf-page:18 lines:1-60Dataset · public43. Ghose, S. Corn or Maize Leaf Disease Dataset. Available online:
https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (Accessed on 10 July
2025).Open asset ↗pdf-page:21 lines:1-59Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
MilletSorghumRootTissueSegmentationRoot system architecture
Root anatomical features are critical for plant performance characterization, yet phenotyping at the anatomical scale remains limited by the extreme annotation burden of cellular segmentation. We present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions. Our approach decomposes multi-class segmentation into species-agnostic tissue identification followed by tissue type classification. By designing robust input representations invariant to imaging artifacts and morphological variations, our framework enables rapid adaptation to new species with fewer than 40 labeled images. Additionally, the first stage automatically generates tissue boundaries, transforming tedious manual tracing into simple tissue labeling. We validate our method on pearl millet, and sorghum root cross-sections from different imaging protocols, achieving state-of-the-art performance while dramatically reducing deployment time. This efficiency breakthrough enables scalable root phenotyping across diverse crop species, accelerating the development of climate-resilient varieties for global food security.
Why it matches plant phenotyping methods植物根の解剖学的形質を対象とする画像セグメンテーション手法を開発し、複数種・撮像条件で検証しているため、方法が研究の中心である。
abstractWe present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the annotated root image dataset (Zenodo 17726414), trained segmentation models (Zenodo 17737703), and the authors' source code (GitHub janetkok/Root-Segmentation-Beyond-Species-Boundaries), all directly reproducing this paper's root anatomical phenotyping andDataset · publicThe dataset and models are available at https://doi.org/10.5281/zenodo.17726414 and https://doi.org/10.5281/zenodo.17737703 , respectively.Open asset ↗Zenodo · 10.5281/zenodo.17726414lines:242-251Code · publicThe source code is hosted at https://github.com/janetkok/Root-Segmentation-Beyond-Species-Boundaries .Open asset ↗GitHub · janetkok/Root-Segmentation-Beyond-Species-Boundarieslines:242-251Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11-57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near-Infrared spectra measured on leaves reflect phenotypic evolution unrelated to domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate phenotypic divergence index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild vs domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいmPDI指標を開発しており、植物形質の統合・比較手法が明示的な貢献であるため。
abstractestablished a framework for cross-species comparisons
Reproduction assets foundThe paper's phenotypic data, NIR spectra, and trait ontology are deposited at doi 10.57745/QWEKVK, and the authors' R analysis scripts are publicly available on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Maize is a vital global crop, but its productivity is often threatened by plant diseases, highlighting the need for precise and timely diagnostic methods. Traditional manual inspection is inefficient and prone to errors, motivating the development of automated solutions. Recent advances in computer vision and deep learning have enabled effective automated plant disease diagnosis. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown promise in plant disease classification, CNNs struggle to capture global contextual information, and ViTs require large datasets and high computational resources. Inspired by mixture-of-experts (MoE) architectures, we propose a lightweight hybrid model that integrates CNN and ViT components, adaptively emphasizing local or global features based on input characteristics. Evaluated on a novel, real-world dataset of full maize plant images, our approach achieves 99.90% classification accuracy, significantly outperforming state-of-the-art baselines such as MobileViT, PiT, EdgeNeXt, and DeiT. These results demonstrate that lightweight hybrid architectures can deliver high-performance disease diagnosis suitable for practical agricultural deployment. The code is available at: https://www.github.com/sabermehdipour/MXiT .
Why it matches plant phenotyping methodsトウモロコシ全身画像から病害状態を推定する軽量CNN-ViT手法を開発・評価しており、植物表現型取得・判定が中心的です。
abstractwe propose a lightweight hybrid model that integrates CNN and ViT components
Reproduction assets foundThe paper's authors' MXiT analysis code is publicly available via a GitHub URL stated in the abstract, and the PlantVillage image dataset used for evaluation is publicly available. The Plant Scanner maize dataset is paper-specific but only available upon request, so it is listed as request_only.Code · publicThe code is available at: https://www.github.com/sabermehdipour/MXiT.Open asset ↗sabermehdipour/MXiThtml-lines:1-77Dataset · publicThe PlantVillage dataset is publicly available (https://github.com/spMohanty/PlantVillage-Dataset).Open asset ↗spMohanty/PlantVillage-Datasethtml-lines:707-785Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Phenotypic characters have long been central to species diagnosis and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex-spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling-posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan , an R package designed to provide a reproducible, easy-to-implement framework for comparing groups using mensural and meristic data. Orangutan integrates statistical analysis and visualization for species diagnosis and population comparisons within a single workflow. The package streamlines the identification of diagnostic, nonoverlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences in overlapping traits. Core features include optional allometric correction to remove size effects, optional outlier removal, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including tables and publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Empirical validation with real-world datasets-including animal and plant species-demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package could empower researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative good practices for species diagnoses, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available as open-source software with comprehensive documentation to facilitate broad adoption.
Why it matches plant phenotyping methods植物を含む表現型データの解析・可視化を統合するRパッケージを開発し、実データで検証しているため、植物表現型解析ソフトウェアとして方法論が中心である。
abstractI introduce Orangutan , an R package designed to provide a reproducible, easy-to-implement framework for comparing groups using mensural and meristic data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産3件を確認しました。Code · publicThe data to reproduce this work and software are freely and publicly available at https://github.com/metalofis/Orangutan‐R , https://cran.r‐project.org/web/packages/Orangutan/index.html and https://zenodo.org/records/18488056 .Open asset ↗GitHub · metalofis/Orangutan‐Rlines:328-395Code · publicThe data to reproduce this work and software are freely and publicly available at https://github.com/metalofis/Orangutan‐R , https://cran.r‐project.org/web/packages/Orangutan/index.html and https://zenodo.org/records/18488056 .Open asset ↗Zenodo · 18488056lines:396-502Dataset · publicThe anole datasets can be downloaded from https://github.com/metalofis/Orangutan‐R/tree/main/example_datasets .Open asset ↗GitHub · metalofis/Orangutan‐Rlines:88-96Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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Sample data are shared in the following link:
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Plant diseases continue to pose a significant threat to worldwide food security, resulting in notable yield reductions and economic consequences. Automated disease diagnosis through machine learning has arisen as a potential solution; nevertheless, current methods frequently have difficulty in capturing both detailed local attributes and overarching contextual patterns found in plant leaf images. This study presents a thorough comparative examination of conventional and deep learning methods—such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), YOLO, Support Vector Machines (SVMs), and Random Forests—for the classification of multi-class plant diseases. To overcome the constraints of individual CNN and transformer models, a new hybrid framework that integrates EfficientNet-B7 for strong spatial feature extraction with a Vision Transformer (ViT-B16) for comprehensive contextual modeling is suggested. The system is assessed on an extensive dataset consisting of 21,534 images covering 38 classes of plant diseases and healthy specimens. Experimental findings show that the suggested hybrid model reaches an accuracy of 98.13%, surpassing standalone CNN baselines and other rival models, while consistently achieving high precision, recall, and F1-scores for all classes. The results emphasize the success of combining convolutional and transformer-based models for scalable and precise plant disease detection, aiding the creation of smart decision-support systems for precision farming.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ハイブリッド画像解析手法を提案し、複数手法との比較評価と大規模データセットでの検証を行っており、フェノタイピング手法が中心である。
abstractAutomated disease diagnosis through machine learning has arisen as a potential solution
Reproduction assets foundThe paper's plant disease image dataset (New Plant Diseases Dataset on Kaggle) and the authors' complete hybrid CNN–ViT implementation (GitHub repository with Zenodo DOI) are both publicly and explicitly available.Dataset · publicThe data that support the findings of this study are openly available in the New Plant Diseases Dataset at Kaggle [https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data].Open asset ↗Kaggle · new-plant-diseases-datasethtml-lines:296-327Code · publicThe source code, including model architecture, training scripts, evaluation routines, and Google Colab notebooks for inference, is hosted on GitHub at: https://github.com/mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosis.Open asset ↗GitHub · mohdzunaidahmed15-ui/hybrid-cnn-vit-plant-disease-diagnosishtml-lines:296-327Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security. This study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of a vision transformer (ViT) and a Swin transformer to accurately predict crop diseases. While ViT captures global image features, the Swin Transformer excels at extracting fine-grained local details. Evaluated on two benchmark datasets, Corn and PlantDoc, our model achieved accuracies of 98.81% and 81.81%, respectively, surpassing recent works. Here, we demonstrate the effectiveness of combining complementary transformer architectures to improve disease identification in diverse agricultural settings. The code, data and the hybrid model are available at https://github.com/hema2107/Swin-HViT.
Why it matches plant phenotyping methods植物画像から病害状態を推定するハイブリッド画像解析モデルを開発し、2つのベンチマークデータセットで評価しており、病害フェノタイピング手法が中心である。
abstractThis study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of a vision transformer (ViT) and a Swin transformer to accurately predict crop diseases.
Reproduction assets foundThe paper reports a hybrid ViT-Swin crop disease classification model evaluated on two public Kaggle plant image datasets (Corn/maize leaf disease and PlantDoc). The authors explicitly state that the code, data, and trained hybrid model are publicly available in their GitHub repository, and both image datasets are usedCode · publicThe code, data and the hybrid model are available at
https://github.com/hema2107/Swin-HViT.Open asset ↗hema2107/Swin-HViTpdf-page:2 lines:1-60Dataset · publicThe first dataset used for hybrid model evaluation is available on Kaggle at
https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on August 2025).Open asset ↗pdf-page:7 lines:1-31Dataset · publicThe second dataset is also from Kaggle and is available at the link
https://www.kaggle.com/datasets/abdulhasibuddin/plant-doc-dataset (accessed on August 2025) [25].Open asset ↗pdf-page:7 lines:1-31Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Pepper / chilliField / plotLiDAR / point cloudWhole plant / canopy / plot / fieldClassificationObject detectionSegmentationTrackingGrowth / development / phenology
Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants.
Why it matches plant phenotyping methods植物の器官レベル表現型解析を支援する4D点群データセットを構築し、成長状態分類・器官分割・追跡などを可能にする研究であり、フェノタイピング用データ基盤が中心です。
abstractPepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages.
Reproduction assets foundThe authors publicly release the Pepper-4D spatiotemporal 3D pepper point cloud dataset (with plant- and organ-level annotations) and associated code via a GitHub repository stated in the Data Availability Statement. CloudCompare is a generic third-party tool, not a paper-specific asset.Dataset · public.J.; writing—original draft preparation, F.A.; writing—review and editing, D.L.; visualization, F.A. and D.L.; supervision, D.L.; project administration, H.Y.; funding acquisition, D.L. and H.Y. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This work was supported in part by the Shanghai Sailing Program under Grant 24YF2701200, in part by the Fundamental Research Funds for the Central Universities under Grant 2232025D-50, and in part by Donghua UnOpen asset ↗foysalahmed10/Pepper-4Dlines:238-264Code · publicang Q., Zeng Y., Hou J., Zhe X. WarpingGAN: Warping multiple uniform priors for adversarial 3D point cloud generation; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; New Orleans, LA, USA. 18–24 June 2022; pp. 6397–6405.
Associated Data
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).Open asset ↗foysalahmed10/Pepper-4Dlines:308-314Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Medicinal plants including Ocimum tenuiflorum L. (Tulsi), Azadirachta indica A. Juss. (Neem), and Kalanchoe pinnata (Lam.) Pers. (Patharkuchi) are essential sources of bioactive compounds, yet leaf diseases threaten their yield and phytochemical integrity. This study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks, achieving 99.72% accuracy, 1.00 macro F1-score, and AUC = 1.00 across 12 disease classes (1,000 images/class post-augmentation) using only 9.38 M parameters and 2.50 GFLOPs. Five-fold cross-validation yielded 99.74% ± 0.14% accuracy, with rapid convergence and no overfitting. Explainable Artificial Intelligence (XAI) via Gradient-weighted Class Activation Mapping (Grad-CAM) (mean intensity: 0.1664-0.2702), Local Interpretable Model-agnostic Explanations (LIME), and t-distributed Stochastic Neighbor Embedding (t-SNE) (silhouette score: 0.87) confirmed biologically meaningful attention on pathological regions. External validation on the independent BD-MediLeaves dataset (8 classes, 8,000 samples) achieved 99.42% accuracy and 0.99 macro F1. With 6.98 ms/image inference latency and 35.81 MB memory, LSeTNet enables real-time, edge-based deployment. It significantly outperforms DenseNet169 (95.56%), ViT-B16 (95.61%), and LW-CNN+SE (95.39%) ([Formula: see text], paired t-tests), establishing a transparent, efficient, and generalizable benchmark for precision phytopathology and sustainable medicinal plant cultivation.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するCNN・Transformer手法を開発し、交差検証、外部データセット、既存モデルとの比較で検証しており、植物フェノタイピング手法が中心である。
abstractThis study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks
Reproduction assets foundThe paper publicly releases its primary medicinal leaf image dataset (MedicinalLeaf-12) on Mendeley Data, uses a public external validation dataset (BD-MediLeaves, also on Mendeley), and provides full training/evaluation code for LSeTNet on GitHub. All three are paper-specific, public, and actionable.Dataset · publicThe primary dataset used in this study is available in the Mendeley Data Repository: https://data.mendeley.com/datasets/ncg7kk3gwx/1 .Open asset ↗Mendeley Data · ncg7kk3gwx/1lines:712-750Code · publicThe full training and evaluation code for the proposed LSeTNet model is publicly available on GitHub at: https://github.com/mdtuhinkhan101/LSeTNet .Open asset ↗GitHub · mdtuhinkhan101/LSeTNetlines:712-750Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Segmentation of hyperspectral image data is a well-established technique in remote sensing. While it is commonly applied to individual field crops, its use for individual trees is less prevalent. Conifers are crucial in forestry, and assessing physiological status, or genetic diversity is required for effective early-age treatment in nurseries and hyperspectral imaging (HSI) combined with high-throughput phenotyping (HTP) offers faster and non-destructive evaluation. NDVI-based thresholding is sufficient for detection of leaves with large projection areas, but needles of conifers present challenges due to spatial resolution constraints and increased proportion of border pixels. This study monitored the offspring of three locally adapted Scots pine (Pinus sylvestris L.) populations, representing distinct upland and lowland ecotypes. This study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings. Using a K-means algorithm, 23 hyperspectral centroids were successfully derived and subsequently classified into ten biologically distinct groups. Random forest classification model effectively differentiated Scots pine seedlings based on origin during water stress and recovery periods. This study highlights the potential of hyperspectral imaging and machine learning in evaluating the physiological state of conifer seedlings, demonstrating promising applications in forest tree physiology research and tree breeding.
Why it matches plant phenotyping methods個体のマツ苗を分離・セグメンテーションするハイパースペクトル画像処理パイプラインを開発し、機械学習で生理状態や由来を評価しており、表現型取得手法が中心である。
abstractThis study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings.
Reproduction assets foundThe paper's Data availability statement explicitly deposits demonstration hyperspectral sample data on Zenodo and the segmentation/classification scripts on GitHub; both are paper-specific, public, and actionable. Full experimental data is request-only and not listed as a public asset.Dataset · publicDemonstration sample data and their accompanying descriptions are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17167809).Open asset ↗Zenodo · 10.5281/zenodo.17167809lines:161-192Code · publicThe scripts developed for this study are available on GitHub at: https://github.com/JCepl/Pine-hyperspectral-image-segmentaionCompleteOpen asset ↗GitHub · JCepl/Pine-hyperspectral-image-segmentaionCompletelines:161-192Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by available field surveys and trait measurements. Recent expansions in biodiversity data aggregation-including vegetation surveys, citizen science observations, and trait measurements-offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution. Our approach achieves correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance understanding of plant community properties and ecosystem functioning, while serving as tools for modeling global biogeochemical processes and informing conservation efforts. Our framework highlights the power of crowdsourced biodiversity data in addressing longstanding extrapolation challenges in global plant trait modeling, with continued advancements in data collection and remote sensing poised to further refine trait-based understanding of the biosphere.
Why it matches plant phenotyping methods地球観測データと多様な植物形質データを統合して植物形質を推定・検証する方法が研究の中心であり、単なる生態学的測定ではないため。
abstractcombining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution
Reproduction assets foundThe paper's own trait maps (Zenodo), source data (Zenodo), and analysis code (GitHub + Zenodo archive) are explicitly public. Core trait inputs (TRY, sPlot) are restricted-access and require requests; GBIF citizen-science occurrence datasets are public inputs.Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗GitHub · dluks/cit-sci-trait-mapslines:249-343Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗Zenodo · 10.5281/zenodo.18269445lines:249-343Dataset · publicSource data underlying the figures are available at https://doi.org/10.5281/zenodo.18108765 .Open asset ↗Zenodo · 10.5281/zenodo.18108765lines:240-248Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security. This study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of Vision Transformer (ViT) and Swin Transformer to accurately predict crop diseases. While ViT captures global image features, Swin Transformer excels at extracting fine-grained local details. Evaluated on two benchmark datasets, Corn and PlantDoc, our model achieved accuracy of 98.81% and 81.81%, respectively, surpassing recent works. Here, we demonstrate the effectiveness of combining complementary transformer architectures to enhance disease identification in diverse agricultural settings. The code, data and the hybrid model are available at https://github.com/hema2107/Swin-HViT.
Why it matches plant phenotyping methods植物画像から病害状態を推定する画像解析モデルの開発・ベンチマークが中心であり、植物病害の表現型推定手法に該当する。
abstractThis study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of Vision Transformer (ViT) and Swin Transformer to accurately predict crop diseases.
Reproduction assets foundThe paper explicitly states that the code, data, and hybrid model are publicly available in the authors' GitHub repository, and it evaluates on two public Kaggle plant-disease image datasets (Corn/maize leaf disease and PlantDoc) that serve as the phenotyping image inputs for the study.Code · publicThe code, data and the hybrid model are available at
https://github.com/hema2107/Swin-HViT.Open asset ↗hema2107/Swin-HViTpdf-page:2 lines:1-60Dataset · publicThe first dataset used for hybrid model evaluation is available on Kaggle at
https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on August 2025).Open asset ↗pdf-page:7 lines:1-31Dataset · publicThe second dataset is also from Kaggle available at the link
https://www.kaggle.com/datasets/abdulhasibuddin/plant-doc-dataset (accessed on August 2025) [24].Open asset ↗pdf-page:7 lines:1-31Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published1 Jan 2026The Plant journal : for cell and molecular biologyCited by 4 · OpenAlex ↗
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Why it matches plant phenotyping methods植物細胞のライブ画像から細胞形状・先端位置・成長方向・成長動態を定量化する解析ソフトウェアを開発しており、植物表現型取得・抽出が研究の中心である。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Potato yield is a key indicator for optimizing cultivation practices in agriculture. Potato yield can be estimated on harvesters using RGB-D cameras, which capture three-dimensional (3D) information of individual tubers moving along the conveyor belt. However, point clouds reconstructed from RGB-D images are incomplete due to self-occlusion, leading to systematic underestimation of tuber weight. To address this, we introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds. Rather than reconstructing full 3D geometry, PointRAFT infers target values directly from raw 3D data. Its key architectural novelty is an object height embedding that incorporates tuber height as an additional geometric cue, improving weight prediction under practical harvesting conditions. PointRAFT was trained and evaluated on 26,688 partial point clouds collected from 859 potato tubers across four cultivars and three growing seasons on an operational harvester in Japan. On a test set of 5,254 point clouds from 172 tubers, PointRAFT achieved a mean absolute error of 12.0 g and a root mean squared error of 17.2 g, substantially outperforming a linear regression baseline and a standard PointNet++ regression network. With an average inference time of 6.3 ms per point cloud, PointRAFT supports processing rates of up to 150 tubers per second, meeting the high-throughput requirements of commercial potato harvesters. Beyond potato weight estimation, PointRAFT provides a versatile regression network applicable to a wide range of 3D phenotyping and robotic perception tasks. The code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git.
Why it matches plant phenotyping methods部分点群からジャガイモ塊茎重量を推定する3D深層学習手法を開発・評価しており、植物形質取得が研究の中心である。
abstractwe introduce PointRAFT, a high-throughput point cloud regression network that directly predicts continuous 3D shape properties, such as tuber weight, from partial point clouds.
Reproduction assets foundThe paper publicly releases its authors' analysis code and trained network weights on GitHub, and a subset of its potato tuber partial point cloud dataset (with ground truth weights) on Hugging Face. Both are paper-specific, public, and actionable.Code · publicThe code, network weights, and a subset of the dataset are publicly available at https://github.com/pieterblok/pointraft.git .Open asset ↗pieterblok/pointraftlines:1-93Dataset · publicA subset of the datasets generated and/or analyzed during this study is publicly available at: https://huggingface.co/datasets/UTokyo-FieldPhenomics-Lab/3DPotatoTwinOpen asset ↗UTokyo-FieldPhenomics-Lab/3DPotatoTwinlines:447-463Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate and efficient leaf trait measurement is essential for plant phenotyping, agronomy, and ecological studies. In this work, we introduce Leaf Analyzer, a novel open-source, fully automated computer vision-based tool for high-throughput leaf morphological trait measurement such as leaf area, dimensions, perimeter, count, and percent damage. Unlike existing methods that rely on strong foreground-background contrast or controlled imaging conditions, Leaf Analyzer employs an unsupervised clustering approach based on the K-means++ clustering algorithm and a novel Leaf Background Separation (LBS) feature, which combines the L∗ and b∗ channels from CIEL∗a∗b∗ color space and the saturation channel from HSV color space. The proposed method and the LBS feature can effectively distinguish leaves from the background across varying lighting conditions, leaf colors, and camera orientations. To evaluate the performance of the new software, we conducted comprehensive quantitative and qualitative comparison experiments with two widely used software tools - Petiole Pro and LeafByte, demonstrating that Leaf Analyzer achieves superior accuracy and consistency, particularly under challenging imaging conditions. Additionally, we explore methods to further enhance measurement precision, including leaf flattening and the integration of supplementary leaf features such as texture features and color specific features. Beyond leaf trait measurement, we showcase the versatility of Leaf Analyzer in a range of applications, including nondestructive plant phenotyping, seed counting, root trait analysis, leaf area measurement for petri dish-grown plants, plant projected silhouette area or crown projection area estimation, leaf damage assessment, and broader plant science applications, making it a valuable tool for researchers working in laboratory and field environments.
Why it matches plant phenotyping methods葉形態形質を自動抽出するオープンソース画像解析ツールの開発と、既存ツールとの定量比較検証が研究の中心であるため。
abstractIn this work, we introduce Leaf Analyzer, a novel open-source, fully automated computer vision-based tool for high-throughput leaf morphological trait measurement such as leaf area, dimensions, perimeter, count, and percent damage.
Reproduction assets foundThe authors state that the Leaf Analyzer source code, installer files, and all data (including evaluation images) used in this study are publicly available on their GitHub repository.Code · publicThe Leaf Analyzer source code, platform-specific installer files, and all data used in this study are publicly available on our GitHub repository at https://github.com/squashking/Leaf-Analyzer .Open asset ↗squashking/Leaf-Analyzerlines:239-277Dataset · publicAll the images used in the evaluation have been published on our Github repository ( https://github.com/squashking/Leaf-Analyzer ).Open asset ↗squashking/Leaf-Analyzerlines:134-155Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Effective pest and disease detection plays a crucial role in minimizing crop losses and improving decision-making in precision agriculture. Among the most destructive pests affecting maize crops globally is the Fall Army Worm (FAW), known for its rapid spread and high impact on yield. Existing detection practices often rely on manual scouting, which can be inefficient, labour intensive and prone to human error. This study proposes a novel deep learning based framework for the automatic classification of FAW infested and healthy maize crops by integrating RGB and thermal image modalities. The core objective is to enhance detection accuracy through multimodal image fusion. A hybrid DNN-ViT model is introduced, combining two complimentary pipelines: (i) feature-level fusion, where CNN extracted features from RGB and thermal images are fused and classified using a Deep Neural Network (DNN) and (ii) image-level fusion, where a 6 channel RGB-thermal image is directly processed using a modified Vision Transformer (ViT). Experimental results demonstrate that the fused model achieved superior performance with an accuracy of 0.98, precision, recall and F1-score of 0.98 and AUC-ROC of 0.98 on the test set, outperforming models trained on RGB-only, thermal-only and unfused data. The ablation study confirms the effectiveness of multimodal fusion, with the no-fusion model showing significantly lower performance (accuracy-0.60 and AUC-ROC-0.67). This work highlights the benefits of integrating complementary data sources for robust crop health monitoring. Future research will explore enhanced fusion strategies, environmental robustness and field level deployment to validate the model's practical applicability.
Why it matches plant phenotyping methodsRGB・熱画像融合によるFAW被害・健全状態の画像判定モデルを開発し、融合方式や性能を比較検証しているため、植物の健康状態を取得する方法が中心である。
abstractThis study proposes a novel deep learning based framework for the automatic classification of FAW infested and healthy maize crops by integrating RGB and thermal image modalities.
Reproduction assets foundThe paper's paired RGB/thermal maize FAW image dataset is publicly deposited on Figshare (part of a peer-reviewed data publication), and the authors' custom Python analysis code is released as a public supplementary file (Supplementary Code.zip) with explicit availability language. The Figshare URL matches an allowed, Dataset · publicThe dataset has been made publicly available in the Figshare Data repository as a part of a peer reviewed data publication54. Detailed information on data acquisition, sensor specifications, environmental conditions and annotation protocols is provided in the associated data article. The dataset can be accessed at: https://figshare.com/s/677d2384ba6e02db9230 (10.6084/m9.figshare.28388018).Open asset ↗Figshare · 10.6084/m9.figshare.28388018html-lines:324-345Code · publicThe custom python code developed for this study is available as supplementary file (“Supplementary Code.zip”) and includes all scripts necessary to reproduce the multimodal feature fusion, image-level fusion and ablation experiments described in the manuscript. The dataset used is publicly available on Figshare. All dependencies are listed within the code file. Readers can execute the python script to reproduce the reported results.Open asset ↗html-lines:324-345Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly available datasets, the PlantVillage Potato Leaf Disease (2,152 images) dataset and the Kaggle Mango Leaf Disease dataset (4,000 images). Images were pre-processed, augmented, and split into training and testing datasets (80:20), to enable better model generalization. Four deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, Residual Networks (ResNet), and EfficientNet, were evaluated in the context of multi-class disease classification. The baseline CNN achieved a training accuracy of 93.67% and a testing accuracy of 92.61%, with balanced precision and recall (92.5%), thus providing a very strong feature extraction and classification capability. AlexNet showed moderate performance (91.3% training, 90.2% validation), and a very small overfitting was observed. ResNet had an efficient convergence, and attained 96.7% validation accuracy in just a few epochs, thus pointing out the advantage of residual connections in the context of deeper learning. EfficientNet surpassed all the other architectures, since it reached a training accuracy of 98.2% and a validation accuracy of 97.8%, with very small loss (≈ 0.015) and no overfitting, thus proving to have the best generalization ability. The models demonstrated stability and discriminative ability with the support of confusion matrices and accuracy and loss plots produced on an epoch-wise basis. Therefore, the findings indicate that DL models can be adapted for real-time and accurate plant disease diagnosis, establishing a pathway for early remediation, and supporting precision agriculture. The research establishes the opportunity for EfficientNet to be considered a promising solution for scalable smart farming.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法を開発・比較し、複数データセットで精度を検証しており、植物の病害状態の取得・推定が研究の中心である。
abstractdeep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (PlantVillage potato, mango leaf disease) and states that all code, preprocessing scripts, dataset splits, and model artifacts are publicly available in a GitHub repository (also archived on Zenodo). All three are paper-specific, public, and actionable.Dataset · publicThe datasets analyzed during the current study are available in (https://www.kaggle.com/datasets/aarishasifkhan/plantvillage-potato-disease-dataset)Open asset ↗html-lines:473-503Code · publicAll code, preprocessing scripts, dataset splits, and model artifacts used in this study are publicly available in the GitHub repository at: [https://github.com/logeswarig/PROJECT_1].Open asset ↗logeswarig/PROJECT_1html-lines:473-503Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
FlowerAnnotation / quality controlObject detectionGrowth / development / phenology
ABSTRACT Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found in-house expert-curated annotations were essential for producing reliable results. Expert validation found relatively strong accuracy for detecting present floral structures, but still had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts. Still, this dataset represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly-available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses, and discuss remaining challenges and next steps.
Why it matches plant phenotyping methods植物標本画像から花の存在を自動検出し、精度検証と大規模な phenology データセット化を行う機械学習手法が研究の中心であるため、植物フェノタイピング手法として適格です。
abstractwe present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the ensemble models, training/validation/test images, training data and final ensemble output on Zenodo, and the analysis code on GitHub; machine-labeled records are also served via the public Phenobase portal. All are paper-specific, public, and actionable.Dataset · publicors contributed to drafts and gave final
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the SupportinOpen asset ↗Zenodo · 17675089pdf-raw-page:19 lines:1-55Dataset · publictps://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotate genera and families removed from training and
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Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
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(https://doi.org/10.5281/zenodo.17675089).463
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Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Aim Phenotypic characters have long been central to species diagnosis and delimitation and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex— spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling—posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan, an R package designed to provide a flexible, easy-to-implement framework for comparing groups using mensural and meristic data. Innovation Orangutan provides a flexible and efficient framework for analyzing mensural and meristic data, supporting a full suite of statistical and visualization tools optimized for species delimitation and population comparisons. The package streamlines the identification of diagnostic, non-overlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences. Core features include optional allometric correction to remove size effects, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including summary statistics and annotated publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Main Conclusions Empirical validation with real-world datasets—including animal and plant species— demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package empowers researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative best practices for species delimitation, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available with comprehensive documentation to support widespread adoption.
Why it matches plant phenotyping methods植物を含む形態形質データの解析・可視化を標準化するRパッケージの開発論文であり、植物種データでの検証も行っているため、表現型解析手法が中心です。
titleOrangutan: an R package for analyzing and visualizing phenotypic data in the context of ecology and systematics
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThe data to reproduce this work and software are freely and publicly available at
https://github.com/metalofis/Orangutan-R.Open asset ↗metalofis/Orangutan-Rpdf-page:15 lines:1-28Dataset · publicThe anole datasets can be downloaded from
https://github.com/metalofis/Orangutan-R/tree/main/example_datasets.Open asset ↗metalofis/Orangutan-R · example_datasetspdf-page:5 lines:1-51Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
Above-ground vertical structure is a critical variable for ecosystem monitoring, carbon accounting, and land management. However, the high cost and limited coverage of airborne lidar hinder its widespread application. To address this, we developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery. Unlike forestry-specific models that exclude human-made features, NAIP-CHM characterizes the full vertical structure of the landscape including vegetation, buildings, and infrastructure. We utilized a U-Net convolutional neural network with attention mechanisms and environmental conditioning, training and validating the model with a peer-reviewed, publicly available dataset of 22.8 million co-registered NAIP imagery and lidar-derived CHM pairs, with stratified sampling to ensure robustness in open-canopy ecosystems. The model achieved a pixel-wise root mean square error (RMSE) of 2.28 meters and an r2 of 0.87. Forested sites alone produced an r2 of 0.82 and RMSE of 3.82 meters. We provide the dataset, source code, and cloud-based tools to enable broad application without requiring specialized computational resources.
Why it matches plant phenotyping methods植生を含む景観の樹冠高・構造を航空画像から推定するモデルを開発し、公開データセットで検証している。植物キャノピーの明示的な構造形質推定が中心だが、建造物等も含むため植物以外の構造も対象とする点には留意が必要。
abstractwe developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery.
Reproduction assets foundThe paper's NAIP-CHM canopy height model, its CONUS 0.6 m dataset, trained weights, and full training/inference code are all publicly released with explicit availability statements and author-hosted URLs (Rangeland Analysis Platform server, GitHub, Zenodo, Colab notebook, Earth Engine app).Dataset · publicFor bulk download, COGs and associated index files are available via HTTP from the Rangeland Analysis Platform server ( http://rangeland.ntsg.umt.edu/data/naip-chm/ ).Open asset ↗Rangeland Analysis Platform serverlines:76-83Model / weights · publicThe source code, trained model weights, validation data, and auxiliary datasets required to reproduce the results are permanently archived in a Zenodo repository 31 .Open asset ↗Zenodolines:89-134Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset includes 1045 high-quality point clouds of field-grown maize collected using a terrestrial laser scanner (TLS). Point clouds of 520 plants from this dataset were segmented and annotated using a graph-based segmentation method to isolate individual leaves and stalks, ensuring consistent labeling across all samples. This labeled data was then used for fitting procedural models that provide a structured parametric representation of the maize plants. The leaves of the maize plants in the procedural models are represented using Non-Uniform Rational B-Spline (NURBS) surfaces that were generated using a two-step optimization process combining gradient-free and gradient-based methods. We conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset also includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled point cloud data (100k, 50k, 10k points), which can be readily used for different downstream computational tasks. MaizeField3D will serve as a comprehensive foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.
Why it matches plant phenotyping methods3D点群の収集・分割・注釈・手続き型モデル化を中核とする、植物表現型解析向けの再利用可能なデータセットである。
abstractwe present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research.
Reproduction assets foundThe paper's own MaizeField3D dataset (1045 TLS point clouds, 520 segmented/annotated plants, metadata, STL/DAT procedural model outputs) is publicly available on Hugging Face, with a project website and public GitHub code for the procedural NURBS surface generation used in the analysis.Dataset · publicThe MaizeField3D dataset is publicly available on the Hugging Face Datasets platform at https://huggingface.co/datasets/BGLab/MaizeField3D. It includes high-resolution point clouds, segmented plant models, metadata, and reconstructed outputs in STL and DAT formats.Open asset ↗BGLab/MaizeField3Dhtml-lines:343-354Code · publicThe code for procedural NURBS surface generation used in this work is available at https://github.com/baskargroup/ProceduralMaize3D.Open asset ↗baskargroup/ProceduralMaize3Dhtml-lines:343-354Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Coffee, the world’s most traded tropical crop, is vital to the economies of many producing countries. However, coffee leaf diseases pose a serious threat to coffee quality and sustainable production. Deep learning has shown strong performance in plant disease identification through automatic image classification. Nevertheless, reliance on a single convolutional neural networks (CNNs) architecture restricts feature variability and real-world generalization. Moreover, limited work has systematically combined feature selection/reduction with CNNs, which constrains the advancement of hybrid models capable of capturing complementary features while ensuring computational efficiency without accuracy loss. This article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms. GoogLeNet and ResNet18 are paired for complementary feature extraction, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are employed for dimensionality reduction, and ANOVA and Chi-square are applied to select the most informative features. An Adam optimizer (learning rate = 0.001, batch size = 20, epochs = 50) with early stopping is used for training. Experiments on the BRACOL dataset achieved 99.78% accuracy, with precision, recall, and F1-score all exceeding 99% across classes. To the best of our knowledge, this study systematically integrates GoogLeNet and ResNet18 with PCA/SVD dimensionality reduction and analysis of variance (ANOVA)/Chi-square feature selection, for coffee disease classification, thereby addressing a key gap in prior research.
Why it matches plant phenotyping methodsコーヒー葉の病害を画像から分類する深層学習フレームワークが研究の中心であり、植物の病害状態を直接推定する実質的な表現型解析手法である。
abstractThis article presents an enhanced deep learning-based framework for coffee disease classification incorporating a hybrid strategy that integrates CNNs and advanced feature selection algorithms.
Reproduction assets foundThe paper uses the public BRACOL/RoCoLe coffee leaf image dataset (Mendeley) and provides authors' analysis code publicly on GitHub and Zenodo, all explicitly linked in the text.Dataset · publicWe utilized the BRACOL dataset, a publicly available dataset of coffee leaf images. The dataset can be accessed at the following DOI: ( https://data.mendeley.com/datasets/c5yvn32dzg/2 ).Open asset ↗lines:30-49Code · publicAll implementation details, including preprocessing scripts, model training, and evaluation codes, are available in the following GitHub repository: ( https://github.com/DrMaherAlrahhal/coffe-code ).Open asset ↗GitHublines:30-49Code · publicThe code is available at GitHub and Zenodo:
- https://github.com/DrMaherAlrahhal/coffe-code .
- w. (2025). coffee code. Zenodo. https://doi.org/10.5281/zenodo.17470672 .Open asset ↗Zenodo · 10.5281/zenodo.17470672lines:2688-2696Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.
Why it matches plant phenotyping methodsリンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。
abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
Reproduction assets foundThe authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.Dataset · publicData accessibility
Repository name: Github
Data identification number: DOI 10.5281/zenodo.15600557
Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.Open asset ↗GitHub · DOI 10.5281/zenodo.15600557html-lines:84-123Code · publicAll data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].Open asset ↗GitHubhtml-lines:1-83Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Polyploidy (whole-genome duplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.
Why it matches plant phenotyping methodsiSPyは画像から組織内の核倍数性を空間的・高スループットに推定する教師なし学習パイプラインであり、Arabidopsisで実証されている。植物の状態を抽出する計算フェノタイピング手法が中心である。
abstractHere, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest.
Reproduction assets foundThe paper deposits its paper-specific phenotyping assets publicly: confocal images of A. thaliana, D. melanogaster, and human cardiomyocytes, ilastik segmentation files, and A. thaliana cotyledon flow cytometry data are all in an OSF repository, and the iSPy analysis code is available both on OSF and in a public GitLabDataset · publicAll data presented in the study are publicly available in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3).Open asset ↗10.17605/osf.io/um7r3html-lines:253-271Code · publicThe code for iSPy can also be found in the OSF data repository (https://osf.io/um7r3/; https://doi.org/10.17605/osf.io/um7r3), as well as in a GitLab repository, https://gitlab.gwdg.de/devplantpatterning/Publications/ispy-inferring-spatial-ploidy.Open asset ↗gitlab.gwdg.de · devplantpatterning/Publications/ispy-inferring-spatial-ploidyhtml-lines:253-271Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Plant cell walls are dynamic composites whose architecture determines growth, mechanics, and environmental resilience. Efforts to link pectin structure to function have been limited by the lack of molecular probes with sufficient specificity, a gap that becomes even more pronounced for the intricately branched rhamnogalacturonon-II (RG-II) subclass. Here we report the first fluorescent probes with defined specificity to RG-II, engineered from catalytic site mutants of Bacteroides thetaiotaomicron glycoside hydrolases BT1010 and BT0996. These enzyme-derived probes bind RG-II monomer with high affinity, discriminate against dimeric forms, and localize to cell corners and junctions in Arabidopsis thaliana stems, consistent with RG-II’s unique ability among wall polysaccharides to form borate-mediated, covalent crosslinkages between molecules. Application of these probes revealed spatial partitioning distinct from the homogalacturonan (HG)- and rhamnogalacturonan I (RG-I)-enriched middle lamella, highlighting functional specialization among pectic domains, with RG-II reinforcing cell junctions while HG and RG-I mediate wall flexibility. Our work establishes a generalizable framework for transforming CAZymes into high-precision imaging reagents, enabling molecular-level visualization of structurally complex polysaccharides in the cell wall.
Why it matches plant phenotyping methodsRG-IIを特異的に可視化する蛍光プローブを開発し、植物細胞壁内の空間分布という植物状態を画像で測定する手法を示しているため、フェノタイピング手法が中心的です。
abstractHere we report the first fluorescent probes with defined specificity to RG-II
Reproduction assets foundThe paper deposits its raw microscopy z-stacks and maximum projections on OSF and its custom MATLAB image-analysis code on GitHub, both with explicit availability statements and public URLs.Dataset · publicMicroscopy data that support the findings of this study have been deposited in Open Science Framework. Raw z-stacks, output maximum intensity projections, and annotated figure images in greyscale are available at (https://osf.io/8utvs/overview).Open asset ↗Open Science Frameworklines:319-349Code · publicMATLAB code used for image analysis is available at https://github.com/kristenthorne/GHprobes.git, with usage instructions and example input and output files provided.Open asset ↗GitHub · kristenthorne/GHprobeslines:319-349Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In accordance with human life, agriculture has main role in it, and in addition to that most people are involved in some kind of agricultural activity either in a direct or indirect manner. Moreover, the agricultural sectors acquired a major role in supplying better quality food and thus made the greatest attribution to the growth of populations and economics. But, the disease over the crop has influenced the growth of the corresponding species and thus requires an earlier diagnosis of plant disease by utilizing the most adequate and automatic detection approach for improving the quality of the production of food as well as to reduce the loss in economic. But, there are no techniques in the conventional system for identifying the disease in diverse crops in the agricultural environment. In modern times, deep learning approaches have acquired tremendous enhancement in the identification of image categorization as well as the object detection system. For precise detection of plant disease, an improved classification model is developed. Initially, from the standard publicly available database, the images of the plants are aggregated. The gathered images are segmented using Dilated, Adaptive, and Attention-based Mask Recurrent Convolutional Neural Networks (DAA-MRCNN). Then, it is fed into a hybrid classification phase, where the new model namely Dilated, Adaptive, and Attention-based Multiscale DenseNet termed as (DAA-MDeNet) for classification. The classifier performance is improved by optimizing the parameter in Mask RCNN and Multiscale DenseNet using the hybrid optimization algorithm named African Vulture and Lemur Optimizer (AVLO). When compared with the other model, a superior performance is shown in the proposed model.
Why it matches plant phenotyping methods植物画像から病害をセグメンテーション・分類する深層学習手法を開発しており、罹病状態の推定が中心的な方法論的貢献である。
abstractFor precise detection of plant disease, an improved classification model is developed.
Reproduction assets foundThe paper uses the public PlantifyDr Kaggle dataset of plant disease images and provides the authors' implementation code on GitHub with explicit availability statements.Dataset · publica total of 12,500 images in it from 10 different plant types, where the 10 different types are considered as 10 individual datasets. (1) Apple, (2) Cherry, (3) Citrus, (4) Corn, (5) Grape, (6) Peach, (7) Pepper, (8) Potato, (9) Strawberry, and (10) Tomato. It contains a total of 37 as plant diseases. It was collected through “ https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset ”: “Access Date: 2023-08-09”.
Thus, the images are significantly aggregated, and it has been termed as \documentclass[12pt]{minimal}
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\setlength{\oddsidemargin}{-Open asset ↗lines:103-129Code · publicThis research did not receive any specific funding.
Data availability
In case of benchmark data: The data underlying this article are available in the dataset link as: https://www.kaggle.com/datasets/lavaman151/plantifydr-dataset .
Code availability
The code for the implementation of the developed model is available at the link https://github.com/kalicharan8u/Plant-Disease-Detection-using-Mask-RCNN-with-Multiscale-DenseNet - and it has been given in Section " Simulation setup ".
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Ashourloo D Matkan AA Huete A Aghighi H Mobasheri MR
Developing an index for detection and identification of disease stages
IOpen asset ↗lines:1529-1559Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Invasive plant pests and pathogens cause substantial environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified. As knowing sensitivity and specificity of visual inspection is critical for effective contingency planning and outbreak management, we address this deficiency using empirical data and statistical analyses. Twenty-three citizen scientist surveyors assessed up to 175 labelled oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these individual trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the 'gold-standard' (i.e., assuming perfect sensitivity and specificity). The utility of an approach using Bayesian modelling to estimate the sensitivity and specificity of visual inspection in the absence of a rarely available 'gold-standard' dataset was then examined with simulated plant health survey datasets. There was large variation in sensitivity and specificity between surveyors and between different symptoms, although the sensitivity of detecting a symptom was positively related to the frequency of the symptom on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with reliable prior knowledge of sites with a higher (~0.6) and lower (~0.3) true disease prevalence we show that sensitivity and specificity can be estimated without 'gold-standard' data using Bayesian modelling. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management.
Why it matches plant phenotyping methods植物の病徴を対象とする目視検査の感度・特異度を定量化し、ゴールドスタンダードなしで推定するベイズモデルを検討しており、植物病害状態の取得・評価法が研究の中心である。
abstractVisual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the code and data used for the study (plant health survey sensitivity/specificity analysis) via a public GitHub repository and an archived Zenodo DOI, both listed in allowed_urls.Code · publicne represents perfect agreement between estimated values and actual values.
(TIF)
Acknowledgments
We would like to thank all involved in the AOD survey days and the National Trust and Royal Parks for allowing workshops to take place on their properties.
Data Availability
The code and data used for this paper are available from: https://github.com/MCombess/Plant_Health_sens_spec_workflow and are archived: https://doi.org/10.5281/zenodo.15730414 .
Funding Statement
MC, NB, PC, SP undertook the work with funding from the United Kingdom’s Department for Environment, Food & Rural Affairs ( https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs ) through the FutuOpen asset ↗Plant_Health_sens_spec_workflowlines:244-268Dataset · publicIF)
Acknowledgments
We would like to thank all involved in the AOD survey days and the National Trust and Royal Parks for allowing workshops to take place on their properties.
Data Availability
The code and data used for this paper are available from: https://github.com/MCombess/Plant_Health_sens_spec_workflow and are archived: https://doi.org/10.5281/zenodo.15730414 .
