Abstract Maize plant plays crucial role not only in the field of agriculture but also in global economy, since it is the third most cultivated crop across the globe. However, these plants are usually affected by various types of diseases such as blight, common rust, gray leaf spot, etc., protecting the plants from these disease is very important. This research proposes a new deep learning model for disease detection to perform well than other models. The hybrid model uses MobileNetV3 as the backbone architecture integrated with attention, fusion and head. The framework includes data preprocessing, feature extraction, classification and interpretation. We will compare the performance of this model with other models such as VGG16, ResNet50, DenseNet121, ALEXNET, etc. criteria for the final evaluation includes Accuracy, Precision, Recall, F1score, Specificity, Logloss, AUC-ROC curve. Through this proposed model we have achieved an accuracy of 98% which is high than the other models compared. The lightweight nature of MobileNetV3 enables us to implement the model in the mobile and IoT devices also. The present study contributes to the development of deep learning model in the field of agriculture, offering a efficient solution for early maize leaf disease detection.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から検出・分類する深層学習手法の開発と比較が中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThis research proposes a new deep learning model for disease detection to perform well than other models.
Reproduction assets foundThe paper's maize leaf disease detection model (MAFH) was trained and evaluated entirely on a public Kaggle image dataset, which the authors explicitly declare in the Data Availability statement. No author code, trained model checkpoints, or other paper-specific assets are stated as publicly available.Dataset · publicig and real-
time datasets and in all the environmental situations.
Funding: This research received no external funding.
Disclosure statement: The authors declare no conflict of interest.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CORN OR
MAIZE LEAF DATASET repository,
https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-datasetOpen asset ↗Kaggle · corn-or-maize-leaf-disease-datasetpdf-raw-page:27 lines:1-34Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Maize is the staple crop for millions of people in Sub-Saharan Africa, particularly for Zambia. Unfortunately, maize crops are exposed to several serious threats due to their susceptibility to foliar diseases like Maize Rust, Leaf Blight, Leaf Spot, Maize Streak Virus, and Maize Lethal Necrosis that may lead to great yield losses. Conventional methods of crop disease identification consist of field surveys that are not only subjective but also difficult to conduct for smallholder farmers. This paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios. The proposed model utilized the concept of transfer learning with ImageNet pre-trained weights and was trained on the Mendeley Maize Crop Disease (Leaf) Dataset which consists of 30,120 images in nine maize disease classes. The developed fine-tuned EfficientNet-B0 yielded 97.57% classification accuracy, macro precision of 97.61%, macro recall of 97.64%, and macro F1-score of 97.61%. From these results, it is evident that transfer learning and fine-tuning greatly boost maize disease classification accuracy while ensuring high computational efficiency. This study makes a significant contribution to precision agriculture as it offers an accurate and computationally efficient AI-based maize disease classification model, which could help smallholder farmers in early maize disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害状態を推定する深層学習モデルを開発・評価しており、植物病害表現型の取得・分類手法が中心的な研究です。
abstractThis paper presents the design and evaluation of a highly optimized version of the EfficientNet-B0 Convolutional Neural Network for the automatic detection of maize leaf diseases using maize leaf images obtained from real-world scenarios.
Reproduction assets foundThe paper's sole qualifying asset is the public Mendeley Maize Crop Disease (Leaf) Dataset of maize leaf images used for all phenotyping/classification measurements, explicitly declared publicly available with a URL. No author analysis code, trained model checkpoints, or other paper-specific assets are disclosed.Dataset · publiconflicts of interest to publish the paper.
Consent to Publish
All authors have read and approved the final version of the manuscript and agree to its
submission to Discover Networks.
Consent to Participate
Not applicable
Data Availability
The Mendeley Maize Crop Disease (Leaf) Dataset used in this study is publicly available at
https://data.mendeley.com/datasets/6w6gsvghfw
Clinical Trial Number
Not applicable.
Ethics Declaration:
Not applicable.
Competing interests
All authors declare no competing interests.Open asset ↗Mendeley · 6w6gsvghfwpdf-raw-page:55 lines:1-22Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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 confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Published18 Aug 2026Journal of King Saud University - Computer and Information SciencesCited by 0 · OpenAlex ↗
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Code / 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 confirmedCrossref · checked 14 Sept 2026
Published11 Aug 2026Engineering, Technology & Applied Science ResearchCited by 0 · OpenAlex ↗
Corn is a staple crop of global significance; however, foliar diseases may lead to 30–60% yield loss if not detected at an early stage. Conventional visual inspection is time-consuming, subjective, and difficult to scale for smallholder farmers worldwide. To overcome these issues, we present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification. CTL-Net, which combines Inception-ResNet-v2 as the backbone and MobileNetV3 as a feature extractor in parallel convolutional streams, simultaneously learns diverse scales of texture information from low-level textures, mid-level structural patterns, and high-level disease semantics of RGB leaf images. Adaptive feature fusion is formulated through learnable weighting coefficients and bi-directional spatial–channel attention mechanisms, which further enhance feature discriminability and robustness. The proposed approach is tested on an extensive dataset of 12,456 images from 10 corn diseases, including Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Cercospora Leaf Spot, acquired under controlled and real-field conditions. CTL-Net attains the highest classification accuracy of 99.42%, outperforming DenseNet121 (97.92%), EfficientNetB3 (97.35%), and general stacking models (97.89%). Robustness experiments demonstrate the effectiveness of the proposed method against illumination variations, additive noise, and partial occlusions. CTL-Net enables real-time inference with a latency of 42 ms on an NVIDIA RTX 3090 GPU. Gradient-weighted Class Activation Mapping++ (Grad-CAM++)-based interpretability analysis results in a mean Intersection over Union (IoU) of 87.6% with expert-annotated disease regions. Five-fold cross-validation, ablation studies, and statistical significance testing (p
Why it matches plant phenotyping methodsトウモロコシ葉画像から病徴・病害状態を推定する深層学習手法を開発し、複数モデルとの比較、頑健性評価、交差検証、アブレーションを行っており、植物フェノタイピング手法が中心である。
abstractwe present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification.
Reproduction assets foundThe paper states its final curated corn leaf disease dataset (12,456 images, 10 classes) is publicly available via the authors' GitHub repository vishruthkp/maizedataset (reference [30] and Data Availability statement). The Kaggle PlantVillage and Corn or Maize Leaf Disease datasets are cited source inputs, not paper-Dataset · public"Prediction of Crop Yield using Machine Learning," International Available: https://github.com/vishruthkp/maizedataset.Open asset ↗vishruthkp/maizedatasetpdf-page:7 lines:1-53Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. "Performer") were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Why it matches plant phenotyping methods固定・ドローン搭載サンプラー、蛍光測定、風況モデルを組み合わせて植物由来の花粉放出率を推定し、花粉測定技術を評価することが中心であるため、植物の生殖状態・放出特性に関するフェノタイピング手法として採用。
abstractPollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices
Reproduction assets foundThe paper's Data Availability statement deposits all sampling data, modeling code, and simulation results on the Virginia Tech Data Repository (DOI 10.7294/25733604), which is an allowed URL. This directly covers the paper's pollen concentration measurements and Lagrangian stochastic dispersal modeling. Other URLs (e.gDataset · publicAll sampling data, modeling code, and simulation results underlying this manuscript are made available on the Virginia Tech Data Repository at https://doi.org/10.7294/25733604 .Open asset ↗Virginia Tech Data Repository · 10.7294/25733604lines:201-219Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published23 Jul 2026Journal of Intelligent Decision Making and Information ScienceCited by 0 · OpenAlex ↗
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effectiveness of disease detection and facilitates well-informed decision-making. But conventional manual inspection techniques are still tedious, unpredictable, and prone to errors. In order to overcome these constraints, this research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time. The proposed YOLOv12-CropNet approach makes use of the Convolutional Block Attention Module (CBAM) for adaptive attention, the Content-Aware Reassembly of Features (CARAFE) up-sampling module to preserve fine-grained disease characteristics, the YOLOv12 architecture improved with Ghost Convolution for effective feature extraction, and Involution layers to capture spatially specific patterns. Inspection techniques are still laborious, arbitrary, and prone to mistakes. A substantial set of data of 38 classes of both healthy and sick leaves from a variety of crops, including tomato, potato, apple, grape, corn, mango and sugarcane, was put together for training and evaluation. Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection. Accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances. The suggested technique provides a scalable and field-deployable way to assist effective identification of diseases and precision agricultural decision-making. The proposed YOLOv12-CropNet model exhibits better performance than the other evaluated models, attaining a 98.45% peak accuracy, 98.10% precision ,98.20 % sensitivity and a 98.18% F1 score, thereby highlighting its efficacy in multi-crop leaf disease detection.
Why it matches plant phenotyping methods複数作物の葉の病徴を画像から検出・分類する深層学習手法を開発し、データセットと性能評価を伴うため、植物病害状態のフェノタイピング手法が中心である。
abstractthis research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time.
Reproduction assets foundThe paper uses public Kaggle datasets as its phenotyping image inputs: the PlantVillage dataset (38 crop-disease classes) and the Sugarcane Leaf Disease dataset, both cited with explicit public URLs. No author code, models, or checkpoints are reported as publicly available.Dataset · public[37] PlantVillage Dataset. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗pdf-page:22 lines:1-61Dataset · public[39] Sugarcane leaf Disease Dataset available online: https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-Open asset ↗pdf-page:22 lines:1-61Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings. Here, we propose Lite U-net FiLM (LUF-net), a lightweight U-net framework integrated with feature-wise linear modulation (FiLM) layers, which incorporates both multispectral-derived irradiance and camera exposure parameters as auxiliary inputs. This metadata-aware design enables dynamic modulation of intermediate image features, allowing the network to disentangle unified canopy color from environmental artifacts. Experiments conducted across diverse crop types (soybean, rice, maize), flight altitudes (6m, 12m, 25m, 40m), and illumination conditions (overcast skies, cloudy, sunny, early morning) demonstrate that the LUF-net model substantially reduces the mean absolute percentage error to below 5.5%, outperforming conventional Gray-world, U-net, AlexNet-MLP, and SIDBlock-MLP methods. Ablation experiments further show that both irradiance and exposure metadata provide complementary information, while FiLM-based conditioning effectively integrates these acquisition parameters into feature learning, jointly contributing to improved color reconstruction performance. Moreover, correlation analysis indicates that the model's color reconstruction errors are weakly dependent on irradiance and exposure settings, confirming that LUF-net reduces sensitivity to external imaging conditions while maintaining physically meaningful and robust corrections.
Why it matches plant phenotyping methodsUAV画像の色校正手法を開発し、複数作物・撮影条件で性能検証しており、作物フェノタイピングの画像取得・補正が中心である。
abstractAccurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings.