Funding Statement
MC, NB, PC, SP undertook the work with funding from the United Kingdom’s Department for Environment, Food & Rural Affairs ( https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs ) through the Future Proofing Plant Health Programme (Project Reference: TH42222FR09: citizen sOpen asset ↗10.5281/zenodo.15730414lines:244-268Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Double haploid (DH) technology can significantly shorten the breeding cycle and improve the breeding efficiency, and it is favored by breeders. The metrics for evaluating the effect of haploid genome doubling mainly include anther emergence and ear seed setting. The evaluation of fertility restoration ability is mainly conducted through visual inspection at present, which is time-consuming, and easy to be affected by fatigue, resulting in errors and inconsistencies. Therefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers. In this work, we propose a grading evaluation model (Maize-IRNet) of haploid anther emergence and ear seed setting based on Inception-ResNet. Firstly, the modules of Stem and Inception-ResNet are utilized for image feature extraction and multi-scale feature learning. Then, the Reduction module is used for spatial downsampling and feature compression, and the global attention mechanism (GAM) is used to enhance the recognition of key regions of the image. The experimental results show that the Maize-IRNet's classification accuracy of haploid ear seed setting and anther emergence is 84.2 % and 84.0 %, which is higher than six baseline methods (VGG11_bn, ResNet50, ResNet101, ViT-Base-16, gMLP, MLP-Mixer). In order to facilitate the practical application for breeding researchers, we have developed a mobile application that integrates the Maize-IRNet model. This study helps to achieve high-throughput collection of fertility restoration phenotypes, improves the evaluation efficiency of fertility restoration, reduces breeding costs, and provides technical support for the promotion of engineering breeding of DH technology.
Why it matches plant phenotyping methodsトウモロコシの葯出現と穂の種子着生という生殖形質を画像から自動評価する深層学習モデルを開発・比較し、モバイルアプリにも実装しており、表現型取得法が中心である。
abstractTherefore, it is urgent to develop efficient and accurate evaluation technology to reduce the field work burden of researchers.
Reproduction assets foundThe paper's data availability statement explicitly provides the maize haploid fertility image dataset (1897 ear images, 6443 tassel images), the Maize-IRNet source code, and the Android APK, all hosted on the authors' public GitHub repository.Dataset · publicThe maize haploid fertility image dataset collected by smartphones is available at https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/datasetOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Code · publicThe source code: https://github.com/wyzwyz666/maize-haploid-fertility/blob/main/sourcecodeOpen asset ↗wyzwyz666/maize-haploid-fertilitylines:506-531Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.
Why it matches plant phenotyping methods植物の生体電気信号をセンサーで取得し、スペクトログラムとCNNで環境状態を分類する手法を開発・評価しており、植物の生理状態に基づく表現型取得が中心です。ただし、人間の感情分類は植物表現型ではありません。
abstractUsing an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification.
Reproduction assets foundThe paper's Data Availability Statement provides explicit public URLs for both the phenotype/bioelectrical signal dataset (figshare project) and the authors' analysis code (GitHub), directly reproducing this paper's plant-phenotyping measurements and computational analysis.Dataset · publicThe data is available at https://figshare.com/projects/Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783 (accessed on 25 October 2025).Open asset ↗figshare · Plant_Bioelectrical_Signals_for_Environmental_and_Emotional_State_Classification/265783lines:129-206Code · publicCode is available at https://github.com/pgloor/hiddenbiosignals (accessed on 25 October 2025).Open asset ↗github · pgloor/hiddenbiosignalslines:129-206Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Premise Deep learning-based classification of herbarium images is hampered by background heterogeneity, which introduces noise and artifacts that can potentially mislead models and degrade their accuracy. Addressing these effects is essential to enhance overall performance. Methods We introduce PlantSAM, an automated segmentation pipeline that integrates YOLOv10 for plant region detection and the Segment Anything Model (SAM2) for segmentation. YOLOv10 generates bounding box prompts to guide SAM2, enhancing segmentation accuracy. Both models were fine-tuned on herbarium images and evaluated using intersection over union (IoU) and Sørensen-Dice coefficient metrics. Results PlantSAM achieved state-of-the-art segmentation performance, with an IoU of 0.94 and a Sørensen-Dice coefficient of 0.97. Incorporating segmented images into classification models led to consistent performance improvements across five tested botanical traits, with accuracy gains of up to 4.36% and F1 score improvements of 4.15%. Conclusions Our findings highlight the importance of background removal in herbarium image analysis, as it significantly enhances classification performance by enabling models to focus more effectively on the foreground plant structures.
Why it matches plant phenotyping methods植物画像から背景を除去して植物領域を抽出するセグメンテーション手法の開発・評価が中心であり、植物形質分類への有用性も検証している。
abstractWe introduce PlantSAM, an automated segmentation pipeline that integrates YOLOv10 for plant region detection and the Segment Anything Model (SAM2) for segmentation.
Reproduction assets foundThe paper's data availability statement provides public GitHub repositories with segmentation source code, examples, and trained models, plus figshare DOIs for the segmentation dataset, the YOLOv10 plant region detection dataset, and the SAM fine-tuning/out-of-distribution dataset — all paper-specific and directly usedCode · publicThe source code for segmentation, including examples and trained models, is available at: https://github.com/IA-E-Col/PlantSAMOpen asset ↗IA-E-Col/PlantSAMlines:570-695Code · publicThe source code of the segmentation application is available at: https://github.com/IA-E-Col/plantsam-appOpen asset ↗IA-E-Col/plantsam-applines:570-695Dataset · publicthe segmentation dataset used to train the UNet model is available at https://doi.org/10.6084/m9.figshare.27685914Open asset ↗10.6084/m9.figshare.27685914lines:570-695Dataset · publicthe object detection dataset used to train YOLOv10 for plant region detection is available at https://doi.org/10.6084/m9.figshare.29528882Open asset ↗10.6084/m9.figshare.29528882lines:570-695Dataset · publicthe dataset used to fine‐tune SAM (a subset of the segmentation images from Sklab et al. [ 2024b ]) and the out‐of‐distribution dataset, used for evaluating segmentation robustness under challenging conditions, are available at https://doi.org/10.6084/m9.figshare.29538065Open asset ↗10.6084/m9.figshare.29538065lines:570-695Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物の画像ベース表現型を含むマルチオミクス統合と機械学習解析を自動化するツールを開発・適用しており、表現型解析ワークフローが中心的です。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Summary We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11 to 57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near Infrared spectra measured on leaves reflect phenotypic evolution unrelated with domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild versus domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいPhenotypic Divergence Index（mPDI）を開発しており、植物表現型の定量・比較手法が主要な貢献です。
abstractBuilding on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication.
Reproduction assets foundThe paper's phenotypic data, NIR spectra, trait ontology, and R analysis scripts are explicitly deposited publicly: phenotype/NIRS data and MIAPPE trait ontology at doi 10.57745/QWEKVK, and R scripts on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction: spp.) is a perennial legume traditionally cultivated as a forage crop and is now emerging as a promising candidate for development as a perennial grain legume. Despite its potential, no research has addressed the breeding of sainfoin varieties with superior grain processing properties. Methods: We conducted a multifactorial experiment to evaluate the depodding and dehulling efficiency of five commercially available sainfoin varieties. Seeds were processed using two different methods (belt thresher and impact dehuller) across five sample sizes. A pre-trained Faster R-CNN (Region-based Convolutional Neural Network) object detection model was fine-tuned to identify intact pods, whole seeds, and split seeds from images of the processed mixtures. These predictions were used to calculate processing efficiency (PE) for each variety. A comprehensive power analysis was performed to determine the minimum sample size of sainfoin pods required to detect differences in PE with high statistical power. Results: We observed strong varietal differences in PE, as well as clear effects of the processing method. Belt threshing produced mixtures with more intact pods, while the impact dehuller generated a higher proportion of split seeds. Increasing sample size led to more intact pods across all varieties and methods, and notably decreased seed proportion in belt-threshed samples. Statistical modeling combined with object detection outputs revealed that a minimum of 2 g of pods is required to reliably detect an absolute proportional difference of 0.25 in PE between two breeding lines with 80% power. Discussion: Our findings demonstrate that sainfoin varieties differ significantly in processing efficiency and that processing outcomes depend strongly on both method and sample size. Integrating deep learning-based phenotyping with robust statistical design enables efficient evaluation of processing traits and provides actionable guidelines for breeding programs. While deep learning models offer powerful, cost-effective tools for plant phenotyping, their outputs must be paired with rigorous statistical design to yield reliable and actionable insights for crop improvement.
Why it matches plant phenotyping methods画像からポッド・種子を検出し、処理効率という植物由来形質を算出する深層学習ベースの表現型解析が研究の中心であるため。
titleDeep learning driven, image-based phenotyping of seed processing efficiency in sainfoin
Reproduction assets foundThe paper's data availability statement explicitly deposits the seed image dataset and Faster R-CNN model weights in two public Zenodo repositories and all Python/R analysis code in a public GitHub repository, all with direct URLs.Dataset · publicThe image dataset and FasterRCNN model weights presented in the study are deposited in publicly available Zenodo repositories under accession numbers https://doi.org/10.5281/zenodo.8346923Open asset ↗Zenodo · 10.5281/zenodo.8346923lines:501-517Code · publicAll Python and R code used in this study are deposited in a public GitHub repository at https://github.com/BoMeyering/sainfoin_seed_RCNNOpen asset ↗GitHub · BoMeyering/sainfoin_seed_RCNNlines:501-517Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Protecting crops from diseases is vital for the sustainable agricultural systems that are needed for food security. Introducing functional resistance genes to enhance the plant immune system is highly effective for disease resistance, but identifying new immune receptors is resource intensive. We observed that functional immune receptors of the nucleotide-binding domain leucine-rich repeat (NLR) class show a signature of high expression in uninfected plants across both monocot and dicot species. Here, by exploiting this signature combined with high-throughput transformation, we generated a wheat transgenic array of 995 NLRs from diverse grass species to identify new resistance genes for wheat. Confirming this proof of concept, we identified new resistance genes against the stem rust pathogen Puccinia graminis f. sp. tritici and the leaf rust pathogen Puccinia triticina, both major threats to wheat production. This pipeline facilitates the rapid identification of candidate NLRs and provides in planta gene validation of resistance. The accelerated discovery of new NLRs from a large gene pool of diverse and non-domesticated plant species will enhance the development of disease-resistant crops.
Why it matches plant phenotyping methods995個のNLRを対象とする高スループット形質評価パイプラインを構築し、植物体内で病害抵抗性表現型を検証することが研究の中心であるため、単なる生物学的測定ではない。
abstractHere, by exploiting this signature combined with high-throughput transformation, we generated a wheat transgenic array of 995 NLRs from diverse grass species to identify new resistance genes for wheat.
Reproduction assets foundThe authors deposited the paper's raw phenotyping data, uncropped images, and analysis/figure scripts in a public figshare repository, explicitly linked in the Data availability and Code availability sections. Other URLs (TGRC, NASC, FAT-CAT, QKbusco, iTOL, HMMER) are stock centers or third-party tools, not paper-qualiDataset · publicThe raw data and uncropped images are available via figshare at https://doi.org/10.6084/m9.figshare.28680800.v1 (ref. 139 ).Open asset ↗figshare · 10.6084/m9.figshare.28680800.v1lines:159-171Code · publicThe scripts used for data analysis and figure preparation are available via figshare at https://doi.org/10.6084/m9.figshare.28680800.v1 (ref. 139 ).Open asset ↗figshare · 10.6084/m9.figshare.28680800.v1lines:159-171Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R 2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R 2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
Why it matches plant phenotyping methods葉の傾斜角分布という植物形質を画像から推定する深層学習手法を開発し、大規模データセット、既存モデル比較、レーザースキャンによる検証、水ストレス下での追跡評価まで実施しており、フェノタイピング手法が研究の中心です。
abstractWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public access to the authors' analysis code (Anonymous GitHub), the phenotyping image/trait dataset (Zenodo), and the pretrained AngleCam V2 model weights (Zenodo). All three are paper-specific, public, and actionable.Code · publicLK and TK conceived the ideas, designed the methodology, and led the analysis. TK, JP, RR, JF, LK,
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and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
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All authors declare that they have no conflicts of interest.
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CC-BY 4.0 International license
perpetuity. It is made available underOpen asset ↗anonymous.4open.science/r/AngleCamV2-2B38pdf-raw-page:2 lines:1-30Dataset · publicK, JP, RR, JF, LK,
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and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
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All authors declare that they have no conflicts of interest.
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, whoOpen asset ↗zenodo · 10.5281/zenodo.17086253pdf-raw-page:2 lines:1-30Model / weights · publicanuscript. All authors contributed
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critically to the drafts and gave final approval for publication.
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
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All authors declare that they have no conflicts of interest.
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for tOpen asset ↗zenodo · 10.5281/zenodo.17101166pdf-raw-page:2 lines:1-30Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model's ability to detect small objects-particularly sugarcane stem nodes-and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.
Why it matches plant phenotyping methodsサトウキビ茎節という植物器官の検出を対象に、改良YOLOモデルの開発と性能評価を中心的に行っており、再利用可能な画像ベース表現型取得手法に該当する。
abstractThe experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sugarcane stem node dataset and the study's source code on ScienceDB with public DOIs, both matching allowed URLs.Dataset · publicppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 ThOpen asset ↗ScienceDB · 10.57760/sciencedb.27078lines:65-90Code · publicno pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .Open asset ↗ScienceDB · 10.57760/sciencedb.27287lines:65-90Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Floral area is a major predictor of the attractiveness of a flowering plant for pollinators, yet the measurement of floral area is time-consuming and inconsistent across studies. Here, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image. The FloralArea algorithm has two main components: an object segmentation module and an area estimation module. The object segmentation module extracts the pixels of flowers and the reference object in an image. The area estimation module predicts floral area based on the ratio between flower and reference object pixels. We fine-tuned two YOLOv8 segmentation models for flower and reference object segmentation. The flower segmentation model achieved moderate precision, recall, mAP0.5, and mAP0.5-0.95 of 0.794, 0.68, 0.741, and 0.455 on the test dataset, while the reference object model achieved an impressive performance of 0.907, 0.940, 0.933, and 0.832. We evaluated FloralArea using 75 images of flowering plants. We used ImageJ to calculate the actual floral area for all the images and compared them with the predicted floral area from FloralArea. The predicted floral area correlated well with the measured floral area with a coefficient of determination (R 2 ) of 0.93 and a root mean square error of 20.58 cm 2 . The FloralArea algorithm reduced the time it takes to calculate floral area from an image by 99.24% compared with traditional methods with image processing tools like ImageJ. By streamlining floral area estimation, the FloralArea algorithm provides a scalable, efficient, consistent, and accessible tool for researchers, particularly to aid in assessing plant attractiveness to different pollinator groups.
Why it matches plant phenotyping methods花画像から花の面積という植物形質を自動抽出するAI手法を開発し、実測値との比較検証と処理時間評価を行っており、植物フェノタイピング手法が研究の中心です。
abstractHere, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image.
Reproduction assets foundThe paper's authors publicly released the FloralArea source code on GitHub and the flower image dataset (used for fine-tuning YOLOv8 models and evaluating the algorithm) on Penn State's ScholarSphere repository, as stated in the Data Availability statement.Code · publicThe source code for the FloralArea algorithm is available on GitHub ( https://github.com/eai6/FloralArea_Web.git ).Open asset ↗GitHub · eai6/FloralArea_Weblines:137-148Dataset · publicThe image dataset used to fine-tune the YOLOv8 models and evaluate the FloralArea algorithm is on the ScholarSphere repository of the Pennsylvania State University ( https://scholarsphere.psu.edu/resources/33452dff-b807-44b0-8783-71c8c47b5242 ).Open asset ↗ScholarSphere · 33452dff-b807-44b0-8783-71c8c47b5242lines:137-148Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Observing biodiversity across space and time is essential for advancing and verifying conservation efforts toward global biodiversity and sustainability goals. Spaceborne imaging spectroscopy has emerged as a revolutionary tool for quantifying and tracking forest diversity, yet its application at large spatial scales remains a central challenge. We develop a framework to map multiple dimensions of forest community composition and diversity by integrating imaging spectroscopy from two spaceborne sensors (DESIS and EMIT) with taxonomic, phylogenetic, and functional trait datasets, and 43,155 forest inventory plots across the Eastern United States. We find that spectral dissimilarity among forest communities is positively correlated with β-diversity matrices of compositional dissimilarity. We then show that imaging spectroscopy can be used to predict ordination axes of β-diversity and to map multiple dimensions of forest diversity at high spatial resolution (30 or 60 m). Predicted β-diversity axes can be used to model forest attributes, including forest types, plant lineages, and community plant traits. On average, β-diversity axes explain more than 48% of the variance—outperforming climatic and topographic predictors—and enable accurate mapping of 95 forest attributes. Our framework shows that spaceborne imaging spectroscopy, when combined with inventory data, allows indirect yet comprehensive observation of forest diversity attributes across broad spatial extents. This integrative approach sets the stage for scalable forest monitoring in support of global biodiversity conservation and forthcoming satellite missions.
Why it matches plant phenotyping methods宇宙空間イメージング分光と在庫データを統合し、森林群集の多様性や植物形質を推定・マッピングする枠組みが研究の中心であり、植物状態の大規模な表現型推定に該当する。
abstractWe develop a framework to map multiple dimensions of forest community composition and diversity by integrating imaging spectroscopy from two spaceborne sensors (DESIS and EMIT) with taxonomic, phylogenetic, and functional trait datasets, and 43,155 forest inventory plots across the Eastern United States.
Reproduction assets foundThe paper's plant-phenotyping/community-composition analysis relies on FIA forest inventory data (public via FIA DataMart), author analysis code publicly hosted on GitHub, and paper-specific spaceborne data products (Level 3/4 maps of β-diversity and forest attributes) released via Harvard Dataverse. The SDS link only Dataset · publicen
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concentration. The application of our mapping efforts to all the scenes used from DESIS and EMIT are
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available at Harvard Dataverse.
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Data, Materials, and Software Availability
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Forest inventory data were obtained from the USDA Forest Service’s FIA Program and are available
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through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html). However, as noted in the
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Methods, we used a federally protected version of the FIA database to access actual plot locations for our
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analyses (for more information on federally protected FIA data, see
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https://research.fs.usda.gov/programs/fia/sds). All code associated with this research is available on
4Open asset ↗FIA DataMartpdf-raw-page:14 lines:1-100Code · public). However, as noted in the
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Methods, we used a federally protected version of the FIA database to access actual plot locations for our
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analyses (for more information on federally protected FIA data, see
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https://research.fs.usda.gov/programs/fia/sds). All code associated with this research is available on
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GitHub (https://github.com/Antguz/mapping-communities), and will be archived in Zenodo under version
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1.0 upon publication. Data that do not compromise federally protected information are being prepared for
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release in the Harvard Dataverse. The spaceborne data products developed in this research are also
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available through the Harvard Dataverse
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(https://dataversOpen asset ↗GitHub · Antguz/mapping-communitiespdf-raw-page:14 lines:1-100Dataset · publicz/mapping-communities), and will be archived in Zenodo under version
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1.0 upon publication. Data that do not compromise federally protected information are being prepared for
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release in the Harvard Dataverse. The spaceborne data products developed in this research are also
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available through the Harvard Dataverse
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(https://dataverse.harvard.edu/previewurl.xhtml?token=cfb44b92-ec7f-4cd8-9c7c-c2b4b43612d6).498
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Acknowledgments
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501Open asset ↗Harvard Dataversepdf-raw-page:14 lines:1-100Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models. Eight pre-trained deep learning models - ResNet50, InceptionResNetV2, DenseNet 201, InceptionV3, EfficientNetB0, Xception, VGG16 and AlexNet,were evaluated using a three-stage methodology. First, the models are assessed using traditional classification metrics. Second, Local Interpretable Model-agnostic Explanations (LIME) is employed to visualize and quantitatively evaluate feature selection using metrics such as Intersection over Union (IoU) and the Dice Similarity Coefficient (DSC). Third, a novel overfitting ratio metric is introduced to quantify the reliance of the models on insignificant features. In the experimental analysis, ResNet50 emerged as the most accurate model, achieving 99.13% classification accuracy as well as the most reliable model demonstrating superior feature selection capabilities (IoU: 0.432, overfitting ratio: 0.284). Despite the high classification accuracies, models such as InceptionV3 and EfficientNetB0 showed poor feature selection capabilities with low IoU scores (0.295 and 0.326) and high overfitting ratios (0.544 and 0.458), indicating potential reliability issues in real-world applications. This study introduces a novel quantitative methodology for evaluating deep learning models that goes beyond traditional accuracy metrics, enabling more reliable and trustworthy AI systems for agricultural applications. This methodology is generic and researchers can explore the possibilities of extending it to other domains that require transparent and interpretable AI systems.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する深層学習モデルについて、性能と説明可能性を評価する三段階の方法論を開発・検証しており、植物病害状態のフェノタイピング手法が中心である。
abstractThe main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models.
Reproduction assets foundThe paper uses the public Kaggle rice leaf disease dataset and explicitly states that the authors' analysis code is publicly available on GitHub and MATLAB File Exchange via short URLs, both of which are in the allowed URL list.Dataset · publicChinna Gopi Simhadri: Formal analysis, investigation, resources, data collection, writing an original draft, Review & editing. All authors have read and agreed to the published version of the manuscript.
Data availability
In this work, the publicly available dataset was used. The data set is available through the link to Kaggle https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset.
Code availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be Open asset ↗Kaggle · dedeikhsandwisaputra/rice-leafs-disease-datasetlines:1332-1347Code · publicode availability
The code developed and used in this study has been uploaded and is available on GitHub and MATLAB File Exchange. These repositories include all the necessary scripts, tools, and instructions required to replicate the results presented in this work. The repository can be accessed via the following links: Github: https://shorturl.at/Github_Quant_XAI MATLAB File Exchange link: https://shorturl.at/MATLAB_Quant_XAI
Declarations
Competing interestsOpen asset ↗GitHublines:1332-1347Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract The development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits. We used imagery from unoccupied aerial vehicles (UAVs) with red/green/blue (RGB) and multispectral cameras flown over multiple site‐years in Saskatchewan, Canada, and Metaponto, Italy, to gather data for crop height, area, and volume in a lentil diversity panel (324 genotypes). The temporal nature of the UAV image‐derived data enabled the modeling of growth curves for volume, height, and area, something that would be impractical under traditional phenotyping procedures in such a large population grown in multiple environments. A principal component analysis and hierarchical clustering revealed differential growth patterns across contrasting environments, with large variations in temperature and photoperiod, within our lentil diversity panel. Combining this analysis with genome‐wide genotyping data, we identified markers, from an exome capture array (267,845 single nucleotide polymorphisms), associated with crop growth that could be used for marker‐assisted selection. Our study demonstrates the potential for UAV‐based imaging to obtain large‐scale time‐series data across multiple environments to model growth curves and investigate genotype‐by‐environment interactions. In addition, we can now use phenotypic traits that were once impractical to collect and derive novel phenotypes to improve our understanding of crop growth and the genetics underlying adaptation in lentil, approaches that will be useful for both researchers and breeders.
Why it matches plant phenotyping methodsUAV画像からレンティルの高さ・面積・体積を時系列推定し、大規模集団で成長曲線をモデル化するフェノタイピング手法の実質的な適用・評価が中心である。
abstractThe development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits.
Reproduction assets foundThe paper's UAV-derived lentil growth phenotypes are publicly available on KnowPulse, and the authors' full analysis code/workflow is public on GitHub with a rendered vignette. Both are explicitly stated in the data availability statement and methods.Dataset · publiciluppo e di Innovazione
in Agricoltura) in Metaponto, Italy. Special thanks to Laura
Jardine for help with editing.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are
available online at https://knowpulse.usask.ca/research-experiment/AGILE-UAV and https://github.com/derekmichaelwright/AGILE_LDP_UAV or from the authors
upon request.
O RC I D
DerekM. Wright https://orcid.org/0000-0002-9639-7596
SandeshNeupane https://orcid.org/0000-0003-3679-1046
Tania Gioia https://orcid.org/0000-0001-8980-3034
Giuseppina Logozzo https://orcid.org/0000-0002-7951-2425
SOpen asset ↗knowpulse.usask.ca · AGILE-UAVpdf-raw-page:11 lines:1-84Code · publical user-
calculated traits as described in Figure 2. G × E analysis was
done with “lme4” using linear mixed models (Bates et al.,
2015). Principal component analysis (PCA) and hierarchical
k-means clustering were performed using the “FactoMineR”
R package (Lê et al., 2008). The source code for all data
analyses is available at: https://derekmichaelwright.github.io/AGILE_LDP_UAV/LDP_UAV_Vignette.html.25782703,
2025,
1,
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[20/08/2025].
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and
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(https://onlinelibrary.wiley.com/terms-and-conditions)
on
WileyOpen asset ↗derekmichaelwright.github.io/AGILE_LDP_UAV · LDP_UAV_Vignettepdf-raw-page:3 lines:1-106Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
In recent years, accurate and low-cost variant calling has enabled the genotyping of large diversity panels for genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. This has created a strong need for high-throughput, accurate, and low-cost in-field phenotyping. Here, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera. Our high-throughput phenotyping approach integrates an RGB- and MSP camera to measure the color and height of lettuce in this large-scale field experiment. We used the mean and other summary statistics, such as median, quantiles, skewness, kurtosis, minimum, and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using these summary statistics as traits for GWAS, we confirm several previously described genetic associations, now under field conditions, and identify additional novel associations for color and height traits in lettuce.
Why it matches plant phenotyping methodsドローン搭載RGB・マルチスペクトルカメラを用いて、レタスの色と高さを大規模・非破壊・定量測定する高スループット表現型解析手法が研究の中心であり、GWASへの応用も行っている。
abstractHere, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera.
Reproduction assets foundThe paper's authors publicly deposited their image processing, GWAS, and figure scripts on GitHub (SnoekLab/Dijkhuizen_etal_2025_Drone) and all raw/intermediate phenotyping data (including weather data) at a UU Yoda DOI (10.24416/UU01-S5FCM9). Both are paper-specific, public, and actionable.Code · publicThe scripts for making the SNP map from the filtered VCF file and for the image processing, GWAS, and figures in this manuscript are available on https://github.com/SnoekLab/Dijkhuizen_etal_2025_Drone .Open asset ↗SnoekLab/Dijkhuizen_etal_2025_Dronelines:362-531Dataset · publicData available at https://doi.org/10.24416/UU01‐S5FCM9 . This includes all raw data, all intermittent steps, the data required to generate all figures, and data on the weather during the experiment.Open asset ↗10.24416/UU01‐S5FCM9lines:362-531Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, andDataset · publicason, therefore,
from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the
field. The factor to convert radiation in MJ m−
2 to PPFR was 2.04 according to [26].
4.1 Data Files and Structure
The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing
pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable
and widely-used ‘.csv‘ and ‘.png‘ format.
• data/Design 2015 2022 Eschikon.csv: Experimental design file, including pOpen asset ↗ETH Research Collection · 10.3929/ethz-b-000742401pdf-raw-page:6 lines:1-47Code · publicCollection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing
interoperability and standardization with other datasets.
9 Code availability
Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code
is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover
10 Author contributions
BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK,
CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design:
BK, LK, LR, AH; all authors improved and approved the manuscript
5Open asset ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycoverpdf-raw-page:7 lines:1-46Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型別の光合成パラメータを推定するニューラルネットワーク手法であり、植物生理形質の抽出が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper deposits its maize gas exchange phenotype measurements (Zenodo 15966533), the synthetic neural-network training dataset (Zenodo 15926601), and the C4TUNE analysis/training code with predicted genotype parameters (GitHub pwendering/C4TUNE), all with explicit availability statements and public URLs.Dataset · publicwere tuned as described above (“Surrogate
648
model”). The final model was trained for 30 epochs with a batch size of 8.
649
The neural networks were implemented using Python 3.10.14 using the PyTorch library version
650
2.5.1 42
.
651
Data availability
652
The gas exchange measurements for maize genotypes are available at
653
https://doi.org/10.5281/zenodo.15966533. Part of these data has been used in another study
654
linking photosynthesis-related traits and hyperspectral reflectance data 43
. The generated
655
artificial data set for neural network training is available at
656
https://doi.org/10.5281/zenodo.15926601.657
.
CC-BY-NC-ND 4.0 International license
made available under a
(whOpen asset ↗zenodo · 10.5281/zenodo.15966533pdf-raw-page:21 lines:1-94Code · public22
Code availability
658
Custom code for the generation of the artificial dataset as well as code for neural model
659
definition and training are available at https://github.com/pwendering/C4TUNE. This
660
repository also contains the predicted parameters for the maize genotypes.
661
References
662
1. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater
663
yield. Annu. Rev. Plant Biol. 61, 235–261 (2010).
664
2. Croce, R. et al. Perspectives on improving photosynthesis to increase crop yOpen asset ↗github · pwendering/C4TUNEpdf-raw-page:22 lines:1-69Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
A novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits, revealing latent patterns related to dense root clusters in digital images. Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm, 27 ARTs were extracted reflecting dense root cluster size and spatial location. These ARTs were then used independently and in combination with Traditional Root Traits (TRTs) to classify wheat genotypes differing in drought tolerance. ART-based models outperformed TRT-only models in drought classification (e.g., 96.3 % vs. 85.6 % accuracy). Combining ARTs and TRTs further improved accuracy to 97.4 %. Notably, 4 selected ARTs matched the performance of all 23 TRTs, offering 5.8 × higher information density (0.213 vs. 0.037 accuracy/feature). This superiority reflects the ability of ARTs to capture richer, more complex architectural information, evidenced by higher internal variability (35.59 ± 11.41 vs. 28.91 ± 14.28 for TRTs) and distinct data structures in multivariate analyses; PERMANOVA confirmed that ARTs and TRTs provide complementary insights. Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes, ART offers a scalable, customisable toolset for high-throughput phenotyping of plant roots. By bridging conventional, visually derived traits with autonomous computational analyses, this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception. ART thus emerges as a promising framework for revealing hidden features in plant imaging, with broader applications across plant science to deepen our understanding of crop adaptation and resilience.
Why it matches plant phenotyping methodsデジタル画像から根の潜在形質を抽出する計算法を開発し、圃場・制御環境で検証した、植物フェノタイピング手法が中心の研究。
abstractA novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits
Reproduction assets foundThe authors explicitly state that all code, data, and segmented root images from this study are publicly available in their GitHub repository (shoaibms/ART), which directly reproduces the paper's root phenotyping measurements and analysis.Code · publicAll code, data and segmented images are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:277-403Dataset · publicAll code and data are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:120-154Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Object detection is essential for precision agriculture applications like automated plant counting, but the minimum dataset requirements for effective model deployment remain poorly understood for arable crop seedling detection on orthomosaics. This study investigated how much annotated data is required to achieve standard counting accuracy (R2 = 0.85) for maize seedlings across different object detection approaches. We systematically evaluated traditional deep learning models requiring many training examples (YOLOv5, YOLOv8, YOLO11, RT-DETR), newer approaches requiring few examples (CD-ViTO), and methods requiring zero labeled examples (OWLv2) using drone-captured orthomosaic RGB imagery. We also implemented a handcrafted computer graphics algorithm as baseline. Models were tested with varying training sources (in-domain vs. out-of-distribution data), training dataset sizes (10–150 images), and annotation quality levels (10–100%). Our results demonstrate that no model trained on out-of-distribution data achieved acceptable performance, regardless of dataset size. In contrast, models trained on in-domain data reached the benchmark with as few as 60–130 annotated images, depending on architecture. Transformer-based models (RT-DETR) required significantly fewer samples (60) than CNN-based models (110–130), though they showed different tolerances to annotation quality reduction. Models maintained acceptable performance with only 65–90% of original annotation quality. Despite recent advances, neither few-shot nor zero-shot approaches met minimum performance requirements for precision agriculture deployment. These findings provide practical guidance for developing maize seedling detection systems, demonstrating that successful deployment requires in-domain training data, with minimum dataset requirements varying by model architecture.
Why it matches plant phenotyping methodsトウモロコシ幼苗の個体数という植物形質を画像から推定する物体検出手法について、複数モデル、データ量、アノテーション品質を系統的に比較・評価しており、手法の性能検証が中心である。
abstractThis study investigated how much annotated data is required to achieve standard counting accuracy (R2 = 0.85) for maize seedlings across different object detection approaches.
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' handcrafted-method analysis code on a GitHub gist and the ID (in-distribution) annotation datasets created for this study on Zenodo. Both have explicit availability language and public URLs.Code · publicThe code for the handcrafted methods used in this study is available at
https://gist.github.com/SamueleBumbaca/4a227bbe7b78d6be3424899c16c60bb4 (accessed on 20
June 2025).Open asset ↗gist.github.com/SamueleBumbacapdf-page:23 lines:1-52Dataset · publicThe datasets created during this study (ID datasets) are available at the Zenodo repository
https://doi.org/10.5281/zenodo.15235602 (accessed on 20 June 2025)Open asset ↗Zenodo · 10.5281/zenodo.15235602pdf-page:23 lines:1-52Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Seed / grainGrowth / time-series analysisGrowth / development / phenology
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian inference, which utilizes previous measurements, historical data, and an expected model. Five Bayesian methods are assessed in this study: Important sampling (IS), Markov chain Monte-Carlo (MCMC), Gaussian process (GP), Extended Kalman filtering (EKF) and Sampling Importance Resampling particle filtering (SIR-PF). We test these five Bayesian sampling methods for the monitoring of germination rate in terms of compression, distortion and computation cost. The best trade-off is found by the MCMC method, which offers a compression rate of 0.2 with very little distortion. GP offers the most unbiased parameter estimation and the capability to adapt to various germination speeds. It also has reasonable computational times.
Why it matches plant phenotyping methods発芽率の時系列フェノタイピングに対するベイズ適応サンプリング法を開発・比較し、圧縮率、歪み、計算コストで評価しており、表現型取得・監視手法が研究の中心である。
abstractWe propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage.
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository with the code implementing the five Bayesian adaptive sampling methods, and a public germination kinetics dataset (red clover accessions) deposited at doi.org/10.57745/JECJUI, which is the raw phenotype data analyzed in the study.Code · publicwe provide the codes and data to perform the computation and discuss the convergence of the process: The code is available at the following address: https://github.com/Fatryuk/BayesianAdaptivSampling.gitOpen asset ↗https://github.com/Fatryuk/BayesianAdaptivSampling.gitlines:29-41Dataset · publicData used in the article are table in.csv format containing raw germination along time available at the following repository https://doi.org/10.57745/JECJUIOpen asset ↗https://doi.org/10.57745/JECJUI · 10.57745/JECJUIlines:297-334Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant disease classification using deep learning techniques has shown promising results, especially when models are trained on high-quality images. However, these models often suffer from a significant drop in their accuracies when tested in real-world agricultural settings. In the wild, models encounter images that are significantly different from the training data in aspects like lighting conditions, capturing conditions, image resolution, and the severity of disease. This discrepancy between the training images and images in-the-wild conditions poses a major challenge for deploying these models in agricultural settings. In this paper, we present a novel approach to address this issue by combining a Vision Transformer backbone with a Mixture of Experts, where multiple expert models are trained to specialize in different aspects of the input data, and a gating mechanism is implemented to select the most relevant experts for each input. The use of Mixture of Experts allows the model to dynamically allocate specialized experts to different types of input data, improving model performance across diverse image conditions. The approach significantly improves performance on diverse datasets that contain a range of image capturing conditions and disease severities. Furthermore, the model incorporates entropy regularization and orthogonal regularization, aiming to enhance the robustness and generalization capabilities. Experimental results demonstrate that the proposed model achieved a 20% improvement in accuracy compared to Vision Transformer (ViT). Furthermore, it demonstrated a 68% accuracy on cross-domain datasets like PlantVillage to PlantDoc, surpassing baseline models such as InceptionV3 and EfficientNet. This highlights the potential of our model for effective deployment in dynamic agricultural environments.
Why it matches plant phenotyping methods野外画像から植物病害の状態・重症度を推定する分類手法を開発し、異なるデータセットや撮影条件で性能検証しているため、植物フェノタイピング手法が中心です。
abstractIn this paper, we present a novel approach to address this issue by combining a Vision Transformer backbone with a Mixture of Experts
Reproduction assets foundThe paper's authors publicly release implementation details and trained models on GitHub, and the study analyzes two public plant disease image datasets (PlantVillage and PlantDoc) hosted on Kaggle, all directly used for this paper's phenotyping/classification analysis.Code · publicment in the agricultural sector. Future work could focus on further optimizing the model architecture, exploring additional data augmentation techniques, and testing the model in diverse field conditions to validate its applicability in complex farming environments. The implementation details and trained models are available at https://github.com/salman32140/Vit_MoE/ .
By offering a solution that effectively bridges the gap between lab-controlled datasets and complex image conditions, this research paves the way for more reliable and scalable plant disease detection systems that can support sustainable agricultural practices and enhance the role of technology in crop management strategies.Open asset ↗https://github.com/salman32140/Vit_MoE/ · salman32140/Vit_MoElines:674-698Dataset · public56354) supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), in part by the National Program for Excellence in SW, supervised by the IITP in 2025” (2024- 0-00037).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage , https://www.kaggle.com/datasets/abdulhasibuddin/plant-doc-dataset .
Author contributions
ZS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. AM: Supervision, Validation, Writing – reviewOpen asset ↗https://www.kaggle.com/datasets/mohitsingh1804/plantvillage · mohitsingh1804/plantvillagelines:674-698Dataset · publiccations Technology Planning and Evaluation (IITP), in part by the National Program for Excellence in SW, supervised by the IITP in 2025” (2024- 0-00037).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage , https://www.kaggle.com/datasets/abdulhasibuddin/plant-doc-dataset .
Author contributions
ZS: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. AM: Supervision, Validation, Writing – review & editing. DH: Funding acquisition, Project administration, ROpen asset ↗https://www.kaggle.com/datasets/abdulhasibuddin/plant-doc-dataset · abdulhasibuddin/plant-doc-datasetlines:674-698Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Natural biocomposites such as wood and plant cell walls exhibit remarkable mechanical properties largely attributed to their nanoscale chiral organization of fibrous components, such as cellulose. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization opens new understanding of materials properties as well as opportunities for the design of bio-inspired materials with tunable mechanical and functional properties.
Why it matches plant phenotyping methods植物細胞壁中のセルロース fibril の3D配向を定量マッピングする画像計測・再構成法が研究の中心であり、植物構造形質の取得手法を開発している。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
Reproduction assets foundThe article's Data and Code Availability statement declares that the SED datasets (diffraction data from oat husk and birch wood) and the authors' custom Python analysis script are publicly available on Zenodo (DOI: 10.5281/zenodo.15647651). This is a paper-specific, public, actionable asset directly reproducing the 3DDataset · publicData and Code Availability
SED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code · publicSED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Humans need food to sustain their lives. Therefore, agriculture is one of the most important issues in nations. Agriculture also plays a major role in the economic development of countries by increasing economic income. Early diagnosis of plant diseases is crucial for agricultural productivity and continuity. Early disease detection directly impacts the quality and quantity of crops. For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. Disease classification was performed using seven state-of-the-art pretrained convolutional neural network architectures: VGG16, ResNet50, SqueezeNet, Xception, ShuffleNet, DenseNet121 and MobileNetV2. A simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification. GAPNet was designed to be a lightweight and fast model with 337.872 parameters. To address the data imbalance between classes, oversampling was carried out using the synthetic minority oversampling technique. The proposed model achieves accuracy rates of 99.72%, 99.53%, and 99.83% for grape, apple and potato leaf disease classification, respectively. A success rate of 99.64% was achieved in multiplant leaf disease classification when the grape, apple and potato datasets were combined. Compared with the state-of-the-art methods, the lightweight GAPNet model produces promising results for various plant species.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する軽量CNN手法を開発・評価しており、病害表現型の抽出が研究の中心です。
abstractIn this study, a simple and effective leaf disease classification method was developed.
Reproduction assets foundAuthors publicly release GAPNet implementation code via GitHub and Zenodo, and the paper's leaf image datasets (PlantVillage, New Plant Disease, Plant Pathology 2020) are publicly available at listed URLs.Code · publiccle, and approved the final draft.
Asuman Günay Yılmaz conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data and code are available at GitHub and Zenodo:
- https://github.com/ozgenurr/GAPNet.git .Open asset ↗ozgenurr/GAPNetlines:727-762Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases uOpen asset ↗PlantVillage-Datasetlines:763-789Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing SympOpen asset ↗lines:763-789Dataset · publicGAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing Symposium (IDAP-2023) 2023:67–74. doi: 10.53070/bbd.1349566.
BanjaOpen asset ↗lines:763-789Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf color patterns in nature, shaped by genetic and environmental factors, can be analyzed using hyperspectral reflectance imaging. This protocol details step-by-step procedures for hyperspectral image acquisition, correction of uneven lighting, and spectral component analysis to reveal distinct and sometimes previously undetectable features on leaves. We outline how to identify key spectral components and project hyperspectral cubes onto them to highlight specific spectral traits. For complete details of this protocol, please refer to Krishnamoorthi et al. 1 .
Why it matches plant phenotyping methods葉の色・スペクトル形質を抽出するハイパースペクトル画像取得、補正、成分分析の手順を扱うプロトコルであり、植物フェノタイピング手法が中心です。
abstractThis protocol details step-by-step procedures for hyperspectral image acquisition, correction of uneven lighting, and spectral component analysis to reveal distinct and sometimes previously undetectable features on leaves.
Reproduction assets foundThe protocol's authors publicly release their analysis code (Python script, Jupyter notebook, conda environment) and sample hyperspectral images of ornamental plants via GitHub, Figshare, and a Zenodo-archived repository version. These are paper-specific phenotyping assets (hyperspectral leaf images and spectral unmix/Code · publicontacts, Shalini Krishnamoorthi ( kshalini@tll.org.sg ) and Dr. Daisuke Urano ( daisuke@tll.org.sg ).
Materials availability
No new experimental materials were utilized in this protocol.
Data and code availability
The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), SinOpen asset ↗Plant-Hyperspectral · dr-daisuke-urano/Plant-Hyperspectrallines:398-433Dataset · publicand sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High Performance Precision Agriculture system (A19E4a0101), and by the Singapore-MIT Alliance for Research & Technology, National Research Foundation: DisOpen asset ↗Zenodo · 10.5281/zenodo.15354496lines:398-433Dataset · publicano ( daisuke@tll.org.sg ).
Materials availability
No new experimental materials were utilized in this protocol.
Data and code availability
The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High PeOpen asset ↗Figsharelines:398-433Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows measurements to be collected in p @0.5 of 97.1% for stomatal detection. When validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress.
Why it matches plant phenotyping methodsFieldDinoは、圃場で気孔の生理・解剖形質を高スループットに取得するフェノタイピング手法として開発され、大規模圃場試験で検証されているため含める。
abstractWe present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public GitHub repository containing the 3D-printed leaf clip STL files, the Python stomatal annotation/measurement script, and the FieldDino app, plus a public Roboflow dataset hosting the training/validation stomatal image set used to train the YOLOv8-M模型.Code · publicrse.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtsalter/FieldDinoMicroscopy . Validation datasets for the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x .
References
Baloch , M. J.
,
J.
Dunwell
,
K.
DrN
, et al. 2013 . “
Morpho‐Physiological Characterization of Spring Wheat Genotypes Under Drought Stress
.” InterOpen asset ↗williamtsalter/FieldDinoMicroscopylines:337-498Dataset · publicResearch Infrastructure Strategy (NCRIS). Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Data Availability Statement
The data that support the findings of this study are openly available in Roboflow at https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtOpen asset ↗narrabri-plant-physiology-hclvi/fielddino-training-set-200xlines:337-498Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30-5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN . caas-4A2, QSN . caas-4D and QSN . caas-5B2 , were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN . caas-4A2 and QSN . caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.
Why it matches plant phenotyping methodsRGB画像とコンピュータビジョンによりコムギの穂数を自動推定する手法を開発・比較し、精度を評価しているため、植物フェノタイピング手法が中心である。
abstractHence, there is an urgent need to develop efficient and accurate methodologies for SN counting.
Reproduction assets foundThe paper publicly releases two paper-specific assets: (1) a wheat spike number image dataset (subsets CD&DD&XX) on GitHub, and (2) the YOLOX-P analysis code on Google Drive. Both have explicit availability statements with author-provided URLs.Dataset · publicThe image set for CD&DD&XX is publicly available on GitHub ( https://github.com/lileimax/YOLOXP-wheat-spike-identification ).Open asset ↗https://github.com/lileimax/YOLOXP-wheat-spike-identificationlines:223-246Code · publicThe code for YOLOX-P is publicly available on Google Drive ( https://drive.google.com/drive/folders/1urCDUdyrq14FuwG2I3YwCZraUGEEl_8X?usp=sharing ).Open asset ↗lines:247-250Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Premise This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao . Methods Despite recent stagnation in accuracy improvements in computer vision for image classification, our research demonstrates significant advancements in performance through semi-supervised learning, specialised loss functions, and the inclusion of a non-cocoa class. Results Semi-supervised learning reduced overfitting and enhanced generalisability, particularly for subtle symptoms. The non-cocoa class exposed models to a broad range of relevant features, significantly improving model robustness and performance in difficult cases. Grad-CAM for qualitative assessment provided valuable insights into model behaviour, highlighting cases of overfitting missed by summary statistics. We also describe dynamic focal loss, a novel loss function that uses an empirical measure of difficulty to weight each image. Our results suggest that while PhytNet shows promise in terms of computational efficiency and superior handling of difficult images, ResNet18 with semi-supervised learning and dynamic focal loss emerged as the strongest contender for real-world deployment. Discussion This research underscores the potential of semi-supervised learning and advanced loss functions in enhancing the applicability of deep learning models in agricultural disease management. It also presents a new high-quality benchmark dataset of 7220 images of diseased and healthy cocoa trees, offering a much greater and more realistic challenge than the Plan Village dataset.
Why it matches plant phenotyping methodsカカオ葉・樹体の病徴画像から植物の病害状態を推定する深層学習手法を開発・比較し、性能評価とベンチマークデータセット構築を行っており、フェノタイピング手法が中心である。
abstractThis study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, Theobroma cacao .
Reproduction assets foundThe paper's data availability statement explicitly provides the paper-specific cocoa image dataset and the FAIGB dataset on OSF, plus authors' analysis code on GitHub, all with public URLs.Dataset · publicThe cocoa image data is available at https://osf.io/2fw6gOpen asset ↗osf · 2fw6glines:753-923Dataset · publicthe FAIGB web‐scraped dataset of crop disease images is available at https://osf.io/nuafhOpen asset ↗osf · nuafhlines:753-923Code · publicAll code necessary to reproduce these results is available on GitHub ( https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet/PhytNet_Cocoa )Open asset ↗github · jrsykes/CocoaReaderlines:753-923Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Plant cells are contained within a rigid network of cell walls. Cell walls serve as a structural material and a crucial signaling hub vital to all aspects of the plant life cycle. However, many features of the cell wall remain enigmatic, as it has been challenging to map its functional properties in live plants at subcellular resolution. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag uses a small molecular motif, a pyridine boronic acid, that directs its cargo to the cell wall. We designed a suite of cell wall imaging probes based on CarboTag in various colors for multiplexing. Additionally, we developed new functional reporters for live quantitative imaging of key cell wall characteristics: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag paves the way for dynamic and quantitative mapping of cell wall responses at subcellular resolution. Subject terms: Plant cell biology, Fluorescence imaging
Why it matches plant phenotyping methods植物細胞壁のライブ機能イメージング用ツールボックスを開発し、孔隙率、pH、活性酸素などの細胞壁特性を定量化する手法が中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
Reproduction assets foundThe paper's Data availability and Code availability statements both point to a public 4TU repository DOI containing the raw imaging/phenotyping data and the analysis code for this paper's CarboTag cell wall imaging measurements.Dataset · publicThe raw data associated with the figures in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 . Source data are provided with this paper.Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code · publicCode developed to process and analyze data in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 .Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Remote sensing holds promise for ecosystem-level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field-based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory-based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of "space for time" approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress.
Why it matches plant phenotyping methods樹木の干ばつストレスを対象に、航空機VSWIR画像による含水量推定と水ポテンシャルとの関係を時空間的に検証しており、リモートセンシング手法の妥当性・限界評価が中心です。
abstractwater content (measured in situ and via repeat airborne VSWIR imaging)
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's data and analysis code on Zenodo (DOI 10.5281/zenodo.15110087), and the AVIRIS-NG reflectance data used for the canopy water content analysis is publicly archived at ORNL DAAC (DOI 10.3334/ORNLDAAC/2376). Both are paper-specific, public, and match Code · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:245-270Dataset · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:467-475Dataset · publicReflectance data was obtained from ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2376 .Open asset ↗ORNL DAAC · 10.3334/ORNLDAAC/2376lines:245-270Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.
Why it matches plant phenotyping methods航空画像とESGANを用いて、ススキの出穂（開花期）を低アノテーションで自動推定する手法を開発・比較しており、植物表現型取得が中心である。
abstractThis study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (UAV imagery/annotations) in the Illinois Databank and the analysis code in a public GitHub repository, both with author-provided URLs.Dataset · publicons, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy.
Data availability
The data sets used and coding implementation during the current study are publicly available from the online Illinois Databank at https://doi.org/10.13012/B2IDB-8462244_V2 and GitHub repository https://github.com/pixelvar79/ESGAN-Flowering-Detection-paper .
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35680968
PMC9184641
Ahmed SF , Alam MDSB , Hassan M , RozbOpen asset ↗Illinois Databank · B2IDB-8462244_V2lines:164-219Code · publichis publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy.
Data availability
The data sets used and coding implementation during the current study are publicly available from the online Illinois Databank at https://doi.org/10.13012/B2IDB-8462244_V2 and GitHub repository https://github.com/pixelvar79/ESGAN-Flowering-Detection-paper .
References
Ahmad W , Ali H , Shah Z , Azmat S
.
A new generative adversarial network for medical images super resolution
. Sci Rep . 2022 : 12 ( 1 ): 9533 .
10.1038/s41598-022-13658-4
35680968
PMC9184641
Ahmed SF , Alam MDSB , Hassan M , Rozbu MR , Ishtiak T , Rafa N , Mofijur M , Shawkat Ali ABM , GandomOpen asset ↗GitHub · pixelvar79/ESGAN-Flowering-Detection-paperlines:164-219Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate and efficient assessment of highland barley (Hordeum vulgare L.) density is crucial for optimizing cultivation and management practices. However, challenges such as overlapping spikes in unmanned aerial vehicle (UAV) images and the computational requirements for high-resolution image analysis hinder real-time detection capabilities. To address these issues, this study proposes an improved lightweight YOLOv5 model for highland barley spike detection. We chose depthwise separable convolution (DSConv) and ghost convolution (GhostConv) for the backbone and neck networks, respectively, to reduce the parameter and computational complexity. In addition, the integration of convolutional block attention module (CBAM) enhances the model's ability to focus on target object in complex backgrounds. The results show that the improved YOLOv5 model has a significant improvement in detection performance. Precision and recall increased by 3.1% to 92.2% and 86.2%, respectively, with an F1 score of 0.892. The AP0.5 reaches 92.7% and 93.5% for highland barley in the growth and maturation stages, respectively, and the overall mAP0.5 improved to 93.1%. Compared to the baseline YOLOv5n model, the number of parameters and floating-point operations (FLOPs) were reduced by 70.6% and 75.6%, respectively, enabling lightweight deployment without compromising accuracy. In addition,the proposed model outperformed mainstream object detection algorithms such as Faster R-CNN, Mask R-CNN, RetinaNet, YOLOv7, and YOLOv8, in terms of detection accuracy and computational efficiency. Although this study also suffers from limitations such as insufficient generalization under varying lighting conditions and reliance on rectangular annotations, it provides valuable support and reference for the development of real-time highland barley spike detection systems, which can help to improve agricultural management.
Why it matches plant phenotyping methodsUAV画像からハダンオオムギの穂を検出し密度評価に用いる軽量化画像解析モデルを開発・比較しており、植物形質状態の取得手法が中心である。
abstractthis study proposes an improved lightweight YOLOv5 model for highland barley spike detection
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' highland barley UAV spike-detection dataset on ModelScope and their analysis code on GitHub, both paper-specific and publicly actionable.Dataset · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗Cai121/highland_barleylines:182-211Code · publicThe dataset can be available from https://modelscope.cn/datasets/Cai121/highland_barley and the code can be available from https://github.com/trangle666ddd/YOLOv5-highland-barley-detection .Open asset ↗trangle666ddd/YOLOv5-highland-barley-detectionlines:182-211Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.
Why it matches plant phenotyping methodsカシャバ葉の病害状態を画像から推定するセグメンテーションと深層学習手法の開発が研究の中心であり、植物病害フェノタイピングに該当する。
abstractWe propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings.
Reproduction assets foundThe paper uses the public Kaggle Cassava Leaf Disease Classification dataset (21,367 labeled cassava leaf images) and releases the authors' analysis code on Zenodo; both are paper-specific, public, and actionable.Dataset · publicapproved the final draft.
Data Availability
The following information was supplied regarding data availability:
The code is available at Zenodo: lizh0019. (2025). lizh0019/cassava: Cassava leaf disease recognition (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.14739855 .
The cassava leaf disease dataset is available at Kaggle: https://www.kaggle.com/competitions/cassava-leaf-disease-classification/data .
References
Ahmed, Jones & Marks (2015)
Ahmed E
Jones M
Marks TK
An improved deep learning architecture for person re-identification
2015 Computer Vision and Pattern Recognition
Piscataway
IEEE
3908 3916
Amid et al. (2019)
Amid E
Warmuth MK
Anil R
Koren T
Robust bi-tempered logistic loss basOpen asset ↗Kaggle · cassava-leaf-disease-classificationlines:1680-1767Code · publicang conceived and designed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The code is available at Zenodo: lizh0019. (2025). lizh0019/cassava: Cassava leaf disease recognition (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.14739855 .
The cassava leaf disease dataset is available at Kaggle: https://www.kaggle.com/competitions/cassava-leaf-disease-classification/data .
References
Ahmed, Jones & Marks (2015)
Ahmed E
Jones M
Marks TK
An improved deep learning architecture for person re-identification
2015 Computer Vision and Pattern Recognition
Piscataway
IEEOpen asset ↗Zenodo · 10.5281/zenodo.14739855lines:1680-1767Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Recent years have seen significant technological advancements in precision farming and plant phenotyping. Remote sensing along with deep learning (DL) techniques can increase phenotyping efficiency and help on-farm decision making with rapid stress detection. In this work, we use these techniques to evaluate drought stress in soybean plants, a crop whose yield is significantly affected by water availability. Images were taken from a high vantage in the field at various times throughout the day. Each image is given a wilting score ranging from 0 to 4 by expert scorers. We implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image. Given the significant overlap between adjacent classes in our dataset, we were able to achieve an overall classification accuracy of 64% and a one-off accuracy of 96% on our holdout test set. Our model outperformed DenseNet121 in most metrics, and provided comparable performance to a vision transformer (ViT) while having fewer parameters overall, less complexity (useful for edge implementations), and some interpretability. Furthermore, we were able to show that our model outperformed expert human annotators by predicting more consistent and accurate wilt levels when considering single-image re-annotation. The results show that our proposed methodology can be a useful approach in detecting drought stress in soybean fields to facilitate efficient crop management and aid selection of drought-resilient varieties.
Why it matches plant phenotyping methods画像からダイズ葉の萎凋・干ばつストレスを推定するMIL手法の開発と性能比較が中心であり、植物状態の表現型推定に該当する。
abstractWe implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image.
Reproduction assets foundThe paper's soybean leaf-wilt image dataset (1788 field images with expert wilt scores) is openly available on Zenodo, and the authors' MIL classification/analysis code is publicly available on GitHub, both explicitly stated in the Data Availability Statement.Dataset · publicThe original data presented in the study are openly available on the
data sharing platform Zenodo, accessed on 6 September 2023 https://zenodo.org/records/8256382
with DOI 10.5281/zenodo.8256382.Open asset ↗Zenodo · 10.5281/zenodo.8256382pdf-page:16 lines:1-58Code · publicWe have also made our code available on github and can be accessed
at https://github.com/ARoS-NCSU/Soybean-Leaf-Wilt-Classification, accessed on 4 March 2025.Open asset ↗GitHubpdf-page:16 lines:1-58Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
MaizeMorphology / geometry measurementRoot system architecture
Abstract Root System Architecture (RSA) plays an essential role in influencing maize yield by enhancing anchorage and nutrient uptake. Analyzing maize RSA dynamics holds potential for ideotype‐based breeding and prediction, given the limited understanding of the genetic basis of RSA in maize. Here, we obtained 16 root morphology‐related traits (R‐traits), 7 weight‐related traits (W‐traits), and 108 slice‐related microphenotypic traits (S‐traits) from the meristem, elongation, and mature zones by cross‐sectioning primary, crown, and lateral roots from 316 maize lines. Significant differences were observed in some root traits between tropical/subtropical and temperate lines, such as primary and total root diameters, root lengths, and root area. Additionally, root anatomy data were integrated with genome‐wide association study (GWAS) to elucidate the genetic architecture of complex root traits. GWAS identified 809 genes associated with R‐traits, 261 genes linked to W‐traits, and 2577 key genes related to 108 slice‐related traits. We confirm the function of a candidate gene, fucosyltransferase5 ( FUT5 ), in regulating root development and heat tolerance in maize. The different FUT5 haplotypes found in tropical/subtropical and temperate lines are associated with primary root features and hold promising applications in molecular breeding. Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. Collectively, our study offers a valuable tool for dissecting the genetic architecture of RSA, along with resources and predictive models beneficial for molecular design breeding and genetic enhancement.
Why it matches plant phenotyping methodsトウモロコシ根系形態を大規模に取得し、根スライス形質に基づく機械学習予測モデルと再利用可能な資源を構築しており、表現型取得・推定が研究の主要部分です。
Reproduction assets foundThe paper's root phenotyping images, phenotypic data, and RNA-seq data are deposited on figshare, and the authors' GWAS analysis pipeline code is publicly available on GitHub, both explicitly stated in the Data Availability Statement.Dataset · publicAll the images, phenotypic data, and RNAs‐seq data are available at https://doi.org/10.6084/m9.figshare.27605208.v1 .Open asset ↗figshare · 10.6084/m9.figshare.27605208.v1lines:197-303Code · publicThe original data and code for GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizerootphenomics .Open asset ↗GitHub · GUOWEIJUN/maizerootphenomicslines:197-303Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
In the early stages of selection, many plant breeding programmes still rely on visual evaluations of traits by experienced breeders. While this approach has proven to be effective, it requires considerable time, labour and expertise. Moreover, its subjective nature makes it difficult to reproduce and compare evaluations. The field of automated high-throughput phenotyping aims to resolve these issues. A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. This approach was used in the present study to assess the dry matter yield of tall fescue and its accuracy was compared to that of the breeder's evaluations, using field measurements as ground truth. RGB images of tall fescue individuals were processed by two types of predictive models: a random forest and convolutional neural network. In addition to computing dry matter yield, the two methods were applied to identify the top 10% highest-yielding plants and predict the breeder's score. The convolutional neural network outperformed the random forest method and exceeded the predictive power of the breeder's eye. It predicted dry matter yield with an R² of 0.62, which surpassed the accuracy of the breeder's score by 8 percentage points. Additionally, the algorithm demonstrated strong performance in identifying top-performing plants and estimating the breeder's score, achieving balanced accuracies of 0.81 and 0.74, respectively. These findings indicate that the tested automated phenotyping approach could not only offer improvements in cost, time efficiency and objectivity, but also enhance selection accuracy. As a result, this technique has the potential to increase overall breeding efficiency, accelerate genetic progress, and shorten the time to market. To conclude, phenotyping by means of RGB-based machine learning models provides a reliable alternative or addition to the visual evaluation of selection candidates in a tall fescue breeding programme.
Why it matches plant phenotyping methodsRGBドローン画像と機械学習により乾物収量などの植物形質を推定し、育種家評価および実測値と比較検証しており、フェノタイピング手法が中心である。
abstractThe field of automated high-throughput phenotyping aims to resolve these issues.
Reproduction assets foundThe paper's data availability statement explicitly deposits the study's datasets (RGB image-derived phenotyping data for tall fescue yield prediction) on Zenodo and all analysis scripts on a public GitHub repository, both with URLs matching allowed_urls.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/14289667 .Open asset ↗zenodo · 14289667lines:530-564Code · publicAll scripts used are provided in the following GitHub repository: https://github.com/SarahGhysels/Estimation-of-individual-plant-performance-in-tall-fescue-through-RGB-image-analysis .Open asset ↗github · SarahGhysels/Estimation-of-individual-plant-performance-in-tall-fescue-through-RGB-image-analysislines:530-564Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Field / plotWhole plant / canopy / plot / fieldVisualization / data managementStress response / tolerance
An understanding of fire-response traits is essential for predicting how fire regimes structure plant communities and for informing fire management strategies for biodiversity conservation. Quantification of these traits is complex, encompassing several levels of data abstraction scaling up from field observations of individuals, to general categories of species responses. We developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels. Key features include: (a) concise and documented trait and method vocabularies; (b) documented uncertainty in observations and aggregation; and (c) documented origin of data including field observations, laboratory experiments, and expert elicitation. We demonstrated application of our framework using data from new field surveys and existing data sets in New South Wales, Australia. The database includes 14 traits for 6,287 plant species derived from 8,936 field work records from 2007 to 2018, 7,054 field records from surveys after 2019, and 48,306 records from 301 existing sources.
Why it matches plant phenotyping methods火災応答形質を体系的に収集・標準化するデータベースとデータパイプライン自体が中心的な方法論的貢献であり、植物形質データの不確実性・測定法・由来も記録しているため、フェノタイピング用データ基盤として含める。
abstractWe developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels.
Reproduction assets foundThe paper's core outputs (Fire Ecology Database v1.1 SQL dump, R data frames, CSV/XLSX exports on FigShare/OSF, and the Python import scripts/Jupyter notebooks) are stated to be publicly available, but no concrete repository URL or identifier for them appears in the supplied blocks, and none matches an allowed URL, so Code · publicCustomised scripts were written in Python to automate the importation of field data from the spreadsheets
into the database. These scripts are available for download (see Code availability section)Open asset ↗pdf-page:6 lines:1-78Dataset · publicStatic versions of the Fire Ecology Database, including version 1.1 used in this descriptor, are available via
FigShare or OSF in three different formatsOpen asset ↗FigSharepdf-page:9 lines:1-78Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research. However, the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models. In this work, we present a comprehensive multicultivar and multiview rice plant image dataset (CVRP) created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields. The dataset includes images capturing rice plants in their natural environment, as well as indoor images focusing specifically on panicles, allowing for a detailed investigation of cultivar-specific differences. A semiautomatic annotation process using deep learning models was designed for annotations, followed by rigorous manual curation. We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art (SOTA) semantic segmentation models. We also conducted 3D plant reconstruction with organ segmentation via images and annotations. The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable resources for challenging tasks such as automatic rice cultivar identification, panicle and grain counting, and 3D plant reconstruction. The database and the model for image annotation are available at https://bic.njau.edu.cn/CVRP.html.
Why it matches plant phenotyping methodsイネ画像データセットとアノテーションモデルを開発・評価し、セグメンテーション、器官再構成、穂・粒数計測などの再利用可能な表現型解析を中心に扱っているため。
abstractwe present a comprehensive multicultivar and multiview rice plant image dataset (CVRP)
Reproduction assets foundThe paper's own CVRP rice image dataset (images + annotations), accompanying code, and trained Mask2Former annotation model are explicitly stated as publicly available on Hugging Face and the authors' NJAU site.Dataset · publicThe CVRP dataset is publicly available on Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP for academic use under the specified license.Open asset ↗CVRPDataset/CVRPhtml-lines:236-252Code · publicThe accompanying code and trained models are available at https://huggingface.co/CVRPDataset/Model.Open asset ↗CVRPDataset/Modelhtml-lines:236-252Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Mar 2025The Plant journal : for cell and molecular biologyCited by 6 · OpenAlex ↗
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.
Why it matches plant phenotyping methodsRGB-D画像からレタスの構造・体積・葉密度を抽出し、生体重推定を検証する手法開発が研究の中心であり、データセットも構築している。
abstractIn this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce.
Reproduction assets foundThe paper's own lettuce 3D point cloud dataset (Pii, 402 point clouds with fresh weight references) is deposited on Zenodo, and the vacuum-package surface reconstruction code plus data processing scripts are publicly available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicData used in this study and developed models are available on Zenodo storage service https://zenodo.org/records/8410252 .Open asset ↗Zenodo · 8410252lines:158-220Code · publicThe code used at this study is available at https://github.com/VicB18/LettuceFW (accessed on 1 November 2024).Open asset ↗GitHub · VicB18/LettuceFWlines:158-220Code · publicThe code for the vacuum package method, along with the data processing scripts used in this study, are available in the Supplementary Information.Open asset ↗lines:98-114Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Premise Deep learning has become increasingly important in the analysis of digitized herbarium collections, which comprise millions of scans that provide valuable resources for studying plant evolution and biodiversity. However, leveraging deep learning algorithms to analyze these scans presents significant challenges, partly due to the heterogeneous nature of the non-plant material that forms the background of the scans. We hypothesize that removing such backgrounds can improve the performance of these algorithms. Methods We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds. The semi-automatic preprocessing stages involve the identification and removal of non-plant elements, substantially reducing the manual effort required to prepare the training dataset. Results The results highlight the importance of effective image segmentation, which achieved an F1 score of up to 96.6%. Moreover, when used in classification models for plant morphological trait identification, the images resulting from segmentation improved classification accuracy by up to 3% and F1 score by up to 7% compared to non-segmented images. Discussion Our approach isolates plant elements in herbarium scans by removing background elements to improve classification tasks. We demonstrate that image segmentation significantly enhances the performance of plant morphological trait identification models.
Why it matches plant phenotyping methodsハーバリウム画像から植物マスクを生成する深層学習セグメンテーション手法を開発し、形態形質識別への効果も検証しており、表現型取得・抽出手法が中心である。
abstractWe propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' segmentation source code and trained models on GitHub and the herbarium image–mask dataset (2277 image–mask pairs) on figshare; both are paper-specific, public, and actionable.Code · publicThe source code for segmentation, examples, and trained models for networks with black and white backgrounds are available at https://github.com/IA-E-Col/Herbarium-Image-Segmentation .Open asset ↗IA-E-Col/Herbarium-Image-Segmentationlines:531-531Dataset · publicThe used dataset is available at https://doi.org/10.6084/m9.figshare.27685914.v1 (Sklab et al., 2024b ).Open asset ↗figshare · 10.6084/m9.figshare.27685914.v1lines:531-531Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The likelihood that a host will be susceptible to infection is influenced by the interaction of diverse biotic and abiotic factors. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between the host, the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, bacterial strain identity and dose, and pathogen dose. Elucidating the interactions between these factors in vivo has been challenging because testing combinations of these variables quickly becomes experimentally intractable. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbe model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and non-pathogenic bacterial dose can overwhelm these effects. Host susceptibility to infection is therefore driven by complex interactions between multiple factors that can both mask and compensate for each other. However, regardless of host or inoculation conditions, the ratio of pathogen to non-pathogen emerged as a consistent correlate of disease. Our results demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host susceptibility to disease. Increasing the scale at which we can screen drivers of disease, such as microbiome community structure, will facilitate both disease predictions and treatments for medicine and agricultural applications.
Why it matches plant phenotyping methodsMYCroplantersという植物の高スループット生育・感染評価システム自体を開発・記述し、宿主の感染感受性（病害状態）を測定する方法が研究の中心であるため。
abstractHere, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, non-pathogenic bacteria, and pathogen variables predict host health.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits paper-specific assets: raw plant scan images and 3D printing files in Dryad, and processed data plus analysis/figure-generation code on GitHub. Both are public, actionable, and directly reproduce this paper's plant-phenotyping measurements and analysis.Dataset · publicThe datasets (raw images and 3D printing files) supporting the conclusions of this article are available in the Dryad repository ( https://doi.org/10.5061/dryad.w9ghx3fxd ).Open asset ↗Dryad · 10.5061/dryad.w9ghx3fxdlines:199-208Code · publicProcessed data, code for analysis and figure generation, and a copy of 3D printing files can be found on our GitHub ( https://github.com/mech3132/mycroplanter ).Open asset ↗GitHub · mech3132/mycroplanterlines:199-208Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity and sustaining ecosystem services in cities. However, comprehensive field assessments are resource-intensive, and landscape-level approaches may overlook heterogeneity within urban regions. To address this challenge, we combined remote sensing with field inventories to comprehensively map and analyze urban forest attributes in forest patches across the Minneapolis-St. Paul Metropolitan Area (MSPMA) in a multistep process. First, we developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP). These models enabled accurate predictions of forest attributes, specifically nine metrics of plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, dbh, and canopy density), and structural complexity (variability in canopy height, dbh, and canopy density) with relative errors ranging between 11% and 21%. Second, we applied these machine learning models to predict diversity metrics for 804 additional plots from GEDI and Sentinel-2. Finally, we applied Bayesian multilevel models to the predicted diversity metrics to assess the influence of multiple factors-patch dimensions, landscape attributes, plot position, and jurisdictional agency-on these forest attributes across the 804 predicted plots. The models showed all predictors have some degree of effect on forest attributes, presenting varying explanatory power with R 2 values ranging from 0.071 to 0.405. Overall, plot characteristics (e.g., distance to nearest trail, proximity to forest edge) and jurisdictional agency explained a large portion of the variability across patches, whereas patch and landscape characteristics did not. The relative effect of plot versus management sets of predictors on the marginal ΔR 2 was heterogeneous across metrics and ecological subsections (an ecological classification designation). The multiplicity of determinants influencing urban forests emphasizes the intricate nature of urban ecosystems and highlights nuanced, heterogeneous relationships between urban ecological and anthropogenic factors that determine forest properties. Effectively enhancing biodiversity in urban forests requires assessments, management, and conservation strategies tailored for context-specific characteristics.
Why it matches plant phenotyping methodsGEDI・Sentinel-2と機械学習を統合し、植物の多様性・構造属性を予測する測定手法を開発、誤差評価し、追加プロットへ適用しているため、表現型取得が中心的である。
abstractwe developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP).
Reproduction assets foundThe paper's data availability statement provides three paper-specific public assets: the field vegetation inventory data on EDI, the machine learning ensemble R script on Zenodo, and the Bayesian model summaries on Zenodo.Dataset · publicVegetation data are available (Marcilio‐Silva et al., 2022 ) on the Environmental Data Initiative (EDI) data portal: https://doi.org/10.6073/pasta/166a4b954ecaaabcda75bd51004804a5Open asset ↗Environmental Data Initiative · 10.6073/pasta/166a4b954ecaaabcda75bd51004804a5lines:317-357Code · publicThe R script used for the machine learning model ensemble (Marcilio‐Silva, 2024 ) is available on Zenodo: https://doi.org/10.5281/zenodo.14395998Open asset ↗Zenodo · 10.5281/zenodo.14395998lines:317-357Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This dataset on eggplant leaf diseases has been meticulously developed to provide a valuable resource for agricultural research and the advancement of automated disease detection systems. It comprises 4,089 high-resolution images of eggplant leaves, systematically categorized into six distinct classes: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, and Wilt Disease. The images were captured using smartphone cameras under controlled conditions with a consistent white background to ensure clarity and uniformity. To reflect real-world agricultural scenarios, data collection was conducted across multiple geographic locations and in varying lighting conditions. This approach enhances the dataset's diversity and applicability. The dataset underwent thorough manual labelling and preprocessing to ensure accuracy and consistency across all samples. Each image is clearly labelled according to its respective disease class, making the dataset readily usable for machine learning applications. The balanced representation of healthy and diseased leaves allows for comprehensive training and testing of classification models. Designed to support the development of machine learning models for the early detection and classification of eggplant diseases, this dataset holds significant reuse potential in various research domains. It is particularly suitable for applications in plant pathology, precision agriculture, and disease forecasting, where timely and accurate diagnosis is crucial. The dataset is freely available for academic and research purposes, making it a valuable resource for researchers and developers aiming to innovate in agricultural technology and crop management. With its robust design and practical focus, the dataset has the potential to drive advancements in sustainable farming practices and enhance agricultural productivity.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するための大規模データセットであり、植物病害表現型の取得・再利用可能な基盤が中心です。
titleA comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications.
Reproduction assets foundThe paper is a Data in Brief article describing an eggplant leaf disease image dataset (4,089 images, six classes) publicly deposited on Mendeley Data, plus an authors' GitHub repository containing the preprocessing code. Both are paper-specific, public, and directly actionable.Dataset · publiclant field in Char Keshabpur, Shibchar, Madaripur (Latitude: 23°21′32.9″N, Longitude: 90°11′48.5″E)
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Eggplant field in Daffodil Smart City, Khagan, Ashulia (Latitude: 23°52′37.6″N, Longitude: 90°19′16.2″E).
Data accessibility
Repository name: Mendeley Data.
Data identification number: 10.17632/d3ypkphghb.2
Direct URL to data: https://data.mendeley.com/datasets/d3ypkphghb/2
Access the dataset at https://data.mendeley.com/datasets/d3ypkphghb/2 and cite using Data ID 10.17632/d3ypkphghb.2 .
Related research articleOpen asset ↗Mendeley Data · 10.17632/d3ypkphghb.2lines:1-49Code · publicand facilitate classification tasks.
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Classification: Images were organized into six predefined classes: Healthy Leaf, Insect Pest, Leaf Spot, Mosaic Virus, White Mold, and Wilt, forming a structured dataset ready for analysis.
4.5.
Code used for data preprocessing
GitHub Repository name: Data_Preprocessing
Direct URL of Code: https://github.com/paradoxicalProfessor/Data_Preprocessing
Limitations
The Eggplant Leaf Disease dataset has some limitations. It was collected from specific regions in Bangladesh, which may limit its applicability to other environments. Our dataset includes only six disease classes, which may not represent all eggplant diseases in different regions. Some disease clasOpen asset ↗Data_Preprocessinglines:223-258Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
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Value of the Data
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The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
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Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
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Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ArabidopsisMicroscopyCell / cellular structureRootTissueVisualization / data management
Abstract Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200–300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants' challenging optical properties. Expansion microscopy (ExM) improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that ExM can be applied to increase effective imaging resolution in Arabidopsis root specimens.
Why it matches plant phenotyping methods植物組織に適用可能な超解像イメージング手法を開発し、Arabidopsis根で解像度向上を実証しており、画像取得法が研究の中心である。
abstractHere, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy.
Reproduction assets foundThe paper's PlantEx expansion microscopy imaging data are deposited in ISTA's public repository, and the authors' custom analysis code (including the BigWarp-based expansion-factor script) is publicly available on GitHub. The Click-ExM repository is cited prior work whose method was adapted, not a paper-specific asset.Dataset · publicThe data that support the findings of this study are available via ISTA's data repository at https://doi.org/10.15479/AT:ISTA:18837 .Open asset ↗ISTA's data repository · 10.15479/AT:ISTA:18837lines:219-252Code · publicThe custom-written code used and described in this manuscript is available via Github ( https://github.com/danzllab/PlantEx ).Open asset ↗github.com/danzllab/PlantExlines:219-252Code · publicThe expansion factor was extracted as the linear scaling factor of the similarity transformation minimizing squared landmark residuals using the script https://github.com/danzllab/CATS/tree/master/rcats_image-analysis/bigwarp .Open asset ↗github.com/danzllab/CATSlines:154-159Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness.
Why it matches plant phenotyping methodsブドウ葉の毛密度という形態形質を画像とCNNで自動定量する高スループット手法を開発し、専門家評価および大規模集団で検証しており、表現型取得・抽出法が研究の中心である。
abstractTherefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN).
Reproduction assets foundThe authors publicly released the ResNet CNN training code, the leaf disc image datasets, and the full leaf hair quantification pipeline in a GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicAll datasets and the code to train the CNNs are available in the GitHub repository.Open asset ↗lines:91-99Dataset · publicThe script of the ResNet CNN along with the images are available in the GitHub repository: https://github.com/1708nagarjun/ResNet-CNN-Leaf-hair.Open asset ↗1708nagarjun/ResNet-CNN-Leaf-hairlines:143-183Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these challenges. To address this, we propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet. LeafNet draws inspiration from the block-wise VGG19 architecture but incorporates several optimizations, including a reduced number of parameters, smaller input size, and faster inference time while maintaining competitive accuracy. The proposed LeafNet leverages small, uniform convolutional filters to capture fine-grained details of plant disease features, with an increasing number of channels to enhance feature extraction. Additionally, it integrates channel attention mechanisms to prioritize disease-related features effectively. We evaluated the proposed method on four datasets: the benchmark plant village (PV), the data repository of leaf images (DRLIs), the newly curated plant composite (PC) dataset, and the BARI Sunflower (BARI-Sun) dataset, which includes diverse and challenging real-world images. The results show that the proposed performs comparably to state-of-the-art methods in terms of accuracy, false positive rate (FPR), model size, and runtime, highlighting its potential for real-world applications.
Why it matches plant phenotyping methods植物病害の画像ベース診断を目的とする軽量CNNを開発し、複数データセットで精度・誤検出率・モデルサイズ・推論時間を評価しており、病害状態の表現型推定手法が中心である。
abstractwe propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet.
Reproduction assets foundThe paper's authors publicly released their LeafNet analysis code on GitHub, and the plant leaf image datasets used for their phenotyping experiments (PV, DRLI, BARI-Sun) are openly available. The PC dataset is a composite of PV and DRLI and is not independently deposited.Code · publicTo promote
reproducibility and facilitate further research, the source code is publicly available at:
(https://github.com/sanaparez/LeafNet)Open asset ↗sanaparez/LeafNetpdf-page:3 lines:1-54Dataset · publicThe datasets utilized in this study are openly available at PV Dataset
(https://github.com/spMohanty/PlantVillage-Dataset)Open asset ↗spMohanty/PlantVillage-Datasetpdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The selection and promotion of high-yielding and nitrogen-efficient wheat varieties can reduce nitrogen fertilizer application while ensuring wheat yield and quality and contribute to the sustainable development of agriculture; thus, the mining and localization of nitrogen use efficiency (NUE) genes is particularly important, but the localization of NUE genes requires a large amount of phenotypic data support. In view of this, we propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots, propose a wheat 3D plot segmentation dataset, quantify the plot canopy height via combination with PointNet++, and generate 4 nitrogen utilization-related vegetation indices via index calculations. Six height-related and 24 vegetation-index-related dynamic digital phenotypes were extracted from the digital phenotypes collected at different time points and fitted to generate dynamic curves. We applied height-derived dynamic numerical phenotypes to genome-wide association studies of 160 wheat cultivars (660,000 single-nucleotide polymorphisms) and found that we were able to locate reliable loci associated with height and NUE, some of which were consistent with published studies. Finally, dynamic phenotypes derived from plant indices can also be applied to genome-wide association studies and ultimately locate NUE- and growth-related loci. In conclusion, we believe that our work demonstrates valuable advances in 3D digital dynamic phenotyping for locating genes for NUE in wheat and provides breeders with accurate phenotypic data for the selection and breeding of nitrogen-efficient wheat varieties.
Why it matches plant phenotyping methods航空画像・3D点群・マルチスペクトル画像から小麦区画の草冠高と植生指数を抽出するデジタルフェノタイピング手法を開発・適用しており、表現型取得が研究の中心である。
abstractwe propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' source code, testing data, and supporting datasets (including the W3DPS 3D plot segmentation dataset and phenotyping/GWAS data) at two public Quark pan links under CC BY 4.0. These are paper-specific, publicly actionable assets. Other allowed URLsCode · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Dataset · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In contemporary agriculture, experts develop preventative and remedial strategies for various disease stages in diverse crops. Decision-making regarding the stages of disease occurrence exceeds the capabilities of single-image tasks, such as image classification and object detection. Consequently, research now focuses on training visual question answering (VQA) models. However, existing studies concentrate on identifying disease species rather than formulating questions that encompass crucial multiattributes. Additionally, model performance is susceptible to the model structure and dataset biases. To address these challenges, we construct the informed-learning-guided VQA model of crop disease (ILCD). ILCD improves model performance by integrating coattention, a multimodal fusion model (MUTAN), and a bias-balancing (BiBa) strategy. To facilitate the investigation of various visual attributes of crop diseases and the determination of disease occurrence stages, we construct a new VQA dataset called the Crop Disease Multi-attribute VQA with Prior Knowledge (CDwPK-VQA). This dataset contains comprehensive information on various visual attributes such as shape, size, status, and color. We expand the dataset by integrating prior knowledge into CDwPK-VQA to address performance challenges. Comparative experiments are conducted by ILCD on the VQA-v2, VQA-CP v2, and CDwPK-VQA datasets, achieving accuracies of 68.90%, 49.75%, and 86.06%, respectively. Ablation experiments are conducted on CDwPK-VQA to evaluate the effectiveness of various modules, including coattention, MUTAN, and BiBa. These experiments demonstrate that ILCD exhibits the highest level of accuracy, performance, and value in the field of agriculture. The source codes can be accessed at https://github.com/SdustZYP/ILCD-master/tree/main.
Why it matches plant phenotyping methods作物病害の視覚属性と発生段階を画像から推定するVQAモデルと専用データセットを開発しており、植物状態の表現型推定手法が研究の中心である。
abstractwe construct the informed-learning-guided VQA model of crop disease (ILCD).
Reproduction assets foundThe paper's authors publicly release both the ILCD analysis code and the paper-specific CDwPK-VQA dataset (crop disease images with question–answer annotations) via GitHub URLs stated in the article.Code · publicd 86.06%, respectively. Ablation experiments are conducted on CDwPK-VQA to evaluate the effectiveness of various modules, including coattention, MUTAN, and BiBa. These experiments demonstrate that ILCD exhibits the highest level of accuracy, performance, and value in the field of agriculture. The source codes can be accessed at https://github.com/SdustZYP/ILCD-master/tree/main.
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 May 24; Revised 2024 Oct 18; Accepted 2024 Nov 12; Collection date 2024.
Introduction
The Food and Agriculture Organization of the United Nations has reports that diseases are respOpen asset ↗SdustZYP/ILCD-masterlines:1-26Dataset · publicgnment between the question text information and image region features. This process results prior knowledge dataset comprising 272 images and 2,180 questions. CDwPK-VQA integrates prior knowledge to expand the dataset and regulate the learning behavior of the model, as shown in Fig. 2 . The dataset of CDwPK-VQA is available at https://github.com/SdustZYP/CDwPK-VQA/tree/main.
The ILCD model
This research constructs a novel ILCD. The model architecture of ILCD is shown in Fig. 3 , and divided into the following steps: (a) Image features V and question features Q are extracted using a pretrained feature extraction model. (b) The coattention mechanism captures the interaction between the image Open asset ↗SdustZYP/CDwPK-VQAlines:52-91Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Background Small object detection via unmanned Aerial vehicle (UAV) is crucial for smart agriculture, enhancing yield and efficiency. Methods This study addresses the issue of missed detections in crowded environments by developing an efficient algorithm tailored for precise, real-time small object detection. The proposed Yield Health Robust Transformer-YOLO (YH-RTYO) model incorporates several key innovations to advance conventional convolutional models. The model features an efficient convolutional expansion module that captures additional feature information through extended branches while maintaining parameter efficiency by consolidating features into a single convolution during validation. It also includes a local feature pyramid module designed to suppress background interference during feature interaction. Furthermore, the loss function is optimized to accommodate various object scales in different scenes by adjusting the regression box size and incorporating angle factors. These enhancements collectively contribute to improved detection performance and address the limitations of traditional methods. Result Compared to YOLOv8-L, the YH-RTYO model achieves superior performance in all key accuracy metrics, with a 13% reduction in the scale of model. Experimental results demonstrate that the YH-RTYO model outperforms others in key detection metrics. The model reduces the number of parameters by 13%, facilitating deployment while maintaining accuracy. On the OilPalmUAV dataset, it achieves a 3.97% improvement in average precision (AP). Additionally, the model shows strong generalization on the RFRB dataset, with AP 50 and AP values exceeding those of the YOLOv8 baseline by 3.8% and 2.7%, respectively.
Why it matches plant phenotyping methods作物の生育異常検出を目的とする画像ベースの物体検出モデルを開発し、複数データセットで性能評価しており、表現型状態の抽出法が中心である。
titleYH-RTYO: an end-to-end object detection method for crop growth anomaly detection in UAV scenarios.
Reproduction assets foundThe paper's Data Availability section explicitly lists public repositories for the authors' code (GitHub and Zenodo) and for the two UAV crop-detection datasets used in the experiments (MOPAD and RFRB). The ultralytics repository is a generic library and is excluded.Code · publicThe code is available at Github and Zenodo:Open asset ↗lines:795-926Dataset · publicThe MOPAD dataset is available at Github and at Zheng et al. (2021):Open asset ↗lines:795-926Dataset · publicThe RFRB dataset is available at Github and is described in Ji et al. (2023):Open asset ↗lines:795-926Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から分類する深層学習手法を開発・比較しており、植物病害表現型の取得・推定が研究の中心である。
abstractWe have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease.
Reproduction assets foundThe paper's tomato leaf disease classification study uses the public PlantVillage-derived Mendeley dataset, provides its restructured training/validation data on Kaggle, and releases its T-Net analysis source code on GitHub and Zenodo, all with explicit availability statements and public URLs.Dataset · publicThe dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .Open asset ↗Mendeley Data · 10.17632/tywbtsjrjv.1lines:498-522Dataset · publicThe dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .
The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeDOpen asset ↗Kagglelines:498-522Code · publicon of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .
The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–Open asset ↗GitHublines:498-522Code · publicing and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data .
The source code is available at GitHub and Zenodo:
- https://github.com/Amreen-source/Tomato-leaf-disease-detection-
- Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 .
References
Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–301. doi: 10.1016/j.procs.2020.03.225.
Ahmad, Saraswat & El Gamal (2023) Ahmad A, Saraswat D, El Gamal A. A survey on using deep learnOpen asset ↗Zenodo · 10.5281/zenodo.14020689lines:498-522Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Field / plotLaboratory / benchtopX-ray / CTRootWhole plant / canopy / plot / field
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Why it matches plant phenotyping methods根周辺のひずみ場という根の力学的形質を、X線CT・回折と有限要素解析で測定・検証する新規プロトコルが研究の中心である。
abstractWe map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Reproduction assets foundThe paper deposits its X-ray diffraction and X-ray imaging (XCT) measurements in the Southampton Pure repository (DOI 10.5258/SOTON/D3309.274) and its processing scripts in a companion deposit (DOI 10.5258/SOTON/D3309.276), both with explicit availability statements and public URLs.Dataset · public∇uT ), F(σ′) > 0,x ∈ Ω
σ′ = Cep
: (∇u+∇uT ), F(σ′) = 0,x ∈ Ω
u·ê1 = 0, x ∈ ΓAxis
u = 0, x ∈ ΓC,Top
u = [0,wstep]T , x ∈ Γbot ∪Γout
n̂·∇u = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report ofOpen asset ↗Pure repository · 10.5258/SOTON/D3309.274pdf-raw-page:9 lines:1-96Code · publicu = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
277
1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
278
groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, h.
279
lee and j. romero (eds.)]. ipcc, geneva, switzerland. (2023).
2Open asset ↗Pure repository · 10.5258/SOTON/D3309.276pdf-raw-page:9 lines:1-96Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Common beanAerial / UAVField / plotLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldCountingMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenology
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Why it matches plant phenotyping methodsドローン画像と深層学習を用いて成熟度、株数、草丈を推定する手法を開発・評価し、パイプラインとオープンソースソフトウェアも提示しており、植物表現型取得が研究の中心である。
abstractIn this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH)
Reproduction assets foundThe paper explicitly states that all R/Python analysis code, apps, and the complete datasets (orthomosaics, shapefiles, ground notes, clipped plots) are publicly available via the authors' GitHub organization and three Zenodo deposits for RM, SC, and PH.Dataset · publicof the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vuOpen asset ↗zenodo · 10.5281/zenodo.7922565lines:677-730Dataset · publicauthors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainabOpen asset ↗zenodo · 10.5281/zenodo.7922584lines:677-730Dataset · publicrests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review
. LeguOpen asset ↗zenodo · 10.5281/zenodo.7922589lines:677-730Code · publics from each individual breeding plot were extracted from the time series of images (6 and 9 flights date), and the RM was estimated using an optimized threshold value of 0.06. To perform the VI extractions from each breeding plot in the field, an open-source Streamlit app in Python was implemented and can be accessed online at: https://msudrybeanbreeding-vegetation-index--vi-extractions-v0-3-9knpzt.streamlit.app/ . Additionally, to accommodate user preferences, an R script is available to perform VI extractions analysis (Data S7 ).
SC DL model
The SC pipeline deployed in this study comprised 6 distinct steps, starting from the raw images and annotations, and ending with the final SC predictiOpen asset ↗lines:139-147Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.
Why it matches plant phenotyping methodsNIRスペクトルから植物形質を予測するソフトウェアおよびデータベースを開発しており、形質取得・推定手法が研究の中心である。
abstractWe developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits
Reproduction assets foundThe paper's NIRS spectra and 81 trait measurements for 5,325 Arabidopsis thaliana individuals are publicly hosted in the authors' NIRSpredict Shiny application, and the application's R code is deposited on the authors' GitHub repository (AxelVaillant/NirsPredict), as stated in the Data availability section. Both are直接,Dataset · publicTrait values are publicly available in the NIRSpredict database atOpen asset ↗pdf-page:10 lines:1-65Code · publicThe R code of the application is available on a GitHubOpen asset ↗pdf-page:10 lines:1-65Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.
Why it matches plant phenotyping methodsコーヒー葉画像から病害の種類と重症度を推定する画像・深層学習手法が研究の中心であり、植物状態の表現型評価に該当する。
abstractThis study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images.
Reproduction assets foundThe authors explicitly state that the data, algorithms, and code for the DeepEMPR coffee leaf disease detection study are publicly available on GitHub and Zenodo, and the leaf image dataset (LeafData.zip) is deposited on Figshare. These are paper-specific, publicly actionable assets directly supporting the study's phenCode · publicd the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
ArjOpen asset ↗ArticleCodeHub/DeepEMPRlines:660-763Code · publicgures and/or tables, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
Arjaria S
Sinha A
Gupta S
2020
ToLeD: tomato leaf disease detection using convolutiOpen asset ↗10.5281/zenodo.13823450lines:660-763Dataset · publicdata, algorithms and code are available at GitHub and Zenodo:
- https://github.com/ArticleCodeHub/DeepEMPR
- Topal, A. (2024). DeepEMPR. Zenodo. https://doi.org/10.5281/zenodo.13823450 .
The code is available in the Supplemental File .
The data is also available at Figshare: Topal, Ahmet (2024). LeafData.zip. figshare. Dataset. https://doi.org/10.6084/m9.figshare.26060464.v1 .