Reproduction assets foundThe paper's data and code availability statement explicitly points to a public GitHub repository containing training/evaluation/inference code, pretrained model weights, and example data for the LUF-net color calibration method.Code · publicThe data and source code for model training, evaluation, and inference, together with pretrained model weights, example data, and detailed usage instructions, is publicly available at: https://github.com/wangchufeng3652/color-correction.Open asset ↗wangchufeng3652/color-correctionhtml-lines:278-299Code / 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 14 Sept 2026
Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two detection approaches have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning classifiers utilizing handcrafted features, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of maximum 0.90 on the Receiver Operating Characteristic in one of the classifiers, showing relatively high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect validation accuracy (AUC: 0.99-1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource as a proof-of-concept for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.
Why it matches plant phenotyping methodsトウモロコシの感染症状を画像から自動検出・スコア化する低コスト撮像プラットフォームと機械学習手法の開発が中心であり、植物病害表現型の取得・抽出方法に該当する。
abstractwe developed a rotating camera system that captures videos of plants under customized lighting and shutter settings.
Abstract Zero-shot visual anomaly detection in complex textured domains remains a fundamental challenge for building adaptive, self-organizing cyber-physical systems. Conventional deep learning approaches often rely on closed-set assumptions, require prohibitive pixel-level annotation costs, and suffer severe performance degradation under cross-domain shifts---limiting their deployability in real-world agricultural CPS where novel disease types and unseen crop species continuously emerge. To address these issues, we present TopoLeaf, a training-free and annotation-free anomaly detection framework. By leveraging the robust semantic representations of foundation models (specifically DINOv2), our method introduces two complementary scoring mechanisms: a geometric anomaly score based on local KNN distance in a stability-selected feature subspace, and a topological anomaly score derived from local persistent homology. The topological score effectively captures subtle structural deviations and micro-texture mutations that geometric distances often miss. Extensive experiments on cross-species plant disease benchmarks (3,100+ images across 40 source--target pairs) demonstrate that TopoLeaf achieves highly competitive and structurally robust zero-shot performance, providing a robust perception layer for closed-loop agricultural cyber-physical systems that must maintain diagnostic stability under previously unseen perturbations. Under well-aligned domains, our geometric score achieves near-perfect detection (e.g., 0.994 AUROC on Strawberry). The method exhibits informative failure modes on structurally isolated domains such as Corn (0.169 AUROC), revealing fundamental structural properties of the foundation model's feature manifold. Furthermore, the topological score demonstrates structural complementarity, achieving 0.542 AUROC on the challenging Corn-to-Apple pair where geometric scoring degenerates to 0.221. Module ablation studies confirm that stability-based dimensionality selection consistently improves cross-domain generalization.
Why it matches plant phenotyping methods植物病害の視覚的異常（植物の病徴・状態）を推定する新規画像解析手法を開発し、複数種の病害ベンチマーク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 TopoLeaf, a training-free and annotation-free anomaly detection framework.
Reproduction assets foundThe paper publicly releases its complete TopoLeaf source code (implementation, baselines, evaluation scripts) under the MIT License on GitHub, and all experimental image data derives from the publicly available PlantVillage dataset, which is the leaf-image input used for the paper's anomaly-detection phenotyping and isCode · public427 7.4 Consent to Publish
428 Not applicable.
429 7.5 Data Availability
430 All experimental data used in this study is derived from the publicly available
431 PlantVillage dataset [17], which can be accessed at https://github.com/spMohanty/
432 PlantVillage-Dataset.
433 7.6 Code Availability
434 The complete source code, including implementation of TopoLeaf, baseline com-
435 parisons, and evaluation scripts, is publicly available at https://github.com/
436 Shutong-Hou/TopoLeaf under the MIT License.
437 7.7 Funding
438 This research received no specificOpen asset ↗Shutong-Hou/TopoLeafpdf-layout-page:26 lines:1-44Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
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1件を確認しました。Dataset · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Maize is a globally important food crop, and its yield and quality are vulnerable to various leaf diseases. To address issues such as blurred edges of disease spots and difficulty in small target detection, this study proposes the MAC-YOLO11 model improved on the basis of YOLOv11m. The model introduces the MSPP module to enhance the extraction capability of edge and directional features, and incorporates the spatial position attention module CDSA to strengthen the modeling capability for differences in lesion morphology, texture, and spatial distribution. We designed the APC module to expand the effective receptive field at a low parameter cost through asymmetric convolution branches. The dataset covers 11 categories: northern leaf blight, brown spot, common rust, smut, downy mildew, fall armyworm larval damage, gray leaf spot, maize streak virus disease, adult corn borer damage, corn borer larval damage, and healthy maize leaves. Results show that the MAC deep learning model achieves mAP50 and mAP50:95 of 94.1% and 83.5%, respectively, with overall performance superior to YOLOv11m and other mainstream models. This study provides a technical solution for intelligent identification of maize diseases and holds significant value for disease monitoring and precise control in smart agriculture.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑形態・テクスチャ・空間分布を画像から識別する深層学習モデルを開発・評価しており、植物病害状態の画像ベース表現型計測が中心である。
abstractthis study proposes the MAC-YOLO11 model improved on the basis of YOLOv11m.
Reproduction assets foundThe paper's authors explicitly state that the source code and implementation details of the proposed MCA-YOLO11 maize leaf disease detection model are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated for the 17,729-image maize leaf disease dataset.Code · publicThe source code and implementation details for the proposed model are publicly
available on GitHub at: https://github.com/xuzhiheng0402/MCA-modelOpen asset ↗xuzhiheng0402/MCA-modelpdf-page:18 lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Testing for distinctness, uniformity, and stability (DUS) is a requirement for plant variety registration and based on phenotypic traits, which is time-consuming and sensitive to environmental variation. Advances in genomics allow to complement DUS testing with molecular markers, for which two models in DUS testing were proposed by the Union for the Protection of New Varieties of Plants (UPOV). A use cases was described for maize, but an implementation has been hindered by a lack of suitable markers and validated analytical frameworks. We address these challenges by integrating historical DUS characteristics scores from 352 European hybrid maize varieties with high-density genome-wide single nucleotide polymorphism (SNP) data. Using genome-wide association studies (GWAS), we identified 18 genomic regions and candidate genes associated with 12 DUS characteristics, enabling the development of diagnostic markers consistent with the UPOV model “Characteristic-Specific Molecular Markers”. Since most DUS traits are polygenic, we combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics. This approach achieved strong predictive performance across multiple traits (mean accuracy 0.67), demonstrating its potential for managing reference collections under UPOV model “Combining phenotypic and molecular distances in the management of variety collections”. Both approaches were validated for two characteristics using independent public USDA-NPGS maize datasets (>1,700 accessions) highlighting the value of public data for method validation. We also identify key limitations of historical DUS data, including imbalanced and sparse trait representation, and discuss mitigation strategies. Despite these constraints, our results demonstrate that molecular markers may improve maize DUS testing, enabling faster, more accurate variety registration and supporting accelerated crop improvement. Key message Historical DUS datasets can be used to identify marker-trait associations of DUS characteristics using genome-wide association study (GWAS) and to develop a genomic prediction framework for an accurate prediction of DUS character notes from marker data.
Why it matches plant phenotyping methodsGWASと機械学習によるDUS形質ノート予測フレームワークを開発し、独立データで検証しており、植物表現型評価の技術的手法が中心である。
abstractwe combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics
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1件を確認しました。Code · publicthe R scripts and computational pipelines used for the analysis of genetic and phenotypic variation in both the European maize hybrid panel and the USDA dataset have been deposited in the Zenodo repository (DOI: 10.5281/zenodo.20610279 )Open asset ↗Zenodo · 10.5281/zenodo.20610279lines:213-244Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha−1) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を選定し、農業形質を予測する方法論の開発・評価が研究の中心であり、植物形質の取得・推定に直接関与している。
titleMethodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.
Reproduction assets foundThe paper's supplementary file contains the REML-BLUP adjusted values for all vegetation indices and field variables, which directly reproduce the paper's phenotyping measurements and underpin its computational analysis. The raw UAV imagery and field data are only available on request, and the EstimateBreed R package (Dataset · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15121782/s1 , Table_Supplementary_1. This table contains all vegetation indices and field variables with values adjusted using the RELM-BLUP methodology.
Author Contributions
C.d.S.L.: Conceptualization, methodology, validation, visualization, writing—original draft, writing—review and editing. A.J.T.S.: Data collection and iOpen asset ↗lines:76-146Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesion-profile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
Why it matches plant phenotyping methods植物病斑と病原菌感染ネットワークを対象に、ライトシート顕微鏡とトレーシング法を適応し、感染構造を定量化する指標を開発・適用しており、表現型取得法が研究の中心である。
abstractUsing light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue.
Reproduction assets foundThe paper's Data availability statement explicitly deposits metadata, data, and computer code as Supplementary Files accompanying the open-access publication (Supplementary Materials 1-5, including XLSX datasets and an untyped Supplementary Material 5 likely holding code). These are paper-specific phenotyping assets (eCode · publicMetadata, data, and computer code from this study are available in Supplementary Files included with the publication.Open asset ↗lines:147-204Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Genomic selection (GS) has revolutionized modern breeding by utilizing genome-wide single nucleotide polymorphisms (SNPs). While traditional models such as GBLUP and Bayesian approaches remain prevalent, several deep learning approaches have recently been introduced for plant GS, demonstrating superior predictive performance. Here, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures. It features two distinct architectures: CSA, which combines convolutional neural networks (CNNs) with self-attention to capture local and long-range genomic dependencies, and NSA, which employs a native sparse attention mechanism to enhance computational efficiency by focusing on the most informative features. We evaluated Gsformer on six datasets spanning animal and plant species-pig, cattle, chicken, mouse, wheat, and maize-and compared its phenotypic prediction performance against five established GS methods: DNNGP, MLP, LightGBM, SVR, and GBLUP. Results Gsformer generally ranked among the top two models across six diverse animal and plant genomic prediction datasets. Specifically, Gsformer-CSA yielded notable improvements in predicting cattle fat percentage, while Gsformer-NSA was more accurate in predicting chicken first egg weight, pig age at 100 kg body weight, and mouse anxiety. With the topN hyperparameter set to 20%, Gsformer-NSA matched or marginally exceeded Gsformer-CSA for most traits-though it showed lower accuracy for a subset of traits. Adjusting the topN value further enhanced Gsformer-NSA's performance, allowing it to match that of Gsformer-CSA. Ablation studies confirmed the complementary roles of CNN and self-attention modules in the CSA architecture. To enhance interpretability, we applied SHAP (SHapley Additive exPlanations) to identify influential SNPs and annotate candidate genes associated with growth and body size traits in pigs. Functional enrichment analysis revealed biologically relevant pathways involved in nervous system development, glycolytic process regulation, and digestive tract morphogenesis. Conclusions In summary, Gsformer establishes a flexible and powerful framework for genomic prediction, demonstrating broad applicability across both animal and plant breeding. Owing to its lower computational cost, Gsformer-NSA is recommended over Gsformer-CSA in scenarios where the minor sacrifice in prediction accuracy is acceptable.