References
Agarwal et al. (2020)
Agarwal M
Singh A
Arjaria S
Sinha A
Gupta S
2020
ToLeD: tomato leaf disease detection using convolution neural network
Procedia Computer Science 167 293 301
10.1016/j.procs.2020.03.225
Ahmed et al. (2019)
Ahmed K
Shahidi TR
Alam SMI
Momen S
2019
Rice leaf disease detection using machineOpen asset ↗10.6084/m9.figshare.26060464.v1lines:660-763Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.
Why it matches plant phenotyping methodsドローン画像、物体検出、写真測量を統合し、ナシ樹のさび病症状を検出・局在化して樹体ごとの感染強度を推定するデジタル表現型解析システムの開発が中心である。
abstracta digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the annotated UAV image dataset on Mendeley Data and the trained model, detection workflow, and Metashape loading script on figshare, both with public URLs matching allowed entries.Dataset · publicsource repository Mendeley Data (https://data.mendeley.com/datasets/44kjgc4gkc/1, accessed on 8Open asset ↗Mendeley Data · 44kjgc4gkc/1pdf-page:15 lines:1-67Code · publicThe model, the detection workflow with instructions and the script for
loading the detections into Agisoft’s Metashape are available in the open-source figshare repository
(https://doi.org/10.6084/m9.figshare.27225312.v2, accessed on 28 October 2024).Open asset ↗figshare · 10.6084/m9.figshare.27225312.v2pdf-page:15 lines:1-67Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 13 Sept 2026
Abstract In recent years, the automation of genotyping has significantly enhanced the efficiency of genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. For this reason, there is a strong need to further automate in-field phenotyping. Here we present a GWAS study on 194 field-grown accessions of lettuce ( Lactuca sativa ). These accessions were non-destructively phenotyped at two time points 15 days apart using an unmanned aerial vehicle. Our high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation. We used the mean and other descriptives such as median, quantiles, minimum and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using this approach, we confirm several previously described QTLs, now in populations grown under field conditions, and identify several new QTLs for plant-height and color.
Why it matches plant phenotyping methodsドローン搭載RGB・マルチスペクトルカメラと高さ推定を統合した圃場フェノタイピング手法を開発・適用し、画像からレタスの色と草丈を定量化しているため、方法が研究の中心です。
abstractOur high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation.
Reproduction assets foundThe paper explicitly states that analysis scripts are publicly available on GitHub (SnoekLab/Dijkhuizen_etal_2025_Drone) and that extended data (raw data, intermediate steps, figure data, weather data) is deposited at the Utrecht University repository DOI 10.24416/UU01-S5FCM9. Both are paper-specific, public, and verbiCode · publicScripts used for this study are available on github:
https://github.com/SnoekLab/Dijkhuizen_etal_2025_Drone.Open asset ↗SnoekLab/Dijkhuizen_etal_2025_Dronepdf-page:8 lines:1-120Dataset · publicExtended data available on https://doi.org/10.24416/UU01-S5FCM9. This includes all raw
data to reproduce results, all intermittent steps, the data required to generate all figures and
the weather data.Open asset ↗10.24416/UU01-S5FCM9pdf-page:8 lines:1-120Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant population counts are highly valued by crop producers as important early-season indicators of field health. Traditionally, emergence rate estimates have been acquired through manual counting, an approach that is labor-intensive and relies heavily on sampling techniques. By applying deep learning-based object detection models to aerial field imagery, accurate plant population counts can be obtained for much larger areas of a field. Unfortunately, current detection models often perform poorly when they are faced with image conditions that do not closely resemble the data found in their training sets. In this paper, we explore how specific facets of a plant detector's training set can affect its ability to generalize to unseen image sets. In particular, we examine how a plant detection model's generalizability is influenced by the size, diversity, and quality of its training data. Our experiments show that the gap between in-distribution and out-of-distribution performance cannot be closed by merely increasing the size of a model's training set. We also demonstrate the importance of training set diversity in producing generalizable models, and show how different types of annotation noise can elicit different model behaviors in out-of-distribution test sets. We conduct our investigations with a large and diverse dataset of canola field imagery that we assembled over several years. We also present a new web tool, Canola Counter, which is specifically designed for remote-sensed aerial plant detection tasks. We use the Canola Counter tool to prepare our annotated canola seedling dataset and conduct our experiments. Both our dataset and web tool are publicly available.
Why it matches plant phenotyping methods航空画像からカノーラ個体数（個体群密度）を推定する検出モデルの汎化性能を検証し、注釈付きデータセットと専用Webツールを提示しており、植物表現型取得手法が中心である。
abstractBy applying deep learning-based object detection models to aerial field imagery, accurate plant population counts can be obtained for much larger areas of a field.
Reproduction assets foundThe paper's aerial canola seedling dataset (images and annotations) is publicly deposited on Zenodo, and the authors' Canola Counter analysis/annotation tool is open source on GitHub. The arXiv 2108.05789 entry is a cited prior work (CropAndWeed dataset), not a paper-specific asset.Dataset · publicData Availability Statement
The canola seedling dataset used in this study is publicly available and can be found at: https://doi.org/10.5281/zenodo.11055599 . The Canola Counter tool is open source and is available at: https://github.com/eandvaag/agricounter .Open asset ↗zenodo · 10.5281/zenodo.11055599lines:236-237Code · publicData Availability Statement
The canola seedling dataset used in this study is publicly available and can be found at: https://doi.org/10.5281/zenodo.11055599 . The Canola Counter tool is open source and is available at: https://github.com/eandvaag/agricounter .Open asset ↗github · eandvaag/agricounterlines:236-237Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.
Why it matches plant phenotyping methods植物のクロロフィルおよび糖含量を定量可能なオンチップ・ハイパースペクトル画像センサーを開発しており、センサー技術と植物形質取得への応用が中心的である。
abstractHere we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution.
Reproduction assets foundThe paper explicitly states that all data generated or analysed are available in a public GitHub repository (hyperspectral image/video dataset collected with the HyperspecI sensors) and that demo code is available in another public GitHub repository. Both are paper-specific, public, and actionable.Dataset · publiced the project.
Peer review
Peer review information
Nature thanks Yidong Huang, Yunfeng Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, Open asset ↗bianlab/Hyperspectral-imaging-datasetlines:148-189Code · publiche peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, ZL 2022 1 0764143.4, ZL 2022 1 0764141.5, ZL 2019 1 0441784.4, ZL 2019 1 0482098.1 and ZL 2019 1 1234638.0) and submitted the related patent applications.Open asset ↗bianlab/HyperspecIlines:148-189Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.
Why it matches plant phenotyping methodsトマト葉の病斑を画像から直接検出・識別する手法を開発し、複雑背景下での精度を比較評価しており、植物病害状態の表現型推定が中心である。
abstractthis article proposes a visual leaf disease identification method based on an enhanced attention mechanism.
Reproduction assets foundThe authors publicly deposited the paper's code and processed data on Figshare, and the study's plant image input (PlantDoc dataset) is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publiced drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The code and processed data are available on Figshare: Zhang, Xu (2024). code of “Automatic visual recognition for leaf disease based on enhanced attention mechanism”. figshare. Software. https://doi.org/10.6084/m9.figshare.27210138.v1 .
The original PlantDoc dataset is available at GitHub: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master .
References
Al Bashish, Braik & Bani-Ahmad (2011) Al Bashish D, Braik M, Bani-Ahmad S. Detection and classification of leaf diseases using k-means-based segmentation and. Information TechnOpen asset ↗figshare · 10.6084/m9.figshare.27210138.v1lines:336-366Dataset · publicsupplied regarding data availability:
The code and processed data are available on Figshare: Zhang, Xu (2024). code of “Automatic visual recognition for leaf disease based on enhanced attention mechanism”. figshare. Software. https://doi.org/10.6084/m9.figshare.27210138.v1 .
The original PlantDoc dataset is available at GitHub: https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master .
References
Al Bashish, Braik & Bani-Ahmad (2011) Al Bashish D, Braik M, Bani-Ahmad S. Detection and classification of leaf diseases using k-means-based segmentation and. Information Technology Journal. 2011;10(2):267–275. doi: 10.3923/itj.2011.267.275.
Al-Hiary et al. (2011) Al-Hiary H, BanOpen asset ↗GitHublines:336-366Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUASマルチスペクトル時系列画像から収量・生育期形質を予測する機械学習手法を比較・評価し、米のベンチマークデータセットも提供しており、表現型取得・推定手法が研究の中心である。
abstractprediction models often fail to generalize well from one year to the next or to new environments.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw and processed UAS imagery, extracted features, and agronomic data on Dryad, and the authors' analysis code on GitHub. Both are paper-specific, public, and actionable.Dataset · publicts complied with the
current laws of the United States, the country in which they
were performed.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
Emily S. Bellis is a full time employee of Avalo, Inc., a crop
improvement company.
DATA AVA I L A B I L I T Y S TAT E M E N T
Raw and processed UAS images are available on Dryad
(https://doi.org/10.5061/dryad.v41ns1s4z) along with
extracted features and agronomic data for the 2021 and 2022
field seasons. Code to reproduce the analyses are available at
https://github.com/FareedFarag/TPPJ-Modeling-Code.O RC I D
FaredFarag https://orcid.org/0000-0002-4659-6781
Trevis D. Huggins https://orcid.org/0000-0002-1937-6687
JeremyD. Edwards https://orcidOpen asset ↗Dryad · 10.5061/dryad.v41ns1s4zpdf-raw-page:16 lines:1-86Code · public61/dryad.v41ns1s4z) along with
approaches for rice trait prediction using UAS imagery as extracted features and agronomic data for the 2021 and 2022
the primary data source. While showcasing the potential of field seasons. Code to reproduce the analyses are available at
various modeling approaches, it also emphasizes the trade- https://github.com/FareedFarag/TPPJ-Modeling-Code.
offs between performance and interpretability for applications
in precision agriculture and plant breeding. Looking for- ORCID
ward, extending the study over multiple years, extending to Fared Farag https://orcid.org/0000-0002-4659-6781
hyperspectral sensors, and exploring additional remotely Trevis D. Huggins https:/Open asset ↗GitHub · FareedFarag/TPPJ-Modeling-Codepdf-layout-page:16 lines:1-54Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Identification of high carotenoid germplasm is crucial to assist breeders in provitamin-A biofortification of sorghum (Sorghum bicolor [L.] Moench). High-performance liquid chromatography is the gold standard for carotenoid quantification, however, it is not feasible for large scale phenotyping due to its high cost and low throughput. In this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding. We hypothesized that visual, color-based selection can be an effective strategy to identify high-carotenoid accessions. Yellow grain had significantly higher carotenoid content than red, brown, and white grain. The degree of yellowness could distinguish the presence or absence of carotenoids, but could not distinguish carotenoid concentrations within yellow-only accessions. The degree of luminosity of the grain, however, was able to better predict carotenoid concentrations within yellow-only accessions. Genome-wide association studies identified significant marker-trait associations for qualitative and quantitative grain color traits and carotenoid concentrations near carotenoid pathway genes-ZEP, PDS, CYP97A, NCED, CCD, and LycE-three of which were common between grain color and carotenoid traits. These findings suggest that using grain color as a method for screening germplasm may be an effective high-throughput selection tool for prebreeding and early-stage breeding in carotenoid biofortification.
Why it matches plant phenotyping methods穀粒色を用いたカロテノイド含量推定・高スループット選抜法の実現可能性を検証しており、植物形質取得法が研究の中心である。
abstractIn this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding.
Reproduction assets foundThe paper's grain-color/carotenoid phenotyping data are in public supplementary files (Supplementary Data S1–S3: GRIN color traits, visual scores, colorimeter measurements), and the authors' analysis code is publicly deposited on GitHub with an explicit availability statement.Code · publicAll other data files are available in the supplemental files and code is available at: https://github.com/rmcdower/sorghumbiofortification/tree/a8457f87068867eb687235c255a6222863102e1aOpen asset ↗rmcdower/sorghumbiofortification · a8457f87068867eb687235c255a6222863102e1alines:134-147Dataset · publicThree grains each per accession were scored independently by two individuals and classified as white, yellow, red, or brown (Supplementary Data S2).Open asset ↗lines:71-78Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cotton is one of the major cash crop in the agriculture led economies across the world. Cotton leaf diseases affects its yield globally. Determining cotton lesions on leaves is difficult when the area is big and the size of lesions is varied. Automated cotton lesion detection is quite useful; however, it is challenging due to fewer disease class, limited size datasets, class imbalance problems, and need of comprehensive evaluation metrics. We propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50. The proposed method offers a more precise, efficient and scalable method for automated detection of diseases of cotton crops. We have implemented the proposed method on publicly available dataset with seven disease and one health classes and have achieved highest accuracy of 95% and F-1 score of 98%. The proposed method performs better than existing state of the art methods.
Why it matches plant phenotyping methods綿花葉の病変・病害状態を画像から推定する深層学習手法の開発と評価が中心であり、植物病害フェノタイピングに該当する。
abstractWe propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50.
Reproduction assets foundThe authors publicly released their analysis code (GitHub + Zenodo DOI) and used two publicly available Kaggle cotton leaf image datasets for their phenotyping/disease-detection analysis; all are paper-specific and actionable.Code · publicon-disease-dataset/data .
The code is available at GitHub and Zenodo:
- https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b
- FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 .
References
Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin Z, Arce J, Guo W. Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model. Biosystems Engineering. 2024;237:220–231. doi: 10.1016/j.biosystemseng.2023.12.014.Open asset ↗Zenodo · 10.5281/zenodo.13324708lines:551-578Code · public:
The cotton plant disease data is available at Kaggle:
https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data , DOI: 10.34740/kaggle/dsv/5127834 .
The Cotton Disease Dataset is available at Kaggle: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
The code is available at GitHub and Zenodo:
- https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b
- FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 .
References
Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin ZOpen asset ↗GitHub · FrnazAkbar/Cotton-Lesion-Detectionlines:551-578Dataset · publice dataset to conduct an analysis that involved the application of various deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on theOpen asset ↗Kaggle · dhamur/cotton-plant-diseaselines:410-480Dataset · publicious deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on the bases of voting in order to extract a wide range of leaf features frOpen asset ↗Kaggle · janmejaybhoi/cotton-disease-datasetlines:410-480Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
PoplarField / plotRootCalibration / preprocessingRoot system architecture
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Why it matches plant phenotyping methods複数の根スキャン画像から根形質を統合する画像連結・統計集約法を開発し、比較検証した研究であり、根形質取得ワークフローが中心です。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
Reproduction assets foundThe paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,Dataset · publicThe imageset is available at doi: 10.5281/zenodo.12667583Open asset ↗Zenodo · 10.5281/zenodo.12667583pdf-raw-page:7 lines:1-85Code · publicthe R code for statistical aggregation along
with the figures and statistics presented here are available at doi:
10.5281/zenodo.12668177Open asset ↗Zenodo · 10.5281/zenodo.12668177pdf-raw-page:7 lines:1-85Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods高解像度画像時系列と複数の植物形質を含む大規模な公開圃場フェノタイピングデータセットであり、再利用可能なフェノタイピング基盤・ベンチマークとして中心的です。
abstractThis data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Reproduction assets foundThis data note directly publishes its own phenotyping measurements and image time series: the FIP 1.0 dataset (images, aligned image sequences, eight wheat traits, environmental and marker data) is publicly available on the ETH Research Collection and Hugging Face, and the authors' analysis/processing code is publicly,Dataset · publicn
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and Hugging Face Data set): https://doi.org/10.5061/dryad.n02v6wwzc
• Private Agroscope marker data repository: Confidential (Con-
tact: Boulos Chalhoub, boulos.chalhoub@agroscope.admin.ch).
This repository contains marker data (Illumina InfiniumOpen asset ↗mikeboss/FIP1pdf-raw-page:7 lines:1-110Dataset · publicn with FAIR principles [26]:
• Findable: This publication and the Hugging Face data set card
(https://doi.org/10.57967/hf/3191) provide detailed meta-
data and a comprehensive description of the data set’s contents,
making it discoverable to researchers.
• Accessible: The data is hosted on the Research Collection of ETH
Zurich (https://doi.org/20.500.11850/697773), a reliable and
openly accessible data storage.
• Interoperable: The use of the open-source Hugging Face
datasets [27] package makes it easy to use and export to differ-
ent formats. The data is fully MIAPPE v1.1 [28] conform. Given
the shared genotypes the data set can be used to enhance the
data by Gogna et al. [13] by 6 envOpen asset ↗20.500.11850/697773pdf-raw-page:2 lines:84-130Code · publiclly aggregating the derived data into the final
data set using the fip1-dataset repository.
In addition, the data set can be recreated using the fip1-dataset
repository from the derived data that is freely available in the ETH
research collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL Open asset ↗fip-1.0-data-set-traitspdf-raw-page:7 lines:1-110Code · publicresearch collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data AvailabiOpen asset ↗fip1-alignmentpdf-raw-page:7 lines:1-110Code · publicramming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and HOpen asset ↗fip1-datasetpdf-raw-page:7 lines:1-110Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2003 to 2015 improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art techniques, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R 2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.
Why it matches plant phenotyping methods作物収量という植物形質を遠隔センシング画像から推定する前処理法を開発し、CNN・LSTM・ViTとの比較で性能を検証しており、フェノタイピング手法が中心です。
abstractThis paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers.
Reproduction assets foundThe paper's declarations section explicitly states that the code for data collection, processing, and analysis is openly available on GitHub, and that a sample dataset used in the study is available via a Google Drive link. Both are paper-specific, public, and actionable.Code · publicThe code used in this study is openly available on GitHub at
https://github.com/mananthakkar24/RemoteSensingBlobDetection. This reposi-
tory contains all the necessary code for data collection, processing, and analysis as
described in this paper.Open asset ↗mananthakkar24/RemoteSensingBlobDetectionpdf-page:3 lines:1-48Dataset · publicA sample dataset used in this study is available at:
https://drive.google.com/drive/folders/18Z3hcqRf0nnE5vjqDat99qh3o-Open asset ↗pdf-page:3 lines:1-48Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
BarleyRootMorphology / geometry measurementSegmentationRoot system architecture
Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify single nucleotide polymorphisms (SNPs) significantly associated with angle and length, shedding light on the genetic basis of root architecture.
Why it matches plant phenotyping methods根の角度を画像から自動抽出するオープンソース手法を開発し、手動測定との相関で検証しているため、植物表現型取得法が中心です。
abstractWe introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images.
Reproduction assets foundThe paper's rhizobox root image dataset is publicly deposited on Zenodo, the SeminalRootAngle analysis code/installer is open-sourced on GitHub, and the BVS QTL analysis materials are on a second authors' GitHub repository. All are paper-specific, public, and actionable.Dataset · publicTo promote transparency and reproducibility, we makeour image dataset freely available under a CreativeCommons license at https://zenodo.org/records/7870965#.ZEp5iXZByUkOpen asset ↗zenodo · 7870965lines:30-44Code · publicwe open-source our code and make our downloadable installer available at https://github.com/Abe404/SeminalRootAngleOpen asset ↗github · Abe404/SeminalRootAnglelines:30-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.
Why it matches plant phenotyping methods熱ストレス耐性という植物状態をクロロフィル自家蛍光で定量するプロトコルを開発しており、表現型取得法が研究の中心的要素として明示されている。
abstractWe developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C.
Reproduction assets foundThe authors publicly deposited both the analysis scripts and the numerical source data (including raw fluorescence intensity values underlying the heat-stress phenotyping) in a Zenodo repository, with explicit availability statements and URLs in the Data availability and Code availability sections.Code · publicAll scripts used in this study are available in a Zenodo repository at https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:168-242Dataset · publicNumerical source data for the graph presented in Figs. 1 – 3 , and Fig. 5 can be found in the Zenodo repository here: https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:148-167Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.
Why it matches plant phenotyping methods非構造化音声による植物表現型記述を定量化し、草丈を推定する手法を開発・実証した研究であり、表現型取得・抽出法が中心である。
abstractWe present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits.
Reproduction assets foundThe paper's Data Availability statement provides public CyVerse and figshare deposits containing the authors' analysis code, the spoken-phenotype/phenotypic dataset, and the genotypic input data used for the GWAS analyses.Code · publicCode to recreate the analysis in this manuscript is available at CyVerse Data Commons from ( Yanarella et al . 2023b ) and can be accessed from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023 . The deidentified spoken data described in this manuscript is exempted by Iowa State University’s Institutional Review Board (IRB ID: 21-179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_DOpen asset ↗CyVerse Data Commons · Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023lines:526-547Dataset · publiccommons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023 . The deidentified spoken data described in this manuscript is exempted by Iowa State University’s Institutional Review Board (IRB ID: 21-179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023 . Genotypic data was obtained from ( Mural et al . 2022b , 2022a ) and can be accessed from: https://figshare.com/articles/dataset/Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888/1 . Gene Ontology data was obtained fOpen asset ↗Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023lines:526-547Dataset · public179-00). Phenotypic data was obtained from ( Yanarella et al . 2023a ) and is available from: https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_Maize_WiDiv_Summer_2021_Dataset_June_2023 . Genotypic data was obtained from ( Mural et al . 2022b , 2022a ) and can be accessed from: https://figshare.com/articles/dataset/Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888/1 . Gene Ontology data was obtained from ( Wimalanathan and Lawrence-Dill 2017 ) and is available from https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence-Dill_maize-GAMER_maize.B73_RefGen_v4_Zm00001d.2_Oct_2017.r1 .
SupplementalOpen asset ↗figshare · Maize_WiDiv_SAM_1051Genotype_vcf_gz_genotype_file/19175888lines:526-547Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 13 Sept 2026
Abstract Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits. Using genetic markers and data from high-throughput phenotyping of a maize multi-parent advanced generation inter-cross population, we show that dynamicGP outperforms a state-of-the-art genomic prediction approach for multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and phenotype over crop development to improve the prediction accuracy of agronomically relevant traits.
Why it matches plant phenotyping methods遺伝マーカーと高スループット表現型データを統合し、作物形質の時系列動態を予測する計算手法dynamicGPが研究の中心であり、複数形質で既存手法と比較検証している。
abstractHere we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits.
Reproduction assets foundThe preprint explicitly provides public availability statements for both the maize HTP phenotypic datasets (IPK DOI repository) and the authors' R implementation of the dynamicGP algorithms (GitHub).Dataset · publicThe phenotypic data sets used in this study are available at: https://doi.ipk-gatersleben.de/DOI/0c9c6237-41f2-411f-a51e-809eb23d1088/f844533e-d775-46dd-8523-d485591f6ea8/2/1847940088Open asset ↗lines:88-96Code · publicAn R implementation of Algorithms 1 and 2 is available at https://github.com/dobby978/dynamicGPOpen asset ↗dobby978/dynamicGPlines:131-142Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model's adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.
Why it matches plant phenotyping methodsイネ葉の病斑部を画像からセグメンテーションする手法を開発・評価しており、植物病害状態の取得が研究の中心である。
abstractAccurate semantic segmentation techniques are crucial for segmenting diseased leaf parts
Reproduction assets foundThe authors explicitly state that the rice leaf disease segmentation dataset (2,005 annotated images of Tungro and brown spot) and the analysis code for AISOA-SSformer are publicly available on GitHub.Dataset · publicwith a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code .
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 Mar 24; Accepted 2024 Jun 21; Collection date 2024.
Introduction
Rice is one of the most important food crops in the world [ 1 – 3 ], but its production Open asset ↗ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Codelines:1-28Code · publicThe datasets and code used in this study have been posted on the website https://github.com/ZhouGuoXiong/ Rice-Leaf-Disease-Segmentation-Dataset-Code .Open asset ↗lines:470-494Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products — food, feed, fuel and fiber — will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, a.k.a duckweeds, are one such species that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of 6 different varieties of L. gibba .
Why it matches plant phenotyping methods小型水生植物の成長率を高スループットに定量する自動フェノタイピング装置を開発・実証しており、表現型取得法が研究の中心です。
abstractHerein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae.
Reproduction assets foundThe authors state that all source code for the phenotyping system, the PlantCV image analysis pipeline, the R growth-curve analysis, 3D models, and the data generated for this study (including PlantCV_Output_Salinity.csv and Barcode_Sample_Map.csv) are publicly available in the ALPHA GitHub repository.Code · publicAll source code used in the phenotyping system, 3D models for printed parts and data generated
for this study are available in the ALPHA Github repository.Open asset ↗pdf-raw-page:2 lines:1-49Dataset · publicThis code requires data output from the quantification pipeline
“PlantCV_Output_Salinity.csv” and the barcode map “Barcode_Sample_Map.csv”. Both are also
available in the Github repository.Open asset ↗pdf-raw-page:7 lines:1-33Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. Despite the proliferation of deep neural network architectures for segmentation and phenotyping of 3D plant models in the last decade, the amount of data, and diversity in terms of species and data acquisition modalities are far from sufficient for evaluation of such tools for their generalization ability. To contribute to closing this gap, we introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. PLANesT-3D introduces diversity to existing datasets by adding point clouds of two new species and providing 3D data acquired with the low-cost SfM/MVS technique as opposed to laser scanning or expensive setups. Point clouds reconstructed with SfM/MVS modality exhibit challenges such as missing data, variable density, and illumination variations. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. The advantages of SP-LSCnet over other deep learning methods are its modular structure and increased interpretability. Two existing deep neural network architectures, PointNet++ and RoseSegNet, were also tested on the point clouds of PLANesT-3D for semantic segmentation.
Why it matches plant phenotyping methods3D植物点群の注釈付きデータセットを構築し、植物器官のセマンティック・インスタンス分割手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractwe introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants.
Reproduction assets foundThe paper introduces PLANesT-3D, an annotated 3D plant point cloud dataset, and SP-LSCnet segmentation code, both explicitly stated as publicly available at the authors' Aperta record and GitHub repository.Dataset · publicThe PLANesT-3D dataset is publicly available at https://aperta.ulakbim.gov.tr/record/286354 and https://github.com/visionlab-ogu/PLANesT-3D/tree/main/dataOpen asset ↗aperta.ulakbim.gov.tr · 286354lines:83-145Dataset · publicThe 2D color images for all the 34 plants together with their estimated camera poses and parameters are also open to the public to provide input data for recent 3D reconstruction techniques 3 3
3
The data is available at https://github.com/visionlab-ogu/PLANesT-3D/tree/main/data .Open asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:494-505Code · publicThe code for SP-LSCnet is available at https://github.com/visionlab-ogu/PLANesT-3DOpen asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:146-154Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain. The utility of dense versus sparse temporal collections (e.g., daily, weekly, and monthly flights) has important implications for experimental design, resource allocation, and the scope of scientific questions investigated through UAS. UAS‐derived image data were collected on over 1500 maize hybrid yield trial plots with a temporal (longitudinal, 4D) sampling density of 2.8 days on average between 43 flights throughout the growing season. Correlations of vegetation index (VI) phenomic features between flight dates were generally high between flights separated by only 1 or 2 days but dropped when 3, 4, or more days separated the flights. These varied depending on specific dates and the VI used. Correlations between flights were lower around flowering time than during other parts of the season indicating the phenotypic uniqueness of this developmental period. The cross‐validation accuracy of end of season yields prediction models on untested genotypes from the UAS data (0.59 and 0.62) far exceeded genomic prediction accuracy (0.24) for the same test set hybrids regardless of whether all flight dates were used for prediction or only dates before flowering. Phenomic prediction accuracy marginally increased as additional flight dates were added throughout the season.
Why it matches plant phenotyping methodsUAS画像を用いた高頻度植物フェノタイピングの時間分解能と予測性能を評価しており、取得・解析方法の技術的検証が中心です。
abstractUnoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools
Reproduction assets foundThe paper's data availability statement explicitly provides the authors' analysis/figure-generation scripts on GitHub and both the scripts and phenotypic tabular data on Zenodo, directly enabling reproduction of this paper's UAS phenomic prediction analyses.Code · publicd by USDA award # 2022-
70412-38454 Agriculture Genome to Phenome Initiative
(AG2PI) seed grant.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E SOpen asset ↗JacobWashburn-USDA/dense_UAVpdf-raw-page:10 lines:1-332Dataset · publicE N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E S
Adak, A., Anderson, S. L., & Murray, S. C. (2023). Pedigree-
management-flight interaction for temporal phenotype analysis and
temporal phenomOpen asset ↗10.5281/zenodo.11085557pdf-raw-page:10 lines:1-332Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods植物・土壌フラックスを分離推定する5手法を47地点で実装し、手法間の一致度を評価した長期データセットであり、植物の生理状態（蒸散・光合成）の取得・推定法が中心的です。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites.
Reproduction assets foundThe paper's five-year NEON flux-partitioning dataset and the authors' partitioning-method scripts are explicitly deposited on Zenodo with public DOIs.Dataset · publicows the availability
of flux components as a fraction of the total number of half-
hour periods in the record. Overall, all the methods cover
a similar temporal distribution of flux partitioning and are
potential candidates for ensemble averaging.
4 Description of the final dataset
The final dataset is available for download at
https://doi.org/10.5281/zenodo.12191876 (Zahn and Bou-
Zeid, 2024). It is organized into different folders for each
site, with each site containing a .csv file for each method.
This format is selected to be user-friendly and accessible in
various programming languages and software packages. For
FVS and CECw, in addition to their ensemble averages for
https://doi.org/Open asset ↗Zenodo · 10.5281/zenodo.12191876pdf-raw-page:9 lines:136-149Code · publicnthesis, transpiration and stomatal conduc-
tance: potential and limitations, Plant Cell Environ., 35, 657–
667, https://doi.org/10.1111/j.1365-3040.2011.02451.x, 2011.
Zahn, E.: einaraz/PartitioningMethods: Processing Eddy-
Covariance Data: Five Evapotranspiration Flux Parti-
tioning Methods (v1.0.1) [Software], Zenodo [code],
https://doi.org/10.5281/zenodo.11510363, 2024.
Zahn, E. and Bou-Zeid, E.: Partitioning of water and CO2 fluxes at
NEON sites into soil and plant components: a five-year dataset
for spatial and temporal analysis [dataset], Zenodo [data set],
https://doi.org/10.5281/zenodo.12191876, 2024.
Zahn, E., Chor, T. L., and Dias, N. L.: A Simple Methodology for
Quality ControlOpen asset ↗Zenodo · 10.5281/zenodo.11510363pdf-raw-page:22 lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy ( F-score = 0.77) and branch angle calculation accuracy ( r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine ( F-score = 0.53) and density-based methods ( F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.
Why it matches plant phenotyping methodsLiDAR点群からダイズの枝を検出し枝角度などの形態形質を抽出するアルゴリズムを開発・検証しており、植物表現型取得手法が研究の中心である。
abstractwe developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data
Reproduction assets foundThe paper's Soybean3D point cloud dataset, source code, and software are explicitly stated as openly available on the authors' public GitHub repository, directly supporting the paper's soybean branch angle phenotyping analysis.Code · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractorlines:178-220Dataset · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor · Soybean3Dlines:291-296Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Wheat stripe rust poses a marked threat to global wheat production. Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases. In this study, we propose a practical solution using mobile-based deep learning and model-assisted labeling. StripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds. Additionally, StripeRust-Pocket facilitates image acquisition, result storage, organization, and sharing. The underlying model employed by StripeRust-Pocket, called StripeRustNet, is a balanced lightweight 2-stage model. The first stage utilizes MobileNetV2-DeepLabV3+ for leaf segmentation, followed by ResNet50-DeepLabV3+ in the second stage for lesion segmentation. Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area. StripeRustNet achieves 98.65% mean intersection over union (MIoU) for leaf segmentation and 86.08% MIoU for lesion segmentation. Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores. To address the challenges in manual labeling, we introduce a 2-stage labeling pipeline that combines model-assisted labeling, manual correction, and spatial complementarity. We apply this pipeline to our self-collected dataset, reducing the annotation time from 20 min to 3 min per image. Our method provides an efficient and practical solution for wheat stripe rust severity assessments, empowering wheat breeders and pathologists to implement timely disease management. It also demonstrates how to address the "last mile" challenge of applying computer vision technology to plant phenomics.
Why it matches plant phenotyping methodsコムギ赤さび病の葉画像から病斑面積比として病害重症度を推定する深層学習モデル、モバイルアプリ、アノテーション手順を開発・検証しており、植物表現型取得法が中心である。
abstractStripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds.
Reproduction assets foundThe paper's Data Availability section explicitly links public GitHub repositories containing the authors' self-collected wheat stripe rust leaf image dataset, the StripeRust-Pocket application, and the application source code used for the disease severity phenotyping analysis.Dataset · publicThe wheat stripe rust leaf image dataset collected by smartphones is available at https://github.com/WeizhenLiuBioinform/StripeRustNet/tree/master/Dataset .Open asset ↗WeizhenLiuBioinform/StripeRustNet · Datasetlines:385-435Code · publicThe source code: https://github.com/WeizhenLiuBioinform/StripeRust-Pocket/tree/master/Application_source_code .Open asset ↗WeizhenLiuBioinform/StripeRust-Pocket · Application_source_codelines:385-435Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Manual segmentation of the petals of flower computed tomography (CT) images is time-consuming and labor-intensive because the flower has many petals. In this study, we aim to obtain a three-dimensional (3D) structure of Camellia japonica flowers and propose a petal segmentation method using computer vision techniques. Petal segmentation on the slice images fails by simply applying the segmentation methods because the shape of the petals in CT images differs from that of the objects targeted by the latest instance segmentation methods. To overcome these challenges, we crop two-dimensional (2D) long rectangles from each slice image and apply the segmentation method to segment the petals on the images. Thanks to cropping, it is easier to segment the shape of the petals in the cropped images using the segmentation methods. We can also use the latest segmentation method for the task because the number of images used for training is augmented by cropping. Subsequently, the results are integrated into 3D to obtain 3D segmentation volume data. The experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping. The 3D segmentation results were also obtained and visualized successfully.
Why it matches plant phenotyping methods花弁のCT画像から3D構造を抽出する画像セグメンテーション手法の開発と精度比較が中心であり、植物形態フェノタイピングに該当する。
abstractThe experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping.
Reproduction assets foundThe paper's CT volume data of Camellia japonica flowers (with ground-truth annotations) is publicly deposited on Figshare, and the authors' segmentation/integration code is publicly available on GitHub, both with explicit availability statements.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.25264774.v1Open asset ↗figshare · 10.6084/m9.figshare.25264774.v1lines:447-494Code · publicThe code implementing the proposed method is available at https://github.com/yu-NK/petal_ct_crop_seg.gitOpen asset ↗github · yu-NK/petal_ct_crop_seglines:447-494Code / dataset availability confirmedOpenAlex · checked 7 Sept 2026
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.
Why it matches plant phenotyping methods小麦の形質・表現型抽出とエンティティ linking のための手動アノテーションコーパスを構築し、言語モデルの訓練・評価に用いており、表現型情報の取得手法とデータセットが研究の中心である。
titleA manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature
Reproduction assets foundThe paper's core assets are publicly available: the TaeC annotated corpus (trait/phenotype/species annotations of 528 PubMed wheat references) on Recherche Data Gouv, the Wheat Trait and Phenotype Ontology on AgroPortal, the AlvisNLP bread wheat workflow on Forgemia, and the ToMap method code on GitHub, all with authorDataset · publicThe corpus dataset TaeC is available under CC-BY-ND License at: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3QOpen asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qlines:142-152Code · publicThe code of the ToMap method is available under Apache License at https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-bibliome/src/main/java/fr/inra/maiage/bibliome/alvisnlp/bibliomefactory/modules/tomapOpen asset ↗github.comlines:142-152Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Fruit appearance of apple (Malus domestica Borkh.) is accession-specific and one of the main criteria for consumer choice. Consequently, fruit appearance is an important selection criterion in the breeding of new cultivars. It is also used for the description of older varieties or landraces. In commercial apple production, sorting devices are used to classify large numbers of fruit from a few cultivars. In contrast, the description of fruit from germplasm collections or breeding programs is based on only a few fruit from many accessions and is mostly performed visually by pomology experts. Such visual ratings are laborious, often difficult to compare and remain subjective. Results Here we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit. Recording of approximately 100 fruit from each of 15 apple cultivars using the FruitPhenoBox was rapid, with an average handling and recording time of less than eleven seconds per fruit. Comparison of fruit images from the 15 apple cultivars identified significant differences in shape index, fruit width, height and weight. Fruit shape was characteristic for each cultivar, while fruit color showed larger variation within sample sets. Assessing a subset of 20 randomly selected fruit per cultivar, fruit height, width and weight were described with a relative margin of error of 2.6%, 2.2%, and 6.2%, respectively, calculated from the mean value of all available fruit. Conclusions The FruitPhenoBox allows for the rapid and consistent description of fruit appearance from individual apple accessions. By relating the relative margin of error for fruit width, height and weight description with different sample sizes, it was possible to determine an appropriate fruit sample size to efficiently and accurately describe the recorded traits. Therefore, the FruitPhenoBox is a useful tool for breeding and the description of apple germplasm collections.
Why it matches plant phenotyping methodsリンゴ果実の画像取得・コンピュータ解析・重量測定を統合した装置を開発し、測定速度、再現性、誤差、適切なサンプルサイズを評価しており、果実形態形質の取得法が研究の中心である。
abstractHere we report on a morphometric device, the FruitPhenoBox, for automated fruit weighing and appearance description using computer-based analysis of five images per fruit.
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw fruit images, extracted datasets, and R scripts in the ETH Research Collection (doi:10.3929/ethz-b-000590509), and the Matlab/R image-analysis scripts (apple fruit feature extractor, affe) on SourceForge. Both are paper-specific, public, and actionable.Dataset · publicSupplementary files for this article, which include raw images in tif format, datasets extracted from the images and R scripts used in this study are available from the ETH Research collection under following doi: https://doi.org/10.3929/ethz-b-000590509Open asset ↗ETH Research collection · 10.3929/ethz-b-000590509lines:125-170Code · publicThe Matlab- and R-scripts are available via sourceforge, project apple fruit feature extractor (affe), https://sourceforge.net/projects/affeOpen asset ↗sourceforge · affelines:125-170Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024Forestry An International Journal of Forest ResearchCited by 45 · OpenAlex ↗
Abstract Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error <2 cm, bias <1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin.
Why it matches plant phenotyping methods森林個体の樹高・胸高直径などの植物形質を点群から自動抽出するソフトウェアの開発と技術検証が中心であり、植物フェノタイピング手法に該当する。
abstractwe have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds.
Reproduction assets foundThe paper's DBH/tree-metric analysis was run on the public SilviLaser 2021 Benchmark Dataset (TU Wien Research Data, DOI 10.48436/afdjq-ce434), and the authors' analysis software 3DFin is publicly available (GitHub releases, CloudCompare plugin, PyPI). Both are paper-specific, public, and actionable.Dataset · publicoptimized presets that facilitate the
effective application of 3DFin in various forest inventory scenarios. Data availability
Another direction for the research linked to 3DFin is the devel-
The data underlying this article are available in TU Wien Research
opment of a complementary software tool focused on the seman-
Data, at https://doi.org/10.48436/afdjq-ce434.
tic segmentation of point clouds into different vegetation struc-
tures. This tool will build upon the capabilities of 3DFin, employing
advanced deep learning techniques to distinguish between var- References
ious types of vegetation elements within a forested scene. The
Bentley JL. Multidimensional binary search trees used foOpen asset ↗10.48436/afdjq-ce434pdf-layout-page:17 lines:1-66Code · publichines as a plugin
in CloudCompare via the CloudCompare PythonRuntime (Montaigu,
2024). The latest alpha-version of CloudCompare (version 2.13.1,
March 2024) including the 3DFin plugin can be downloaded from
the official site https://www.danielgm.net/cc/release/. 3DFin is
also downloadable on Windows as a standalone program from
https://github.com/3DFin/3DFin/releases. Additionally, 3DFin
and its dependencies may be installed and launched on any
OS (Windows, Linux and macOS) as a Python package, available
in PyPI. A script entry point is also installed by pip in Python
installation’s bin | script directory. This enables launching 3DFin’s
GUI from the command line, which avoids the need to wrOpen asset ↗pdf-raw-page:7 lines:1-72Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
Why it matches plant phenotyping methods根系のランドマーク検出・形状復元・形質抽出を行う深層学習ベースの植物フェノタイピング手法を開発・検証し、専用ライブラリも提供しているため。
abstractHere, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly make all paper-specific assets public: the sleap-roots trait-extraction codebase on GitHub, a separate repository with figure-replication code, and an OSF deposit containing labeled training data, trained pose-estimation models, and analysis files for the root phenotyping measurements.Code · publicthe specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-rootsOpen asset ↗talmolab/Berrigan_et_al_sleap-roots · Berrigan_et_al_sleap-rootslines:485-526Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files, which can be accessed via the following link: https://osf.io/k7j9g/Open asset ↗osf.io/k7j9g · k7j9glines:485-526Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、専用データセットを構築して性能評価しているため、方法が中心的である。
abstractwe propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes.
Reproduction assets foundThe paper introduces the RGWHD dataset (oriented-bounding-box annotations of GWHD wheat images), publicly released via Baidu pan with extraction code, and makes its experiment scripts publicly available on GitHub. The underlying GWHD image dataset (public on Kaggle) is the image source used for the paper's phenotyping.Dataset · publiction, Validation, Writing—original draft preparation. N.L.: Formal analysis, Resources, Project ad-ministration.C .F.: Investigation, Data curation. All authors have read and agreed to the published version of the manuscript.
Data availability
The datasets generated and analysed during the current study are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors deOpen asset ↗RGWHDlines:445-520Code · publicdy are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Sharma, S., Kooner, R., Arora, R., Insect pests and crop losses. Breeding insect resistant crops for suOpen asset ↗cwr0821/OFPNlines:445-520Dataset · public. The GWHD dataset is a comprehensive collection of well-annotated wheat head images, compiled by nine research institutions across seven countries. It serves as a valuable resource for training robust models to accurately estimate the location and density of wheat heads in seven categories. The GWHD dataset can be accessed at https://www.kaggle.com/competitions/global-wheat-detection/data . The SPIKE dataset comprises 335 images captured at three distinct growth stages, covering ten different wheat varieties. The UWHD dataset consists of 550 images captured by a drone at an altitude of 10 m. The ACID dataset consists of 520 images taken in controlled greenhouse conditions, featuring 4158 laOpen asset ↗lines:52-149Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Trunk diameter is related to the overall health and level of carbon sequestration in a tree. Trunk diameter measurement, therefore, is a key task in both forest plot and urban settings. Unlike the traditional approach of manual measurement with a measuring tape or calipers, several recent approaches rely on sophisticated technologies such as Terrestrial Laser Scanning (TLS), LiDAR, and time-of-flight sensors that provide fine-grain depth maps, which are used for depth-assisted image segmentation in downstream processing. These technologies are supported only on specialized devices or high-end smartphones. We present a mobile application that uses coarse-grain depth maps derived from an optical sensor, and so can be run on most common Android devices. Moreover, we use a state-of-the-art deep neural network to estimate trunk diameter from an image and its corresponding coarse depth map (RGB-D). We tested our app using a dataset collected from four countries and under challenging conditions including occlusion, leaning trees, and irregular shapes and found that our algorithm has a MAE of 2.58 cm and an RMSE of 3.57 cm, which is comparable to accuracy from fine-grain depth maps. Moreover, diameter measurement using our app is more than 5 times faster than traditional manual surveying.
Why it matches plant phenotyping methodsRGB-D画像と粗い深度マップから樹幹直径を推定するモバイル手法を開発し、複数国のデータセットと困難条件で精度検証しており、植物形質取得が中心である。
abstractWe present a mobile application that uses coarse-grain depth maps derived from an optical sensor
Reproduction assets foundThe paper's DBH estimation evaluation dataset (154 RGB + depth tree images with metadata and ground-truth DBH) is publicly deposited on Zenodo, and the app/algorithm source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicing the quality of the DBH estimate, includ-
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ing non-cylindrical trunks, burl presence, degrees of leaning and occlusion, and poor lighting, as illustrated in Fig. 4. Sample images
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from the dataset are available in Supplement 10.9, and the complete set of RGB and depth images, along with metadata, is acces-
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sible at https://zenodo.org/records/10199711.200
In Thailand, we collected data in Bangkok’s Lumphini Park and Chulalongkorn Centenary Park. As a tropical location, Bangkok is
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home to many tropical trees, such as rain trees (Samanea saman), banyan trees, palm trees, and coconut trees52. At Lumphini Park,
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where most of our data came from, small forests grow next to watOpen asset ↗zenodo.org · 10199711pdf-raw-page:8 lines:1-31Code · public; Z.F. analyzed the data and led the writing of the manuscript.
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A.H. and S.K. reviewed the manuscript and provided constructive suggestions. All authors contributed critically to the drafts and
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gave final approval for publication.
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8 DATA AVAILABILITY
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The algorithm and app code are publicly available on GitHub at https://github.com/MingyueX/GreenLens, with APK available from
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APKPure at https://apkpure.com/p/com.cleeg.greenlens. All the data for the app evaluation can be accessed at https://zenodo.org/357
records/10199711.
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9 AUTHOR COMPETING INTERESTS
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The authors declare no conflict of interest.
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REFERENCES
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[1] Kenneth G MacDicken. Global forest resourOpen asset ↗github.com/MingyueX/GreenLenspdf-raw-page:15 lines:1-92Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
Why it matches plant phenotyping methods植物病徴を画像から定量化するソフトウェアの開発が研究の中心であり、苗の色・形態パラメータを自動抽出して病害症状を評価する。
abstractIn this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.
Reproduction assets foundThe paper's authors publicly released the AraDQ software package (system code, pretrained deep learning models, installation manual) and the datasets generated and analyzed in the study, including the case-study image files, in their GitHub repository.Code · publicThe portable software, system code, and installation manual are available at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:94-101Dataset · publicThe image files used in this case study are provided in the released dataset on GitHub.Open asset ↗lines:137-212Dataset · publicThe AraDQ software package, including the installation manual, and the datasets generated and analyzed during the current study are available in the GitHub repository at https://github.com/kist-smartfarm/AraDQ .Open asset ↗kist-smartfarm/AraDQlines:137-212Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated AA $\text{AA}$ and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species 'ilima (Sida fallax). Results Coastal 'ilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and have lower CO 2 conductance through the internal airspace and (2) that they benefit more because they have similar conductance on each surface, as opposed to most conductance being through the lower surface. Conclusions This is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO 2 supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in "sun" leaves, but the mechanistic basis remains uncertain.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい実験手法を開発し、複数集団で適用しているため、植物の生理形質取得法が中心である。
abstractWe developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy).
Reproduction assets foundThe paper's raw phenotyping data (stomatal traits, leaf thickness, gas exchange) are publicly deposited on Dryad, and the authors' custom analysis scripts are on GitHub with a Zenodo archive; both are paper-specific and directly actionable.Dataset · public7341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. Yellow ʻilima (Sida fallax). https://www.inaturalist.org/taxa/54995-Sida-fallax. iNaturalist.
Ball, J. T., I. E. Woodrow, and J. A. Berry. 1987. A model prediOpen asset ↗Dryad · 10.5061/dryad.rxwdbrvfwpdf-raw-page:9 lines:1-93Code · publicfor advice on leaf sectioning. Startup
funds were provided by the University of Hawaiʻi, NSF
Award 1929167 to C.D.M., and T.N.B. received NSF Award
2307341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. YellowOpen asset ↗GitHub · cdmuir/stomata-ilimapdf-raw-page:9 lines:1-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.
Why it matches plant phenotyping methodsトマト葉の病害症状と重症度を、画像・疑似ハイパースペクトル・深層学習で直接推定する方法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractthis study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity
Reproduction assets foundThe paper uses two publicly available tomato leaf disease image datasets (Kaggle tomatoleaf; Google Drive dataset) as phenotyping inputs and provides the authors' analysis code (RGB-to-SHSI conversion, VGG-16 feature extraction, GMM/K-means severity pipeline) via a public Colab notebook listed in the Data Availability.Dataset · publicards in the field. In summary,
the compilation of our diverse dataset and the incorporation of benchmark datasets form the
foundation of this research endeavor, ensuring a thorough and principled evaluation of our proposed
approaches in the context of plant disease assessment [8].
2.1.1. Dataset 1:
This data was collected from "https://www.kaggle.com/datasets/kaus-tubhb999/tomatoleaf:
Access Date: 2023-10-25." This dataset includes diseases for tomato leaves such as "Septoria leaf spot,
tomato healthy, Spider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photosOpen asset ↗kaggle · kaus-tubhb999/tomatoleafpdf-raw-page:7 lines:1-31Dataset · publicider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photos in total.
2.1.2. Dataset 2:
Dataset-2 is also a publicly available one which can be downloaded and utilized from the drive
link provided. "https://drive.google.com/file/d/1DVy0LyUUfJciyo7BUFm1sHKSRdTVJgjF/view:
Access Date: 2023-10-25." This dataset is divided into seven classes: yellow curving, tomato mosaic,
Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 January 2024 doi:10.20944/preprints202401.1973.v1Open asset ↗pdf-raw-page:7 lines:1-31Code · public.B.; writing— S.K., M.M., B.B., Y.S., and A.B.; writing—review
and editing, M.M, B.B.; supervision, S.K., M.M., B.B. All authors have read and agreed to the published version
of the manuscript.
Funding: This research was partly funded by Zayed University, grant number 12091.
Data Availability Statement: Our code is available at
https://colab.research.google.com/drive/1wMvqsuZNY_lB2INmyWWqSZYm87wVckv0?usp=sharing
Acknowledgments: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the resultsOpen asset ↗pdf-raw-page:19 lines:1-52Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. Newly desirable wheat variety traits include disease resistance to reduce pesticide use, adaptation to climate change, resistance to heat and drought stresses, or low gluten content of grains. Wheat breeding experiments are documented by a large body of scientific literature and observational data obtained in-field and under controlled conditions. The cross-referencing of complementary information from the literature and observational data is essential to the study of the genotype-phenotype relationship and to the improvement of wheat selection. The scientific literature on genetic marker-assisted selection describes much information about the genotype-phenotype relationship. However, the variety of expressions used to refer to traits and phenotype values in scientific articles is a hinder to finding information and cross-referencing it. When trained adequately by annotated examples, recent text mining methods perform highly in named entity recognition and linking in the scientific domain. While several corpora contain annotations of human and animal phenotypes, currently, no corpus is available for training and evaluating named entity recognition and entity-linking methods in plant phenotype literature. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 540 PubMed references fully annotated for trait, phenotype, and species named entities using the Wheat Trait and Phenotype Ontology and the species taxonomy of the National Center for Biotechnology Information. A study of the performance of tools trained on the Triticum aestivum trait Corpus shows that the corpus is suitable for the training and evaluation of named entity recognition and linking.
Why it matches plant phenotyping methods小麦の形質・表現型を文献から抽出・リンクするための注釈付きデータセットを開発し、ツール性能も評価しており、植物表現型情報の計算的抽出が中心である。
abstractThe Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat.
Reproduction assets foundThe paper's core asset, the TaeC annotated wheat trait/phenotype corpus, is publicly deposited on Recherche Data Gouv under CC-BY-ND. The authors' AlvisNLP wheat text-mining workflow and the ToMap method code are also publicly available. The WTO ontology used for annotation is public on AgroPortal.Dataset · publicTaeC is
available under CC-BY-ND License at:
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3Q.Open asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qpdf-page:9 lines:1-56Code · publicThe AlvisNLP bread wheat workflow is available at : https://forgemia.inra.fr/migale/wheat-tm.
It includes the wheat-specific lexica of ToMap.Open asset ↗forgemia.inra.frpdf-page:13 lines:1-53Code · publicThe code of the ToMap method is available at
https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-Open asset ↗github.com/Bibliome/alvisnlppdf-page:13 lines:1-53Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Abstract Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.
Why it matches plant phenotyping methods植物病害を対象としたコンピュータビジョンによる症状・病害の検出手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的整理と評価が主題である。
abstractHere, we review the application and development of computer vision and machine learning methods for the detection of plant disease.
Reproduction assets foundThe paper's data availability statement provides public OSF deposits (view-only links) containing image data, annotations, training data, and semi-supervised model weights for the cocoa disease-detection case studies, plus public GitHub repositories with the authors' custom training/analysis code (CocoaReader, CocoaNetDataset · publicThe image data, annotations, and link to the accompanying GitHub repository for Case Study 1 can be found at: https://osf.io/79kx3/?view_only=4a2c1dccee1a4baeb85de5002c702f10 .Open asset ↗osflines:411-466Dataset · publicFor Case Study 2, the data used to train the initial supervised model, the .csv search terms file for the below web scraper, and the final semi‐supervised model weights can be found at: https://osf.io/h5gj7/?view_only=dbf9f245e21a41e185f5b73e718b4cad .Open asset ↗osflines:411-466Code · publicThe custom code used to train both the initial model and the final semi‐supervised model can be found at: https://github.com/jrsykes/CocoaReader/blob/main/PlantNotPlant .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Code · publicThe custom code to run the sweep in Case Study 4 can be found in the following GitHub repository: https://github.com/jrsykes/CocoaReader/tree/main/CocoaNet .Open asset ↗github · jrsykes/CocoaReaderlines:411-466Dataset · publicThe data used to generate these results and the full wandb report can be found at: https://osf.io/2fw6g/?view_only=adc66ba66f83465a9e7b111515a60bf2 .Open asset ↗osflines:411-466Code · publicThe “contaminated” data used to train the semi‐supervised model were generated using the code at: https://github.com/jrsykes/Google-Image-Scraper .Open asset ↗github · jrsykes/Google-Image-Scraperlines:411-466Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusions The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Why it matches plant phenotyping methods植物の3D顕微鏡画像から細胞形態を抽出するセグメンテーション手法を開発し、植物データセットで性能評価・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractWe propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data.
Reproduction assets foundThe paper provides public author code (CellStitch implementation and experiment-reproducing notebooks on GitHub) and the plant image datasets analyzed (Ovules, ATAS, Arabidopsis 3D Digital Tissue Atlas), all with explicit availability statements and URLs.Code · publicopen source code implementing the stitching algorithm from the top to the bottom layer is available at https://github.com/imyiningliu/cellstitchOpen asset ↗imyiningliu/cellstitchlines:116-127Dataset · publicAll the datasets analyzed in this paper are publicly available online. Ovules: https://osf.io/uzq3w/ ; ATAS: https://www.repository.cam.ac.uk/handle/1810/262530 ; Arabidopsis 3D Digital Tissue Atlas: https://osf.io/fzr56Open asset ↗lines:175-223Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisTissueClassificationCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly available Arabidopsis thaliana data with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing. Author Summary Large datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly available Arabidopsis thaliana RNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Why it matches plant phenotyping methodsRNA-seqデータから植物の年齢・組織型を予測する機械学習について、必要サンプル数と正規化法を大規模Arabidopsisデータで評価しており、表現型推定手法の検証が中心である。
abstractWe use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required.
Reproduction assets foundThe paper's normalized gene expression matrices, curated phenotype annotation datasets, and intermediary files are publicly deposited on Zenodo, and all analysis code is publicly available on GitLab. These directly reproduce the paper's plant-phenotyping measurements (Arabidopsis age/tissue annotations) and modeling/MLDataset · public(NoNo). TMM normalization [24,29] and MRN normalization [25] were performed using
the Python “conorm” package 1.2.0 [30]. TPM and NoNo normalization values were an
output of Kallisto [27]. How these normalizations impacted sample count is visualized as
S2 Figure. We have made these GEMs publicly available on Zenodo at the link
https://zenodo.org/records/10183151
Sample Phenotype Annotations Pre-Processing
Sample phenotype annotations were retrieved from the NCBI BioProject database
[16,17] using BioSampleParser which was slightly modified to check for successful data
retrieval [31]. Phenotype annotations were retrieved for 48696 NCBI BioSamples,
representing data from 2643 BioProjects.Open asset ↗Zenodopdf-raw-page:9 lines:1-55Dataset · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. BostanciOpen asset ↗Zenodo · 10.5281/zenodo.10183150pdf-raw-page:43 lines:1-51Code · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning
Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Open asset ↗GitLabpdf-raw-page:43 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from bright-field microscopy images containing mesophyll tissue as noisy backgrounds. By combining a You Only Look Once X-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved a mean average precision with an intersection of union (IoU) threshold of 50% value of 0.875 (stomata detection performance) and an IoU of 0.745 (pore segmentation performance) against images of leaf discs taken with a bright-field microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We demonstrated that this device in combination with fine-tuned models of the pipeline we generated here provides robust measurements that can substitute for manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.
Why it matches plant phenotyping methods葉画像から気孔開度を自動抽出する画像解析パイプラインと携帯型撮像装置を開発・性能評価しており、植物表現型取得が中心である。
abstractwe present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' ONNX model weights and pipeline code, plus masked/unmasked test images, on a public GitHub repository and Zenodo (DOI 10.5281/zenodo.7549843). These are paper-specific, publicly actionable assets for the stomatal aperture phenotyping pipeline. The YCode · publicPlant Cell Physiol. 00(00): 1–10 (2023) doi:https://doi.org/10.1093/pcp/pcad018
Supplementary Data
Supplementary data are available at PCP online.
Data Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative ResearcOpen asset ↗phytometrics/arabidopsis_leaf_stomata_quantificationpdf-raw-page:9 lines:1-84Dataset · publicData Availability
The model weights and codes in ONNX format to execute
the Arabidopsis stomata quantification pipeline and mask
and unmasked test data are available at https://github.com/phytometrics/arabidopsis_leaf_stomata_quantification.Model weights and the test images are also available on Zen-
odo with the following DOI: https://doi.org/10.5281/zenodo.7549843. The software used to process image streams from the
portable device is under development at https://github.com/phytometrics/cvgui_linux.Funding
Grant-in-Aid for Transformative Research Areas (21H05151 and
21H05149 to A.M. and 21H05152 to Y.T.), Grant-in-Aid for Sci-
entific Research (B) (19H02960 to A. M.), and Grant-in-Aid foOpen asset ↗10.5281/zenodo.7549843pdf-raw-page:9 lines:1-84Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Characterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort Marchantia polymorpha is a model system for plant biology and exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep-learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features and analyzed pictures of M. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sex chromosomes. To isolate the effects of sex chromosomes from autosomal polymorphisms, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations. Furthermore, we validated our trained models by an 'eXplainable AI' technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.
Why it matches plant phenotyping methods植物画像から形態表現型を客観的に分類・解析する深層学習手法を開発し、モデル検証と説明可能AIによる妥当性確認を行っており、表現型取得・抽出法が研究の中心である。
abstractwe utilized a deep-learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features
Reproduction assets foundThe paper's gemmaling image dataset is publicly deposited in the RIKEN SSBD repository, and the authors' training/analysis code is on GitHub. The R script repo (PMB-KU/Rit-dev) concerns genomic polymorphism counting, and the Grad-CAM/saliency repos are generic third-party libraries, so they are excluded.Dataset · publicImage data underlying this article are available in the RIKEN SSBD:repository (Systems Science Biological Dynamics repository) with the https://ssbd.riken.jp/repository/290/ .Open asset ↗RIKEN SSBD:repository · 290lines:116-134Code · publicOur code used for training neural networks is available at https://github.com/nyunyu122/Marchantia_sex_classifier .Open asset ↗nyunyu122/Marchantia_sex_classifierlines:116-134Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/ .
Why it matches plant phenotyping methods植物根の形態ランドマークをポーズ推定で検出し、根系形質を抽出する手法とソフトウェアを開発・検証した研究であり、植物フェノタイピング手法が中心である。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe paper makes its root phenotyping assets public: labeled training data, trained SLEAP models, and analysis files on OSF, the sleap-roots trait-extraction codebase on GitHub, and a separate figure-replication code repository.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files which can be accessed via the following link: https://osf.io/k7j9g/ .Open asset ↗osf.io/k7j9glines:752-811Code · publicAdditionally, the specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-roots .Open asset ↗talmolab/Berrigan_et_al_sleap-rootslines:752-811Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.
Why it matches plant phenotyping methods植物画像から成長状態を抽出する画像セグメンテーション手法を開発し、ハイスループット表現型解析プラットフォームで評価しているため、方法が研究の中心である。
abstractThe Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework
Reproduction assets foundThe paper's authors publicly released both their analysis code (OSC-CO2 framework on GitHub) and the paper-specific plant phenotyping image dataset (CosegPP+, a VSTEM plant imagery dataset from the UNL LemnaTec platform) with explicit availability statements and URLs.Code · publicthe object’s (plant’s) shape, orientation, and size at a specific point in time. OSC-CO 2 is designed to process datasets that contain a variety of features, such as perspective (V), species (S), temporality (T), environmental conditions (E) and modality (M) (VSTEM) (
Figure 1
). The code for OSC-CO 2 is publicly available at: https://github.com/rubiquinones/OSC-CO2 .
Figure 1
A preview of a VSTEM Dataset. This work will use the CosegPP dataset ( Quiñones et al., 2021 ) and modify it as CosegPP+ and categorize it as a VSTEM dataset for our problem definition. The first row shows the growth sequence of a Buckwheat plant from 3 rd July 2019 to 27 th July 2019. The second row shows the threeOpen asset ↗rubiquinones/OSC-CO2lines:37-49Dataset · publicasets through segmentation using Otsu’s method ( Otsu, 1979 ) and cosegmentation using Subdiscover ( Meng et al., 2016 ). These two methods were chosen since ( Quiñones et al., 2021 ) defined these as the top methods for being able to segment some of the challenging features of computer vision. CosegPP+ is publicly available at https://doi.org/10.5281/zenodo.6863013 .
We replaced the original images with the outputs generated by Otsu’s method and Subdiscover. Meaning that each time point
i
will have at most
a
binary images where
a
is the number of algorithms (i.e., Otsu’s method and Subdiscover) used. Some groups do not contain Subdiscover binary masks due to the method’s limitation in notOpen asset ↗10.5281/zenodo.6863013lines:350-411Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
Why it matches plant phenotyping methodsトウモロコシの収量・開花形質に関する大規模な再利用可能データセットを構築し、農業モデリングのベンチマークとして提示しており、表現型データセットが研究の中心である。
abstractThe resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids.
Reproduction assets foundThe paper's curated maize G×E dataset (phenotypes, SNP genotypes, environmental covariates) is publicly deposited on Figshare, and the authors' analysis scripts are publicly available on GitHub (MAIZE-HUB). Both are paper-specific, public, and actionable.Dataset · publicThe aggregated curated data set (including the SNP genotypes, phenotypes, and ECs) is available in the Figshare repository [ https://doi.org/10.6084/m9.figshare.22776806 ] 58 .Open asset ↗Figshare · 10.6084/m9.figshare.22776806lines:207-229Code · publicThe scripts used to implement all the analyses described in this study are provided in the GitHub repository [ https://github.com/QuantGen/MAIZE-HUB ].Open asset ↗GitHub · QuantGen/MAIZE-HUBlines:207-229Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rice ( Oryza sativa ) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM 2 ) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM 2 trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM 2 trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM 2 trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM 2 trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.
Why it matches plant phenotyping methodsイネ穂数という植物形質をドローン画像から定量化するAIプラットフォーム、データセット、検出モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractwe present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.
Reproduction assets foundThe paper's Data Availability statement provides a public GitHub releases page containing the authors' source code and the paper-specific DRPD dataset (5,372 annotated rice panicle subimages), plus a public cloud platform URL for panicle detection. These directly reproduce the paper's phenotyping measurements and are,Code · publicRelease page and source code can be found via https://github.com/changcaiyang/Panicle-AI/releases/; the DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repository.Open asset ↗https://github.com/changcaiyang/Panicle-AI/releases/lines:230-241Dataset · publicthe DRPD dataset: 5,372 RGB subimages with annotate 259,498 panicles collected from 229 rice varieties can also be downloaded for the GitHub repositoryOpen asset ↗DRPDlines:230-241Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our results suggest the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物中の植物化学物質量を測定する低コスト反射計を商用分光光度計と比較検証しており、植物の化学的形質取得が研究の中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all data derived from the Bionutrient Institute methods (the crop phytochemical measurements underlying this study) are publicly available in the authors' GitLab repository, and that the automated data pipeline scripts used to calculate, merge, and QC the sample data are also publicly可用Dataset · publicd redefined extraction protocols utilized for crop phytochemical assessment, developed protocols, and provided technical oversight for the usage of reflectometer and offered editorial review of the manuscript.
3 Data Availability
All data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset. The data used in this manuscript covers samples submitted up to 7/31/2022 . Our Sci seeks to increase transparency and access to research via open-source hardware and software and open-access data [19].
4 Competing Interests Statement
Author G. Austic and D. Ter Avest are co-founders of OurSci, LLC, the companyOpen asset ↗our-sci/bionutrient-institute/datasetlines:108-124Code · publice created to guide users through each aspect of the protocol, including Reflectometer measurements, instructions, and questions for entering metadata (ex: crop type, amount of extractant used, etc). An automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.
4.2 Crop Phytochemical Variability Study
4.2.1 Crop Sample characteristics
Crop samples were submitted from both producer and consumer volunteers from 2019–2022 representing 10,000 unique samples with accompanying geographical and management datOpen asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:86-97Code · publiccation was built using NodeJS/express with mongoDB on the Server and Vue with Vuetify on the Client. Hardware integration between SurveyStack and the Reflectometer occurs via the SurveyStack Kit mobile application written in Kotlin for Android devices. The source code for all applications is available and documented on Gitlab ( https://gitlab.com/our-sci/software/surveystack ). The Android application is available for download over the Google Play store ( https://play.google.com/store ). SurveyStack and SurveyStack Kit are licensed under the GNU General Public License v3.0.
All data collection was completed using SurveyStack forms. For each data collection activity, forms were created to guiOpen asset ↗our-sci/software/surveystacklines:86-97Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
Why it matches plant phenotyping methods葉色画像から個体を自動検出し、深層学習で葉色およびアントシアニン含量を推定する撮像・解析手法を開発し、大規模フィールドデータで適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically.
Reproduction assets foundThe paper deposits its field time-series plant images and annotation/labeling data in two Dryad repositories, and the PlantServation demo set (scripts plus demo data) on Zenodo. All are paper-specific, public, and actionable.Dataset · publicThe time-series image data for the Swiss site generated in this study have been deposited in a Dryad repository [ https://doi.org/10.5061/dryad.1g1jwsv11 ] 93 .Open asset ↗Dryad · 10.5061/dryad.1g1jwsv11lines:200-258Dataset · publicThe time-series image data for Japanese site generated in this study as well as the labeling data for image analysis used in this study are available in a Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Dryad · 10.5061/dryad.h70rxwdnklines:200-258Code · publicThe PlantServation demo set (ca. 600 MB) including scripts and demo data for PlantServation software is available at Zenodo [ https://zenodo.org/record/7321725 ] 95 accessible via Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Zenodo · 7321725lines:200-258Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice ( Oryza sativa ). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
Why it matches plant phenotyping methods自動ハイパースペクトル画像からイネの生理形質を推定するモデルを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository containing the code for the physiological trait prediction models (RReliefF-PLSR, SVM classification of rice hyperspectral data), and a Purdue PURR repository deposit containing the study's datasets (HSI-derived and ground-reference phenCode · publicThe code for each physiological trait prediction model can be accessed through GitHub ( https://github.com/To-Chia/rice_imaging_ms ).Open asset ↗https://github.com/To-Chia/rice_imaging_mslines:384-394Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://purr.purdue.edu/publications/4079/ .Open asset ↗https://purr.purdue.edu/publications/4079/lines:442-452Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
Why it matches plant phenotyping methodsアヒルウキクサの画像ベース成長アッセイを含む、培養・実験管理ソフトウェアとデータ解析ワークフローの開発が中心であり、植物表現型取得を支援する方法論的貢献である。
abstractcomputational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis.
Reproduction assets foundThe paper's duckweed growth-assay experimental data and Python analysis scripts are publicly available in the authors' GitHub repository, explicitly stated in the availability statement and results sections. The Aquarium platform itself is a generic pre-existing tool, not a paper-specific asset.Code · publicAll code used in this study is available on Github.Open asset ↗lines:119-135Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Github repository, https://github.com/mtscott321/duckweed_data_analysis .Open asset ↗mtscott321/duckweed_data_analysislines:119-135Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.
Why it matches plant phenotyping methodsスマートフォン画像・深度情報と3D再構成を用いて樹高という植物構造形質を自動測定する手法を開発し、誤差評価とAndroidアプリ化まで行っているため、植物フェノタイピング手法が中心である。
abstractWe propose a tree height measurement method based on three-dimensional (3D) reconstruction.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides two paper-specific public assets: the source code of the TreeHeight prototype app on GitHub and the authors' annotated tree image dataset (300 annotated images augmented to 1000 pairs of color images, relative depth maps, and tree masks) on Google Drive. Both,Code · publicThe source code of the prototype app is publicly available on GitHub at https://github.com/LisaShen0509/Tree_Height_Measurement (accessed on 27 July 2023).Open asset ↗LisaShen0509/Tree_Height_Measurementlines:432-615Dataset · publicTree image dataset is available at https://drive.google.com/file/d/1kG6LWMOAiA2KvGF_suZ5cG_4C-udUV0m/view?usp=sharing (accessed on 27 July 2023).Open asset ↗lines:432-615Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands (e.g., hydraulic conductivity, transpiration efficiency, and tolerance to damage and blockage) constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. To better understand the morphological diversities and constraints on leaf vein networks, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images. Because the one-dimensional distribution patterns align with the Pareto front that optimizes transport efficiency, construction cost, and robustness to damage, as predicted by the earlier theoretical study, our findings suggested that venation patterns are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins.
Why it matches plant phenotyping methods葉画像の取得、深層学習による葉脈セグメンテーション、ネットワーク抽出、特徴量計算から成る高スループット表現型解析ワークフローを開発しており、植物形態の定量化が研究の中心である。
abstractwe developed a simple, high-throughput phenotyping workflow for the quantification of vein networks
Reproduction assets foundThe paper's Data Availability statement and Methods text explicitly deposit the untreated leaf image dataset on Zenodo (10.5281/zenodo.7070266) and the analysis code and trained U-Net model weights on Zenodo (10.5281/zenodo.8020856) and GitHub (MorphometricsGroup/iwamasa-2022). These are paper-specific, public, and可直接可Dataset · publicData Availability: All relevant data and code are available on Zenodo at links https://doi.org/10.5281/zenodo.7070266 and https://doi.org/10.5281/zenodo.8020856 , and on GitHub at links https://github.com/MorphometricsGroup/iwamasa-2022 .Open asset ↗Zenodo · 10.5281/zenodo.7070266lines:104-116Code · publicThe analysis code and model weights have been publicly available at Zenodo [ 40 ] and GitHub ( https://github.com/MorphometricsGroup/iwamasa-2022 ).Open asset ↗Zenodolines:145-157Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vivo microscopy of plants with high-frequency imaging allows observation and characterization of the dynamic responses of plants to stimuli. It provides access to responses that could not be observed by imaging at a given time point. Such methods are particularly suitable for the observation of fast cellular events such as membrane potential changes. Classical measurement of membrane potential by probe impaling gives quantitative and precise measurements. However, it is invasive, requires specialized equipment, and only allows measurement of one cell at a time. To circumvent some of these limitations, we developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3). In this protocol, we describe how to prepare experiments for agar media and microfluidics, and we detail the image analysis. We take an example of the rapid plasma membrane depolarization induced by the phytohormone auxin to illustrate the method. Relative membrane potential measurements using DISBAC 2 (3) fluorescence increase the spatio-temporal resolution of the measurements and are non-invasive and suitable for live imaging of growing roots. Studying membrane potential with a more flexible method allows to efficiently combine mature electrophysiology literature and new molecular knowledge to achieve a better understanding of plant behaviors. Key features Non-invasive method to relatively quantify membrane potential in plant roots. Method suitable for imaging seedlings root in agar or liquid medium. Straightforward quantification.
Why it matches plant phenotyping methods植物根の膜電位を蛍光画像から定量する非侵襲的手法の開発と画像解析プロトコルが中心であり、植物表現型計測法に該当する。
abstractwe developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3).
Reproduction assets foundThe protocol explicitly states that the raw imaging data re-analyzed in the paper are deposited on Zenodo and that all analysis scripts (R and Python) are available in a public SourceForge repository. Both are paper-specific, public, and actionable.Dataset · publicluorescence only in the root transition zone. Moreover, we focus on the interface between the cortex and the epidermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
QuantOpen asset ↗Zenodo · 4922659lines:168-214Code · publicermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
Quantify root elongation either as an average growth (agar experiment) or over time (microfluidics).
Normalize the microfluidOpen asset ↗SourceForge · disbac2-3-data-analysislines:168-214Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.
Why it matches plant phenotyping methods植物の画像から種子・シュート・根を認識し、形態形質を自動抽出して統計評価するオープンソースツールの開発・精度検証が中心である。
abstractIn this study, we developed Morley, a free, open-source graphical user interface written in Python.
Reproduction assets foundThe paper's authors publicly released the Morley analysis code (GitHub repo dashabezik/Morley) and example data/user guide (dashabezik/plants), both explicitly stated in the Data Availability Statement and Methods. These directly support the paper's seedling image analysis and morphometric measurements.Code · publicths and plant surface areas, and figures characterizing
distributions of measured parameters, bar plots with mean values and standard deviations (95% CI),
and heatmaps visualizing the conclusions on statistical significance of the morphometric differences.
Code, graphical user interface, user guide and examples are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/, respectively.
Morley is available as a graphical user interface and a command line tool.
3. Results
3.1. Comparison of Morley with ImageJ and Manual Measurements Demonstrates Agreement between
Results
ImageJ [23] is widely applied for image analysis of plants and seedlings [24–28] andOpen asset ↗dashabezik/Morleypdf-layout-page:6 lines:1-47Dataset · publicon,
IAT; funding acquisition, IAT. All authors have read and agreed to the published version of the manuscript.
Funding: The study was supported by Russian Science Foundation, grant #22‐26‐00109.
Data Availability Statement: Program code, GUI, user guide and example data are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/.
Acknowledgments: The authors thank Dr. Olga M. Zhigalina and Dr. Dmitri N. Khmelenin (Shubnikov Institute
of Crystallography, FSRC “Crystallography and Photonics”, RAS) for collecting high‐quality TEM images of
iron nanoparticles and Dr. Nadezhda G. Berezkina (N.N. Semenov Federal Research Center for Chemical
Physics, RAS) forOpen asset ↗dashabezik/plantspdf-layout-page:13 lines:1-65Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen's kappa, κ ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the F1w score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year's data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.
Why it matches plant phenotyping methodsRGB画像からコムギ赤かび病の重症度を推定する分類モデルを開発し、複数年データで性能を検証した、中心的な画像ベース植物フェノタイピング研究です。
abstractThe present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images
Reproduction assets foundThe authors explicitly state that the FHB RGB image dataset with annotations and the source code are publicly available via their GitHub repository.Dataset · publicAll images and corresponding annotations can be downloaded from the link provided in our GitHub repository: https://github.com/cvims/FHB_classification .Open asset ↗cvims/FHB_classificationlines:663-678Code · publicThe source code and the dataset are available at https://github.com/cvims/FHB_classification .Open asset ↗cvims/FHB_classificationlines:1-28Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Plant phenotyping experiments are conducted under a variety of experimental parameters and settings for diverse purposes. The data they produce is heterogeneous, complicated, often poorly documented and, as a result, difficult to reuse. Meeting societal needs (nutrition, crop adaptation and stability) requires more efficient methods toward data integration and reuse. In this work, we examine what "making data FAIR" entails, and investigate the benefits and the struggles not only of reusing FAIR data, but also making data FAIR using genotype by environment and QTL by environment interactions for developmental traits in potato as a case study. We assume the role of a scientist discovering a phenotypic dataset on a FAIR data point, verifying the existence of related datasets with environmental data, acquiring both and integrating them. We report and discuss the challenges and the potential for reusability and reproducibility of FAIRifying existing datasets, using metadata standards such as MIAPPE, that were encountered in this process.
Why it matches plant phenotyping methods植物フェノタイピングデータセットのFAIR化、統合、再利用性・再現性を扱う研究であり、フェノタイピングデータ基盤とデータ標準化が中心です。
titleThe benefits and struggles of FAIR data: the case of reusing plant phenotyping data
Reproduction assets foundThe paper's FAIRified potato CxE phenotyping datasets (original and processed) and its analysis code (Jupyter notebooks, FDP/triple-store scripts) are publicly deposited on Zenodo and GitHub, with explicit availability statements.Dataset · publicAll associated data, original and processed, is available on Github 15 . The data is located under the paths “ all_containers/ common_files/data-original ” and “ all_containers/common_files/data-generated ”. These two folders ( data-original and data-generated ) are also available on Zenodo 29 .Open asset ↗Zenodolines:191-241Code · publicAll associated code is available on our Github repository and deposited on Zenodo 15 , including Jupyter notebooks to transform data, scripts to run the FDP and the triple store.Open asset ↗Zenodolines:191-241Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Automatic tree density estimation and counting using single aerial and satellite images is a challenging task in photogrammetry and remote sensing, yet has an important role in forest management. In this paper, we propose the first semisupervised transformer-based framework for tree counting which reduces the expensive tree annotations for remote sensing images. Our method, termed as TreeFormer, first develops a pyramid tree representation module based on transformer blocks to extract multi-scale features during the encoding stage. Contextual attention-based feature fusion and tree density regressor modules are further designed to utilize the robust features from the encoder to estimate tree density maps in the decoder. Moreover, we propose a pyramid learning strategy that includes local tree density consistency and local tree count ranking losses to utilize unlabeled images into the training process. Finally, the tree counter token is introduced to regulate the network by computing the global tree counts for both labeled and unlabeled images. Our model was evaluated on two benchmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our TreeFormer outperforms the state of the art semi-supervised methods under the same setting and exceeds the fully-supervised methods using the same number of labeled images. The codes and datasets are available at https://github.com/HAAClassic/TreeFormer.
Why it matches plant phenotyping methods樹木の個体数・密度という植物状態を航空・衛星画像から推定する画像解析手法を開発し、複数データセットで評価しているため、植物フェノタイピング手法が中心である。
abstractAutomatic tree density estimation and counting using single aerial and satellite images is a challenging task
Reproduction assets foundThe paper's authors publicly release their analysis code and the KCL-London tree counting dataset via GitHub, and the paper's annotation workflow directly uses the public London Datastore local-authority-maintained trees dataset for tree locations.Code · publichmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our TreeFormer outperforms the state of the art semi-supervised methods under the same setting and exceeds the fully-supervised methods using the same number of labeled images. The codes and datasets are available at https://github.com/HAAClassic/TreeFormer .
Index Terms:
Tree counting, semi-supervised model, transformer, pyramid learning strategy, remote sensing.
I Introduction
Trees are the pulse of the earth and are vital organisms in maintaining the ecological functioning and health of the planet [ 1 ] . Tree counting using high-resolution images is useful in various fields suOpen asset ↗HAAClassic/TreeFormerlines:1-71Dataset · publicmages are gathered and stitched together from Google Maps at 0.2 m ground sampling distance (GSD). The gathered images are divided into images with 1024 × 1024 pixels.
To aid the identification of tree locations and numbers of selected images, we employed the accessible tree locations of London in London Datastore website 1 1
1
https://data.london.gov.uk/dataset/local-authority-maintained-trees .
Although these data show the locations and species information for over 880,000 of London’s trees, the data mainly contains information on trees in the main streets and does not cover trees that are dense between houses or parks. We manually annotated the latter.
To this end, Global Mapper as geograOpen asset ↗lines:124-145Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.
Why it matches plant phenotyping methods根圏線虫感染による植物の耐性を定量化するため、画像による緑色キャノピー面積の測定と、960個体を同時測定する高スループット表現型解析プラットフォームを開発しており、表現型取得法が研究の中心である。
abstractThrough imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe paper's authors publicly deposited the full plant image dataset (green canopy phenotyping pictures) on figshare and the analysis code/model (SYLM and R growth analysis scripts) on a WUR GitLab repository, both explicitly linked in the Data availability statement.Dataset · publicAlso, the full picture dataset has been made available at doi: https://doi.org/10.6084/m9.figshare.23518923.v1 .Open asset ↗figshare · 10.6084/m9.figshare.23518923.v1lines:263-263Code · publicUsing these equations, the tolerance limit T SYLM and the minimum yield m were estimated (model and code available via gitlab: https://git.wur.nl/published_papers/willig_2023_camera-setup ).Open asset ↗git.wur.nl · published_papers/willig_2023_camera-setuplines:53-66Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.
Why it matches plant phenotyping methods植物のさび病画像から病斑をセグメンテーションするための適応的画像拡張法をDRLで開発・評価しており、植物病害状態の画像ベース表現型取得が中心である。
abstractThis study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system.
Reproduction assets foundThe paper's authors publicly released their DRL adaptive image augmentation analysis code on GitHub, and the plant rust leaf image dataset used for phenotyping/segmentation is publicly available on Baidu AI Studio per the data availability statement.Code · publicctical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git .
adaptive image augmentation deep reinforcement learning deep Q-learning automated optical inspection semantic segmentation Department of Education of Guangdong Province
10.13039/501100010226 2021KTSCX005 Natural Science Foundation of Guangdong Province
10.13039/501100003453 2022A1515240061, 2023A1515012975 Open asset ↗lynnkobe/Adaptive-Image-Augmentationlines:1-51Dataset · publicwork should consider more advanced image augmentation methods, segmentation targets, and a more flexible and efficient DRL framework to provide more effective detection schemes for complex AOI application scenarios.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://aistudio.baidu.com/aistudio/datasetdetail/11591 .
Author contributions
SW, AK, YL, ZJ, HT, SA, MS and UB were responsible for question formulation, method, experimental design, and manuscript writing. YL, ZJ, HT, SA, MS and UB contributed to the issue investigation. HT contributed to the data analysis and AK funded the research. All authors listed have made a Open asset ↗11591lines:642-707Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
Why it matches plant phenotyping methodsドローン画像から植物高・葉面積・分げつ密度を推定し、これらを用いたフェノミック選抜モデルを評価しており、形質取得と解析ワークフローが研究の中心である。
abstractThe dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (phenotypic/trait data from drone-based wheat phenotyping) in the ETH Research Collection and the phenomics data processing source code in a public ETH GitLab repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated and analyzed during the current study are openly available in the ETH Research Collection repository, http://doi.org/10.3929/ethz-b-000566864 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000566864lines:210-223Code · publicSource code for the phenomics data processing methods used in this study are openly available in the ETH gitlab repository, https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing .Open asset ↗ETH gitlab · crop_phenotyping/htfp_data_processinglines:210-223Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis , formerly known as Mycosphaerella fijiensis . Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.
Why it matches plant phenotyping methodsバナナの健全・罹病状態を画像で記録した公開データセットが論文の中心であり、植物病害状態の画像ベース表現型解析に利用できる。
abstractThe dataset of curated banana crop image is presented in this article.
Reproduction assets foundThe paper's banana leaf/stem image dataset (16,092 images) is publicly deposited on Harvard Dataverse (doi:10.7910/DVN/LQUWXW), and annotation was done with the Makerere AI Lab public web annotation tool on GitHub. Both are paper-specific, public, and actionable.Dataset · publiccation
• Institution: The Nelson Mandela African Institution of Science and Technology (NM-AIST), The International Institute of Tropical Agriculture (IITA)
• City/Town/Region: Arusha
• Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/LQUWXW
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LQUWXW
Value of the Data
•
Machine learning models for early detection of Black Sigatoka and Fusarium Wilt Race 1 diseases that affect productivity can be trained using this dataset.
•
Researchers in the field of machine learning can use the collected imagery dataset of bananas to develop the endOpen asset ↗Harvard Dataverse · doi:10.7910/DVN/LQUWXWlines:1-53Code · publict and Remove Duplicate Pictures
2023 https://visipics.en.softonic.com (Accessed 10 February 2023)
3
Chen Q. Zobel J. Zhang X. Verspoor K.
Supervised learning for detection of duplicates in genomic sequence databases
PLoS ONE 11 2016 1 15
10.1371/journal.pone.0159644
PMC4973881
27489953
4
Makerere AI Lab
Web Annotation Tool
2023 https://github.com/AI-Lab-Makerere/web-annotation-tool (Accessed 5 March 2023)
5
Mduma N. Leo J. Loyani L. Jomanga K. Kamara A. Msaki I. Sanga S.
Banana Dataset Tanzania, Havard Dataverse
2022
10.7910/DVN/LQUWXW
Data Availability
Bananas Dataset Tanzania (Original data) (Dataverse).
Acknowledgments
The authors would like to extend their gratitude to Rockefeller FoundaOpen asset ↗github.com/AI-Lab-Makerere/web-annotation-toollines:89-126Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m 2 m -2 ) (root mean square error (RMSE): 0.23 to 0.89 m 2 m -2 ), including for sparse canopy (LAI less than 0.3 m 2 m -2 ) grown on different soil types (RMSE: 0.02 to 0.25 m 2 m -2 ). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータから小麦LAIを推定する汎用モデルを開発し、異なる土壌・圃場試験で検証しており、植物形質取得手法が中心である。
abstractdevelop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data
Reproduction assets foundThe paper's Data Availability statement points to public source code and data at UQ eSpace (DOI 10.48610/ac9642c), covering the RFR model code and supporting data. Additionally, the BASE soil reflectance dataset used to generate test soil backgrounds is publicly available on Zenodo (record 6265730).Code · publicOther data and source code supporting this work are available at UQ eSpace, and a unique DOI (https://doi.org/10.48610/ac9642c) is provided for public access.Open asset ↗UQ eSpace · 10.48610/ac9642clines:298-372Dataset · publicThe BASE soil reflectance data are available online ( https://zenodo.org/record/6265730 ).Open asset ↗Zenodo · 6265730lines:176-179Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.
Why it matches plant phenotyping methods植物病害画像を用いた事前学習モデルを開発し、病害識別・検出・セグメンテーションで評価する研究であり、植物の病害状態を画像から抽出する方法が中心です。
abstractwe propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis.
Reproduction assets foundThe authors publicly release their PDDD plant disease dataset, pre-trained model weights, and code via their project website and a Zenodo deposit, as stated in the abstract and Data Availability section.Code · publico the website. X.D., Q.H., Q.G., and Xue Wu conducted the experiments. Q.W., L.L. and G.H. provided funding support. All authors contributed equally to the writing of the manuscript. Competing interests : The authors declare that they have no competing interests.
Data Availability
All data and codes are available on the website https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293 .
References
1. Food and Agriculture Organization. World food and agriculture—statistical yearbook 2020. Rome (Italy): FAO; 2020.
2. Bruinsma J. The resource outlook to 2050: By how much do land, water and crop yields need to increase by 2050? How to feed the World in 2Open asset ↗pd.samlab.cnlines:707-779Dataset · publicu conducted the experiments. Q.W., L.L. and G.H. provided funding support. All authors contributed equally to the writing of the manuscript. Competing interests : The authors declare that they have no competing interests.
Data Availability
All data and codes are available on the website https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293 .
References
1. Food and Agriculture Organization. World food and agriculture—statistical yearbook 2020. Rome (Italy): FAO; 2020.
2. Bruinsma J. The resource outlook to 2050: By how much do land, water and crop yields need to increase by 2050? How to feed the World in 2050. Paper presnted at: Proceedings of a Technical MeetingOpen asset ↗Zenodo · 10.5281/zenodo.7856293lines:707-779Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.
Why it matches plant phenotyping methods果実の遮蔽に頑健な画像ベースのアモーダル分割と、リンゴ果径という植物形質の推定手法を開発・検証しており、方法が研究の中心である。
abstractThe predicted amodal masks were used to estimate the fruit diameters in pixels.
Reproduction assets foundThe paper's apple amodal segmentation dataset (RGB-D images, modal/amodal masks, calliper-measured diameters) and the authors' analysis code are both explicitly stated to be publicly available at the authors' GitHub repository GRAP-UdL-AT/Amodal_Fruit_Sizing.Dataset · publictain data from both maturity stages, of different fruit size and with
different fruit visibilities. The dataset split was performed randomly,
obtaining in each partition a similar distribution of diameters (Fig. 4.b)
and apples visibilities (Fig. 4.d) than in the original dataset. The dataset
has been made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.The data used for the case study was acquired in an Elstar apple
orchard located in Randwijk (the Netherlands). Five different trees were
imaged at four different dates (Table 1), obtaining data at different
growth stages: BBCH75, BBCH77, BBCH78 and BBCH85 (Fig. 2b). To
have a complete representation of trees, images Open asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:3 lines:1-74Code · publicft, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The code and the dataset used to evaluate the method have been
made publicly available at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.Acknowledgements
This work was partly funded by the Departament de Recerca i Uni
versitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the
Spanish Ministry of Science, Innovation and Universities (grants
RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00
[PAgPROTECT project] and PID2020-117142GOpen asset ↗GRAP-UdL-AT/Amodal_Fruit_Sizingpdf-raw-page:12 lines:1-75Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation. As a use case, we focus on wheat head segmentation. We synthesize a computationally annotated dataset-using a few annotated images, a short unannotated video clip of a wheat field, and several video clips with no wheat-to train a customized U-Net model. Considering the distribution shift between the synthesized and real images, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set. When further evaluated on a diverse external dataset collected from 18 different domains across five countries, this model achieved a Dice score of 0.73. To expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains to further fine-tune the model. This increased the Dice score to 0.91. The result highlights the utility of the proposed approach in the absence of large-annotated datasets. Although our use case is wheat head segmentation, the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.
Why it matches plant phenotyping methods植物画像から穂を抽出するセマンティックセグメンテーション手法を開発し、複数データセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractwe propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation.
Reproduction assets foundThe paper's wheat head segmentation study provides two paper-specific public assets: the dataset (video frames, synthesized images, manual annotations) hosted on the authors' USask FTP server, and the authors' image synthesis pipeline code on GitHub. Both have explicit availability statements with public URLs.Dataset · publicThe data used for this study is available at https://www.cs.usask.ca/ftp/pub/whs/ .Open asset ↗lines:52-63Code · publicThe code used for image synthesis is available at https://github.com/KeyhanNajafian/ImageSimulatorPipeline .Open asset ↗lines:64-77Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A large amount of training data is usually lacking at the beginning of system development and labeling such a large number of RGB (red, green, blue) images is laborious. Interactive recurrent annotation is beneficial to incrementally gain training images in the stream of the system development and provides an opportunity to reduce human workload. We developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems. Predictions can be performed under both single-image mode and batch mode for multiple images. The prediction results can be used as the initial image labeling and manually adjusted under a single image mode. Human labeling and machine predictions are visualized on the same image. ROOSTER provides fully automatic labeling for abundantly available initial images of wheat stripe rust to gain essential predictability. The navigation of integrating prediction with labeling benefits human adjustment to iteratively improve predictability. The development of a detection system for wheat stripe rust was presented as a use case to demonstrate the efficiency of using interactive deep learning to develop machine vision systems.