Why it matches plant phenotyping methods植物の表現型を予測する深層学習フレームワーク自体を開発し、コムギ・トウモロコシを含むデータセットで既存手法と比較検証しており、表現型推定法が中心である。
abstractHere, we introduce Gsformer, a novel deep learning framework designed to predict phenotypes by modeling complex genetic architectures.
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1件を確認しました。Code · publicThe Gsformer software is available on GitHub at (https://github.com/hajudien/GSformer/tree/master).Open asset ↗https://github.com/hajudien/GSformer/tree/masterhtml-lines:256-299Code / 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 · Crossref · checked 5 Sept 2026
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, the use of large-scale models 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 that 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特異的な光合成動態パラメータを推定するニューラルネットワークであり、植物の生理形質の取得・推定手法の開発と検証が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper's Data Availability Statement provides a public GitHub repository with the authors' custom code for artificial dataset generation, neural network definition/training, and predicted maize genotype parameters. Zenodo datasets (gas exchange measurements and synthetic training data) are mentioned via DOIs but no Code · publicCustom code for the generation of the artificial dataset as well as code for neural model definition and training is available at https://github.com/pwendering/C4TUNE . This repository also contains the predicted parameters for the maize genotypes.Open asset ↗pwendering/C4TUNElines:223-270Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R2 rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m2·m−2. Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval.
Why it matches plant phenotyping methodsLiDAR点群からトウモロコシのLAIを推定する手法を、3D放射伝達モデル、PointNet++、転移学習で開発・検証しており、植物形態形質の取得・推定が研究の中心です。
abstractthis work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques.
Reproduction assets foundThe paper's field-measured LiDAR point cloud and LAI data (Yingke Oasis and Huazhaizi sites) come from a publicly accessible TPDC dataset with an explicit URL in the Data Availability Statement. No author analysis code, trained models, or synthetic dataset deposit is stated.Dataset · public2024WX06.
Data Availability Statement: The dataset used in this study was obtained from the National Tibetan
Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ (accessed on 6 September 2025)), a publicly
accessible scientific data platform providing multi-source geoscientific datasets. The specific dataset
can be accessed via: https://www.tpdc.ac.cn/zh-hans/data/4d60d570-0aa9-417b-8a9d-c32b73b564
(accessed on 6 September 2025). The TPDC database integrates long-term observational and remote
sensing data with standardized quality control, ensuring the reliability and consistency of the datasets
for scientific research.
Acknowledgments: The authors would like to acknowledge the National TibetaOpen asset ↗4d60d570-0aa9-417b-8a9d-c32b73b564pdf-raw-page:19 lines:1-51Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management ( https://maize-nclb.streamlit.app/ ), integrating advanced deep-learning for automated diagnosis and fungicide advisory. Among thirteen Machine-learning and deep-learning models evaluated for classification, the Visual Geometry Group 16-layer convolutional neural network (VGG16) outperformed all others, achieving 94.0% accuracy, with balanced precision, recall, and F1-score of 0.94, and an AUC-ROC of 0.93. Confusion matrix analysis revealed minimal misclassification, with only 12 errors out of 357 samples, confirming the model's high reliability in distinguishing healthy and infected plants, while Grad-CAM visualizations consistently highlighted biologically meaningful lesion regions, supporting the model's interpretability and alignment with plant pathological symptoms. Field validation of DSS-guided fungicide recommendations (Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC) demonstrated significant benefits, reducing disease incidence to 6.8% compared with 67.4% in controls, achieving 90% disease reduction, and enhancing grain yield by 35.4% (8.55 t/ha), with a favorable cost-benefit ratio of 1:2.49. Seasonal disease progression analysis further confirmed DSS effectiveness, with cumulative disease burden reduced by approximately 85% compared with untreated control. These results highlight the potential of integrating deep-learning with field-validated management strategies into a practical DSS, demonstrating its potential for precision disease management in maize.
Why it matches plant phenotyping methods葉の病斑を画像から分類・可視化する深層学習法を開発し、野外で検証した研究であり、植物病害状態のフェノタイピング手法が中心です。
abstractintegrating advanced deep-learning for automated diagnosis and fungicide advisory
Reproduction assets foundThe paper explicitly states that the complete implementation (model training, preprocessing, evaluation, Grad-CAM visualization) and the final trained VGG16 model are publicly available on GitHub, and the deployed Streamlit DSS is publicly accessible. The Scribd link is a cited prior-work bulletin, not a paper-specificCode · publicthe complete implementation, including model training, preprocessing, evaluation, and Grad-CAM visualization, along with deployment instructions, is publicly available at: https://github.com/anuragd02/NCLB-VGG16-Detection.Open asset ↗anuragd02/NCLB-VGG16-Detectionhtml-lines:133-143Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate and real-time detection of maize foliar diseases is important for field disease monitoring and yield protection. However, in complex natural field environments, different diseases often exhibit high visual similarity, and early weak lesions are easily confused with background elements such as dry leaves, soil, and shadows, leading to false positives and missed detections in existing models. To address these challenges, this study proposes an improved lightweight maize foliar disease detection model based on YOLO11, termed CKM-YOLO11. First, a mixed local channel attention mechanism is introduced and adapted to the task in the backbone to construct the C3k2-MLCA module, thereby enhancing joint modeling of local lesion textures, edge details, and global contextual information. Second, a lightweight residual attention module, named MLCA-HeadLite, is designed at the P5 layer of the neck/head to alleviate the suppression of weak lesion responses during deep feature fusion. Experimental results demonstrate that the proposed model achieves an mAP@50 of 81.5% on a self-constructed maize disease dataset with complex field backgrounds, improving mAP@50 and mAP@50-95 by 3.2 and 3.4 percentage points, respectively, compared with the baseline YOLO11, while maintaining a low parameter count and computational cost. Further analyses based on the confusion matrix, comparisons of detection results, and Grad-CAM visualizations indicate that the proposed model performs better in background suppression, retention of weak lesion responses, and robustness in complex scenes. This study provides a reference for the lightweight design of maize foliar disease detection models in complex field environments and their deployment on agricultural edge devices.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から検出する軽量モデルを開発し、データセット上で性能評価しているため、植物病害表現型の取得手法が中心である。
abstractthis study proposes an improved lightweight maize foliar disease detection model based on YOLO11, termed CKM-YOLO11.
Reproduction assets foundThe paper's maize foliar disease detection dataset is built from public image sources (CD&S Dataset from OpenDataLab and PlantDoc-Dataset corn rust leaf folders) that are explicitly cited with public URLs, qualifying as paper-specific public phenotype image inputs. The self-collected images and the authors' code/tranedDataset · publict was constructed using three public-data components together with a small number of self-collected maize leaf images. First, field-acquired maize disease images were obtained from the Corn Disease and Severity (CD&S) Dataset downloaded from OpenDataLab, and only the Dataset_Original folder in the raw dataset package was used ( https://opendatalab.com/OpenDataLab/CD_and_S/tree/main , accessed on 3 May 2026). Second, to supplement the leaf rust category, additional images were collected from the train/Corn rust leaf folder of the PlantDoc-Dataset GitHub repository ( https://github.com/pratikkayal/PlantDoc-Dataset/tree/master/train/Corn%20rust%20leaf , accessed on 3 May 2026). Third, leaf rustOpen asset ↗OpenDataLab/CD_and_Slines:38-47Dataset · publicOpenDataLab, and only the Dataset_Original folder in the raw dataset package was used ( https://opendatalab.com/OpenDataLab/CD_and_S/tree/main , accessed on 3 May 2026). Second, to supplement the leaf rust category, additional images were collected from the train/Corn rust leaf folder of the PlantDoc-Dataset GitHub repository ( https://github.com/pratikkayal/PlantDoc-Dataset/tree/master/train/Corn%20rust%20leaf , accessed on 3 May 2026). Third, leaf rust images from the test folder of the same PlantDoc-Dataset repository were also used ( https://github.com/pratikkayal/PlantDoc-Dataset/tree/master/test , accessed on 3 May 2026). In addition, a small number of self-collected maize leaf images Open asset ↗pratikkayal/PlantDoc-Datasetlines:38-47Dataset · public, additional images were collected from the train/Corn rust leaf folder of the PlantDoc-Dataset GitHub repository ( https://github.com/pratikkayal/PlantDoc-Dataset/tree/master/train/Corn%20rust%20leaf , accessed on 3 May 2026). Third, leaf rust images from the test folder of the same PlantDoc-Dataset repository were also used ( https://github.com/pratikkayal/PlantDoc-Dataset/tree/master/test , accessed on 3 May 2026). In addition, a small number of self-collected maize leaf images were included as negative samples and field-background supplements. Considering that the present study focuses on object detection under complex backgrounds rather than image-level classification under simple-backgOpen asset ↗pratikkayal/PlantDoc-Datasetlines:38-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Ustilago maydis is a biotrophic fungus that causes smut disease in maize, leading to tumor formation on aerial parts of the plant. While U. maydis has been a model for plant-fungal interaction studies, no tool has existed to automatically quantify infection symptoms under laboratory conditions for deep learning analysis. To address this, we developed a rotating camera system that captures videos of plants under customized lighting and shutter settings. These videos were used to train machine learning models to distinguish between healthy and infected plants. Two machine learning models have been presented. In the first approach, by employing a naive masking technique and combining classical machine learning with deep learning classifiers, the model achieved a reasonable performance, with an Area Under the Curve (AUC) of 0.90 on the Receiver Operating Characteristic (ROC), displaying high sensitivity and specificity. The second approach utilizes pre-trained YOLO11 model for object detection and further classification. The YOLO11-based approach outperforms traditional methods, achieving near-perfect accuracy (AUC: 0.99-1.00), demonstrating its superiority for real-time, scalable applications. Our toolset, featuring a cost-efficient and customizable scanning platform with open building-blocks design, provides a valuable resource for unbiased disease symptom detection and scoring, with potential applications in other plant pathology studies. This point enables easy replication and adaptation by other research laboratories which makes the platform robust, scalable and practical beyond our specific application.
Why it matches plant phenotyping methods植物の感染症状を画像から自動検出・定量する低コスト撮像プラットフォームと解析モデルを開発しており、植物表現型取得法が中心的である。
abstractwe developed a rotating camera system that captures videos of plants under customized lighting and shutter settings.
Accurate classification of corn leaf diseases is critical for timely detection and control of pests and diseases. By accurately recognizing different types of leaf diseases, farmers and agricultural experts can quickly take targeted control measures to reduce crop losses and safeguard corn yield and quality. Since these corn leaf disease images usually contain complex backgrounds, similar lesion features, and limited labeling data, it causes traditional convolutional neural networks (CNNs) to easily confuse the lesion region with the background, making it difficult to distinguish between different disease types. To address these limitations, we propose G-ResNet, a hybrid CNN-Vision Mamba network that enhances disease-relevant feature learning through a hierarchical feature attention module and a scale feature attention module. It was demonstrated experimentally that G-ResNet can better classify maize leaf disease images. The code is available at https://github.com/gustafmy/g_resnet.git.