Why it matches plant phenotyping methods植物病害（コムギ縞萎縮病）の画像検出を対象とした対話型画像ラベリング・分類ソフトウェアを開発しており、植物病害状態の取得手法が中心である。
abstractWe developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems.
Reproduction assets foundThe paper's wheat stripe rust use case is supported by a public Zenodo underlying dataset (400 author-captured training images and use case output files) and public author source code (zzlab.net, GitHub, archived Zenodo). The independent test data from Schirrmann et al. is only available on request.Dataset · publicilability
Underlying data
The independent data used to test ROOSTER was sourced from Schirrmann et al.,10
see here: https://doi.org/10.3389/fpls.2021.469689). Please contact the corresponding author of this article (mschirrmann@atb-potsdam.de) to request
access to the test data if interested.
Zenodo: ROOSTER underlying dataset. https://doi.org/10.5281/zenodo.7530460.11
This project contains the following underlying data:
- RawImages.zip (400 input training images used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software aOpen asset ↗Zenodo · 10.5281/zenodo.7530460pdf-raw-page:5 lines:1-44Code · publicmages used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗GitHub · 12HuYang/ROOSTERpdf-layout-page:5 lines:1-63Code · publicseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗Zenodo · 10.5281/zenodo.7320405pdf-layout-page:5 lines:1-63Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeaf2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.
Why it matches plant phenotyping methods静的データから器官の発生時系列と成長軌跡を再構成するモデルベース手法が研究の中心であり、葉の形態形成・成長という植物表現型を推定している。
abstractWe introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data.
Reproduction assets foundThe paper's data availability statement explicitly provides three paper-specific public assets: a new version of the MorphoLeaf phenotyping application, COPASI and R scripts for temporal calibration parameter estimation, and the leaf datasets used in the analysis, each with a public URL.Dataset · publicLeaf datasets are available at https://doi.org/10.15454/BMELNY .Open asset ↗10.15454/BMELNYlines:210-440Code · publicCOPASI and R scripts used to estimate temporal calibration parameters are available at https://doi.org/10.15454/DPFU1T .Open asset ↗10.15454/DPFU1Tlines:210-440Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Over the last few years, the impact of climate change has increased rapidly. It is influencing all steps of plant production and forcing farmers to change and adapt their crop management practices using new technologies based on data analytics. This study aims to classify plant diseases based on images collected directly in the field using deep learning. To this end, an ensemble learning paradigm is investigated to build a robust network in order to predict four different pear leaf diseases. Several convolutional neural network architectures, named EfficientNetB0, InceptionV3, MobileNetV2 and VGG19, were compared and ensembled to improve the predictive performance by adopting the bagging strategy and weighted averaging. Quantitative experiments were conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. Data augmentation was adopted to improve the generalization of the model. The results, evaluated with a range of metrics, including accuracy, recall, precison and f1-score, showed that the proposed ensemble convolutional neural network outperformed the single convolutional neural network in classifying diseases in real field-condition with variation in brightness, disease similarity, complex background, and multiple leaves.
Why it matches plant phenotyping methodsナシ葉の画像から病害状態を推定する深層学習分類法の開発・比較が中心であり、植物病害表現型の取得・抽出手法に該当する。
abstractThis study aims to classify plant diseases based on images collected directly in the field using deep learning.
Reproduction assets foundThe paper's authors publicly released both the DiaMOS Plant dataset (field-collected pear leaf images used for all experiments, on Zenodo) and their analysis code as the LeafBox toolbox (website and GitHub repository), with explicit availability statements.Dataset · publicaMOS Plant dataset is available at https://doi.org/10.5281/zenodo.5557313, accessed on 16 JanuaryOpen asset ↗Zenodo · 10.5281/zenodo.5557313pdf-page:10 lines:1-59Code · publicThe source code is available at https://leafbox.francescamalloci.com/,
https://github.com/mallociFrancesca/leaf-disease-toolbox, accessed on 16 January 2023.Open asset ↗GitHub · mallociFrancesca/leaf-disease-toolboxpdf-page:10 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
Why it matches plant phenotyping methods植物の熱画像・マルチスペクトル画像を用いた表現型取得と解析のためのオープンソース計算パイプラインを開発しており、画像補正・共登録・解析が中心的な方法論的貢献である。
abstractThis study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicAll codes were written in MATLAB to produce a library package which is available at https://github.com/SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipeline.git (accessed on 12 November 2022).Open asset ↗SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipelinelines:35-43Dataset · publicThe data is freely shared in google drive and can be accessed from the following link. https://drive.google.com/file/d/1VSqRu5CUZhyd3MF23kdRjqrtRke7sbJU/view?usp=share_link .Open asset ↗lines:91-241Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceSeed / grainCountingSegmentationGrowth / development / phenology
The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.
Why it matches plant phenotyping methodsイネ種子の発芽率という植物形質を画像処理で自動抽出・定量する手法を開発し、誤差と頑健性を評価しており、表現型取得法が中心である。
abstractIn this paper, a new method is proposed for counting the number of grains and germinated grains.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' analysis code and the 90 rice grain images used for germination-rate phenotyping in a public GitHub repository, matching an allowed URL. The web application URL is a live service, not a deposited asset, and is excluded.Code · publicges of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Acknowledgments
We are deeply grateful to the editor and reviewers for their assistance with reviews and guidance of the paper.
Data Availability
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .
Funding Statement
This study was supported by the National Natural Science Foundation of China (Grants No. 61373004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Wu W, Zhou L, Chen J, Qiu Z, He Y. GainTKW: a measurement sysOpen asset ↗DoctorXiong123456/CodeOfPaperlines:233-285Dataset · publicmages of II you 534 and the germination detection results.
(DOCX)
Click here for additional data file.
S3 Table
30 images of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Data Availability Statement
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .Open asset ↗DoctorXiong123456/CodeOfPaperlines:286-308Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with ≥ 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R 2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHT Data from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice.
Why it matches plant phenotyping methods自動ハイパースペクトル画像を用いてイネの生理形質を非破壊推定し、予測精度を評価しているため、フェノタイピング手法が中心的です。
abstractHere, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system.
Reproduction assets foundThe paper's collected/analyzed datasets (hyperspectral imaging and physiological trait data) are publicly deposited in the Purdue University Research Repository, and the authors' analysis code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicand/or edits.
657 CONFLICT OF INTEREST
658 The authors declare no conflict of interest.
659 FUNDING
660 This work was partially funded by a grant from USDA NIFA to DRW (#2022-67013-36205).
661 DATA AVAILABILITY
662 The datasets collected and analyzed for this study can be found in the Purdue University Research
663 Repository [https://purr.purdue.edu/publications/4079/1].
664
665 REFERENCES
666 Al Makdessi, N., Ecarnot, M., Roumet, P., and Rabatel, G. (2019). A spectral correction method for
667 multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application
668 to nitrogen content assessment in wheat. Precision Agric 20, 237–259. doi: 10.1007/s11119-
669 018-Open asset ↗pdf-layout-page:30 lines:1-64Code · public249
250 Data analysis
251 Data were formatted and analyzed in R 4.1.1 (R Core Team, 2021) with packages dplyr
252 (Wickham et al., 2021) and reshape2 (Wickham, 2007). Plots were made with package ggplot2
253 (Wickham, 2016) or in base R environment. The code for each physiological trait model can be
254 accessed through GitHub (https://github.com/To-Chia/rice_imaging_ms).
255 Physiological trait collection: From the physiological trait measurements, we derived specific
256 leaf area (SLA, cm2g-1), CN ratio (C:N), specific leaf area with respect to carbon (SLA_C (cm2
257 mg-1 (C)) and specific leaf nitrogen (SLN, mg (N) cm-2). The summary statistics are in Table S3.
258 Histograms and normal Open asset ↗GitHub · To-Chia/rice_imaging_mspdf-layout-page:12 lines:1-64Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Why it matches plant phenotyping methods自動化された高スループット画像表現型プラットフォームを開発し、77種類の画像形質を定量化・検証し、機械学習による草丈予測も評価しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages.
Reproduction assets foundThe paper explicitly states that all images and phenotypic data are available on figshare and that the HTP/RGB image and GWAS analysis pipeline code is available on the authors' GitHub repository (maizeHTP). Both are paper-specific, public, and actionable.Dataset · publicAll the images and phenotypic data are available at https://figshare.com/account/home#/projects/141743 .Open asset ↗figshare · projects/141743lines:168-200Code · publicThe code for HTP from LemnaTec and the code for the RGB image and GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizeHTP .Open asset ↗GitHub · GUOWEIJUN/maizeHTPlines:168-200Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Premise The shape of young cotton ( Gossypium ) fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of G. hirsutum and one of G. barbadense . Both narrow and wide fibers exist in G. hirsutum cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material. More efficient cellular phenotyping methods are needed to empower future research efforts. Methods We developed semi-automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered (narrow) or hemisphere (wide) fibers in homogeneous or mixed populations. Results The new methods were accurate for diverse accessions of G. hirsutum and G. barbadense and at least eight times more efficient than manual methods. Narrow fibers dominated in the three G. barbadense accessions analyzed, whereas the three G. hirsutum accessions showed a mixture of tapered and hemisphere fibers in varying proportions. Discussion The use or adaptation of these improved methods will facilitate experiments with higher throughput to understand the biological factors controlling the variable shapes of young cotton fibers or other elongating single cells. This research also enables the exploration of links between early cell shape and mature cotton fiber quality in diverse field-grown cotton accessions.
Why it matches plant phenotyping methods若い綿繊維の形状を対象に、半自動イメージングと機械学習による細胞形状検出法を開発し、精度と効率を検証しているため、植物表現型取得法が中心である。
abstractMore efficient cellular phenotyping methods are needed to empower future research efforts.
Reproduction assets foundThe paper publicly releases its authors' analysis code/workflow on GitHub and the cotton fiber images of six accessions used for phenotyping on USDA Ag Data Commons, both explicitly stated in the Data Availability statement and Open Data badge sections.Code · publicComputational tools and code supporting the project analysis are available through GitHub ( https://github.com/USDA-ARS-GBRU/Cotton_Fiber_Computer_Vision/ )Open asset ↗USDA-ARS-GBRU/Cotton_Fiber_Computer_Visionlines:217-287Dataset · publicimages of the six cotton accessions used are available through USDA Ag Data Commons ( https://data.nal.usda.gov/dataset/data-efficient-imaging-and-computer-vision-detection-two-cell-shapes-young-cotton-fibers )Open asset ↗lines:217-287Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Premise With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness. Methods During a greenhouse experiment on the effects of abiotic stress on Brassica rapa , we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000. Results In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not. Discussion These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.
Why it matches plant phenotyping methods低コストの画像ベース高スループット表現型計測システムを開発・検証し、葉面積と緑色度を自動推定することが研究の中心であるため。
abstractwe develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness.
Reproduction assets foundThe paper's data availability statement deposits both the phenotyping data (images/measurements) and the analysis scripts on Zenodo with explicit public DOIs, making both paper-specific assets directly actionable.Dataset · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.5725224lines:188-243Code · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.6366716lines:188-243Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).
Why it matches plant phenotyping methods植物3D点群から器官を自動分割・識別するグラフベース手法を開発し、合成および実データで評価しており、植物表現型取得の技術が中心である。
abstractAccurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping.
Reproduction assets foundThe paper's Chenopodium 3D point cloud dataset (with ground truth annotations) is publicly deposited on Zenodo, and the reconstruction pipeline code is open source on GitHub (romi/plant-3d-vision).Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/6962994#.YuvYkS8itqs .Open asset ↗zenodo · 6962994lines:641-715Code · publicThe entire code is open source and available online ( https://github.com/romi/plant-3d-vision ).Open asset ↗github · romi/plant-3d-visionlines:428-438Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
Why it matches plant phenotyping methods複数の表現型予測モデルを植物種の実データとシミュレーションで系統比較・評価しており、計算による植物形質推定が研究の中心である。
abstractour aim is to systematically compare 12 different phenotype prediction models
Reproduction assets foundThe paper's authors publicly release their analysis code (easyPheno framework and the phenotype_prediction repository containing simulated phenotypes, hyperparameter optimization results, GWAS results, and figure-generation code), plus the Arabidopsis SNP matrix (figshare) and four AraPheno phenotype datasets used in aCode · publicAll simulated phenotypes, detailed results of the whole hyperparameter optimization, precomputed permutation-based GWAS results, and the code for conducting the simulations and generating all figures can be freely downloaded from our GitHub repository: https://github.com/grimmlab/phenotype_prediction .Open asset ↗grimmlab/phenotype_predictionlines:619-631Dataset · publicThe fully imputed SNP matrix data for Arabidopsis thaliana is publicly available and can be downloaded from https://doi.org/10.6084/m9.figshare.11346893.v1 .Open asset ↗10.6084/m9.figshare.11346893.v1lines:619-631Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Mitigating the effects of global change on biodiversity requires its understanding in the past. The main proxy of plant diversity, fossil pollen record, has a complex relationship to surrounding vegetation and unknown spatial scale. We explored both using modern pollen spectra in species-rich and species-poor regions in temperate Central Europe. We also considered the biasing effects of the trees by using sites in forests and open habitats in each region. Pollen samples were collected from moss polsters at 60 sites and plant species were recorded along two 1 km-transects at each site. We found a significant positive correlation between pollen and plant richness (alpha diversity) in both complete datasets and for both subsets from open habitats. Pollen richness in forest datasets is not significantly related to floristic data due to canopy interception of pollen rather than to pollen productivity. Variances (beta diversity) of the six pollen and floristic datasets are strongly correlated. The source area of pollen richness is determined by the number of species appearing with increasing distance, which aggregates information on diversity of individual patches within the landscape mosaic and on their compositional similarity. Our results validate pollen as a reconstruction tool for plant diversity in the past.
Why it matches plant phenotyping methods現代の花粉データと植物種多様性を比較し、花粉を過去の植物多様性再構築に用いる測定・推定手法を明示的に検証しているため、単なる生態学的なルーチン測定ではない。
abstractOur results validate pollen as a reconstruction tool for plant diversity in the past.
Reproduction assets foundThe paper's pollen and vegetation datasets are deposited on Zenodo and the analysis code is on GitHub, both with explicit availability statements.Dataset · publicPollen data are available in the Neotoma Palaeoecological database. The list of the Neotoma datasets, vegetation data and further data at https://doi.org/10.5281/zenodo.7233824 .Open asset ↗zenodo · 10.5281/zenodo.7233824lines:133-178Code · publicCode to reproduce the numerical analysis is available at https://github.com/vojtechabraham/SpatialScalingPollenDiversity/ .Open asset ↗github · vojtechabraham/SpatialScalingPollenDiversitylines:133-178Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Why it matches plant phenotyping methodsDNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
Reproduction assets foundThe paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.Dataset · publichways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a
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second pathway would be wild gourd marrow straightneck zucchini cocozelle
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(Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the
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‘contours.png’ file, based in (Paris 1989) and available in GitHub
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(https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using
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OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were
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obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a
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generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al.
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2022Open asset ↗https://github.com/miguelperezenciso/dna2image · contours.pngpdf-raw-page:5 lines:1-76Code · publicy, we have shown that very simple networks can be successfully trained in small
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datasets to accurately predict fruit images. Although much work remains to be done, this
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research opens new possibilities in the area of prediction of complex traits.
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Data availability statement
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All data and code are available at https://github.com/miguelperezenciso/dna2image.327
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.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 22, 2022.
;
https://doi.org/10.1101/2022.09.19.Open asset ↗https://github.com/miguelperezenciso/dna2image.327 · dna2image.327pdf-raw-page:10 lines:1-73Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Why it matches plant phenotyping methodsロボティクスによる根系画像取得の自動化と画像解析パイプライン開発が中心で、根系構造・成長動態という植物形質を抽出するフェノタイピング基盤を提示している。
abstractwe present the automation of GLO-Roots using robotics and the development of image analysis pipelines
Reproduction assets foundThe paper deposits its root phenotyping imaging data, image analysis pipelines/scripts, RShiny exploration apps, and rhizotron build files on Zenodo, plus robotics software on GitHub — all paper-specific, public, and actionable.Dataset · publicThe raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009 .Open asset ↗Zenodo · 10.5281/zenodo.5709009lines:160-163Code · publicImage analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430 .Open asset ↗Zenodo · 10.5281/zenodo.5708430lines:224-389Code · publicGeneral code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-server .Open asset ↗GitHub · rhizolab/rhizo-serverlines:224-389Code / dataset availability confirmedCrossref · checked 14 Sept 2026
LeafClassificationGrowth / development / phenology
Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML). A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).
Why it matches plant phenotyping methods標本ラベルから植物の形態形質・構造名を機械学習で抽出・分類するワークフローを開発し、大規模データで性能評価しており、植物表現型の取得・抽出手法が中心である。
abstractThis document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML).
Reproduction assets foundThe paper uses INBio herbarium specimen data (106,804 records) publicly available via GBIF, and provides an authors' replication code package on GitHub for data cleaning and analysis. Both are paper-specific, public, and actionable.Dataset · publiceveraging tagged descriptions from other languages.
For more complex texts, more robust algorithms, such as Recurrent Neural Networks -
LSTM and Transformers, can be applied.
Data and Code
Data from the National Biodiversity Institute of Costa Rica is used in this paper. The full
dataset and documentation can be downloaded from https://www.gbif.org/dataset/3717f916-d983-4a81-bb13-5f91200871a6. Code for data cleaning and analysis is provided
as part of the replication package. It is available at https://github.com/colibri-itcr.References
• Akella LM, Norton CN, Miller H (2012) NetiNeti: discovery of scientific names from text
using machine learning methods. BMC Bioinformatics 13 (1). https://Open asset ↗gbif.org · 3717f916-d983-4a81-bb13-5f91200871a6pdf-raw-page:19 lines:1-35Code · publicied.
Data and Code
Data from the National Biodiversity Institute of Costa Rica is used in this paper. The full
dataset and documentation can be downloaded from https://www.gbif.org/dataset/3717f916-d983-4a81-bb13-5f91200871a6. Code for data cleaning and analysis is provided
as part of the replication package. It is available at https://github.com/colibri-itcr.References
• Akella LM, Norton CN, Miller H (2012) NetiNeti: discovery of scientific names from text
using machine learning methods. BMC Bioinformatics 13 (1). https://doi.org/10.1186/1471-2105-13-211
• Balhoff JP, Dahdul WM, Dececchi T, Lapp H, Mabee PM, Vision TJ (2014) Annotation of
phenotypic diversity: decoupling data curation and Open asset ↗github.com/colibri-itcr.Referencespdf-raw-page:19 lines:1-35Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield – those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.
Why it matches plant phenotyping methods遺伝子型・環境・管理データからトウモロコシ収量という植物形質を予測する深層学習手法を開発・比較しており、表現型推定が研究の中心である。
abstractHere we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models
Reproduction assets foundThe paper uses publicly available Genomes to Fields (G2F) maize phenotype/weather/soil data (2014-2019) and provides authors' custom Python processing/analysis scripts on two public Bitbucket repositories. A Zenodo deposit (10.5281/zenodo.6916775) with PCA eigenvectors is mentioned but its URL is not among the allowed,Dataset · publicrformance, while avoiding
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overfitting the model to any location
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Materials and Methods
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Data Preparation
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We used data from the Genomes to Fields (G2F) initiative for years 2014-2019
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(McFarland et al. 2020), focusing on the sites within the continental United States. Each year’s
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data are publicly available (https://www.genomes2fields.org/resources/), including weather and
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soil data for field sites, genomic data, management schedules (e.g., application of fertilizer,
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herbicides, irrigation) and yield (in addition to other phenotypic variables). We augmented this
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through additional genomic and weather data. Weather data retrieved from Daymet (Thornton et
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alOpen asset ↗Genomes to Fieldspdf-raw-page:7 lines:1-53Code · publicthe eigenvectors
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resulting from the principal components analysis are provided to enable transformation of
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provided genomes. Additional weather measurements were retrieved from Daymet (Thornton et
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al. 2020). Custom python scripts for downloading, aggregating and processing these data are
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available on bitbucket (https://bitbucket.org/washjake/maizemodel and
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https://bitbucket.org/daniel_kick/maizemodel/ ) in the notebooks directory (files with the prefix
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0.0 to 0.5).
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and is also made available for use under a CC0 license.
was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105Open asset ↗washjake/maizemodelpdf-raw-page:17 lines:1-46Code · publicscripts were used to
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aggregate and standardize terminology across years. Rather than itemizing each operation, we
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restrict ourselves to those which are likely to be of interest to those working with similar data
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sets. The scripts used are available through Bitbucket
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(https://bitbucket.org/washjake/maizemodel and https://bitbucket.org/daniel_kick/maizemodel/ ).
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Scripts were written in Python (Van Rossum and Drake 2009 p. 3) and rely on scientific and
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common general libraries (Seabold and Perktold 2010; Pedregosa et al. 2011; fuzzywuzzy
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2017; Virtanen et al. 2020; team 2020; Harris et al. 2020; Da Costa-Luis et al. 2022) along with
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plotting libraries for exploraOpen asset ↗daniel_kick/maizemodelpdf-raw-page:8 lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.
Why it matches plant phenotyping methods収穫ロボット向けの位置検出が主目的だが、果実形状・姿勢・3D直径を画像から抽出し、精度を検証する技術開発が中心であり、再利用可能な植物器官形質の推定に該当する。
abstracta novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed.
Reproduction assets foundThe authors explicitly state that their citrus image dataset (4855 binocular image groups with depth maps) and analysis code are publicly available on GitHub, matching the allowed URL.Dataset · publicnt occlusion conditions in natural orchards. Future work will focus on few-shot learning and reduce the number of citrus fruits in the training dataset to improve citrus detection and localization.
Data availability statement
The original contributions presented in this study are publicly available. This data can be found here: https://github.com/AshesBen/citrus-detection-localization .
Author contributions
All authors contributed to the method and result of the study, dataset generation, model training and testing, analysis of results, and the drafting, revising, and approving of the contents of the manuscript.
Funding
We acknowledged support from the Natural Science Foundation of GuangdongOpen asset ↗AshesBen/citrus-detection-localizationlines:335-356Code · public98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization .
Keywords: citrus detection, citrus localization, binocular vision, YOLO v5s, loss function
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 Jun 18; Accepted 2022 Jul 12; Collection date 2022.
IntroductionOpen asset ↗AshesBen/citrus-detection-localizationlines:1-28Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Plant disease can diminish a considerable portion of the agricultural products on each farm. The main goal of this work is to provide visual information for the farmers to enable them to take the necessary preventive measures. A lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification. In addition to the ViT, the classical convolutional neural network (CNN) methods and the combination of CNN and ViT have been implemented for the plant disease classification. The models have been trained and evaluated on multiple datasets. Based on the comparison between the obtained results, it is concluded that although attention blocks increase the accuracy, they decelerate the prediction. Combining attention blocks with CNN blocks can compensate for the speed.
Why it matches plant phenotyping methods植物画像から病害状態を分類するVision Transformer等の手法開発・比較が研究の中心であり、植物病害フェノタイピングに該当する。
abstractA lightweight deep learning approach is proposed based on the Vision Transformer (ViT) for real-time automated plant disease classification.
Reproduction assets foundThe paper uses the public Wheat Rust Classification Dataset (Kaggle) and the authors' analysis code is publicly available on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe Wheat Rust Classification Dataset is available at: https://www.kaggle.com/sinadunk23/behzad-safari-jalal .Open asset ↗lines:79-99Code · publicThe code of this paper is available at https://github.com/yasaminborhani/PlantDiseaseClassification .Open asset ↗yasaminborhani/PlantDiseaseClassificationlines:136-143Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Why it matches plant phenotyping methods葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。
abstractReflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits
Reproduction assets foundThe paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.Dataset · publicected and curated the spectral and trait data. SK analyzed the data,
597 interpreted the results, and wrote the first draft with substantial contributions from EL. All authors
598 contributed to further revisions of the paper.
599
600 Data availability
601 All fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).
602 Upon publication, we will also upload all spectral data, as well as metadata and trait data, to theOpen asset ↗pdf-layout-page:38 lines:1-58Code · publicnder a CC-BY 4.0 International license.
603 Ecological Spectral Information System (EcoSIS, https://ecosis.org/), and upload models to the
604 Ecological Spectral Model Library (EcoSML, https://ecosml.org/). At that stage, we will update this
605 section accordingly. Analysis scripts are available as a repository on GitHub
606 (https://github.com/ShanKothari/CABO-trait-models).Open asset ↗ShanKothari/CABO-trait-modelspdf-layout-page:39 lines:1-14Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Recognizing rice seedling growth stages to timely do field operations, such as temperature control, fertilizer, irrigation, cultivation, and disease control, is of great significance of crop management, provision of standard and well-nourished seedlings for mechanical transplanting, and increase of yield. Conventionally, rice seedling growth stage is performed manually by means of visual inspection, which is not only labor-intensive and time-consuming, but also subjective and inefficient on a large-scale field. The application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations and its applications in agriculture and high-throughput phenotyping are increasing. This paper presented automatic approaches to detect rice seedling of three critical stages, BBCH11, BBCH12, and BBCH13. Both traditional machine learning algorithms and deep learning algorithms were investigated the discriminative ability of the three growth stages. UAV images were captured vertically downward at 3-m height from the field. A dataset consisted of images of three growth stages of rice seedlings for three cultivars, five nursing seedling densities, and different sowing dates. In the traditional machine learning algorithm, histograms of oriented gradients (HOGs) were selected as texture features and combined with the support vector machine (SVM) classifier to recognize and classify three growth stages. The best HOG-SVM model obtained the performance with 84.9, 85.9, 84.9, and 85.4% in accuracy, average precision, average recall, and F1 score, respectively. In the deep learning algorithm, the Efficientnet family and other state-of-art CNN models (VGG16, Resnet50, and Densenet121) were adopted and investigated the performance of three growth stage classifications. EfficientnetB4 achieved the best performance among other CNN models, with 99.47, 99.53, 99.39, and 99.46% in accuracy, average precision, average recall, and F1 score, respectively. Thus, the proposed method could be effective and efficient tool to detect rice seedling growth stages.
Why it matches plant phenotyping methodsUAV画像からイネ幼苗の生育段階という植物状態を自動推定する機械学習手法を開発・比較し、性能評価しているため、表現型取得が研究の中心である。
abstractThe application of machine learning algorithms on UAV images offers a high-throughput and non-invasive alternative to manual observations
Reproduction assets foundThe paper's data availability statement explicitly deposits the UAV rice seedling image datasets, trained models, and analysis code in public locations: a Google Drive folder (datasets and models) and a GitHub repository (code), both matching allowed URLs.Dataset · publicThe datasets, models and code used in this paper are available at the following locations: Datasets and models: https://drive.google.com/drive/folders/1AY-ro3HID9noOpen asset ↗lines:786-795Code · publicCode: https://github.com/imagevision-lab/rice_seedling_growth_stages_detectionOpen asset ↗imagevision-lab/rice_seedling_growth_stages_detectionlines:786-795Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
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Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
BackgroundMethods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. ResultsIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. ConclusionsBoth image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
Why it matches plant phenotyping methodsカシャバの病斑を画像から分割・定量する2手法を開発し、処理間の病徴・病害重症度の測定性能を比較検証しており、植物表現型取得が中心である。
abstractIn this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem.
Reproduction assets foundThe paper deposits its phenotype measurement datasets and custom R analysis scripts on figshare, and documents the machine learning phenotyping workflow (PhenotyperCV) with a public GitHub wiki URL containing the workflow and software download instructions.Dataset · public9
● CSV: Comma separated plain text file
410
Declarations:
411
Ethics approval and consent to participate: Not applicable
412
Consent for publication: Not applicable
413
Availability of data and materials:
414
The datasets and custom R scripts generated and/or analyzed in this study are
415
available in the figshare repository, https://figshare.com/s/0148e5e4fc7f220ac4c3
416
Competing interests: The authors declare that they have no competing interests
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Funding:
418
National Science Foundation GRFP DGE-2139839 and DGE-1745038 (KE)
419
Bill and Melinda Gates Foundation OPP1125410 (RBS)
420
Authors' contributions
421
.
CC-BY-NC-ND 4.0 International license
available under a
was not certifieOpen asset ↗figsharepdf-raw-page:19 lines:1-45Code · publicned leaf image was converted to a binary mask and referred to as the
365
“labeled image”. The machine learning image analysis tool is part of PhenotyperCV, a
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C++11 header-only library designed for image-based plant phenotyping. The machine
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learning workflow and software download instructions are available on GitHub
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(https://github.com/jberry47/ddpsc_phenotypercv/wiki/Machine-Learning-Workflow).369
All steps of the machine learning workflow were run on the Mac terminal command line.
370
The labeled leaf mask image and original combined leaf graphic were used to create a
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support vector machine learning classifier or YAML file. Individual images of inoculated
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cassava leaves Open asset ↗github · jberry47/ddpsc_phenotypercvpdf-raw-page:17 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Confocal imaging is a well-established method for investigating plant phenotypes on the tissue and organ level. However, many differences are difficult to assess by visual inspection and researchers rely extensively on ad hoc manual quantification techniques and qualitative assessment. Here we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces. We successfully demonstrate the applicability of the approach using confocal imaging of aerial organs in Arabidopsis thaliana. Automatic identification of flower primordia using the surface curvature as an indication of outgrowth allows for high-throughput quantification of divergence angles and further analysis of individual flowers. We demonstrate the throughput of our method by quantifying geometric features of 1065 flower primordia from 172 plants, comparing auxin transport mutants to wild type. Additionally, we find that a paraboloid provides a simple geometric parameterisation of the shoot inflorescence domain with few parameters. We utilise parameterisation methods to provide a computational comparison of the shoot apex defined by a fluorescent reporter of the central zone marker gene CLAVATA3 with the apex defined by the paraboloid. Finally, we analyse the impact of mutations which alter mechanical properties on inflorescence dome curvature and compare the results with auxin transport mutants. Our results suggest that region-specific expression domains of genes regulating cell wall biosynthesis and local auxin transport can be important in maintaining the wildtype tissue shape. Altogether, our results indicate a general approach to parameterise and quantify plant development in 3D, which is applicable also in cases where data resolution is limited, and cell segmentation not possible. This enables researchers to address fundamental questions of plant development by quantitative phenotyping with high throughput, consistency and reproducibility.
Why it matches plant phenotyping methods植物組織の3D画像から形態形質を自動抽出・定量する手法を開発し、高スループット性と再現性を実証しているため、フェノタイピング手法が中心である。
abstractHere we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces.
Reproduction assets foundThe paper's data availability statement explicitly deposits all original source data (confocal phenotyping data of Arabidopsis shoot apical meristems) in the Cambridge Apollo repository and all analysis/segmentation/quantification scripts in a public Sainsbury Laboratory GitLab repository. Both are paper-specific,公开,直接Dataset · publicAll original source data files used in this study are available via the Cambridge University Apollo Repository ( https://doi.org/10.17863/CAM.82442 ).Open asset ↗Cambridge University Apollo Repository · 10.17863/CAM.82442lines:369-397Code · publicAll scripts and software for segmentation, quantification, analysis and visualisation are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/aahl_etal_2022 ).Open asset ↗Sainsbury Laboratory GitLab repositorylines:369-397Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green area index, or to characterize the sanitary state of the crop. We developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest. It is based on a U-net model that separates the vegetation from the background. It was trained over a very large and diverse dataset. The vegetation pixels are then classified using a SVM shallow machine learning technique trained over pixels extracted from grids applied to images. The performances of the SegVeg model are then compared to a three classes U-net model trained using weak supervision over RGB images with predicted pixels by SegVeg as groundtruth masks. Results show that the SegVeg model allows to segment accurately the three classes, with however some confusion mainly between the background and the senescent vegetation, particularly over the dark and bright parts of the images. The U-net model achieves similar performances, with some slight degradation observed for the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent ones. Finally, the models are used to predict the fraction of the three classes over the grids pixels or the whole images. Results show that the green fraction is very well estimated (R 2 =0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R 2 =0.70 and 0.73, respectively). We made SegVeg publicly available as a ready-to-use script, as well as the entire dataset, rendering segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge, or at least, offering a pre-trained model to more specific use.
Why it matches plant phenotyping methodsRGB画像から緑色・枯死植生を分割し、植生割合や緑色面積指数などの植物形質推定に用いるSegVeg手法を開発・比較検証し、モデルとデータセットを公開しているため、植物フェノタイピング手法が中心である。
abstractWe developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest.
Reproduction assets foundThe paper's SegVeg segmentation scripts and the annotated LITERAL/PHENOMOBILE/P2S2 pixel dataset with segmentation masks are publicly released via the authors' GitHub repository, with Zenodo links specified there.Code · publicuthors declare that there is no conflict of interest regarding the publication of this article.
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Data Availability
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Upon acceptance of the paper, SegVeg pixels dataset, images and their corresponding segmentation
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masks will be publicly available. All the SegVeg scripts for computation and analysis are also public:
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https://github.com/mserouar/SegVeg. For simplicity, dataset download links (including Zenodo)
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will be specified in the above repository.
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References
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[1] T. Sakamoto et al., “An alternative method using digital cameras for continuous monitoring of
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crop status,” Agricultural and Forest Meteorology, vol. 154-155, pp. 113–126, Mar. 2012, issn:
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016Open asset ↗mserouar/SegVegpdf-raw-page:25 lines:1-69Dataset · publicAmong the 441 annotated grids (Table 4), the unsure classes represented about 8% of the total
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number of pixels, for the PHENOMOBILE dataset the integrated flashes provided better pixel
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interpretation leading to fewer confusions. This dataset is publicly available on Zenodo following
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this link https://github.com/mserouar/SegVeg (When published linked with ORCID).
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Table 4: Distribution of labeled pixel for the three datasets.
Datasets
Nb. of
labelled
patches
Nb. of
labelled
pixels
% Classes
Green
Veg.
Sen.
Veg.
Background
Green /
Sen. Veg.
Unsure
Unknown Other
LITERAL 68 4260 46.5 15.8 15.0 13.1 9.5 0.1
PHENOMOBILE 173 8266 40.3 31.1 27.6 0.1 0.8 0Open asset ↗mserouar/SegVegpdf-raw-page:10 lines:1-92Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
ClassificationVisualization / data managementArchitecture / morphology / geometry
The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.
Why it matches plant phenotyping methodsCNNとCAMを用いて樹皮画像から識別に有用な形態的特徴を抽出・可視化する方法が研究の中心であり、植物器官の観察可能な形質の推定に該当する。
abstractwe trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species.
Reproduction assets foundThe paper's own bark image dataset (BARK-KR) is publicly deposited on Zenodo, the authors' analysis scripts are on GitHub, and the CAM extended figures are hosted on Figshare. The BarkNet 1.0 dataset is cited prior work and excluded.Dataset · publicthe bark image data collected in this study were published and are available on Zenodo ( https://doi.org/10.5281/zenodo.4749062 ) 48 .Open asset ↗Zenodo · 10.5281/zenodo.4749062lines:134-164Code · publicThe python scripts used in this study are available on GitHub ( https://github.com/snutp/TBKFE ).Open asset ↗GitHub · snutp/TBKFElines:134-164Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Abstract Background: Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results: A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at ZT5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 hours, because it maximizes germination rates while minimizing contamination. The pollen germination medium’s pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions: We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
Why it matches plant phenotyping methodsキノア花粉の生存性を評価するin vitro発芽法を開発・最適化し、PlantCVによる発芽花粉の画像カウントも半自動化しており、植物形質取得法が研究の中心である。
abstractA semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community.
Reproduction assets foundThe paper's pollen germination microscopy images are deposited on Zenodo and the PlantCV analysis workflow/scripts plus extracted numerical data are on the authors' GitHub, both explicitly stated with URLs.Dataset · publicImages are available here:
https://doi.org/10.5281/zenodo.5909573.Open asset ↗Zenodo · 10.5281/zenodo.5909573pdf-page:9 lines:1-47Code · publicScripts and extracted numerical data are available on
GitHub: https://github.com/danforthcenter/quinoa-pollen-germination.Open asset ↗GitHub · danforthcenter/quinoa-pollen-germinationpdf-page:11 lines:1-46Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceAnnotation / quality controlCalibration / preprocessingVisualization / data management
Background Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. Results We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline . Conclusion The analysis workflow and document presented are not limited to IRRI's RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI's RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.
Why it matches plant phenotyping methods作物育種における表現型データの前処理・品質管理・統計解析・可視化を一貫して行う、再現可能なオープンソース解析パイプラインが中心である。
abstractHere, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline
Reproduction assets foundThe paper's authors publicly release their phenotypic data analysis pipeline source codes, sample HTML workflow documents, and demo phenotypic dataset on GitHub, directly reproducing this paper's computational analysis.Code · publicThe analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:1-75Dataset · publicAll the instructions, R source codes, examples, and the data sets are freely available in the GitHub repository at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:80-91Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy. RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model. CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.
Why it matches plant phenotyping methods綿花葉の毛茸という植物形質を対象に、画像取得法と深層学習モデルHairNetを開発・精度評価しており、表現型取得手法が研究の中心である。
abstractCurrently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.
Reproduction assets foundThe paper publicly releases its de-identified cotton leaf hairiness image dataset (~13,600 leaf images, 27 genotypes) via the CSIRO data access portal and the HairNet analysis code via a public Bitbucket repository, both with explicit availability statements and URLs.Dataset · publicThe HairNet image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:242-283Code · publicThe HairNet code is available at https://bitbucket.csiro.au/scm/sth/hairnet.git .Open asset ↗bitbucket.csiro.au/scm/sth/hairnetlines:242-283Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Why it matches plant phenotyping methods植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。
abstractthe assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Reproduction assets foundThe paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather UndergDataset · publicThe field validation data
presented in this study are openly available in the P-band Radiometer Inferred Soil Moisture (PRISIM)
website at https://www.prism.monash.edu/index.htmlOpen asset ↗pdf-page:19 lines:1-59Code · publicThe APSIM-EnKF data assimilation framework used in this study was the version
developed and described by Zhang [28] (source code available on https://github.com/Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methods3D画像からトウモロコシ根系の階層別形態形質を抽出する計算手法を開発し、既存法と精度比較・検証しており、植物表現型取得が中心です。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems.
Reproduction assets foundThe paper's authors publicly distribute the TopoRoot analysis software (C++ pipeline with GUI) together with the 45 X-ray CT scans of maize root crowns, per-image threshold values, and hand-measured nodal root counts in a GitHub repository. The synthetic OpenSimRoot images and ground-truth traits are only available on.Code · publicto a Euclidean distance field (e.g., using [ 29 ]). Fig. 12
Hierarchies of sorghum roots computed by TopoRoot, showing one tiller ( A ), two tillers ( B ), and four tillers ( C ). Hierarchy levels 0, 1, 2, 3 and 4 are colored dark blue, light blue, green, orange, and red.
Software availability
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot .
Included in the page are instructions to run the software, and details on the formats of the input and output files. Currently, the accepted inputs are either image slices (suffixed with.png) or.raw files, with a.dat accompanying the.raw file to specify the dimensions. The output consists of a skeleton, a hierarchy annotationOpen asset ↗https://github.com/danzeng8/TopoRootlines:2051-2060Dataset · public\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$t_{low} ,t_{mid} ,t_{high}$$\end{document} t low , t mid , t high ) and hand measurements of nodal roots for each sample, are available in the TopoRoot Github repository: https://github.com/danzeng8/TopoRoot . The synthetic images of simulated roots and associated ground truth trait measurements are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing inOpen asset ↗https://github.com/danzeng8/TopoRootlines:2061-2116Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
BackgroundThe three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. ResultsWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allows their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availabilityNODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis. The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE.
Why it matches plant phenotyping methods植物核内構造を3D画像から自動抽出・解析するImageJプラグインを開発し、Arabidopsisデータセットで検証しているため、植物フェノタイピング手法が中心である。
abstractWe present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin.
Reproduction assets foundThe paper's authors publicly released NODeJ (source code and releases on GitLab) and the 3D nuclear images used for validation are publicly available via the INDEPTH image site and a data repository DOI.Code · publicNODeJ can be downloaded from
https://gitlab.com/axpoulet/image2danalysis/-/releases with source
code, documentation and further information avaliable at
https://gitlab.com/axpoulet/image2danalysis.Open asset ↗axpoulet/image2danalysispdf-page:1 lines:1-65Dataset · publicThe
images used in this report are available in these links: https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗10.15454/1HSOIEpdf-page:6 lines:1-75Dataset · publicThe images used in this
study are publicly available at
https://www.brookes.ac.uk/indepth/images/ and
https://doi.org/10.15454/1HSOIE.Open asset ↗pdf-page:1 lines:1-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above ground, but largely unexplored below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time. This dataset describes the developmental dynamics of 93 accessions and reveals highly complex and polygenic RSA traits that show significant correlation with climate variables.
Why it matches plant phenotyping methodsロボティクスによる表現型取得の自動化と画像解析パイプライン開発が中心で、根系構造の時系列形質を抽出するフェノタイピング基盤を提示している。
abstractIn this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time.
Reproduction assets foundThe paper's data availability statement deposits the GLORIAv2 phenotyping robot hardware, the image analysis pipelines/scripts used to extract root traits, the RShiny RSA exploration app, and the raw imaging data/images on Zenodo, all directly reproducing this paper's root phenotyping measurements and analysis.Dataset · public10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of Biological and
Environmental Research (DE-SC0008769 and DE-SC0018277) and the Carnegie Institution for
SOpen asset ↗Zenodo · 10.5281/zenodo.5709009pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of BiologOpen asset ↗Zenodo · 10.5281/zenodo.5708422pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.528Open asset ↗Zenodo · 10.5281/zenodo.5574925pdf-raw-page:13 lines:1-35Code / dataset availability confirmedCrossref · checked 9 Sept 2026
The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
Why it matches plant phenotyping methods植物病徴を画像で診断・モニタリングする公開データセットの構築と既存データセット比較が中心であり、植物病害状態の画像ベース表現型解析に該当する。
abstractwe release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant
Reproduction assets foundThe paper releases the DiaMOS Plant dataset (3505 field images of pear leaves/fruits with csv and YOLO annotations) on Zenodo, and the authors' LeafBox analysis code (used for the CNN classification benchmark) on GitHub. Both are paper-specific, public, and directly actionable.Dataset · publicThe dataset is freely available for
academic purposes from a repository at https://doi.org/10.5281/zenodo.5557313 (accessed
on 17 October 2021)Open asset ↗Zenodo · 10.5281/zenodo.5557313pdf-page:4 lines:1-46Code · publicThe source code is available
at https://github.com/mallociFrancesca/leaf-disease-toolbox, accessed on 17 October 2021.Open asset ↗GitHubpdf-page:12 lines:1-59Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.
Why it matches plant phenotyping methodsリンゴ果実の数とサイズを動画から自動抽出するフォトグラメトリ手法を開発し、実験で精度を検証しており、植物フェノタイピング手法が中心である。
abstractThis work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number.
Reproduction assets foundThe authors explicitly state that acquired data, labelled images, code, and network weights for the apple phenotyping pipeline are publicly available on the 3DOM-FBK GitHub account, with a concrete URL given in reference [56]. This is a paper-specific, public, actionable asset covering the Mask R-CNN retraining code/权重Code · publicData Availability Statement: Data acquired and used in the presented experiments, labelled im-
ages, code, and network weights, are available to the scientific community at 3DOM-FBK-GitHub
[56].Open asset ↗pdf-page:16 lines:1-58Dataset · publicAcquired
data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.Open asset ↗pdf-page:1 lines:1-67Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper ( Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented. The proposed image-processing approach is based on the utilization of Normalized Red-Blue Vegetation Index (NRBVI) and Normalized Green-Blue Vegetation Index (NGBVI) as new RGB-based vegetation indices, and its subsequent Jet pallet colored version NRBVI-Jet NGBVI-Jet as pre-processing algorithms. Furthermore, four standard pre-trained deep-learning architectures, Visual Geometry Group-16 (VGG-16), Xception, Inception v3, and MobileNet v2, were implemented for classification purposes. The objective of this methodology was to find the most accurate combination of vegetation index pre-processing algorithms and pre-trained deep- learning classification models. Transfer learning was applied to fine tune the pre-trained deep- learning models and data augmentation was also applied to prevent the models from overfitting. The performance of the models was evaluated using Top-1 accuracy, precision , recall , and F1-score using test data. The results showed that the best model was an Xception-based model that uses the NGBVI dataset. This model reached an average Top-1 test accuracy of 98.3%. A complete analysis of the different vegetation index representations using models based on deep-learning architectures is presented along with the study of the learning curves of these deep-learning models during the training phase.
Why it matches plant phenotyping methods葉の可視症状をRGB画像処理と深層学習で識別する手法を開発・比較しており、植物病害状態の表現型推定が中心である。
abstracta novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper ( Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented.
Reproduction assets foundThe paper publicly releases its authors' analysis code on GitHub and its generated leaf image datasets (RGB plus vegetation-index versions) on Zenodo, both with explicit availability statements.Code · publicThe source code of this article is publicly released and can be downloaded from https://github.com/jrmillan1983/PHYVV_TMV_CNN .Open asset ↗jrmillan1983/PHYVV_TMV_CNNlines:371-472Dataset · publicThe datasets generated during and/or analyzed during the current study are available from https://doi.org/10.5281/zenodo.5500727 (accessed on 19 September 2021).Open asset ↗zenodo · 10.5281/zenodo.5500727lines:474-476Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Laboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.