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CNN・Vision Mamba手法の開発と実験評価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。
titleCorn or maize leaf disease classification based on CNN and vision mamba model.
Reproduction assets foundThe paper's analysis code (G-ResNet) is publicly available on GitHub with explicit availability statements, and the plant image dataset used for the disease classification experiments is a public Kaggle dataset explicitly cited in the Experiment section. The underlying data availability statement also mentions request,Code · publicSource code for the algorithms described in this paper is available at https://github.com/gustafmy/g_resnet.git.Open asset ↗gustafmy/g_resnethtml-lines:300-328Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate identification of maize diseases is crucial for safeguarding global food security. Traditional image-based methods often struggle with lighting variations, occlusions, and noise, limiting their robustness and generalisation. Multimodal approaches that integrate visual and textual information have shown promise. However, these methods frequently require manually curated textual descriptions for each image, increasing data collection costs and limiting scalability and practical implementation. To address these limitations, we proposed a maize image-text framework with Cross-Modal Category Alignment (mIT-CMCA). This approach enforces category-level alignment between image and text modalities, enabling more accurate and interpretable cross-modal mapping. First, we construct cross-modal representations by aligning image and text modalities at the category level within a shared embedding space. Second, inspired by contrastive learning, we introduce a Cross-Modal Category Alignment (CMCA) loss based on category-level textual descriptions, reducing annotation complexity. Finally, we present an Efficient Channel-Spatial Hybrid Attention (CSHA) module that preserves inter-class boundaries while incurring minimal computational overhead, thereby enhancing feature discriminability under complex conditions. Experimental results on the maize subset of the PlantVillage dataset (MPVD) show that mIT-CMCA achieves 99.48% accuracy, 99.28% precision, 99.54% recall, and 99.41% F1-score. These results represent improvements of 0.24%, 0.13%, 0.17%, and 0.15% over the strongest vision-only baseline, MaxViT_tiny. On the self-built Maize Leaf-Field dataset (MLFD), the model achieves 93.67% accuracy, 93.76% precision, 93.67% recall, and 93.71% F1-score. It uses only 8.27 million parameters, which is 71.6% fewer than MaxViT_tiny. Its model size is 32.13 MB, which is 72.3% smaller. The proposed method also outperforms comparative models in robustness experiments under artificially added perturbations. These results demonstrate that mIT-CMCA achieves a favorable balance between accuracy and efficiency, making it suitable for practical agricultural deployment.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害状態を推定する画像・マルチモーダル手法の開発と性能評価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 proposed a maize image-text framework with Cross-Modal Category Alignment (mIT-CMCA).
Reproduction assets foundThe paper's maize disease identification analysis code and trained models are explicitly stated as publicly available in a GitHub repository. The phenotype image datasets (MPVD subset and self-built MLFD) are not publicly available and require contacting the corresponding author.Code · publicCode availability
The code and models are available in the GitHub repository at https://github.com/TANGFEILONG626/mIT-CMCA..Open asset ↗TANGFEILONG626/mIT-CMCAhtml-lines:673-695Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年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。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
686
analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
687
Validation; Visualization; Writing – original draft; Writing – review and editing.
688
D t v il ility Part of the datasets generated and analyzed during the current study are
689
publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
691
be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
693
Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Crop diseases significantly threaten global food security by directly affecting the crop yield and quality. The traditional diagnostic methods are labour intensive and human error prone. However, the existing deep learning solutions suffer with poor generalization due to the sharp loss landscapes. The proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM). This method minimizes both the training loss and loss landscape sharpness and enables the model to converge to a flatter-minima with improved generalization. The proposed work is evaluated on 60,000 corn leaf image samples for four classes with 15,000 balanced samples per class after augmentation. The optimized CNN model has achieved 99.66% test accuracy at 0.33% classification error rate and outperforms the conventional optimizers like Adam (98.44% accuracy) and the Stochastic Gradient Descent (SGD). The state-of-the-art analysis presents a 99% average precision rate along with 99.66% F1-score and 0.0013% mean squared error (MSE). The quantized model achieves an inference latency of 22.7 ms/image (≈44 FPS) on a Raspberry Pi 4 and reduces model overfitting and enhances feature discriminability. These results underscore the potential of SAM-based optimization in precision agriculture by driving a scalable automation of disease management. This work bridges the gap between theoretical advances in deep learning optimization and practical deployment in resource-constrained farming environments.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病害を分類するCNNの最適化と性能評価が中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractThe proposed work addresses this limitation and optimizes the Convolutional Neural Network (CNN) using the Sharpness-Aware Minimization (SAM).
Reproduction assets foundThe paper's Data availability statement lists the public corn leaf image datasets used for its disease-classification experiments (Kaggle corn/maize leaf disease dataset, New Bangladeshi crop disease dataset, New Plant Diseases Dataset, and the MahindiNet maize leaf disease dataset on Science Data Bank). No author codeDataset · publicg.; Gireesh Kumar: Formal Analysis, Visualization, Writing – Review & Editing, Resources.
Funding
Open access funding provided by Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf DOpen asset ↗Kagglelines:2568-2620Dataset · publicby Manipal University Jaipur. Open access funding provided by Manipal University Jaipur, Jaipur. No external funding was received for this research.
Data availability
Corn or Maize Leaf Disease Dataset, https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset [ 33 ] New Bangladeshi crop disease dataset. https://www.kaggle.com/datasets/nafishamoin/new-bangladeshi-crop-disease [ 38 ] New Plant Diseases Dataset, https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset [ 46 ] Mohammed Abo-Zahhad et al. (2023). MahindiNet: Maize Leaf Disease Dataset[DS/OL]. V1. Science Data Bank. https://cstr.cn/31253.11.sciencedb.12556 . CSTR:31,253.11.sciencedb.12556 [ 55 ] Open asset ↗Kagglelines:2568-2620Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract The precise classification of plant diseases is crucial for ensuring food security for all people and boosting agricultural productivity. Although there has been significant progress in this field using deep learning approaches, cross-dataset training hasn’t drawn as much attention from researchers as intra-dataset training has. Moreover, very few models have successfully blended intra-dataset and cross-dataset training approaches. This paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations. The model improves feature extraction and classification accuracy across multiple datasets by using attention mechanisms. It was tested on five datasets (Digipathos, Northern Leaf Blight (NLB), PlantVillage, PlantDoc, and the CD&S dataset) that covered leaf diseases of both corn and potatoes. During intra-dataset training, the model achieved the highest classification accuracy of 99.38% when trained on images of potato leaves from the PlantVillage dataset. During cross-dataset training, the model exhibited the highest average classification accuracy of 82.93% for corn leaf diseases when trained on images from the CD&S dataset with their backgrounds removed. When compared to the techniques taken into consideration in this study under comparable experimental conditions, the results demonstrate improved performance. This study shows how the model may be flexible for both intra- and cross-datasets, offering a flexible way to categorize diseases that affect plants. Because of its ability to generalize across different datasets, it may be helpful in real-world agricultural applications with a wide variety of image quality and situations. This encourages the advancement of precision farming techniques and disease control.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類するCNN手法の開発・データセット間検証が中心であり、植物病害の表現型推定に該当する。
abstractThis paper proposes a novel attention-based Convolutional Neural Network (CNN) to overcome these limitations.
Reproduction assets foundThe paper's plant disease classification experiments rely on five publicly available leaf-image datasets, each cited with an explicit public access URL in the reference list: PlantVillage (GitHub), PlantDoc (GitHub), Digipathos (Embrapa), NLB (SciDB), and CD&S (OSF). No author analysis code or trained model checkpoint,Dataset · publicHughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060. Dataset accessed via GitHub: https://github.com/spMohanty/PlantVillage-DatasetOpen asset ↗GitHub · spMohanty/PlantVillage-Datasethtml-lines:1013-1082Dataset · publicDataset available at: https://github.com/pratikkayal/PlantDoc-DatasetOpen asset ↗GitHub · pratikkayal/PlantDoc-Datasethtml-lines:979-1012Dataset · publicCD&S dataset: Handheld imagery dataset acquired under field conditions for corn disease identification and severity estimation. arXiv preprint arXiv:2110.12084. Dataset available at: https://osf.io/s6ru5/files/osfstorageOpen asset ↗OSF · s6ru5html-lines:1013-1082Code / 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
To address the challenge of balancing model lightweight and detection accuracy in maize leaf disease detection, as well as the limitations of edge device deployment resources, we propose an enhanced target detection model, YOLOv11n-DualPC-Lite.Firstly, the C2fDualPConv module was designed, integrating PartialConv to replace some C3k2 modules in the backbone and neck networks. This approach enhances feature representation while reducing the number of parameters. Secondly, the Slim-Neck architecture is introduced in the neck network. To improve accuracy without increasing the number of parameters, the VoVGSCSPC_SimAm module enables the new Slim-Neck structure to reduce parameters while strengthening feature representation. Finally, an EfficientHead detection head is introduced that uses an inverted bottleneck MBConv module to improve performance. This significantly reduces computational load while efficiently extracting features. This study constructed a maize leaf disease dataset integrating a publicly available Kaggle dataset and a field-collected dataset from Anhui Science and Technology University's experimental plots. The dataset includes four categories: Blight, Common_Rust, Gray_Leaf_Spot, and Health. Through techniques such as rotation and gamma correction, the dataset was expanded from 3,876 to 5,165 images for model training and performance validation. Test results show this improved model performs better than other popular lightweight models overall, with a mAP50 score of 90.9%. Meanwhile, the model has only 2.13 million parameters; its computational complexity is reduced to 4.55 G, and the model size is 4.41 MB. Compared with the original YOLOv11n, its mAP50 is 1.9% higher, while the number of parameters is down by 17.8%, computational complexity is cut by 29.3%, and file size is reduced by 15.7%. When run on a Raspberry Pi 5, the model's detection speed reaches 2.3 FPS, an increase of 27.8%. This model achieves a good balance between detection accuracy and lightweight performance for maize leaf diseases, providing an efficient and practical method for real-time crop disease monitoring.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から検出・分類する軽量モデルを開発し、データセット構築、性能比較、エッジデバイス検証まで行っており、植物病害状態の画像ベース表現型取得が中心である。
abstractwe propose an enhanced target detection model, YOLOv11n-DualPC-Lite
Reproduction assets foundThe paper's maize leaf disease detection study uses a public Kaggle maize leaf disease image dataset (Dataset 1) combined with a field-collected dataset. The Kaggle dataset is a public, paper-specific image asset directly used for the model's training and validation. No author analysis code, trained model checkpoints,或Dataset · publicre, the model was successfully run on a Raspberry Pi 5 edge device, realizing stable, real-time detection and providing a workable technical method for field disease monitoring.