Why it matches plant phenotyping methods根画像から長さ・直径・体積などの植物形質を抽出するオープンソースソフトウェアを開発し、グラウンドトゥルースおよび既存ソフトウェアとの比較検証を行っており、植物フェノタイピング手法が中心である。
abstractRhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements.
Reproduction assets foundThe paper's own phenotyping assets are all publicly available: the RhizoVision Explorer source code (GitHub) and Windows binaries (Zenodo 3747697), the copper wire validation image set (Zenodo 4677546), the scanned root image sets from maize, wheat, herbaceous and tree species (Zenodo 4677751), and the R statistical/分析Code · publicThe open-source code for RhizoVision Explorer written in C++ is available at https://github.com/noble-research-institute/RhizoVisionExplorer on GitHub.Open asset ↗https://github.com/noble-research-institute/RhizoVisionExplorerlines:300-393Dataset · publicThe copper wire image set used here is available in a public repository and can be downloaded at http://doi.org/10.5281/zenodo.4677546 ( Dhakal et al. 2021a ).Open asset ↗zenodo · 10.5281/zenodo.4677546lines:86-101Dataset · publicThese four image sets of roots from several plant species are available in a public repository and can be downloaded at http://doi.org/10.5281/zenodo.4677751 ( Dhakal et al. 2021b ).Open asset ↗zenodo · 10.5281/zenodo.4677751lines:105-118Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔と表皮細胞を検出・計数し、気孔指数を自動推定する画像解析パイプラインの開発とベンチマーク検証が研究の中心である。
abstractThe main aim of this research is to develop an automated stomatal index measurement pipeline.
Reproduction assets foundThe authors explicitly state the stomatal index pipeline code is fully open-source on GitHub and the wheat microscopic image dataset is downloadable as a zip release from the same repository. Both are paper-specific, public, and directly actionable.Code · publicThe code is fully open-source for academic usage and can be downloaded at https://github.com/WeizhenLiuBioinform/stomatal_indexOpen asset ↗WeizhenLiuBioinform/stomatal_indexlines:361-376Dataset · publicThe wheat dataset is available for downloading at https://github.com/WeizhenLiuBioinform/stomatal_index/releases/download/wheat1.0/wheat_dataset.zipOpen asset ↗WeizhenLiuBioinform/stomatal_index · wheat1.0lines:361-376Supplement · publicSupplementary Table 2
Description of the cuticle dataset used for training and testing the stomatal index measurement model.Open asset ↗lines:651-703Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methodsX線CT画像からトウモロコシ根系の階層的形態形質を抽出する計算手法を開発し、実画像・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns.
Reproduction assets foundThe paper's TopoRoot phenotyping software (C++ pipeline computing root hierarchy and fine-grained traits from X-ray CT volumes) and the datasets generated/analysed in the study (including the test dataset) are publicly released on the authors' GitHub repository.Code · publicduce a
697
probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
698
volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
699
need to be converted into a Euclidean distance field.
700
Software availability
701
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
702
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted August 28, 2021.
;
https://doi.org/10.1101/2021.08.24.457522
doi:
bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
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Declarations
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Ethics approval and consent to participate
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Not applicable
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Consent for publication
727
Not applicable
728
Availability of data and materials
729
The datasets generated and analysed during the current study are available in the TopoRoot
730
Github repository: https://github.com/danzeng8/TopoRoot
731
Competing interests
732
The authors declare that they have no competing interests.
733
Funding
734
This material is based upon work supported by the National Science Foundation under award
735
numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
736
2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.
Why it matches plant phenotyping methods市民科学画像とCNNを用いて植物機能形質を自動推定する計算手法を開発・評価しており、形質取得が研究の中心である。
abstractwe test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN).
Reproduction assets foundThe paper's Data/Code availability statements provide public figshare deposits with the trained CNN ensemble models and global trait maps (10.6084/m9.figshare.13312040), raw data tables with image download links and mean trait values (10.6084/m9.figshare.14410379), the iNaturalist raw image dataset via GBIF (10.15468/3Dataset · publicthe raw data tables containing the download links for the plant images as well as the mean trait values that were the basis for further processing are available on figshare ( https://doi.org/10.6084/m9.figshare.14410379 )Open asset ↗figshare · 10.6084/m9.figshare.14410379lines:142-198Dataset · publicThe raw image dataset can be obtained from iNaturalist database via https://doi.org/10.15468/ab3s5x 47Open asset ↗10.15468/ab3s5xlines:142-198Code · publicThe code supporting this manuscript is available online at https://github.com/ChrSchiller/cnn_traitsOpen asset ↗GitHub · ChrSchiller/cnn_traitslines:142-198Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting. However the huge computational complexity and massive storage requirements make it a great challenge to deploy them in real-time applications, especially on devices with limited resources. In this study, a rapid and highly efficient lightweight CNN based on visual attention, namely SeedSortNet, is proposed for seed sorting. First, a dual-branch lightweight feature extraction module Shield-block is elaborately designed by performing identity mapping, spatial transformation at higher dimensions and different receptive field modeling, and thus it can alleviate information loss and effectively characterize the multi-scale feature while utilizing fewer parameters and lower computational complexity. In the down-sampling layer, the traditional MaxPool is replaced as MaxBlurPool to improve the shift-invariant of the network. Also, an extremely lightweight sub-feature space attention module (SFSAM) is presented to selectively emphasize fine-grained features and suppress the interference of complex backgrounds. Experimental results show that SeedSortNet achieves the accuracy rates of 97.33% and 99.56% on the maize seed dataset and sunflower seed dataset, respectively, and outperforms the mainstream lightweight networks (MobileNetv2, ShuffleNetv2, etc.) at similar computational costs, with only 0.400M parameters (vs. 4.06M, 5.40M).
Why it matches plant phenotyping methods種子画像から種子の外観・純度に関わる状態を分類する軽量CNNを開発しており、画像取得・特徴抽出手法が研究の中心である。
abstractSeed sorting based on machine vision provides an effective solution to this problem.
Reproduction assets foundThe paper's Data Availability statement provides public access to both datasets and the authors' analysis code: the haploid/diploid maize seed dataset (from Altuntaş et al. 2019) hosted at rovile.org, and the SeedSortNet code plus the authors' sunflower seed dataset on GitHub.Dataset · publicThe maize seed dataset comes from Altuntaş et al. (2019): https://doi.org/10.1016/j.compag.2019.104874 and is available at: http://www.rovile.org/datasets/haploid-and-diploid-maize-seeds-dataset/ .Open asset ↗lines:571-586Code · publicThe seedsortnet code and sunflower seed dataset are available at GitHub: https://github.com/Huanyu2019/Seedsortnet .Open asset ↗Huanyu2019/Seedsortnetlines:571-586Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC [Formula] of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling "The sum of cross sectional areas after a branch junction equals the one before the branch junction" (Pipe Model Theory) and "Twigs are self-similar" (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B0 * RPRB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC [Formula] of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript. Upload statementSelf-uploaded pre-print for peer-review submitted manuscript. The manuscript was submitted on 26th of July 2021 to Plos Computational Biology: I, Jan Hackenberg uploaded this manuscript because the automated journal upload was rejected for the following reason: Thank you for considering posting your manuscript "SimpleForest - a comprehensive tool for 3d reconstruction of tree from forest plot point clouds." as a preprint. Your manuscript does not meet bioRxivs criteria and therefore we will not be sending it for posting as a preprint. For more information about our checks, see link. We have noted that it contains material that is potentially subject to copyright. In particular, screenshot in Figure 1. Preprints posted to bioRxiv following submission to PLOS journals are done so under the CC BY license. To avoid a potential breach of the copyright that applies to the material listed above, we are unable to make the manuscript publicly available. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/454344v1_fig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1094b17org.highwire.dtl.DTLVardef@120d7a3org.highwire.dtl.DTLVardef@12d2fbcorg.highwire.dtl.DTLVardef@1990a4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Submission system screenshot. C_FIG Please note that this decision does not affect the editorial process at PLOS Computational Biology. Your manuscript is being separately assessed with regards to sending for peer review. From section Abstract on, the pdf you see is same as submitted one.
Why it matches plant phenotyping methods森林プロットの点群から樹木を3D再構成し、DBH・体積などの植物構造形質を定量化するソフトウェアと自動解析パイプラインを開発・検証しており、フェノタイピング手法が中心である。
abstractSimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders.
Reproduction assets foundThe paper explicitly publishes its TLS point cloud datasets (5 datasets with harvested ground-truth volumes), SimpleForest processing/QSM scripts, R validation scripts, combined results table, and GPL v3 source code in a public Zenodo repository (10.5281/zenodo.5131717) and GitLab repository, all directly reproducing QCode · publicipts SimpleForest scripts to process S1 Dataset. 75
• Erythrophleum fordii denoising scripts: 76
https://zenodo.org/record/5131717/files/hackenbergErythrophleumDenoisingScripts.zip 77
• Pinus massoniana denoising scripts: 78
https://zenodo.org/record/5131717/files/hackenbergPinusDenoisingScripts.zip 79
• QSM modeling script: 80
https://zenodo.org/record/5131717/files/hackenbergQsm.xsct2 81
S2 Processing scripts SimpleForest scripts to process S2 Dataset. 82
• Denoising scripts: 83
https://zenodo.org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds.zip 84
• Poisson reconstruction buttress script: 85
https://zenodo.org/record/5131717/files/deTanagoButtressPoisson.xsct2 86
• QSM modeOpen asset ↗zenodopdf-raw-page:4 lines:1-48Code · publicipt: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin publishOpen asset ↗zenodopdf-raw-page:5 lines:1-46Dataset · publicripts to validate results of S4 Processing scripts. 108
• Statistical plotting script: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code withOpen asset ↗zenodopdf-raw-page:5 lines:1-46Code · publiconScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin published: 121
https://gitlab.com/SimpleForest/computree/-/commits/v5.3.1. 122
• Inside a subfolder this repository contains a Win10 compiled executable : 123
https://gitlab.com/SimpleForest/computree/-/tree/master/bin. 124
• Persistent 5.1.3: 125
https://doi.org/10.5281/zenodo.5138255 126
5/25
.
CC-BY-NC 4.0 International license
available under a
was not certified by peer review) Open asset ↗gitlab · SimpleForest/computreepdf-raw-page:5 lines:1-46Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Field / plotRGB / grayscaleMultispectral / hyperspectralThermalWhole plant / canopy / plot / field
A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions. The TERRA-REF program deployed a suite of high-resolution, cutting edge technology sensors on a gantry system with the aim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolution multiple times per week. The system contains co-located sensors including a stereo-pair RGB camera, a thermal imager, a laser scanner to capture 3D structure, and two hyperspectral cameras covering wavelengths of 300-2500nm. This sensor data is provided alongside over sixty types of traditional plant phenotype measurements that can be used to train new machine learning models. Associated weather and environmental measurements, information about agronomic management and experimental design, and the genomic sequences of hundreds of plant varieties have been collected and are available alongside the sensor and plant phenotype data. Over the course of four years and ten growing seasons, the TERRA-REF system generated over 1 PB of sensor data and almost 45 million files. The subset that has been released to the public domain accounts for two seasons and about half of the total data volume. This provides an unprecedented opportunity for investigations far beyond the core biological scope of the project. The focus of this paper is to provide the Computer Vision and Machine Learning communities an overview of the available data and some potential applications of this one of a kind data.
Why it matches plant phenotyping methods植物の高解像度マルチセンサーデータと植物表現型データを含む公開ベンチマーク／データセットを紹介し、コンピュータビジョンでの利用を主目的とするため、フェノタイピング手法・基盤として中心的です。
abstractgenerate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions
Reproduction assets foundThe paper describes the TERRA-REF public domain release of plant phenotyping sensor data (RGB, thermal, laser scanner, hyperspectral, PSII) plus derived phenotypes, and explicitly points to public code repositories for the processing pipeline (terraref GitHub, PhytoOracle, AgPipeline) and a data access portal. All are,Dataset · publicprocessing, reviewing, curating, describing, and hosting the data.
Instead, we focused on an initial public release and plan to make new datasets available based on need.
Access to unpublished data can be requested from the authors, and as data are curated they will be added to subsequent versions of the public domain release ( https://terraref.org/data/access-data ).
In addition to hosting an archival copy of data on Dryad [ 16 ] , the
documentation includes instructions for browsing and accessing these
data through a variety of online portals. These portals provide access
to web user interfaces as well as databases, APIs, and R and Python
clients. In some cases it will be easier to acceOpen asset ↗lines:234-317Code · publicapproach described by Li et al . [ 18 ] .
Herritt et al . [ 14 , 13 ] demonstrate and provide software used in analysis of a sequence of images that capture plant fluorescence response to a pulse of light.
Most of the algorithms used to generate data products have not been published as papers but are made available on GitHub ( https://github.com/terraref ); code
used to release the data publication in 2020 is available on Zenodo [ 25 , 15 , 10 , 6 , 4 , 19 , 8 , 7 , 5 , 9 , 17 ] .
Pipeline development continues to support ongoing use of the field scanner as well as more general applications in plant sensing pipelines.
Recent advances have improved pipeline scalability and modulOpen asset ↗terrareflines:193-233Code · publiclant sensing pipelines.
Recent advances have improved pipeline scalability and modularity by adopting workflow tools and making use of heterogeneous computing environments.
The TERRA-REF computing pipeline has been adapted and extended for continuing use with the Field Scanner with the new name ”PhytoOracle” and is available at https://github.com/LyonsLab/PhytoOracle . Related work generalizing the pipeline for other phenomics applications has been released under the name ”AgPipeline” https://github.com/agpipeline with applications to aerial imaging described by Schnaufer et al . [ 22 ] .
All of these software are made available with permissive open source licenses on GitHub to enable accesOpen asset ↗PhytoOraclelines:193-233Code · publicnvironments.
The TERRA-REF computing pipeline has been adapted and extended for continuing use with the Field Scanner with the new name ”PhytoOracle” and is available at https://github.com/LyonsLab/PhytoOracle . Related work generalizing the pipeline for other phenomics applications has been released under the name ”AgPipeline” https://github.com/agpipeline with applications to aerial imaging described by Schnaufer et al . [ 22 ] .
All of these software are made available with permissive open source licenses on GitHub to enable access and community development.
Figure 4: Summary of public sensor datasets from Seasons 4 and 6. Each dot represents the dates for which a particular daOpen asset ↗agpipelinelines:193-233Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
Why it matches plant phenotyping methods根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。
abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
Reproduction assets foundThe paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.Code · publicand
online Python image analysis and machine learning tutorials.
Availability of data and materials
The latest versions of ACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (GOpen asset ↗sourceforge.net/projects/acorbapdf-raw-page:17 lines:1-45Dataset · publicACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (Grant No. 803048), Charles
University Primus (Grant No. PRIMUS/19/SCI/09).
Author contributions
NBCS and MF conceived the pOpen asset ↗ZENODO · 10.5281/zenodo.5105719pdf-raw-page:17 lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
Why it matches plant phenotyping methodsLiDAR計測とCropQuant-3Dによる作物の3D形質抽出システムを開発・実証しており、植物表現型の取得・解析手法が研究の中心である。
abstractHere, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis.
Reproduction assets foundThe paper's authors publicly deposited CropQuant-3D source code, GUI software, and testing point cloud datasets on GitHub, directly supporting this paper's LiDAR-based wheat phenotyping analysis.Code · publicSource code: https://github.com/The-Zhou-Lab/LiDAR/releases/tag/V2.0Open asset ↗The-Zhou-Lab/LiDAR · V2.0lines:150-195Dataset · publicThe datasets supporting the results presented here are available at https://github.com/The-Zhou-Lab/LiDAR/releases/tag/V2.0Open asset ↗The-Zhou-Lab/LiDAR · V2.0lines:150-195Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha -1 . Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.
Why it matches plant phenotyping methodsUAV画像からキャノピー高を推定し、地上部バイオマスを予測するフォトグラメトリ手法の開発・検証・標準化が研究の中心であるため。
abstractHere we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's UAV aerial images, marker/plot coordinates, and harvested dry biomass weights at the NERC Environmental Information Data Centre, and the photogrammetric processing and statistical analysis code at Zenodo. Both are paper-specific, public, and have作者Dataset · publicThe data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre < https://doi.org/10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424 >.Open asset ↗NERC Environmental Information Data Centre · 10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424lines:222-246Code · publicCode for photogrammetric processing and statistical analysis is available at Zenodo < https://doi.org/10.5281/zenodo.4783021 >Open asset ↗Zenodo · 10.5281/zenodo.4783021lines:222-246Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Laboratory / benchtopRoot2D/3D reconstructionSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Why it matches plant phenotyping methods根系画像の深層セグメンテーションと3D装置を開発し、画像から根系形態・成長動態を自動抽出する方法が研究の中心である。
abstractHere we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.
Reproduction assets foundThe paper publicly releases its root segmentation image/annotation datasets, hardware files, and analysis code via GitHub repositories, plus supporting data in GigaDB. Three qualifying paper-specific assets with allowed URLs are listed; the GigaDB deposit (10.5524/100911) is paper-specific but its URL is not in the允许edCode · publicThe source code corresponding to ChronoRoot imaging controller, namely, the web interface to check and set up the image acquisition parameters:
Project name: ChronoRoot: Module Controller
Project home page: https://github.com/ThomasBlein/ChronoRootControlOpen asset ↗https://github.com/ThomasBlein/ChronoRootControllines:185-222Dataset · publicThe 2 datasets of images and annotations described in the Datasets section, as well as the 3D printing and laser cutting files, are publicly available at https://github.com/ThomasBlein/ChronoRootModuleHardware under the CERN Open Hardware License Version 2—Strongly Reciprocal licence.Open asset ↗https://github.com/ThomasBlein/ChronoRootModuleHardwarelines:223-262Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurements of these trace gas fluxes require enclosing of shoots in closed non-steady-state chambers. Due to plant physiological activity, this type of enclosure, however, leads to CO2 depletion in the enclosed air volume, condensation of transpired water, and warming of the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gas fluxes over extended periods, capturing diurnal and seasonal variations, and linking trace gas exchange to plant physiological functioning and environmental drivers. Initial measurements show daytime CH4 emissions of two pine shoots of 0.056 and 0.089 nmol per gram of foliage dry weight (d.w.) per hour or 7.80 and 13.1 nmolm-2h-1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmolm-2h-1, and transpiration rates were 1.24 and 0.90 molm-2h-1. Concurrent measurement of VOC emissions demonstrated that potential effects of spectral interferences on CH4 flux measurements were at least 10-fold smaller than the measured CH4 fluxes. Overall, this new system solves multiple technical problems that have so far prevented automated plant shoot trace gas flux measurements and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
Why it matches plant phenotyping methods植物シュートからの微量ガス・VOCフラックスを自動・連続測定する装置を開発し、技術的課題を解決しているため、植物生理状態の取得方法が研究の中心である。
abstractHere, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots.
Reproduction assets foundThe paper's Code and data availability section states that raw measurement data and the analysis script are deposited on Zenodo (doi 10.5281/zenodo.4609836) and that the custom control software (Koppi/koppismear) is publicly available on Bitbucket. Both are paper-specific, public, and actionable.Dataset · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ).Open asset ↗Zenodo · 10.5281/zenodo.4609836lines:435-476Code · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ). The software used to operate both systems is available online at https://bitbucket.org/makoskinen/koppismear/ ( Koskinen , 2021 ) .Open asset ↗Bitbucket · koppismearlines:435-476Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high‐throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do‐it‐yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high‐end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r 2 = 0.96, Laser r 2 = 0.86; projected leaf area: EasyDCP r 2 = 0.96, Laser r 2 = 0.96). EasyDCP is an open‐source software tool to measure phenotypic traits of container plants with high‐throughput and low labour and financial costs.
Why it matches plant phenotyping methodsEasyDCPは、フォトグラメトリによる3D植物表現型取得と自動形質抽出のためのソフトウェア・撮像ワークフローを開発し、レーザースキャナおよび実測値と比較検証しており、方法が研究の中心です。
abstractWe introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data.
Reproduction assets foundThe paper's EasyDCP source code is publicly available on GitHub, and the performance-test data (source images, point clouds, trait data, R files) plus code and documentation are archived on Zenodo.Code · public| 1681
Methods in Ecology and Evolu on
FELDMAN et al.
EasyDCP_Creation (Section 2.2), which creates a 3D point
cloud from 2D images; and EasyDCP_Analysis (Section 2.3),
which analyses that point cloud and performs trait calcula-
tion. EasyDCP source code and documentation are available
on GitHub (https://github.com/UTokyo-FieldPhenomics-Lab/EasyDCP).2.1 | Image acquisition
Plants must be imaged prior to EasyDCP measurement, and the
image acquisition area can be set up according to the user's needs
(Figure 2a,b). The image acquisition area should have as little in-
clination as possible. One printed target page (.pdf provided with
the software) must be placed in a corner oOpen asset ↗UTokyo-FieldPhenomics-Lab/EasyDCPpdf-raw-page:3 lines:1-111Dataset · public.
PEER REVIEW
The peer review history for this article is available at https://publo
ns.
com/publon/10.1111/2041-210X.13645.
DATA AVAILABILITY STATEMENT
Data from the performance test (source images, point clouds, trait
data and R files), EasyDCP source code, example scripts and detailed
documentation are archived using Zenodo https://doi.org/10.5281/zenodo.4756537 (Feldman et al., 2021).
ORCID
Alexander Feldman https://orcid.org/0000-0002-1162-5917
Haozhou Wang https://orcid.org/0000-0001-6135-402X
Yuya Fukano https://orcid.org/0000-0001-9057-4742
Yoichiro Kato https://orcid.org/0000-0002-7131-0220
Seishi Ninomiya https://orcid.org/0000-0002-2123-4354
Wei Guo https://orcid.org/0000-0002-Open asset ↗Zenodo · 10.5281/zenodo.4756537pdf-raw-page:6 lines:1-102Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotRootMorphology / geometry measurementSegmentationRoot system architecture
The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.
Why it matches plant phenotyping methodsRoot画像から根長密度などの植物形質を抽出するCNNソフトウェアを検証し、手動測定との比較や異物除去性能を評価しており、フェノタイピング手法が研究の中心です。
abstractWe analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use.
Reproduction assets foundThe paper's Data availability statement deposits the study's root image dataset with manual counts, the created training dataset and final trained models, and a Python analysis script on Zenodo, all with explicit public URLs.Dataset · publicptualization; EH: investigation, data curation, formal
analysis; EH, AGS, RK, R
W, JK, and MA: methodology; EH: writing—
original draft; EH, MA, and KTK: funding acquisition; EH, AGS, RK,
R
W, JK, KTK, and MA: writing—review and editing.
Data availability
The dataset and manual counts used in the study are available on-
line at http://doi.org/10.5281/zenodo.3754081, the created training
dataset and final trained models are available at http://doi.org/10.5281/zenodo.4300127, and the Python script for splitting the segmenta-
tion on profile wall images is available at http://doi.org/10.5281/zenodo.4299944.References
Böhm W. 1976. In situ estimation of root length at natural soil profiles.
JOpen asset ↗zenodo · 10.5281/zenodo.3754081pdf-raw-page:10 lines:1-88Model / weights · public: writing—
original draft; EH, MA, and KTK: funding acquisition; EH, AGS, RK,
R
W, JK, KTK, and MA: writing—review and editing.
Data availability
The dataset and manual counts used in the study are available on-
line at http://doi.org/10.5281/zenodo.3754081, the created training
dataset and final trained models are available at http://doi.org/10.5281/zenodo.4300127, and the Python script for splitting the segmenta-
tion on profile wall images is available at http://doi.org/10.5281/zenodo.4299944.References
Böhm W. 1976. In situ estimation of root length at natural soil profiles.
Journal of Agricultural Science 87, 365.
Dodge S, Karam L. 2016. Understanding how image quality affects deep
nOpen asset ↗zenodo · 10.5281/zenodo.4300127pdf-raw-page:10 lines:1-88Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals’ fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote‐sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote‐sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large‐scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: (1) image segmentation, to identify individual trees and estimate structural traits; (2) an ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and (3) predictions for segmented crowns for the full remote‐sensing footprint at the NEON sites. The R 2 values on held‐out test data ranged from 0.41 to 0.75 on held‐out test data. The ensemble approach performed better than single partial least‐squares models. Carbon performed poorly compared to other traits ( R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over‐segmentation. The pipeline produced good estimates of DBH ( R 2 of 0.62 on held‐out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between 0.07 and 0.26. We used the pipeline to produce individual‐level trait data for ~5 million individual crowns, covering a total extent of ~360 km 2 . This large data set allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.
Why it matches plant phenotyping methods個体樹木の画像分割、ハイパースペクトル推定、アロメトリーを統合し、構造形質・葉形質を大規模に推定する手法を開発・適用しており、植物フェノタイピング手法が中心である。
abstractWe used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites.
Reproduction assets foundThe paper's Data Availability section deposits three paper-specific public assets on Zenodo: the authors' analysis code, the derived crown-level trait dataset for ~5 million trees, and the trait/input data with metadata. All are directly tied to this paper's phenotyping measurements and analysis.Code · publicgle tree extraction by exploiting airborne full-
waveform LiDAR data. Remote Sensing of Environment
123:368–380.
SUPPORTING INFORMATION
Additional supporting information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as describedOpen asset ↗Zenodo · 10.5281/zenodo.3991797pdf-raw-page:15 lines:1-105Dataset · publicrmation may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
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https://esajournals.onlinelibrary.wiley.Open asset ↗Zenodo · 10.5281/zenodo.3991815pdf-raw-page:15 lines:1-105Dataset · publicanalyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
June 2021 LEAFAND STRUCTURAL TRAIT REMOTE SENSING Article e02300; page 15
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2021,
4,
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https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2300,
Wiley
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on
[27/08/2026].
See
the
Terms
and
Conditions
(https://onlinelibrary.wileOpen asset ↗Zenodo · 10.5281/zenodo.4434481pdf-raw-page:15 lines:1-105Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches in ampelography. The objective of this study was the classification of representative cultivars of Vitis vinifera conserved in the Spanish collection of IMIDRA according to seed shape. Thirty eight cultivars representing the diversity of this collection were analyzed. A consensus seed silhouette was defined for each cultivar representing the geometric figure that better adjusted to their seed shape. All the cultivars tested were classified in ten morphological groups, each corresponding to a new model. The models are geometric figures defined by equations and similarity to each model is evaluated by quantification of percent of the area shared by the two figures, the seed and the model (J index). The comparison of seed images with geometric models is a rapid and convenient method to classify cultivars. A large proportion of the collection may be classified according to the new models described and the method permits to find new models according to seed shape in other cultivars.
Why it matches plant phenotyping methods種子画像から形状を抽出し、幾何モデルと面積共有率でブドウ品種を分類する画像解析手法が研究の中心であり、再利用可能な植物形態フェノタイピング法に該当する。
abstractNew methods of image analysis may help to optimize morphological approaches in ampelography.
Reproduction assets foundThe paper deposits its seed-image datasets and analysis materials in public Zenodo records: composed images of 30 seeds per accession (record 4433813), a video protocol for obtaining average silhouettes (record 4478344), a video of the J index calculation process (record 4478315), and the Mathematica code for the ten新的Dataset · publicified in ten groups defined by their
similarity to each of the respective models.
2.4.1. Obtention of an average silhouette for each cultivar
The average silhouette is a representative image of seed shape for each cultivar. It
was obtained in Corel Photo Paint, by the following protocol (a detailed video is available
at Zenodo: https://zenodo.org/record/4478344#.YBPOguhKiM8):
The layers containing the seeds are superimposed and the opacity is given a value of
3 in all layers. All the layers are combined, and the brightness is adjusted to a minimum
value. From this image we are interested in the inner region representing the area where
most of the seeds coincide, which is the darkest area. ToOpen asset ↗Zenodo · 4478344pdf-raw-page:3 lines:1-37Code · publicbelow, the model in white.
Right: Reed zones show the areas quantified in each of the figures. ImageJ gives the total area for
the seed with the model in black, while shared area is obtained with the white model.
3. Results
3.1. New models
The Mathematica code for the ten new models described in this work is stored in
Zenodo: (https://zenodo.org/record/4478500#.YBPetOhKiM8). The following nine models
were obtained from modifications in Model 7 [24] (between parenthesis the cultivars to
which the model applies):
Model Listán Prieto (Listán Prieto and Tortozona Tinta):
(
2
17
(√3300 − 90𝑥2 −
400
24 + 5𝑥2
) + 𝑦) (
25
187
(−√3300 − 90𝑥2 −
1200
60 + 𝑥6
) + 𝑦) = 0;
Model Sylvestris (wild varietiesOpen asset ↗Zenodo · 4478500pdf-raw-page:4 lines:1-48Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images. We uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates. Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と構造を対象に、CO2フラックスの新規ハイスループット法と画像解析ソフトウェアを用いた機能的フェノミクスを中心的に実施しており、植物形質取得法が研究の主要部分である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images.
Reproduction assets foundThe paper explicitly deposits its trait data, GEMMA GWAS output, and R analysis scripts at Zenodo (10.5281/zenodo.4247894), and separately deposits the root respiration measurement protocol and flux-calculation R scripts at Zenodo (10.5281/zenodo.4247873). Both are paper-specific, public, and actionable. The Triticeae-Dataset · publicAll trait data, gemma output, and R analysis scripts necessary for the statistical analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b ).Open asset ↗Zenodo · 10.5281/zenodo.4247894lines:608-654Code · publicThe protocol for the root respiration measurements and the R script for calculating total flux from a directory of text files are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al.,
2020a ).Open asset ↗Zenodo · 10.5281/zenodo.4247873lines:85-97Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.
Why it matches plant phenotyping methods植物の時系列3D点群を登録し、骨格表現に基づいて器官レベルの形質追跡を可能にする新規計算手法を開発・検証しており、フェノタイピング手法が中心である。
abstractIn this paper, we investigate the problem of registering 3D point clouds of the plants over time and space.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides both the 4D plant point cloud datasets (maize and tomato laser-scanner time series used for the phenotyping/registration experiments) and the authors' implementation code, each with a public URL.Dataset · publicavailable at https://www.ipb.uni-bonn.de/data/4d- tems for automated temporal plant-trait analysis by tracking plant traits at an organ level.Open asset ↗pdf-page:1 lines:1-63Code · publicThe code for our approach is
available at https://github.com/PRBonn/4d_plant_
registration.Open asset ↗pdf-page:1 lines:1-63Code / dataset availability confirmedOpenAlex · checked 8 Sept 2026
ABSTRACT Similarities in phenotypic descriptions can be indicative of shared genetics, metabolism, and stress responses, to name a few. Finding and measuring similarity across descriptions of phenotype is not straightforward, with previous successes in computation requiring a great deal of expert data curation. Natural language processing of free text descriptions of phenotype is often less resource intensive than applying expert curation. It is therefore critical to understand the performance of natural language processing techniques for organizing and analyzing biological datasets and for enabling biological discovery. For predicting similar phenotypes, a wide variety of approaches from the natural language processing domain perform as well as curation-based methods. These computational approaches also show promise both for helping curators organize and work with large datasets and for enabling researchers to explore relationships among available phenotype descriptions. Here we generate networks of phenotype similarity and share a web application for querying a dataset of associated plant genes using these text mining approaches. Example situations and species for which application of these techniques is most useful are discussed. Database URLs The database and analytical tool called QuOATS are available at https://quoats.dill-picl.org/ . Code for the web application is available at https://git.io/Jtv9J . Datasets are available for direct access via https://zenodo.org/record/7947342#.ZGwAKOzMK3I . The code for the analyses performed for the publication is available at https://github.com/Dill-PICL/Plant-data and https://github.com/Dill-PICL/NLP-Plant-Phenotypes .
Why it matches plant phenotyping methods植物表現型の自然言語記述をNLPで類似性解析し、遺伝子データセット探索用のWebアプリケーションを開発・提供しており、表現型データの計算的整理・解析手法が中心です。
abstractNatural language processing of free text descriptions of phenotype is often less resource intensive than applying expert curation.
Reproduction assets foundThe paper's phenotype description dataset and analysis code are explicitly deposited with public git.io URLs, and the QuOATS web application is publicly hosted. All are paper-specific and actionable.Dataset · publicThe dataset used in this work is available at https://git.io/JTutQ.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the analysis performed here
is available at https://git.io/JTutN and https://git.io/JTuqv.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the analysis performed here
is available at https://git.io/JTutN and https://git.io/JTuqv.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the web application discussed here
is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the web application discussed here
is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.Open asset ↗pdf-page:1 lines:1-60Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Published1 Feb 2021Proceedings of the National Academy of Sciences of the United States of AmericaCited by 35 · OpenAlex ↗
Artificial mechanical perturbations affect chromatin in animal cells in culture. Whether this is also relevant to growing tissues in living organisms remains debated. In plants, aerial organ emergence occurs through localized outgrowth at the periphery of the shoot apical meristem, which also contains a stem cell niche. Interestingly, organ outgrowth has been proposed to generate compression in the saddle-shaped organ-meristem boundary domain. Yet whether such growth-induced mechanical stress affects chromatin in plant tissues is unknown. Here, by imaging the nuclear envelope in vivo over time and quantifying nucleus deformation, we demonstrate the presence of active nuclear compression in that domain. We developed a quantitative pipeline amenable to identifying a subset of very deformed nuclei deep in the boundary and in which nuclei become gradually narrower and more elongated as the cell contracts transversely. In this domain, we find that the number of chromocenters is reduced, as shown by chromatin staining and labeling, and that the expression of linker histone H1.3 is induced. As further evidence of the role of forces on chromatin changes, artificial compression with a MicroVice could induce the ectopic expression of H1.3 in the rest of the meristem. Furthermore, while the methylation status of chromatin was correlated with nucleus deformation at the meristem boundary, such correlation was lost in the h1.3 mutant. Altogether, we reveal that organogenesis in plants generates compression that is able to have global effects on chromatin in individual cells.
Why it matches plant phenotyping methods植物組織内の核変形を経時イメージングで定量化する解析パイプラインを開発し、核の形態状態を抽出しているため、表現型取得法が研究上実質的に中心である。
abstractHere, by imaging the nuclear envelope in vivo over time and quantifying nucleus deformation, we demonstrate the presence of active nuclear compression in that domain.
Reproduction assets foundThe paper's Data Availability statement deposits original confocal phenotyping data (meristem/nucleus imaging) in the Cambridge repository and provides the authors' segmentation/quantification analysis pipeline scripts on the Sainsbury Laboratory GitLab. Both are paper-specific, public, and actionable.Dataset · publicOriginal confocal data are available via the University of Cambridge Data Repository ( https://doi.org/10.17863/CAM.64310 ).Open asset ↗University of Cambridge Data Repository · 10.17863/CAM.64310lines:76-106Code · publicScripts for the analysis pipeline are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/fal_etal_2020 ).Open asset ↗Sainsbury Laboratory GitLab · slcu/teamHJ/publications/fal_etal_2020lines:76-106Code · publicScripts required to do the segmentation and quantitative analysis are provided via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamhj/publications/fal_et_al_2021 ), where also a more detailed protocol for executing the steps of the pipeline is provided.Open asset ↗Sainsbury Laboratory GitLab · slcu/teamhj/publications/fal_et_al_2021lines:76-106Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F v /F m ) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Why it matches plant phenotyping methods植物病害の症状・重症度・進展を画像から定量化する方法の開発とワークフロー提示が中心であり、植物状態の表現型取得に該当する。
abstractHere, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves.
Reproduction assets foundThe authors explicitly state that all R and ImageJ scripts and the study data are openly available in their public GitHub repository, which directly reproduces this paper's Botrytis symptom phenotyping analysis.Code · publicAll R and ImageJ script generated to process are available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Dataset · publicThe data presented in this study are openly available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Why it matches plant phenotyping methods種子の発芽状態を画像と機械学習で自動抽出し、手動計数と性能比較しているため、植物表現型取得法が中心です。
abstractWe here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.
Reproduction assets foundThe paper's authors publicly released the labeled germination image dataset (~24,000 annotated images of 2449 seeds) on Mendeley Data and their machine learning analysis code on GitHub, both explicitly stated in the Availability of data and materials section.Dataset · publicThe generated and labeled training data is freely available on Mendeley Data: http://dx.doi.org/10.17632/4wkt6thgp6.2 .Open asset ↗Mendeley Data · 10.17632/4wkt6thgp6.2lines:164-248Code · publicThe code for our proposed machine learning–based model can be found on GitHub: https://github.com/grimmlab/GerminationPrediction .Open asset ↗GitHub · grimmlab/GerminationPredictionlines:164-248Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動推定する深層学習・クラウド実装を開発・評価しており、植物フェノタイピング手法が中心です。
abstractAutomated identification and classification of plant leaf diseases is, therefore, essential
Reproduction assets foundThe paper's Data Availability statement explicitly links a public Kaggle plant-disease image dataset used for training/testing and an authors' GitHub code repository. The TensorFlow plant_village catalog URL is a generic mirror of the same public dataset rather than a paper-specific deposit.Dataset · publicData Availability: Dataset is available from the below link: https://www.kaggle.com/emmarex/plantdiseaseOpen asset ↗kaggle · emmarex/plantdiseaselines:123-130Code · publicGithub Code Repo Link: https://github.com/umairnawazz/Plant-Disease-DetectionOpen asset ↗github · umairnawazz/Plant-Disease-Detectionlines:123-130Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Grain size and weight are important yield components in rice ( Oryza sativa L.). There is still uncertainty about the genetic control of these traits under drought stress, the most pressing emerging issue in many rice cultivation areas. To address this lack of knowledge, we investigated the genetic architecture of seed size, shape, and weight using the rice Global Multi-parent Advanced Generation Intercross (MAGIC) population, grown under well-watered and vegetative drought conditions. We measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV). Besides being affordable, rapid, and accurate, our method captured the phenotypic divergence between drought and well-watered samples, expressed as 12 different traits that include traditional size metrics and new grain shape measures. Overall, under water deficit, the MAGIC lines produced smaller and shorter seeds. We identified ten MAGIC lines with traits that make them good candidates for the release of rice cultivars with high yield potential under vegetative drought stress. We ran a marker-trait association analysis for the measured seed-related traits. Most of the identified marker-trait associations showed strong genotype-by-environment interactions (GxE), with most allele effects being conditionally neutral. These results suggest dynamic genetic control of seed size, shape, and weight under vegetative drought stress in rice, highlighting the importance of understanding the contribution of GxE interactions on trait variation to develop resilient and high-yielding rice varieties. Our study confirms that combining low-cost and high-throughput phenotyping strategies with a diverse genetic material suited for multi-environmental trial provides solutions for adapting rice cultivation to current and future environmental adversities.
Why it matches plant phenotyping methodsイネ種子の形状・サイズ・重量を、デスクトップスキャナーとPlantCVによる新規かつ高スループットな表現型取得法で測定しており、方法開発と実質的な適用が研究の中心です。
abstractWe measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV).
Reproduction assets foundThe paper deposits two paper-specific public assets: the authors' PlantCV image-analysis code (Zenodo 4156942) and the raw rice seed scan images used for phenotyping (Zenodo 4158169). Other URLs (PlantCV docs, 3K rice genome registry, R project) are generic resources or cited prior work, not paper-specific assets.Code · publicr standards
179 (white and grey cards) for image exposure normalization, and a ruler as a size standard.
180
181 We processed the RGB (Red Green Blue) images generated with the scanner using a personal
182 laptop with Intel® Core™ i7 8650u CPU @1.90Ghz and 16 GB RAM. The PlantCV code used for
183 this manuscript is available at https://doi.org/10.5281/zenodo.4156942, and more details on
184 the PlantCV functions used in our pipeline can be found in the online user manual of PlantCV
185 (https://plantcv.readthedocs.io/en/latest/). Briefly, for each RGB image, the pipeline first
186 standardizes image exposure using the white standard color. Then, it separates the seeds from
187 the backgrouOpen asset ↗zenodo · 10.5281/zenodo.4156942pdf-raw-page:9 lines:1-32Dataset · publicas described at
196 https://plantcv.readthedocs.io/en/stable/pipeline_parallel/. All trait estimates per seed and per
197 sample are saved in JSON text files, which are then merged and converted to a final CSV table
198 file using the accessory tool “plantcv-utils.py” implemented in PlantCV. All seed images are
199 available at https://doi.org/10.5281/zenodo.4158169.200 We also measured the grain weight of 50 seeds per sample using an analytical scale
201 (Adventurer® Analytical, Ohaus, USA). We then converted the weight of grains to 1000-seed
202 weight for easy comparisons with previous studies.
203 Statistical analyses of phenotypic data
204 We performed all statistical analyses of theOpen asset ↗zenodo · 10.5281/zenodo.4158169pdf-raw-page:10 lines:1-31Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類するGANベースの画像解析手法を開発・比較評価しており、病害フェノタイピング手法が研究の中心である。
abstractWasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data.
Reproduction assets foundThe paper analyzes two public plant disease image datasets (Kaggle plantdisease and PlantVillage raw color images) and provides authors' Python code for data processing and model training on GitHub.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/emmarex/plantdiseaseOpen asset ↗lines:1205-1278Code · publicThe Python code of data processing and model training is available online at https://github.com/lbn-dev/WGAN_plant_diseasesOpen asset ↗WGAN_plant_diseaseslines:1205-1278Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Dec 2020Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of CanadaCited by 16 · OpenAlex ↗
Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.
Why it matches plant phenotyping methods植物由来の植物珪酸体を対象に、顕微鏡画像からの自動識別・分類手法を比較評価しており、画像ベースの形態的特徴抽出が研究の中心です。
abstractThis paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths.
Reproduction assets foundThe paper's phytolith photomicrograph dataset (429 images across 8 morphotypes) is explicitly stated to be publicly available at the UPF repository, and the authors' analysis code is shared on GitHub with explicit availability language. Both are paper-specific, public, and actionable.Dataset · publiccaptured from the side view.
The total number of photomicrographs obtained for each mor-
photype is shown in Table 1. Only nonarticulated (not attached to
any other phytoliths) were considered and just one photomicro-
graph per phytolith was recorded. The total number of samples
was 429. All the images are publicly available at https://reposi-tori.upf.edu/handle/10230/44939, all the morphotypes have at
least 50 samples, and the dataset is fairly balanced (i.e., there is
a similar number of samples per class).
The image of each phytolith was digitized, using an open-
source web annotation tool called VGG Image Annotator.1
This
tool allows a researcher to create a control-points based contour
Open asset ↗reposi-tori.upf.edu · 10230/44939pdf-raw-page:3 lines:1-92Code · publicclassification process. Even though several researchers have
attempted to create automatic tools for the identification of
archaeobotanical remains, none of the attempts has produced a
tool that is accessible online or as a downloadable app. We are
sharing the code used in our research (which is accessible at
https://github.com/alvarag/AutomaticPhytolithClassification) to
stimulate other researchers to join in the effort to build a real
and functional tool that can be trained online, increasing its
accuracy.
Future Research Lines
The development of new features and the application of feature
selection techniques are some of the research avenues we are plan-
ning to explore. It would be imporOpen asset ↗github.com/alvarag/AutomaticPhytolithClassificationpdf-raw-page:9 lines:1-85Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Premise Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives. Methods We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it. Results The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession. Discussion Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.
Why it matches plant phenotyping methodsドローン遠隔センシングとフォトグラメトリで植物群落の生産性・バイオマス・被覆を推定し、地上測定との予測性能を比較検証しており、植物形質取得手法が中心的です。
abstractWe explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity.
Reproduction assets foundThe authors publicly deposited the scripts and data used in this drone-based prairie phenotyping study (biomass, cover, vegetation index measurements, trait data) on GitHub and archived on Zenodo, with explicit availability statements and URLs in the article.Dataset · publicraw measurements can be found in the data sets provided in GitHub ( https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATA )Open asset ↗https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATAlines:88-96Code · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://github.com/lanescher/prairie‐remote‐sensing‐2020lines:956-978Dataset · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://doi.org/10.5281/zenodo.3981500 · 10.5281/zenodo.3981500lines:956-978Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Summary The root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. We uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. Combining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と形態を対象に、CO2フラックスの新規ハイスループット測定法と画像解析ソフトウェアを用いたフェノタイピングが研究の中心である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images.
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: (1) the root respiration measurement protocol and R script for computing CO2 flux from LI-850 text files (Zenodo 4247873), and (2) all trait data, GEMMA output, and R analysis scripts for the statistical analysis and plotting (Zenodo 4247894). Both are theCode · publicThe protocol for the root
respiration measurements and the R script for calculating total flux from a directory of text files
are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al., 2020a).Open asset ↗Zenodo · 10.5281/zenodo.4247873pdf-page:8 lines:1-41Dataset · publicAll trait data, GEMMA output, and R analysis scripts necessary for doing the statistical
analysis and plotting are available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b).Open asset ↗Zenodo · 10.5281/zenodo.4247894pdf-page:12 lines:1-35Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Published1 Nov 2020Journal of the American Society for Horticultural ScienceCited by 29 · OpenAlex ↗
Grape ( Vitis vinifera ) cluster compactness is an important trait due to its effect on disease susceptibility, but visual evaluation of compactness relies on human judgement and an ordinal scale that is not appropriate for all populations. We developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape ( Vitis sp.) population for 2 years. Images were collected from grape clusters immediately after harvest, segmented by color, and analyzed using a custom script. Both automated and conventional phenotyping methods were used, and comparisons were made between each method. A partial least squares (PLS) model was constructed to evaluate the prediction of physical cluster compactness using image-derived measurements. Quantitative trait loci (QTL) on chromosomes 4, 9, 12, 16, and 17 were associated with both image-derived and conventionally phenotyped traits within years, which demonstrated the ability of image-derived traits to identify loci related to cluster morphology and cluster compactness. QTL for 20-berry weight were observed between years on chromosomes 11 and 17. Additionally, the automated method of cluster length measurement was highly accurate, with a deviation of less than 10 mm ( r = 0.95) compared with measurements obtained with a hand caliper. A remaining challenge is the utilization of color-based image segmentation in a population that segregates for fruit color, which leads to difficulty in differentiating the stem from the fruit when the two are similarly colored in non-noir fruit. Overall, this research demonstrates the validity of image-based phenotyping for quantifying cluster compactness and for identifying QTL for the advancement of grape breeding efforts.