2
Materials and methods
2.1
Dataset introduction
The dataset constructed in this study comprises two datasets: Dataset 1 from the Kaggle data website ( https://www.kaggle.com/datasets/hendriyunuswijaya/maize-leaf-disease ) and Dataset 2 collected from the experimental field at Anhui Science and Technology University in Chuzhou City, Anhui Province. Dataset 1 contains a total of 4,188 images, including 1,162 images in the Health category. All images depict only specific regions of healthy maize leaves without complex Open asset ↗Kaggle · hendriyunuswijaya/maize-leaf-diseaselines:46-63Code / 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 · Crossref · OpenAlex · checked 5 Sept 2026
High-density planting is an effective strategy to increase maize yield but imposes greater demands on plant architectural adaptability. To elucidate the structural response mechanisms of maize under varying planting densities, we developed a high-throughput 3D phenotyping system tailored to complex field conditions. High-precision point clouds of field-sampled plants were obtained via multi-view 3D reconstruction. Using a deep learning network, stem and leaf organs were semantically segmented (95.6% accuracy), while leaves were individually separated via clustering (94.8% accuracy). From these data, 31 plant architectural traits and 14 ear-leaf traits were extracted, establishing a hierarchical trait characterization system. Results showed that increased planting density significantly influenced plant architecture reshaping and structural coordination, leading to more compact plant forms and ear height position centralization. Ear leaves exhibited heightened sensitivity to density variation, particularly in leaf area, vertical distribution, and leaf inclination angle, suggesting an early-response role. Principal component analysis and clustering further revealed patterns of structural differentiation and key traits driving these changes under density treatments. The integrated workflow-comprising data acquisition, modeling, segmentation, clustering, trait extraction, and analysis-offers a robust approach for structural phenotyping and intelligent breeding selection in maize and other tall crops. This pipeline provides valuable technical support and data resources for optimizing dense planting strategies and advancing digital agriculture.
Why it matches plant phenotyping methods高スループット3D表現型システムを開発し、点群再構成・器官分割・クラスタリングから多数の植物構造形質を抽出することが中心であるため。
abstractwe developed a high-throughput 3D phenotyping system tailored to complex field conditions
Reproduction assets foundThe paper's authors provide a public GitHub repository for the study's source code (segmentation/trait-extraction pipeline). The phenotype point-cloud dataset itself is only available on request from the corresponding author, so it is not a public asset.Code · publicThe code of this study will be made publicly available upon publication. The source code is available at https://github.com/CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responses .Open asset ↗CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responseslines:330-415Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions.
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1件を確認しました。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate classification of maize yield potential is essential for food security and effective agricultural planning, particularly in regions characterized by environmental variability and socio-economic constraints. This study explores the binary classification of maize kernel weight into low ( n = 160). A Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy. The model's performance was benchmarked against standard classifiers including Logistic Regression, Random Forest, XGBoost, Decision Tree, Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). The proposed HCA-DBN achieved a peak classification accuracy of 94%, demonstrating its potential to outperform conventional baselines even under small sample conditions. Rigorous validation, including bootstrapping and stratified 10-fold cross-validation, confirmed the statistical stability of the results. While these findings serve as a proof-of-concept given the dataset constraints, this study contributes a methodological benchmark for field-based maize yield classification and provides a scalable framework for future validation on larger, multi-season datasets.
Why it matches plant phenotyping methodsトウモロコシの収量ポテンシャル（kernel weight）を分類する計算手法を提案し、複数モデルとのベンチマークおよび交差検証で技術的に評価しているため、植物形質推定法が中心である。
abstractA Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy.
Reproduction assets foundThe paper's maize field phenotyping dataset (plant/ear traits, canopy temperature, chlorophyll from 160 tagged plants at VIT Sevur farm) is explicitly stated as publicly available via a Data in Brief DOI deposit, and the same dataset is cited in the references as a Mendeley Data deposit authored by the paper's authors.Dataset · publicPublicly available datasets were analysed in this study. This data can be found here: https://doi.org/10.1016/j.dib.2024.110367.Open asset ↗html-lines:851-875Dataset · publicRadhakrishnan S., Sandhya P., Venkatramana B., Pradeep Kumar T.
Analyzing various maize varieties grown organically: VIT Vellore’s phenotypic, yield, and canopy data. (2024) 1. Available online at: https://data.mendeley.com/datasets/6py9v57sf2/1Open asset ↗6py9v57sf2/1html-lines:900-924Code / 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 15 Sept 2026
This paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification. This framework relies on a mathematical model based on neural networks that classifies and detects the condition of agriculture, removing the reliance on manual tasks and subjective diagnosis. This paper focuses on three main aspects of our framework: data acquisition, training and prediction. Data is collected using sensors like drones, cameras, and satellite imagery and is pre-processed to filter out noise and improve quality. The training part uses CNN to learn features from the data and become more meaningful. The prediction part of the task classifies, and diagnoses crop health through the trained model using the features. The framework accuracy for crops such as maize, potato, and wheat has been tested and yielded over 90% accuracy. The novelty of this work resides in the development of a multi-modal deep learning architecture that fuses macro-scale satellite imagery with micro-scale drone and IoT sensor data to improve diagnostic reliability. The framework was validated on a multi-source agricultural dataset using a 70% training, 15% validation, and 15% testing protocol. Experimental results demonstrate an accuracy exceeding 90% for staple crops. Using this framework can increase the visibility and quality of information maintained for crop health and improve the decision-making routine of farmers in real time. Additionally, automation of this process can significantly reduce labor costs and increase productivity per crop. Implementing this framework can contribute to precision agriculture and sustainable management practices.
Why it matches plant phenotyping methods作物の健康状態を植物の表現型・状態として推定するマルチモーダル画像・センサ基盤と深層学習手法を開発し、複数作物・データセットで検証しているため、方法が中心である。
abstractThis paper presents a three-phase deep learning framework comprising (i) multi-modal data acquisition from drones and satellites, (ii) standardized pre-processing including interpolation for missing temporal data, and (iii) CNN-based feature extraction for real-time health classification.
Reproduction assets foundThe article's Data availability section points to a public Kaggle dataset used for the crop classification/health diagnosis experiments, matching an allowed URL. No code or model checkpoints are disclosed.Dataset · publicript. The research work was guided by Dr. B.D.K.P. The Corresponding author Shshank Chaube collaborated for review and supervision. All authors reviewed the manuscript.
Funding
Open access funding provided by Symbiosis International (Deemed University). No funds, grants, or other support was received.
Data availability
Dataset: https://www.kaggle.com/datasets/bhagvendersingh/precision-agriculture-dataset .
Declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
References
1. Mohyuddin, G. et al. Evaluation of machine learning approaches for preciOpen asset ↗kaggle · bhagvendersingh/precision-agriculture-datasetlines:473-545Code / 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 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 · checked 14 Sept 2026
Published15 Feb 2026Journal of Artificial Intelligence and Engineering Applications (JAIEA)Cited by 0 · OpenAlex ↗
Monitoring plant health is an important factor in maintaining agricultural productivity. Manual identification of leaf diseases requires expert knowledge and is prone to errors due to visual similarities among disease symptoms. This study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach. The dataset consists of plant leaf images representing three disease classes: Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot. Prior to model training, the images undergo preprocessing steps including image resizing and pixel normalization. The performance of the CNN model is evaluated using a testing dataset that is not involved in the training process, employing accuracy, confusion matrix, precision, recall, and F1-score as evaluation metrics. Experimental results show that the proposed model achieves a test accuracy of 95.56%, with balanced performance across all disease classes. In addition to quantitative evaluation, the trained model is implemented in a Streamlit-based application, allowing users to upload plant leaf images and obtain disease classification results interactively. The findings indicate that the CNN-based approach is effective for plant leaf disease classification and has potential application as an early decision-support system for plant health monitoring.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN分類法を開発し、独立テストデータで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach.
Reproduction assets foundThe paper's phenotyping input is a publicly available PlantVillage image dataset (900 leaf images across three disease classes) obtained from Kaggle, with an explicit authors' URL. No author analysis code or trained model is publicly deposited.Dataset · publicleaf disease images obtained from the PlantVillage Dataset, which is publicly available through the Kaggle platform [17]. The dataset is
organized using a folder-based class structure, where each folder represents a specific leaf disease category. In this study, three disease
classes are used—Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot—with 300 images per class, resulting in a total of
900 images.Open asset ↗Kagglepdf-page:3 lines:1-51Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length ( R 2 = 0.972), ear diameter ( R 2 = 0.905), ear volume ( R 2 = 0.976), ear weight ( R 2 = 0.878), kernel number ( R 2 = 0.98), kernel row number ( R 2 = 0.888), kernel number per row ( R 2 = 0.852), kernel thickness ( R 2 = 0.705), kernel width ( R 2 = 0.515), and thousand kernel weight ( R 2 = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との一致を検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline.
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1件を確認しました。Code · publicAll codes and the manual of command line interface and GUI can be found at the GitHub repository: https://github.com/Chimaco37/OpenEar.Open asset ↗Chimaco37/OpenEarhtml-lines:294-325Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
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1件を確認しました。Dataset · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Code / 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 confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype. To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500–3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500–3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4–768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies. The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features, with performance peaking at optimal context lengths that proved to be trait-dependent (e.g., 1000 bp to 3000 bp), particularly for traits with low-to-moderate heritability (H 2 ∈ (0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (H 2 ∈ (0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers. The proposed feature embedding methods, which leverage DNABERT-2 to capture the contextual features of SNPs, effectively overcome the limitations of traditional prediction models. This study demonstrates that the SNP-context mode is superior for traits with low-to-moderate heritability, while the whole-genome embedding mode excels for highly heritable ones. Our work provides plant breeders with a flexible and powerful analytical framework, enabling them to select the most suitable phenotypic prediction method based on the complexity of the target trait, thereby accelerating genetic gain in the breeding of elite crop varieties.
Why it matches plant phenotyping methodsDNABERT-2を用いた遺伝情報から作物形質を予測する新規特徴埋め込み・機械学習手法の開発と比較評価が中心であり、植物形質推定の計算手法に該当する。
titleCrop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their crop phenotype prediction method (SNP-context and whole-genome DNABERT-2 embedding pipeline) is publicly available on GitHub. Other URLs in the text (samtools, bcftools, pysam, pyfaidx, PyCaret) are generic third-party libraries, not paper-ownCode · publicThe Python implementation of our method is publicly available and downloadable from the GitHub repository: https://github.com/oliveSpring/Crop_DNA_Embedding.git.Open asset ↗oliveSpring/Crop_DNA_Embeddinglines:513-602Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.
Why it matches plant phenotyping methods植物の複雑形質を予測するゲノム・フェノミック選抜用Rパッケージを開発し、データ準備からモデル評価までの再利用可能なワークフローを提供・検証しているため、フェノタイピング関連ソフトウェアとして中心的です。
abstractThis study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction.
Reproduction assets foundThe paper's authors publicly released the MTMEGPS R package (analysis code/workflow) on GitHub, and the independent multi-environment maize validation dataset (phenotypes and genotypes) is publicly available via the Genomes to Fields initiative DOI. Both are paper-specific, public, and actionable.Dataset · publicnal phenotypic information.
2.2
Independent multi-environment maize validation dataset
The datasets analyzed in this study were obtained from the Genomes to Fields (G2F) initiative ( www.genomes2fields.org ). The dataset comprises 135 unique maize hybrids evaluated across nine experimental sites during the 2018 growing season ( https://doi.org/10.25739/anqq-sg86 ). Phenotypic measurements were collected following standardized protocols provided by the G2F consortium, as detailed in the accompanying documentation available on the project website.
The traits evaluated in this study included plant height (distance from the plant base to the ligule of the flag leaf), ear height (distance fOpen asset ↗10.25739/anqq-sg86lines:51-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Crop traits are the integrated outcome of genetic variation, environmental conditions, and their complex interactions, rendering accurate prediction from genetic markers alone a persistent challenge. Here, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes from a multiple-parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model in predicting the photosynthetic rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP enabled us to survey genetic variability in enzyme kinetic parameters, which can be used to identify targets for the improvement of photosynthesis. This approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the accuracy of photosynthetic trait predictions.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワーク自体が研究の中心であり、遺伝マーカーとガス交換測定を統合した植物生理形質の推定手法を開発・評価している。
abstractHere, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes
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1件を確認しました。Code · publicAll codes and data to ensure the reproducibility of the results can be accessed at https://github.com/Rudan-X/KineticGP .Open asset ↗GitHub · Rudan-X/KineticGPlines:231-264Code / 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 15 Sept 2026
Abstract The research develop an accurate and efficient method for detecting multiple corn leaf diseases to support sustainable agricultural practices in Soppeng Regency, Indonesia. The goal is to design a Convolutional Neural Network (CNN) model capable of classifying corn leaf diseases, including rust, blight, and gray leaf spot, using high-resolution image data. The research employed a balanced dataset sourced from open-access repositories, followed by preprocessing, data augmentation, and CNN model optimization. The model’s performance was evaluated using accuracy, precision, recall, and F1-score to ensure comprehensive assessment. Experimental results show that the proposed CNN achieved high accuracy across all disease classes, with strong per-class metrics, indicating robust performance in distinguishing visually similar symptoms. The classification results with the Convolutional Neural Network algorithm have 95% training data accuracy and 93% test data accuracy in detecting leaf diseases in corn plants. The findings contribute to agricultural technology by offering a scalable and field-deployable disease detection system that can be integrated into mobile or edge-based platforms. Limitations include reliance on publicly available datasets, which may not fully capture the variability of local field conditions. The research concludes that the proposed CNN model can significantly enhance early disease detection, reduce dependency on manual inspections, and support precision agriculture. Future research should focus on expanding the dataset with locally captured images, incorporating real-time image acquisition, and optimizing the model for deployment in low-resource environments to improve adaptability and reliability.
Why it matches plant phenotyping methodsトウモロコシ葉の病害症状を画像から分類するCNN手法の開発・性能評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractThe goal is to design a Convolutional Neural Network (CNN) model capable of classifying corn leaf diseases, including rust, blight, and gray leaf spot, using high-resolution image data.
Reproduction assets foundThe paper's phenotyping analysis is based entirely on a public Kaggle corn leaf disease image dataset, explicitly cited with URL and access date in the Data Availability statement and Methods. No author code or trained model is shared.Dataset · publicrch and innovation in the field of agricultural technology and artificial intelligence
Data Availability
The data used in this study comes from a publicly available and curated image repository. The dataset was obtained from the “Corn or Maize Leaf Disease Dataset” . The data source can be seen in the Kaggle – https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset
References
Rozi F, et al. Indonesian market demand patterns for food commodity sources of carbohydrates in facing the global food crisis. Heliyon. 2023;9(6):e16809. 10.1016/j.heliyon.2023.e16809 .
Yu B-G, Chen X-X, Zhou C-X, Ding T-B, Wang Z-H, Zou C-Q. Nutritional composition of maize grain assoOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:224-246Code / 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 confirmedOpenAlex · Crossref · checked 6 Sept 2026
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments.
Why it matches plant phenotyping methods植物の3次元形態を取得・特徴づけるステレオビジョンと深度推定システムの開発が中心であり、明確な植物フェノタイピング手法です。
abstractThis paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants.
Reproduction assets foundThe paper's Data Availability Statement openly deposits the original study data (the 544 stereo RGB maize images and related phenotyping data) on OSF at a DOI, which is a paper-specific, publicly actionable asset. No author analysis code repository is explicitly stated.Dataset · publicData Availability Statement: The original data presented in the study are openly available in OSF at
https://doi.org/10.17605/OSF.IO/MN6P9.Open asset ↗OSF · 10.17605/OSF.IO/MN6P9pdf-page:18 lines:1-59Code / 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 · checked 15 Sept 2026
We present a meticulously curated, long-term (1981-2024) dataset documenting maize phenology dynamics across Northeast China, the nation's most critical commercial grain base. Derived from 61 national agrometeorological stations, it captures the timing of 10 pivotal phenological stages (sowing, emergence, three-leaf, seven-leaf, jointing, tasseling, flowering, silking, milking, maturity) and derives the durations of 4 growth period lengths (sowing-jointing, jointing-silking, silking-maturity, sowing-maturity). The dataset underwent a rigorous, multi-tiered quality control protocol, including automated checks for internal consistency and expert arbitration for ambiguous records, ensuring high integrity. Subsequent analysis employed kernel density estimation to characterize the probability distribution of phenological events and univariate linear regression to quantify decadal trends. The resulting repository is substantial, comprising 976 georeferenced diagnostic plots in JPEG format and two primary data tables in XLSX format, with a total volume of 601.04 MB. Systematically organized by province and station, this dataset serves as a foundational empirical resource for quantifying climate-driven shifts in crop development, enhancing the parameterization and validation of process-based crop models, and informing the development of optimized cultivation practices and regional climate adaptation frameworks.
Why it matches plant phenotyping methodsトウモロコシの複数の生育ステージと生育期間を長期・広域に収録し、品質管理済みデータセットとして構築しているため、植物フェノタイピングデータセットが研究の中心です。
abstractit captures the timing of 10 pivotal phenological stages
Reproduction assets foundThe paper is a data descriptor whose maize phenology dataset (1981–2024, 61 stations, 10 phenological stages, diagnostic plots and XLSX tables) is openly deposited in Science Data Bank. No custom code was created.Dataset · publich stage timing and duration, it empowers farmers and
307
agricultural planners to optimize production systems in response to evolving climatic
308
conditions, thereby enhancing regional food security resilience.
309
Data Availability
310
The dataset generated during this study is openly available in the Science Data Bank at
311
https://doi.org/10.57760/sciencedb.28709 or https://cstr.cn/31253.11.sciencedb.28709.312
Code availability
313
No custom code was created for the production of this dataset.
314
References
315
1.Cai C, Ding T, Chen W. (2024). Potential yield of world maize under global warming based on ARIMA-TR model.
316
Journal of Agrometeorology, 2024, 26(1).
317
2.Li M. RetrospectOpen asset ↗Science Data Bank · 10.57760/sciencedb.28709pdf-raw-page:15 lines:1-94Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Corn is a critical food crop globally, widely cultivated due to its strong adaptability. However, it is susceptible to various diseases, necessitating advanced intelligent detection methods to enhance disease prevention, control efficacy, and production efficiency. Traditional disease recognition models suffer from high computational costs or inadequate feature extraction capabilities, making it challenging to achieve efficient and accurate disease identification in complex environments. To improve the accuracy and efficiency of corn leaf disease identification and to meet the requirements of portable devices, this paper proposes a novel ES-ShuffleNetV2 (Exponential Linear Unit + Spatial Group-wise Squeeze-and-Excitation Block) lightweight recognition model for corn diseases. The proposed model builds upon the ShuffleNetV2 architecture. Firstly, an improved attention mechanism, SGSE, is incorporated immediately following the first convolutional layer to emphasize fine-grained features in corn leaf disease images, enhancing the model's focus on key characteristics. Secondly, the model replaces the ReLU activation function in the down-sampling and basic units with the ELU function, facilitating smoother gradient propagation and faster convergence by allowing a small negative gradient inflow. Additionally, layer pruning techniques are employed to eliminate redundant parameters, reduce model complexity, and enhance operational efficiency on mobile devices. Experimental results demonstrated that the ES-ShuffleNetV2 model achieved recognition accuracy of 97.07%, surpassing the base model's accuracy of 95.43%. After pruning, the new model reduced parameters by 30.45% and FLOPs by 30.26% compared to the original model, meeting the criteria for a lightweight recognition model. Furthermore, the ES-ShuffleNetV2 model outperformed competing models in Accuracy and F1-Score, validating its effectiveness in corn leaf disease recognition and providing valuable insights for future research.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から認識する軽量モデルを開発・評価しており、植物病害フェノタイピング手法が中心です。
abstractExperimental results demonstrated that the ES-ShuffleNetV2 model achieved recognition accuracy of 97.07%
Reproduction assets foundThe paper's Data Availability statement deposits a representative subset of the corn leaf disease image dataset (sample images, partial experimental results, metadata) on Figshare, publicly available. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publico 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 A representative, de-identified subset of the dataset used in this study has been deposited in Figshare under the title Corn Leaf Disease Recognition Dataset and is publicly available at https://doi.org/10.6084/m9.figshare.30264037 . This repository contains sample images, partial experimental results, and metadata for reproducibility. Due to privacy concerns, the full dataset cannot be made publicly available. However, it may be made available upon reasonable request from the Research Center of Intelligent Planting and Processing Technology of Crops Open asset ↗Figshare · 10.6084/m9.figshare.30264037lines:46-55Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In modern agricultural production, accurate monitoring of maize growth and leaf counting is crucial for precision management and crop breeding optimization. Current UAV-based methods for detecting maize seedlings and leaves often face challenges in achieving high accuracy due to issues such as low spatial-resolution, complex field environments, variations in plant scale and orientation. To address these challenges, this study develops an integrated detection and visualization software, DP-MaizeTrack, which incorporates the DP-YOLOv8 model based on YOLOv8. The DP-YOLOv8 model integrates three key improvements. The Multi-Scale Feature Enhancement (MSFE) module improves detection accuracy across different scales. The Optimized Spatial Pyramid Pooling-Fast (OSPPF) module enhances feature extraction in diverse field conditions. Experimental results in single-plant detection show that the DP-YOLOv8 model outperforms the baseline YOLOv8 with improvements of 3.9% in Precision (95.1%), 4.1% in Recall (91.5%), and 4.0% in mAP50 (94.9%). The software also demonstrates good accuracy in the visualization results for single-plant and leaf detection tasks. Furthermore, DP-MaizeTrack not only automates the detection process but also integrates agricultural analysis tools, including region segmentation and data statistics, to support precision agricultural management and leaf-age analysis. The source code and models are available at https://github.com/clhclhc/project.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ個体数と葉数を抽出するソフトウェアを開発しており、植物形質取得が研究の中心です。
abstractthis study develops an integrated detection and visualization software, DP-MaizeTrack
Reproduction assets foundThe paper explicitly states that the authors' source code and trained models for DP-MaizeTrack/DP-YOLOv8 are publicly available on GitHub. No public dataset deposit is stated; the UAV image dataset is described but not declared publicly available.Code · publicThe source code and models are available at https://github.com/clhclhc/project .Open asset ↗https://github.com/clhclhc/projectlines:224-300Code / 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 confirmedEurope PMC · checked 6 Sept 2026
We investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies. Rather than relying on a single vegetation index, we utilize all available data collectively to predict the human-assigned visual score (VS). The conceptual motivation is that when a trained model is available, these predictions may provide a more accurate assessment of disease symptoms than the use of a specific vegetation index (VI). To evaluate the PP approach, we employ the predicted VS in a genome-wide association study (GWAS) and consider strength and position of the detected genetic signal. We use two different sets of predictor variables: i) the five basic wavelengths captured by a multispectral and a thermal camera (basic traits model, BT) or ii) all traits (AT), consisting of the five basic wavelengths plus ten vegetation indices. As statistical methods, we compare a) (linear) ordinary least squares regression (OLS), b) (linear) ridge regression (RR), c) (linear) least absolute shrinkage and selection operator (LASSO) d) an artificial neural network (ANN) and e) a gradient boosted regression tree method (GBRT). Our results indicate that the simple linear OLS regression on the five basic wavelengths (BT-OLS) performs on a level comparable to the best individual vegetation index G. The use of all traits in the OLS regression (AT-OLS) leads to overfitting, which was prevented by the regularization in AT-RR and AT-LASSO. The non-linear ANN approach seems to improve the results further, but the differences between the methods were not statistically significant. The strongest improvement for the purification of the genetic signal was observed when genomic estimated breeding values (GEBVs) for the different traits (VS, basic wavelengths, vegetation indices) instead of their adjusted phenotypes were used. Across all approaches, the combination of GEBVs with Ridge Regression or the non-linear ANN provided the best results.