Why it matches plant phenotyping methodsブドウ房の画像解析パイプラインを開発し、従来法との比較、PLSによる予測評価、測定精度検証を行っており、画像ベース表現型計測が中心である。
abstractWe developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape ( Vitis sp.) population for 2 years.
Reproduction assets foundThe paper explicitly states public availability of both the grape cluster images (University of Minnesota Conservancy) and the custom MATLAB image analysis script (GitHub), both directly supporting this paper's phenotyping measurements and analysis.Dataset · publicStien Iverson and David Tork, who helped with data collection. Soon Li Teh and
James Luby built the GE1025 linkage map.
Cluster images are available at https://conservancy.umn.edu/handle/11299/202560. Image analysis script is available at https://github.com/underhil-lanna/GrapeImageAnalysis.Current address for A.U.: Grape Genetics Research Unit, U.S. Department of
Agriculture, Agricultural Research Service, 630 West North Street, Geneva, NY
14456
M.C. is the corresponding author. Email: clark776@umn.edu.
This is an open accOpen asset ↗conservancy.umn.edu · 11299/202560pdf-raw-page:1 lines:74-81Code · publicStien Iverson and David Tork, who helped with data collection. Soon Li Teh and
James Luby built the GE1025 linkage map.
Cluster images are available at https://conservancy.umn.edu/handle/11299/202560. Image analysis script is available at https://github.com/underhil-lanna/GrapeImageAnalysis.Current address for A.U.: Grape Genetics Research Unit, U.S. Department of
Agriculture, Agricultural Research Service, 630 West North Street, Geneva, NY
14456
M.C. is the corresponding author. Email: clark776@umn.edu.
This is an open access article distributed under the CC BY-NC-ND license
(https://creativecommons.org/licenses/by-nc-ndOpen asset ↗github.com/underhil-lanna/GrapeImageAnalysispdf-raw-page:1 lines:74-81Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm 2 ). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.
Why it matches plant phenotyping methods葉脈画像のセグメンテーションと形態統計抽出を深層学習で開発・検証し、再利用可能なソフトウェアとして提供しているため、植物フェノタイピング手法が中心である。
abstractHere we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks.
Reproduction assets foundThe paper's Data and algorithm availability section explicitly deposits the LEAFVEINCNN GUI software with trained networks (Zenodo 4007731), the down-sampled CNN predictions, ground truths, and MATLAB analysis scripts (Zenodo 4008614), and all results (Zenodo 4008361), all openly available. These are paper-specific,公开,Code · publicfull width of the vein, the P-R analysis was also run fol-
lowing conversion of the binary image at each threshold value to
a single-pixel wide skeleton.
Data and algorithm availability
A MATLAB App or the standalone LEAFVEINCNN GUI software
package, including the trained networks, and manual (Fig. S2)
are openly available from https://doi.org/10.5281/zenodo.4007731. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from htOpen asset ↗zenodo · 10.5281/zenodo.4007731pdf-raw-page:8 lines:1-92Code · publicanual (Fig. S2)
are openly available from https://doi.org/10.5281/zenodo.4007731. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from https://doi.org/10.5281/zenodo.4008361.Results
CNNs provided high accuracy vein network segmentationOpen asset ↗zenodo · 10.5281/zenodo.4008614pdf-raw-page:8 lines:1-92Dataset · public31. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from https://doi.org/10.5281/zenodo.4008361.Results
CNNs provided high accuracy vein network segmentationOpen asset ↗zenodo · 10.5281/zenodo.4008361pdf-raw-page:8 lines:1-92Code / dataset availability confirmedarXiv · checked 9 Sept 2026
Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenotyping falls within this category. Object counting in plant images is further challenged by having plant image datasets with significant domain shift due to different experimental conditions, e.g. applying an annotated dataset of indoor plant images for use on outdoor images, or on a different plant species. In this paper, we propose a domain-adversarial learning approach for domain adaptation of density map estimation for the purposes of object counting. The approach does not assume perfectly aligned distributions between the source and target datasets, which makes it more broadly applicable within general object counting and plant organ counting tasks. Evaluation on two diverse object counting tasks (wheat spikelets, leaves) demonstrates consistent performance on the target datasets across different classes of domain shift: from indoor-to-outdoor images and from species-to-species adaptation.
Why it matches plant phenotyping methods植物器官数を画像から推定するドメイン適応・密度マップ推定法を開発し、コムギ小穂と葉の計数で評価しており、表現型取得手法が中心である。
abstractCounting plant organs for image-based plant phenotyping falls within this category.
Reproduction assets foundThe paper provides two paper-specific public assets: the authors' implementation code for the domain-adversarial counting model on GitHub, and the authors' newly created GWHD dot annotations deposited on figshare. Other URLs are cited prior-work datasets, not paper-specific assets.Code · publicAll experiments were performed on a GeForce RTX 2070 GPU with 8GB memory using the Pytorch framework. The implementation is available at: https://github.com/p2irc/UDA4POCOpen asset ↗p2irc/UDA4POClines:81-104Dataset · publicTo evaluate our method, we created dot annotations for 67 images from the GWHD which are used as ground truth. These annotations are made publicly available at https://doi.org/10.6084/m9.figshare.12652973.v2 .Open asset ↗10.6084/m9.figshare.12652973.v2lines:105-155Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Rice density is closely related to yield estimation, growth diagnosis, cultivated area statistics, and management and damage evaluation. Currently, rice density estimation heavily relies on manual sampling and counting, which is inefficient and inaccurate. With the prevalence of digital imagery, computer vision (CV) technology emerges as a promising alternative to automate this task. However, challenges of an in-field environment, such as illumination, scale, and appearance variations, render gaps for deploying CV methods. To fill these gaps towards accurate rice density estimation, we propose a deep learning-based approach called the Scale-Fusion Counting Classification Network (SFC 2 Net) that integrates several state-of-the-art computer vision ideas. In particular, SFC 2 Net addresses appearance and illumination changes by employing a multicolumn pretrained network and multilayer feature fusion to enhance feature representation. To ameliorate sample imbalance engendered by scale, SFC 2 Net follows a recent blockwise classification idea. We validate SFC 2 Net on a new rice plant counting (RPC) dataset collected from two field sites in China from 2010 to 2013. Experimental results show that SFC 2 Net achieves highly accurate counting performance on the RPC dataset with a mean absolute error (MAE) of 25.51, a root mean square error (MSE) of 38.06, a relative MAE of 3.82%, and a R 2 of 0.98, which exhibits a relative improvement of 48.2% w.r.t. MAE over the conventional counting approach CSRNet. Further, SFC 2 Net provides high-throughput processing capability, with 16.7 frames per second on 1024 × 1024 images. Our results suggest that manual rice counting can be safely replaced by SFC 2 Net at early growth stages. Code and models are available online at https://git.io/sfc2net.
Why it matches plant phenotyping methodsイネ個体数という植物形態・密度形質を画像から自動推定する深層学習手法を開発し、新規データセットで精度検証しているため、方法が中心である。
abstractwe propose a deep learning-based approach called the Scale-Fusion Counting Classification Network (SFC 2 Net)
Reproduction assets foundThe paper's rice plant counting (RPC) dataset (382 field images with 211,971 dot annotations) and the authors' SFC2Net code and trained models are explicitly stated to be publicly available at the authors' URL https://git.io/sfc2net, which matches an allowed URL.Dataset · publicThe RPC dataset has been made available at https://git.io/sfc2net .Open asset ↗lines:403-419Code · publicCode and models are available online at https://git.io/sfc2net .Open asset ↗lines:1-28Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Premise Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods High-throughput time series image data from water-deprived maize ( Zea mays subsp. mays ) and sorghum ( Sorghum bicolor ) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
Why it matches plant phenotyping methodsLAXは画像から葉角度を抽出・定量するために開発された高スループット画像処理フレームワークであり、植物表現型取得手法が研究の中心です。
abstractA MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.
Reproduction assets foundThe paper's authors explicitly state that the LAX source code and GUI are publicly available on GitHub, and the paper's time-lapse image data (Video S1) is publicly hosted on Vimeo. Both are paper-specific, public, and actionable.Code · publicnowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The source code and GUI interface are available at https://github.com/Kenchanmane‐Raju/Leaf‐Angle‐eXtractor .
LITERATURE CITED
Araus , J. L.
,
S. C.
Kefauver
,
M.
Zaman‐Allah
,
M. S.
Olsen
, and
J. E.
Cairns
. 2018
Translating high‐throughput phenotyping into genetic gain
. Trends in Plant Science
23 ( 5 ): 451 – 466 .
29555431
10.1016/j.tplants.2018.02.001
PMC5931794
Awada , L.
,
P. W. B.
Phillips
, and
S. J.
Smyth
.Open asset ↗Kenchanmane‐Raju/Leaf‐Angle‐eXtractorlines:182-386Dataset · publicgle boxes and leaf number. Clicking the ‘Export Data’ icon at the bottom outputs leaf angle measurements for the selected leaves as a .csv file .
Click here for additional data file.
VIDEO S1. Time‐lapse video showing the drop of maize leaves in response to water deficit stress over a single day. This video is also available at https://vimeo.com/256137800 .
Click here for additional data file.
Acknowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The sourOpen asset ↗lines:182-386Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Why it matches plant phenotyping methods植物組織を細胞単位で抽出する画像解析パイプラインを開発し、異なる組織・スケール・撮像条件で性能を示しているため、植物フェノタイピング手法が中心である。
abstractHere, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells.
Reproduction assets foundThe paper publicly deposits all plant phenotyping image/ground-truth datasets on OSF (https://osf.io/uzq3w), including ovule, lateral root, meristem, and leaf confocal/light-sheet volumes with hand-curated segmentations, and releases the PlantSeg analysis code and pre-trained 3D U-Net models on GitHub.Dataset · publicAll datasets used to support the findings of this study have been deposited in https://osf.io/uzq3w .Open asset ↗osf.io/uzq3wlines:38-47Code · publicThe code used for training and inference can be found at Wolny, 2020b
https://github.com/wolny/pytorch-3dunet copy archived at https://github.com/elifesciences-publications/pytorch-3dunet .Open asset ↗GitHub · wolny/pytorch-3dunetlines:212-223Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields, at the phloem unloading interfaces of Arabidopsis ( Arabidopsis thaliana ) roots. Pit fields were visualized and quantified. As the wall environment of plasmodesmata is highly specialized, we also designed a tool to extract the thickness of the extracellular matrix at and outside of plasmodesmata positions. We detected and quantified clear wall thinning around plasmodesmata with differences between genotypes, including the recently published plm-2 sphingolipid mutant. Our tools open avenues for quantitative approaches in the analysis of symplastic trafficking.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布、クラスター、細胞壁厚を定量化する計算パイプラインとツールを開発しており、植物形態・構造形質の取得が中心です。
abstractWe developed a computational pipeline to study plasmodesmata distributions and detect the presence/absence of plasmodesmata clusters, or pit fields
Reproduction assets foundThe paper's authors publicly released their Matlab plugins for plasmodesmata distribution and cell-wall thickness analysis on GitHub, a guided R analysis pipeline tutorial, and the Col-0 SB-EM data sets with segmented wall models and PD annotations on Figshare. Generic tools (MIB, matGeom, CRAN packages) and the EMPIARCode · publicA guided tutorial with all the necessary code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/ (last accessed March 2020).Open asset ↗lines:148-159Dataset · publicThe Col-0 data sets used in this article, with corresponding models and annotations, are available on Figshare ( https://doi.org/10.6084/m9.figshare.12488702.v1 ). They can be used as example data sets to test our pipeline.Open asset ↗figshare · 10.6084/m9.figshare.12488702.v1lines:148-159Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
LettuceAerial / UAVFlowerSegmentationGrowth / time-series analysisGrowth / development / phenology
Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time. One sentence summaryMachine learning and Bayesian analyses of drone-mediated remote phenotyping data revealed two genetic loci regulating differential daily flowering time in lettuce (Lactuca spp.).
Why it matches plant phenotyping methodsドローン画像、機械学習、ベイズ推定を用いた花開花時刻の表現型取得・抽出が研究の中心であり、方法の精度も評価されている。
abstractthe floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping
Reproduction assets foundThe paper's Data Availability statement provides two paper-specific public assets: authors' analysis scripts (machine learning and Bayesian inference) on GitHub, and the GPS-anchored drone aerial image data on HydroShare. Both are directly used for this paper's phenotyping and analysis.Code · public51 2015-51181-24283 to RWM.
452
453 Data Availability
454 GBS data of the RILs and WGS data of the parents are available on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗rkbhan/FloralOpeningpdf-layout-page:20 lines:1-57Dataset · publicavailable on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗pdf-layout-page:20 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
PREMISE: High-resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above-ground plant attributes. However, the acquisition of high-resolution images of plant roots is more challenging than above-ground data collection. An effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. METHODS: We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non-plant-root images, (ii) training with plant-root images, and (iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. The architectures of the SR models were based on two state-of-the-art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. RESULTS: In our experiments, we observed that the SR models improved the quality of low-resolution images of plant roots in an unseen data set in terms of the signal-to-noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non-root data sets. DISCUSSION: The incorporation of a deep learning-based SR model in the imaging process enhances the quality of low-resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal-to-noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.
Why it matches plant phenotyping methods植物根の低解像度画像を高解像度化する深層学習手法を開発・比較し、根画像の分割性能への効果も検証しており、表現型取得・抽出法が中心である。
abstractAn effective super-resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors
Reproduction assets foundThe paper's authors explicitly state that their source code and pre-trained SR models are publicly available on GitHub and Zenodo, and they used five publicly available image datasets (DIV2K, 91-Image, Arabidopsis thaliana root data, wheat seedling roots, 3D MRI barley roots) plus a SegRoot soybean test set as phenotypCode · publicbidopsis thaliana data set ( https://zenodo.org/record/50831#.XjIAPVNKhQI ), wheat seedling data set ( http://gigadb.org/dataset/100346 ), and barley data set ( https://www.quantitative‐plant.org/dataset/3d‐magnetic‐resonance‐images‐of‐barley‐roots ). The source code and pre‐trained SR models are available at GitHub and Zenodo (https://github.com/GatorSense/SRrootimaging; https://doi.org/10.5281/zenodo.3940562 ; Ruiz‐Munoz, 2020 ).
LITERATURE CITED
Akinnifesi , F. K.
,
B. T.
Kang
, and
D. O.
Ladipo
. 1998
Structural root form and fine root distribution of some woody species evaluated for agroforestry systems
. Agroforestry Systems
42 : 121 – 138 .
Araus , J. L.
, and
J. E.
Cairns
. 2014
FielOpen asset ↗GatorSense/SRrootimaginglines:155-276Dataset · publicWinRHIZO images of the barley roots.
In our experiments, we grouped the three plant‐root data sets into a single data set named “Roots.” Figure 3 shows examples of the plant‐root data sets used for training the SR model. To test the performance of the SR models, we used a data set of 65 soybean ( Glycine max (L.) Merr.) roots ( https://github.com/wtwtwt0330/SegRoot [accessed 11 June 2020]) (Wang et al., 2019 ).
SR model training
Many CNN architectures that enable the mapping of LR images into SR images can be found in the machine learning literature. In this study, we used two state‐of‐the‐art CNN‐based models, FSRCNN and SRGAN, to convert LR root images to SR images. FSRCNN is a model thOpen asset ↗wtwtwt0330/SegRootlines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). TheOpen asset ↗lines:93-102Dataset · publicIn this study, we used five publicly available data sets to train the SR models. We used two non‐plant‐root data sets, DIV2K ( https://data.vision.ee.ethz.ch/cvl/DIV2K/ [accessed 11 June 2020]) and 91‐Image ( https://www.kaggle.com/ll01dm/t91‐image‐dataset [accessed 11 June 2020]). DIV2K is a data set of natural images that has been used by others to train and test SR algorithms (Timofte et al., 2017 ). We trained our models on the grayscale version of this training data set (800 images). The 91‐Image information is a classical data set commonly used in SR studies. We also used tOpen asset ↗lines:93-102Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
PREMISE: X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers. METHODS AND RESULTS: We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations. By training the model on each scan using six hand-segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. CONCLUSIONS: Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
Why it matches plant phenotyping methods3DマイクロCT画像から葉の内部解剖形質を抽出する機械学習セグメンテーションと定量化パイプラインの開発が中心であり、植物フェノタイピングへの適用性も明示されている。
abstractWe present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification
Reproduction assets foundThe paper's authors publicly released their random forest segmentation/leaf-traits analysis code on GitHub and the microCT image dataset, hand-labeled training slices, and segmentation outputs on Zenodo.Code · publicThe code and an in-depth user manual are available at https://Open asset ↗pdf-raw-page:8 lines:1-78Dataset · publicgithub.com/plant-microct-tools/leaf-traits-microct. Future updates
will be integrated to this repository. The microCT data set, training
hand-labeled slices, and all image outputs of the program including
one full stack segmentation are available on Zenodo at https://doi.org/10.5281/zenodo.3694973 (Théroux-Rancourt et al., 2020b).
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Average proportion of pixels per tissue in the 24
slices of the training data set.
APPENDIX S2. Standard deviation of thickness estimates pre-
sented inOpen asset ↗Zenodo · 10.5281/zenodo.3694973pdf-raw-page:8 lines:79-106Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.
Why it matches plant phenotyping methods珪藻の顕微鏡動画から形態・細胞内特徴・群体運動を抽出する画像処理および深層学習手法が研究の中心であり、植物表現型解析手法の開発に該当する。
abstractTo acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources.
Reproduction assets foundThe paper's Bacillaria phenotyping data and analysis assets are publicly available: raw data, processed numeric/image data, and code in the authors' Digital-Bacillaria GitHub repository; skeleton-creation scripts in the Image-Skeletons subrepository; the OpenDevoCell segmentation platform (GitHub and web app); and a GfDataset · publicstrains contained in our primary data (videos) have been harvested from the Neckar
river in Germany (49°04'41.8"N 9°09'17.9"E). Samples were collected on September 14, 2019.
The average size of each cell (filament) is approximately 81µm.
Data Availability
Select unprocessed (raw) data are available at our Github repository
(https://github.com/devoworm/Digital-Bacillaria), processed numeric and image data (numeric
tables and skeletonized images), and select video files are available on the Open Science
Framework (DOI 10.17605/OSF.IO/AR8C3).
17Open asset ↗devoworm/Digital-Bacillariapdf-layout-page:17 lines:1-49Code · publicckground
color (select the background by color) to RGB value 0,0,0. To create a thick skeleton from a thin
skeleton, select the thin skeleton by color and then select the border function. The border width
should be set to 4, hard border, and filled with RGB value 0,217,0. The pseudo-code for GIMP
script-fu is located on Github (https://github.com/devoworm/Digital-Bacillaria/tree/master/Image-Skeletons).Image Tracking for Movement. We also employ image tracking for the primary microscopy
data. The tracking of a partial image (template) of a diatom can be used under certain conditions
to obtain its trajectory. In particular, a movement of the diatoms in a plane perpendicular to the
optical axiOpen asset ↗devoworm/Digital-Bacillariapdf-raw-page:10 lines:1-44Code · publicn-source software with a web interface called OpenDevoCell (based on DeepLearning 4J).
DeepLabv3 (Google, MountainView, California, USA) is a package for TensorFlow, and Deep
Learning 4J (Eclipse Foundation, Ottawa, Canada), a Java-based library that works with
TensorFlow. OpenDevoCell is open-source software located on Github
(https://github.com/devoworm/GSOC-2019/tree/master/OpenDevoCell) and as a web-based
application (https://open-devo-cell.herokuapp.com).11
.
CC-BY 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (wOpen asset ↗devoworm/GSOC-2019pdf-raw-page:11 lines:1-35Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The shapes of grapevine leaves have been critical to correctly identify economically important varieties throughout history. The correspondence of homologous features in nearly all grapevine species and varieties has enabled advanced morphometric approaches to mathematically classify leaf shape. These approaches either model leaves through the measurement of numerous vein lengths and angles or measure a finite number of corresponding landmarks and use Procrustean approaches to superimpose points and perform statistical analyses. Hand illustrations, too, play an important role in grapevine identification, as details omitted using the above methods can be visualized. Here, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations. Using these points, averaged leaf shapes for 60 varieties of wine and table grapes are calculated that preserve features. A pairwise Procrustes distance matrix of the overall morphological similarity of each variety to the other classifies leaves into two main groups--deeply lobed and more entire--that correspond to the measurements of sinus depth by Pierre Galet. Using the system of Galet, pseudo-landmarks are converted into relative distance and angle measurements. Both Galet-inspired and Procrustean methods allow increased accuracy in predicting variety compared to a finite number of landmarks. Using Procrustean pseudo-landmarks captures grapevine leaf shape at the same level of detail as drawings and provides a quantitative method to arrive at mean leaf shapes representing varieties that can be used within a predictive statistical framework.
Why it matches plant phenotyping methodsブドウ葉の形状を擬似ランドマークとプロクルステス解析で定量化し、品種識別・平均葉形状推定に用いる手法の開発が中心である。
abstractHere, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations.
Reproduction assets foundThe paper explicitly links public GitHub repositories and a Dryad DOI containing its leaf photographs, hand-traced landmark/pseudo-landmark raw data, visual-check outputs, interpolation code and outputs, and Procrustes analysis code and outputs — all paper-specific, public, and actionable.Dataset · public) the photo ID of the leaf indicating the vineyard position of
206 the vine it was collected from, 2) an enumerating value 1 through 4 specifying which of four
207 leaves for the variety the data corresponds to, and 3) which vector the data file represents.
208 These files, the raw data, are available at the following link:
209 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/ampel
210 ometry_data. Tracing all data for a single leaf took approximately 15 minutes. Because the data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicthe data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Hunter,
213 2007) to plot the data on the actual photo. The code for plotting vectors onto the original photo
214 can be found here:
215 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/0_visual_check/ampel
216 ometry_visual_check.ipynb. The visual checks for each of the 240 leaves analyzed in this study
217 can be found here:
218 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/outpu
219 t_visual_check
220
5Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicith assigned numbers of points to every vector, interpolation was
240 used to calculate equidistant pseudo-landmarks. A function was created using the scipy
241 (Virtanen et al., 2020) interp1d function to interpolate the correct number of equidistance
242 points for each vector. The code used to interpolate points is here:
243 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/ampe
244 lometry_interpolation.ipynb. The interpolated points can be found here:
245 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/outp
246 ut_interpolated_points.txt
247
248 With corresponding points between all leaves, a Procrustes analysis could be peOpen asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicesults saved as a pairwise distance matrix. The hclust() function in R using the
259 “mcquitty” method was used to hierarchically cluster varieties based on the pairwise distance
260 matrix and overall morphological similarity. The code for performing a Procrustes analysis for
261 each variety and outputs can be found here:
262 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/2_procrustes_by_vari
263 ety
264
6Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicll Procrustes mean
265
shape, super-imposed Procrustes coordinates for all leaves, and eigenvalues and eigenleaves
266
from a PCA. The superimposed Procrustes coordinates of all leaves and the mean shape were
267
plotted together. The code for the Procrustes analysis for all 240 leaves and the outputs can be
268
found here:
269
https://github.com/DanChitwood/grapevine_ampelometry/tree/master/3_overall_procrustes
270
271
Data analysis
272
273
To calculate allometry for each line segment, distances between all points were converted to
274
cm using the pixel to cm scale measured for each leaf. The lm() function in R was used to model
275
the natural log of the distance from each point to the neOpen asset ↗DanChitwood/grapevine_ampelometrypdf-raw-page:7 lines:1-92Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Rapeseed is the second most important oil crop species and is widely cultivated worldwide. However, overcoming the 'phenotyping bottleneck' has remained a significant challenge. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In addition, it is important to explore the dynamic genetic architecture underlying rapeseed plant growth and its contribution to final yield. In this work, a high-throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons. We developed an automatic image analysis pipeline to quantify 43 dynamic traits across multiple developmental stages, with 12 time points. The time-resolved i-traits could be extracted to reflect shoot growth and predict the final yield of rapeseed. Broad phenotypic variation and high heritability were observed for these i-traits across all developmental stages. A total of 337 and 599 QTLs were identified, with 33.5% and 36.1% consistent QTLs for each trait across all 12 time points in the two growing seasons, respectively. Moreover, the QTLs responsible for yield indicators colocalized with those of final yield, potentially providing a new mechanism of yield regulation. Our results indicate that high-throughput phenotyping can provide novel insights into the dynamic genetic architecture of rapeseed growth and final yield, which would be useful for future genetic improvements in rapeseed.
Why it matches plant phenotyping methodsアブラナの高スループット表現型解析施設を用い、自動画像解析パイプラインを開発して43の動的形質を定量化しており、表現型取得・抽出手法が研究の中心である。
abstracta high-throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons.
Reproduction assets foundThe paper explicitly deposits its rapeseed phenotyping data (RGB images, genotypic and phenotypic i-trait data for both growing seasons) in the HZAU plant phenomics database, and its image analysis pipeline source code (LabVIEW, DLL, cpp, test images) on the first author's public GitHub repositories. Both are paper-‐‑Dataset · publice points (every ~7 days starting from 53 to 138 days after sowing). The trials were performed using a randomized block design with five replications in each growing season: 2015–2016 and 2016–2017. The screening generated a total of 1.62 terabytes of RGB images (16,986,93 images; PNG format), which are available in a database ( http://plantphenomics.hzau.edu.cn/search_rape.action , 2015‐2016‐QTL and 2016‐2017‐QTL). A movie of the growth of the recurrent parent and two select ISLs is shown in Movies [Link] , [Link] , [Link] . The inspected lines and inspection dates are shown in Table S1 , where T1‐T12 represent the twelve time points. In our greenhouse experiment, the final yield per plant wOpen asset ↗plantphenomics.hzau.edu.cnlines:165-171Code · publiceasons in this study are available at http://plantphenomics.hzau.edu.cn/search_rape.action under the sections 2015‐2016‐QTL and 2016‐2017‐QTL. The phenotypic data are also shown in Table S13 . All the source code, including that of LabVIEW programs, the dynamic link library, cpp documents and test images, can be downloaded from https://github.com/fenghuifh2006?tab=repositories .
Conflicts of interest
The authors declare that they have no conflicts of interest.
Author Contributions
H.L., H.F. and W.Y. performed the experiments, analysed the data and wrote the manuscript. C.G., S.Y., W.H., X.X., J.L. G.C. and Q.L. assisted in the data analysis and database information construction. W.Y., L.X. Open asset ↗github.com/fenghuifh2006lines:189-223Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Abstract Remote sensing of solar‐induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top‐of‐canopy SIF using ground‐based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco‐physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal‐ and spectral resolution ground‐based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground‐based SIF retrieval. Analysis included field measurements from bi‐hemispherical and hemispherical‐conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer‐based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF‐PAR relationships and diurnal consistency across different sky conditions, while O2A‐based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF‐PAR relationships across sky conditions. Such behavior holds across system configurations, though hemispherical‐conical systems diverge less across sky conditions. O2A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF‐photosynthesis relationships interpreted by previous studies using O2A‐based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer‐based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O2A‐based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far‐red SIF retrievals across sky conditions.
Why it matches plant phenotyping methods高頻度フィールド分光による植物キャノピーの蛍光・光合成関連状態の取得法を、複数のSIF検索手法について系統的に評価・ベンチマークしており、フェノタイピング手法の技術的検証が中心である。
abstractevaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements
Reproduction assets foundThe paper's field spectroscopy data (PhotoSpec and bi-hemispherical system) and SIF retrieval code are explicitly stated to be publicly available at Caltech, Cornell, and GitHub repositories with DOIs.Dataset · publicthe PhotoSpec data used in this study is publicly available
at a data repository hosted at the California Institute of Technology
(https://data.caltech.edu/records/1226) and associated with DOI 10.22002/D1.1226.Open asset ↗data.caltech.edu · 10.22002/D1.1226lines:4420-4536Dataset · publicData from
the bi-hemispherical system is publicly available at a data repository hosted by Cornell
University (https://ecommons.cornell.edu/handle/1813/69711) and associated with DOI
10.7298/wqx5-ba07.Open asset ↗ecommons.cornell.edu · 10.7298/wqx5-ba07lines:4420-4536Code · publicCode used for SIF retrievals can be found on Github
(https://github.com/SunCornell/SIF retrieval methods) and is associated with DOI
10.5281/zenodo.3759965.Open asset ↗github.com/SunCornell/SIF · 10.5281/zenodo.3759965lines:4420-4536Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を画像から自動取得・定量化する撮像システムと解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper explicitly states that the phenoSEED analysis script is publicly available on GitLab and that the BELT-captured seed image datasets are available on the first author's Figshare page. Both are paper-specific, public, and actionable.Code · publicAt the time of publication, a version of the processing script is available at https://gitlab.com/usask-speclab/phenoseed .Open asset ↗usask-speclab/phenoseedlines:145-153Dataset · publicThe image datasets captured by BELT analysed for the sample study are available from https://figshare.com/authors/Keith_Halcro/8363580 . The phenoSEED script is available from https://gitlab.com/usask-speclab/phenoseed .Open asset ↗lines:173-192Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plasmodesmata are small channels that connect plant cells. While recent technological advances have facilitated the analysis of the ultrastructure of these channels, there are limitations to efficiently addressing their presence over an entire cellular interface. Here, we highlight the value of serial block electron microscopy for this purpose. We developed a computational pipeline to study plasmodesmata distributions and we detect presence/absence of plasmodesmata clusters, pit fields, at the phloem unloading interfaces of Arabidopsis thaliana roots. Pit fields can be visualised and quantified. As the wall environment of plasmodesmata is highly specialised we also designed a tool to extract the thickness of the extracellular matrix at and outside plasmodesmata positions. We show and quantify clear wall thinning around plasmodesmata with differences between genotypes, namely in the recently published plm-2 sphingolipid mutant. Our tools open new avenues for quantitative approaches in the analysis of symplastic trafficking. Sentence summary We developed computational tools for serial block electron microscopy datasets to extract information on the spatial distribution of plasmodesmata over an entire cellular interface and on the wall environment the plasmodesmata are in.
Why it matches plant phenotyping methods植物組織の電子顕微鏡画像から原形質連絡の分布や細胞壁厚を定量抽出する計算ツールとパイプラインが研究の中心であり、植物形態状態の測定法に該当する。
abstractWe developed a computational pipeline to study plasmodesmata distributions
Reproduction assets foundThe paper publicly releases its authors' MIB plugins for plasmodesmata distribution and wall-thickness analysis (GitHub), a guided R analysis tutorial/pipeline (GitHub Pages), and the Col-0 SB-EM datasets with segmented wall models and PD annotations (Google Drive), all with explicit availability statements and URLs.Code · publicA guided tutorial with all the necessary
code for this analysis is available at https://andreapaterlini.github.io/Plasmodesmata_dist_wall/Open asset ↗pdf-page:6 lines:1-49Dataset · publicThe Col-0 datasets used in this paper, with corresponding models and
annotation are available from https://drive.google.com/file/d/1g-Open asset ↗pdf-page:6 lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using Multi-Task Learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification. We adopted a modified ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count Mean Squared Error (MSE) by more than 40 %, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75 % fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available.
Why it matches plant phenotyping methods植物画像から複数形質を同時推定するマルチタスク深層学習手法の開発・評価が中心であり、明確な植物フェノタイピング方法論研究である。
abstractWe present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification.
Reproduction assets foundThe paper's authors provide public analysis code (MTL phenotyping framework) on GitHub, and the study analyzes publicly available CVPPP plant image datasets (Ara2013, A1, A4) hosted on plant-phenotyping.org. Both are paper-specific, public, and actionable.Code · publicCode available at https://github.com/andobrescu/Multi_task_plant_phenotyping .Open asset ↗andobrescu/Multi_task_plant_phenotypinglines:224-295Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017-challenge .Open asset ↗lines:607-694Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.
Why it matches plant phenotyping methodsイネ穂の画像セグメンテーション手法を開発し、UAV画像で性能評価しているため、植物形質取得法が研究の中心である。
abstractan unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV)
Reproduction assets foundThe paper's UAV rice panicle image dataset and the authors' MATLAB analysis code are both explicitly stated as publicly available with direct URLs in the Availability of data and materials section.Dataset · publicThe dataset analyzed during the current study are available at https://wuj.hosted.uark.edu/research/datasets/panicle/UBLRPSUI.zip .Open asset ↗lines:221-277Code · publicAlso, the MATLAB code and related materials can be downloaded from https://github.com/i2pt/UBLRPSUI .Open asset ↗i2pt/UBLRPSUIlines:221-277Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 9 Sept 2026
Hyperspectral imaging techniques have been expanding considerably in recent years. The cost of current solutions is decreasing, but these high-end technologies are not yet available for moderate to low-cost outdoor and indoor applications. We have used some of the latest compressive sensing methods with a single-pixel imaging setup. Projected patterns were generated on Fourier basis, which is well-known for its properties and reduction of acquisition and calculation times. A low-cost, moderate-flow prototype was developed and studied in the laboratory, which has made it possible to obtain metrologically validated reflectance measurements using a minimal computational workload. From these measurements, it was possible to discriminate plant species from the rest of a scene and to identify biologically contrasted areas within a leaf. This prototype gives access to easy-to-use phenotyping and teaching tools at very low-cost.
Why it matches plant phenotyping methods低コスト単一画素ハイパースペクトル撮像プロトタイプを開発し、反射率測定を計量学的に検証して植物フェノタイピングへの利用を示しており、測定手法が研究の中心である。
abstractA low-cost, moderate-flow prototype was developed and studied in the laboratory
Reproduction assets foundThe paper's supplementary materials publicly host the two hyperspectral hypercube datasets (leaf discrimination and leaf health-state experiments) directly used for the paper's phenotyping measurements, and the authors' acquisition/reconstruction MATLAB scripts are publicly available on a GitHub repository. Both are onDataset · publicThe following are available online at https://www.mdpi.com/1424-8220/20/4/1132/s1 , Data S1: “discrimination of a leaf within a scene” hypercube, Data S2: “different health states leaf” hypercube, Video S1: “discrimination of a leaf within a scene” hypercubeOpen asset ↗lines:64-76Code · publicAcquisition and reconstruction MATLAB ® scripts are available at https://github.com/mathieuribes/Hyperspectral-Single-Pixel-Imaging- .Open asset ↗github.com/mathieuribes/Hyperspectral-Single-Pixel-Imaging-lines:64-76Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefulness of the multi-view approach and the application of forced air flow to reduce the number of fruit occlusions. The data provided in this article is related to the research article entitled "Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow" [1].
Why it matches plant phenotyping methodsリンゴ果実の位置を含む3D LiDARデータセットを構築し、果実検出、マルチビュー、遮蔽低減の評価に利用できる再利用可能なフェノタイピング基盤であるため。
abstractThis article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth.
Reproduction assets foundThe paper's own LFuji-air dataset (annotated 3D LiDAR point clouds of Fuji apple trees with apple location ground truth) is publicly available at the authors' GRAP-UdL dataset pages, and the authors' point cloud generation and fruit detection code is publicly available on GitHub.Dataset · publicThe repository Lfuji-air dataset ( http://www.grap.udl.cat/en/publications/LFuji_air_dataset.html ) includes 3D LiDAR point clouds of 11 Fuji apple trees ( Malus domestica Borkh. Cv. Fuji) containing 1444 apples ( Fig. 1 ).Open asset ↗Lfuji-air dataset · Lfuji-air datasetlines:61-98Code · publicThe code used to process the row data and generate the georeferenced point clouds has been made publicly available at https://github.com/GRAP-UdL-AT/MTLS_point_cloud_generation .Open asset ↗GRAP-UdL-AT/MTLS_point_cloud_generation · GRAP-UdL-AT/MTLS_point_cloud_generationlines:61-98Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show Hans-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work we introduce the novel setting of explanatory interactive learning (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine and encourages (or discourages, if appropriate) trust into the underlying model.
Why it matches plant phenotyping methods植物フェノタイピング課題を対象に、説明への科学者の介入で深層モデルを修正する新しい機械学習手法を提案しており、方法開発が中心である。
abstractIn this work we introduce the novel setting of explanatory interactive learning (XIL) and illustrate its benefits on a plant phenotyping research task.
Reproduction assets foundThe paper's plant-phenotyping measurements (RGB and hyperspectral images of healthy/Cercospora-inoculated sugar beet leaf discs) are publicly deposited at TU Datalib, and the authors' analysis code, runnable capsule, and pre-trained models are publicly available on Code Ocean. Both are explicitly stated in the Data/CoaDataset · publicnd no post hoc test was performed.
Data availability
The ML benchmark Fashion-MNIST is available at https://github.com/zalandoresearch/fashion-mnist. The
PASCAL VOC2007 dataset is available at http://host.robots.ox.ac.uk/pascal/VOC/voc2007/. The RGB and
hyperspectral data that support the findings of this study are available at https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2278.4 and in the code repository https://codeocean.com/capsule/4559958/tree. The
user study is available at https://github.com/ml-research/xil/tree/master/Trust_Study.Code availability
The code and a fully runnable capsule to reproduce the figures and results of this article, including pre-trained
models, can be Open asset ↗tudatalib · tudatalib/2278.4pdf-raw-page:14 lines:1-41Code · publicailable at https://github.com/zalandoresearch/fashion-mnist. The
PASCAL VOC2007 dataset is available at http://host.robots.ox.ac.uk/pascal/VOC/voc2007/. The RGB and
hyperspectral data that support the findings of this study are available at https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2278.4 and in the code repository https://codeocean.com/capsule/4559958/tree. The
user study is available at https://github.com/ml-research/xil/tree/master/Trust_Study.Code availability
The code and a fully runnable capsule to reproduce the figures and results of this article, including pre-trained
models, can be found at https://codeocean.com/capsule/4559958/tree.Statement of ethical compliance
The aOpen asset ↗codeocean · capsule/4559958pdf-raw-page:14 lines:1-41Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the entity-quality (EQ) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically rich format, as well as numerical vector representations generated using natural language processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.
Why it matches plant phenotyping methods植物表現型記述をNLPで構造化・ベクトル化し、手動キュレーションとの性能比較で検証する計算手法が中心である。
abstractWe computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code on GitHub (irbraun/phenologs) and all files needed to reproduce the results (including the phenotype/EQ datasets used) on Zenodo (doi 10.5281/zenodo.3255020). These are paper-specific, public, and actionable assets for the phenotype-Code · publicThe code used to produce the results of this work is available at github.com/irbraun/phenologs . Files necessary to reproduce the discussed results, datasets used to generate figures presented in this work, and other supplemental files are available at doi.org/10.5281/zenodo.3255020 .Open asset ↗irbraun/phenologs · 10.5281/zenodo.3255020lines:792-814Dataset · publicFiles necessary to reproduce the discussed results, datasets used to generate figures presented in this work, and other supplemental files are available at doi.org/10.5281/zenodo.3255020 . This data repository also includes versions of the previously described datasets available as supplemental data of Oellrich, Walls et al. (2015) and Lloyd and Meinke (2012) , for the purpose of making this study reproducible without any additional external files.Open asset ↗10.5281/zenodo.3255020lines:792-814Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract High‐throughput phenotyping systems provide abundant data for statistical analysis through plant imaging. Before usable data can be obtained, image processing must take place. In this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods. Because obtaining accurate training data is a major obstacle to using supervised learning methods for segmentation, a novel approach to producing accurate labels was developed. We demonstrated that, with careful selection of training data through such an approach, supervised learning methods, and neural networks in particular, can outperform thresholding methods at segmentation.
Why it matches plant phenotyping methods植物画像から背景を分離するセグメンテーション手法を開発・比較し、教師データ生成法も提案しているため、表現型取得の技術が中心である。
abstractIn this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods.
Reproduction assets foundThe paper's DATA AVAILABILITY statement points to the authors' public GitHub repository containing all segmentation analysis code and related data, and to CyVerse Data Commons hosting the raw maize image data used in the study.Code · publicdata were obtained for the rest of the plant. In more challeng-
ing cases of plant segmentation, such as the field environment,
our method serves as an excellent starting point, and its perfor-
mance could be improved by incorporating more training data.
DATA AVAILABILITY
All code along with related data are posted on Github at
https://github.com/jasonradams47/PlantSegmentationCode.The raw image data used in this study are hosted at CyVerse
(Liang & Schnable, 2017).
CONFLICT OF INTEREST
The authors have no competing financial interests.
ORCID
Jason Adams https://orcid.org/0000-0003-2085-4911
Yumou Qiu https://orcid.org/0000-0003-4846-1263
Yuhang Xu https://orcid.org/0000-0003-4351-4602
JamesOpen asset ↗jasonradams47/PlantSegmentationCodepdf-raw-page:10 lines:1-83Dataset · publicThe raw image data used in this study are hosted at CyVerse
(Liang & Schnable, 2017).Open asset ↗pdf-raw-page:10 lines:1-83Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., s p i k e n u m b e r m - 2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. Results In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ( 91.01 % counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on 912 × 1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity , and improving efficiency without sacrificing accuracy . It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.
Why it matches plant phenotyping methodsコムギ穂数という植物形態形質を画像から自動計数する手法を開発し、精度・速度を評価するとともに大規模データセットを構築しており、植物フェノタイピング手法が中心である。
abstractWe thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2.
Reproduction assets foundThe paper explicitly states that the WSC dataset (1764 images, 675,322 annotated wheat spikes) and code/models are made available online at the authors' public URL https://tinyurl.com/TasselNetv2.Dataset · publicThe WSC dataset and other supporting materials are made available online at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:205-231Code · publicCode and models are made available at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:1-72Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.
Why it matches plant phenotyping methodsUAV搭載3D LiDARによる作物高の抽出手法、シミュレーション用ツールチェーン、検証実験、データセットを中心に扱っており、植物表現型取得法が明確に中心である。
abstractWe present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV).
Reproduction assets foundThe authors explicitly release their point cloud processing tools, real-world wheat LiDAR datasets, and simulation farm-generation toolchain on their public GitHub repository. The Turbosquid URL only references the license for commercial third-party soybean 3D models, not a paper-specific asset.Code · publicgorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on our GitHub repository. 1 1
1
https://github.com/hsd1121/PointCloudProcessing
I INTRODUCTION
The goal of precision agriculture is to optimize the growth, maintenance, and harvesting of crops using data-driven technologies [ 1 , 2 ] . This will become especially important as the population grows, leading to a higher demand of efficiency from farms [ 3 , 4 , 5 ] . One way of achieving higher efficieOpen asset ↗hsd1121/PointCloudProcessinglines:1-69Dataset · publicThe dataset released along with this paper has models for three representative environments, simulated 3D LiDAR scans, and ground truth information. This is released for the community-at-large to benchmark their algorithms against.Open asset ↗lines:70-87Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Why it matches plant phenotyping methods圃場ハイパースペクトル反射の時系列特徴量を開発・検証し、コムギの病害存在と重症度を定量化する手法が研究の中心であるため。
abstractwe collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.
Reproduction assets foundThe paper's data availability statement explicitly deposits the datasets generated and analyzed (canopy hyperspectral reflectance, STB scorings, PLACL leaf-scan measurements) in the ETH Zürich research repository with a DOI, and makes all analysis scripts (R/Python, including the stb_placl leaf-image analysis pipeline)Dataset · publicThe datasets generated and analyzed for this study can be found in the ETH Zürich publications and research data repository ( https://www.research-collection.ethz.ch/ ) and can be downloaded from the following link: https://doi.org/10.3929/ethz-b-000370027 .Open asset ↗ETH Zürich publications and research data repository · 10.3929/ethz-b-000370027lines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/Andereggetal2019blines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/stb_placllines:684-694Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
ArabidopsisStem / branchMorphology / geometry measurementGrowth / development / phenology
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis ( Arabidopsis thaliana ). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.
Why it matches plant phenotyping methods幼苗胚軸長を画像から高速・高スループットに推定する深層学習手法の開発であり、植物形質取得が研究の中心です。
abstractTo make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method.
Reproduction assets foundThe paper's authors publicly released their full analysis code (U-Net-based hypocotyl segmentation/measurement pipeline) on GitHub and the training images used for phenotyping on Kaggle. Trained models are only available upon request and are therefore not listed as public assets.Code · publicThe code is fully open source and available at GitHub ( https://github.com/biomag-lab/hypocotyl-UNet ).Open asset ↗biomag-lab/hypocotyl-UNetlines:134-142Dataset · publicImages used for training are also available at https://www.kaggle.com/tivadardanka/plant-segmentation .Open asset ↗tivadardanka/plant-segmentationlines:268-326