Why it matches plant phenotyping methodsリモートセンシング画像・センサーデータからトウモロコシさび病の視覚的症状スコアを推定する複数の統計・機械学習手法を比較評価しており、表現型取得・抽出法が研究の中心である。
abstractWe investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies.
Reproduction assets foundThe paper's phenotypic (visual scores, adjusted phenotypes) and remote-sensing (multispectral/thermal) data, plus genomic marker data, are publicly deposited in the CIMMYT Research Data repository. No authors' analysis code or trained model checkpoints are deposited; R packages cited are generic libraries.Dataset · publicWe use the data previously published by Loldaze et al. [ 21 , 22 ], which is available on the CIMMYT Research Data repository at https://hdl.handle.net/11529/10548898 .Open asset ↗CIMMYT Research Data repository · 11529/10548898lines:35-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurately modeling the nonlinear relationships between near-infrared (NIR) spectral signatures and biochemical traits in corn remains a major challenge. A key difficulty lies in capturing multi-scale contextual dependencies-ranging from local absorption peaks to global spectral patterns-that jointly determine quality constituents such as protein and oil. To address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression. SpecTran integrates three key components: adaptive multi-scale patch embedding which extracts spectral features at multiple resolutions to capture both fine and coarse patterns, spectral-enhanced positional encoding which preserves wavelength order information more effectively than standard encoding, and hierarchical feature fusion for robust multi-task prediction. Evaluated on the public Eigenvector corn dataset, SpecTran had a performance across four key traits-moisture, starch, oil, and protein-with an average R2 of 0.483. It reduced the RMSE by 11.2% for protein and 10.7% for oil compared to the best-performing baseline, which is the standard Transformer model. These results demonstrate SpecTran's superior ability to model complex spectral dynamics while providing interpretable insights, offering a reliable framework for NIR-based agricultural quality assessment.
Why it matches plant phenotyping methodsトウモロコシのNIRスペクトルから水分・デンプン・油・タンパク質という種子品質形質を推定するTransformer手法を開発し、公開データセット上でベースライン比較検証しており、形質取得・推定法が研究の中心である。
abstractTo address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression.
Reproduction assets foundThe paper uses the public Eigenvector corn NIR dataset and provides an authors' GitHub repository containing the spectral data, reference trait values, preprocessing scripts, and model implementation code.Dataset · publicnto wavelength-specific contributions for each quality constituent, thereby bridging data-driven prediction with domain knowledge.
2. Materials and Methods
2.1. Materials
Datasets
The corn near-infrared (NIR) spectral dataset used in this study was obtained from the publicly available repository hosted by Eigenvector Research ( http://www.eigenvector.com/data/Corn , accessed on 31 October 2025). This benchmark dataset has been widely adopted in chemometric studies for evaluating multivariate calibration models in agricultural spectroscopy. It comprises NIR absorbance spectra of 80 corn samples, measured in the wavelength range of 1100–2498 nm at 2 nm intervals, resulting in 700 discreteOpen asset ↗lines:27-35Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy ( R2>0.95 ) for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R 2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.
Why it matches plant phenotyping methods葉の光学特性と表現型形質を取得・予測する測定機器、BRDF解析ソフトウェア、機械学習モデルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractthe ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits.
Reproduction assets foundThe paper's BRDF analysis code (adaptive grid search fitting and Roughness Calculator) is publicly available at github.com/PlantSystemsBiology/brdf, and the modified fastTracer ray tracing software used for canopy light simulations is at github.com/PlantSystemsBiology/fastTracerPublic. Phenotype/measurement data are '…Code · publicAn adaptive grid search algorithm was developed in this study, and this algorithm utilized a 2-layered grid (step sizes of 1 × 10 − 2 and 1 × 10 − 4 respectively) structure to incrementally optimize each parameter, providing a more precise approximation of true values. By iteratively narrowing the search range and increasing resolution, this method gradually converges on the optimal solution. The source code of Python for adaptive grid search algorithm was available at https://github.com/PlantSystemsBiology/brdf .Open asset ↗PlantSystemsBiology/brdflines:212-227Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection.
Why it matches plant phenotyping methodsトウモロコシの病徴・病害重症度をUAV画像および衛星データから推定する深層学習・時系列解析パイプラインが研究の中心であり、植物状態の取得・評価手法に該当する。
abstracttraining deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease
Reproduction assets foundThe paper's UAV maize disease imagery is a public Kaggle dataset (corn disease drone images from Cornell's Musgrave Research Farm) explicitly cited as the source of the 9967 images and 42,117 annotations used for training the deep learning classifiers. No author analysis code, trained models, or processed MODIS/weatherDataset · public25. UAV dataset Musgrave Research Farms. Available online: https://www.kaggle.com/datasets/alexanderyevchenko/corn-Open asset ↗Kaggle · alexanderyevchenko/corn-pdf-page:31 lines:57-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するデータセットが研究の中心であり、植物病害フェノタイピング用の画像データセットとして適格です。
abstractThe PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
None
1
Value of the Data
•
The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing yield prediction models for an entire field or individual plots are based on satellite-derived vegetation indices (VIs) and widely used machine learning-based feature extraction models, including principal component analysis (PCA) and autoencoders (AE). Here, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery. Our approach uses a dataset of approximately 4,000 satellite images collected from replicated plots of 84 hybrid maize varieties grown at five distinct locations across the U.S. Corn Belt. By deploying the CAE model, we improve the separation of genotype and environment effects, enabling more accurate incorporation of genotype-by-environment (GxE) interactions for downstream prediction tasks. Results show that the CAE-based features improve early-stage yield predictions by up to 10% compared to traditional autoencoder-based features and outperform vegetation indices (VIs) by 9% across various growth stages. The CAE model also excels in separating environmental factors, achieving a high silhouette score of 0.919, indicating effective clustering of environmental features. Moreover, the CAE consistently outperforms standard models in unseen environments and unseen genotypes yield predictions, demonstrating strong generalizability. This study demonstrates the value of disentangling G and E effects for providing more accurate and early yield predictions that support informed decision-making in precision agriculture and plant breeding.
Why it matches plant phenotyping methods作物プロットの収量という植物形質を、衛星画像から推定する深層学習特徴抽出法を開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractHere, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery.
Reproduction assets foundThe paper's data availability statement provides public access to both the authors' analysis code (Bitbucket repository) and the paper-specific satellite plot-level images with ground-truth yield data (Dryad DOI deposit), directly reproducing this study's phenotyping measurements and analysis.Code · publicAll code is available at bitbucket at https://bitbucket.org/ JS has equity interests in Data2Bio, LLC, and Dryland GeneticsOpen asset ↗pdf-page:14 lines:1-66Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on phenotypes from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their dynamic and cumulative contributions. We introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph consistently outperforms traditional machine learning and graph-based models in both trait completion and yield prediction. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.
Why it matches plant phenotyping methods多段階の植物形質を補完・統合し、収量という植物形質を予測するグラフ手法を開発・ベンチマークしており、形質取得・推定ワークフローが中心です。
abstractWe introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction.
Reproduction assets foundThe paper's authors publicly release their Yield-Graph analysis code on GitHub; the phenotype datasets themselves are only available on request.Code · publicthe
manuscript. All authors read and approved the final manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable
request.
Code availability
The code developed to generate the results and analysis in this article is available at https://github.com/wjhhh2928/Yield-GraphOpen asset ↗https://github.com/wjhhh2928/Yield-Graphpdf-raw-page:14 lines:1-38Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングし、空間的位置と遺伝子型伝達比を解析するプラットフォームおよび統計パイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds. The edge computing-based computer vision approach, termed Edge CV, was developed using the Jetson Nano, embedded cameras, and deployed with the proposed Edge CV model to enable intelligent evaluation with constrained computing resources and GPU power. The Edge CV model: First, leveraging semantic segmentation in computer vision tasks to differentiate between A. flavus -infected and uninfected; Second, utilizing post-processing techniques to accurately separate connected peanut seeds while merging segments belonging to the same ones; Third, analyzing and quantifying infection indices, as well as results presentation. Finally, deep transfer learning was employed to validate the model's transferability for other crop seeds. As a result, Edge CV inference showed agreement with manual measurements (R 2 = 0.901, RMSE = 0.07) and superior consistency, with only a 0.01 % fluctuation compared to 4.2 % for human assessments. Moreover, Edge CV demonstrated its transferability to other crop seeds, such as maize (R 2 = 0.968, RMSE = 0.13) and rice (R 2 = 0.949, RMSE = 0.26). These results underscore the potential of Edge CV as a transferable solution for assessing toxic fungal infections. The approach developed also offers valuable insights for enhancing proximal machine vision, improving the distinction of adjacent seeds, and enabling more accurate calculation of the infection index.
Why it matches plant phenotyping methods種子の真菌感染状態を画像から定量化するエッジコンピューティング画像手法を開発し、手動測定との一致および他作物種への移 transfer 性を検証しており、植物状態の取得・抽出が研究の中心である。
abstractThis study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' data and code for the Edge CV peanut A. flavus infection assessment pipeline.Code · publicThe data and code will be made available on this URL: https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessment.Open asset ↗https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessmenthtml-lines:349-374Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Plant diseases pose a significant threat to global food security and agricultural productivity. In this work, we propose a novel deep convolutional neural network (CNN) model enhanced with Squeeze-and-Excitation (SE) blocks and Attention Gates (AGs) for multi-class plant disease classification across five crops: apple, maize, grape, potato, and tomato. Leveraging a large image dataset and a comprehensive training regime, the proposed model achieves high performance across all metrics, including 99% accuracy, 0.99 F1-score, and strong specificity. Evaluation includes feature visualization and Grad-CAM interpretability. The model's robustness and interpretability make it a compelling solution for practical agricultural applications.
Why it matches plant phenotyping methods植物画像から病害状態を分類するCNN手法の開発と性能評価が中心であり、植物病害フェノタイピングに該当する。
titleAttention-Based Deep Convolutional Neural Networks for Plant Disease Classification
Reproduction assets foundThe paper's plant disease classification experiments are built directly on the public PlantVillage Kaggle image dataset (21 classes across five crops), which is the paper-specific image input for its phenotyping measurements. No author analysis code, trained model checkpoints, or other paper-specific assets are stated.Dataset · publicThe dataset used in this study is a curated subset of the publicly available PlantVillage dataset [21], originally hosted on
Kaggle.Open asset ↗Kagglepdf-page:9 lines:1-26Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeMilletRiceSeed / grainMorphology / geometry measurementSegmentationRoot system architecture
Radicle length is a critical indicator of seed vigor, germination capacity, and seedling growth potential. However, existing measurement methods face challenges in automation, efficiency, and generalizability, often requiring manual intervention or re-annotation for different seed types. To address these limitations, this paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops. The method leverages the Segment Anything Model (SAM) as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids, which are then fed into SAM for precise segmentation of the seed coat and radicle. The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length. Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944, demonstrating excellent segmentation performance. Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error (MAE) was 0.273 mm and the coefficient of determination (R 2 ) was 0.982, confirming the method's high precision for rice. On the Otherseed dataset, the predicted radicle lengths for maize ( Zea mays ), pearl millet ( Pennisetum glaucum ), and rye ( Secale cereale ) are consistent with the observed radicle length distributions, demonstrating strong cross-species performance. These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice, with validated applicability to other crop species.
Why it matches plant phenotyping methodsイネを中心に複数作物の幼根長を画像から自動抽出・推定する手法を開発し、セグメンテーション性能と測定精度を検証しているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops.
Reproduction assets foundThe authors explicitly state that the LenRuler code and software are publicly available on GitHub, providing the paper's radicle-length phenotyping analysis pipeline (SAM-based segmentation, YOLO detection, Gaussian classification).Code · publicThe code and software are available on GitHub at https://github.com/cccccabbage/LenRuler .Open asset ↗cccccabbage/LenRulerlines:351-417Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The vertical distribution of leaves plays a crucial role in the growth process of maize. Understanding the vertical spectral characteristics of maize leaves is crucial for monitoring their growth. However, accurate estimation of the vertical distribution of leaf area remains a significant challenge in practical investigations. To address this, we used a 3D RTM to simulate the layered canopy spectra of maize, revealing the impact of canopy structure on remote sensing penetration depth across different growth stages and planting densities. The results of this study revealed differences in detection depth across growth stages. During the early growth stage, the depth was concentrated in the bottom 1 to 3 leaves of the canopy, reaching 1 to 4 leaves at the ear stage and 1 to 7 leaves during the grain-filling stage. The planting density had a notable effect on the detection depth at the bottom of the canopy. Moreover, compared with the other spectral bands, the near-infrared spectral range exhibited greater sensitivity to density variations. In terms of LAI inversion, a FuseBell-Hybrid model was constructed. We analyzed VIs across different planting density and canopy structural scenarios and found that compared with lower layers, increased density reduced the relative change rate in the upper leaf layers. The sensitivity patterns differed between plant architectures: VIred exhibited density-dependent sensitivity, with distinct responses between plant types, and MTVI2 demonstrated optimal performance for mid-canopy monitoring. This study highlights the influence of the heterogeneous structural characteristics of maize canopies on remote sensing detection depth during different phenological stages, providing theoretical support for enhancing multilayer crop monitoring in precision agriculture.
Why it matches plant phenotyping methods分光計測と3D放射伝達モデルを用いてトウモロコシ冠層の検出深度およびLAI推定法を構築・評価しており、植物形質取得手法が研究の中心である。
abstractTo address this, we used a 3D RTM to simulate the layered canopy spectra of maize
Reproduction assets foundThe paper states its analysis code was uploaded to a public GitHub repository, which qualifies as an authors' public code asset for the LAI phenotyping analysis. No separate phenotype dataset or model checkpoint deposit is explicitly stated.Code · publicData availability
The code have been uploaded to Github: https://github.com/aaawitch/code .Open asset ↗https://github.com/aaawitch/codelines:290-311Code / 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 confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools for meeting this requirement. Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads, their operational flexibility, and their proximity to crops, which allows for higher imaging resolution. In this study, we introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions. The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field phenotyping, with integrated visual and satellite navigation systems enabling autonomous operation. We validated the performance of the robot through a series of experiments involving various crop canopies. By capturing RGB images of rice and wheat, we independently performed wheat ear detection and rice panicle segmentation. For wheat ear detection, we achieve a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 when the YOLOv8m model is used. For rice panicle segmentation, the SegFormer_B0 model yielded a mean intersection over union (mIoU) of 0.949 and an accuracy of 0.987. Additionally, by capturing RGB-D data of maize canopies, we performed 3D reconstructions to calculate plant height, achieving an R 2 of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R 2 of 0.97. Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories, with classification accuracies ranging from 0.977 to 0.996. Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
Why it matches plant phenotyping methods圃場用自律ロボットと複数の画像・分光センシング、形質抽出手法を開発し、作物キャノピーで性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions.
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the data and code supporting this PhenoRob-F phenotyping study; remaining data are available on request.Code · publicPart of the data and code supporting this study are openly available with the following link: https://github.com/balloonhaha/PhenoRob-F. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.Open asset ↗balloonhaha/PhenoRob-Fhtml-lines:193-220Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Wheat and corn are essential crops for global food security, but wheat yellow rust and the corn northern leaf spot are significant threats. Proper assessment of the severity of the disease is the key to effective control and minimizing crop loss. Traditional methods don’t work effectively, and current deep learning models have problems like focusing too little on severity assessment, only being able to be used for a single crop or disease, and relying on small datasets, all of which make them less reliable in the real world. This paper addresses these issues. It introduces WY-CN-NASNetLarge, a deep-learning model based on the NASNetLarge architecture. The model is trained using transfer learning, fine-tuning, and several datasets, such as Yellow-Rust-19, Corn Disease and Severity (CD&S), and PlantVillage. These help the model work well in a variety of disease conditions. Data augmentation, the AdamW optimizer, dropout training, and mixed precision training enhance performance and prevent overfitting. The model has 97.33% accuracy for classifying disease severity. It is higher than ResNet152v2, InceptionResNetV2, and DenseNet201. This approach is effective and quick for identifying multiple diseases and rating their severity. It can also help manage diseases in agriculture and prevent crop loss.
Why it matches plant phenotyping methods植物病害の重症度を画像等から推定する深層学習手法の開発・比較検証が中心であり、植物の病害状態を直接評価するフェノタイピング研究に該当する。
abstractIt introduces WY-CN-NASNetLarge, a deep-learning model based on the NASNetLarge architecture.
Reproduction assets foundThe paper trains its plant disease severity model on three public image datasets (Yellow-Rust-19, CD&S, PlantVillage) with explicit Kaggle/paperswithcode availability links. Two Kaggle-hosted datasets match allowed URLs exactly; the CD&S link in the text does not exactly match an allowed URL entry, and the authors' ownDataset · publicRust-19 dataset [23, 11]: Available at https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-Open asset ↗Kaggle · yellowrust19-yellow-rust-disease-in-pdf-page:36 lines:1-70Dataset · publiclage dataset [32]: Available at https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset. The integratedOpen asset ↗Kaggle · plantvillage-datasetpdf-page:36 lines:1-70Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する統計手法であり、複数種・多数形質で性能評価され、Rパッケージも開発されているため、表現型推定法が研究の中心です。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
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1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:6 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques demand a large quantity of data to train the models for diagnosis and further classification. Few-shot learning has surfaced to remove the drawbacks of deep learning methods. Therefore, the proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning. Metric Learning calculates the distance to measure the similarity between the data points. Hence, addressing the challenge of limited labeled data, GRCornShot effectively classifies healthy and corn diseases. Furthermore, the Gabor filter is incorporated into the backbone network ResNet-50 to extract the texture features and to enhance the classification performance. The experiments show the promising application of few-shot learning in agronomic applications, providing a robust solution for detecting corn diseases precisely with minimal data requirements. Using a 4-way 2-shot, 3-shot, 4-shot, and 5-shot learning strategy, GRCornShot achieves impressive accuracy of 96.19%, 96.54%, 96.90%, and 97.89%, respectively.
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少数ショット深層学習手法を開発し、精度を評価しており、植物フェノタイピング手法が中心である。
abstractthe proposed work presents a novel GRCornShot model for corn disease diagnosis using few-shot learning with Prototypical Networks based on metric learning.
Reproduction assets foundThe paper's corn disease detection experiments use two publicly available image datasets (Roboflow corn-disease and the Kaggle corn/maize leaf disease dataset), explicitly linked in the Data Availability statement. No author analysis code or trained model checkpoint is deposited.Dataset · publiction, Writing—Original draft preparation. J.S. Conceptualization of this study, Editing, Supervision. S.B. Conceptualization of this study, Editing, Supervision. S.K. Funding, Editing, Supervision. All authors reviewed the manuscript.
Data availability
Publicly available datasets were used in this study which can be found here: https://universe.roboflow.com/final-enlye/corn-disease and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset.
Declarations
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.
JayakOpen asset ↗Roboflow · final-enlye/corn-diseaselines:355-376Dataset · publication of this study, Editing, Supervision. S.B. Conceptualization of this study, Editing, Supervision. S.K. Funding, Editing, Supervision. All authors reviewed the manuscript.
Data availability
Publicly available datasets were used in this study which can be found here: https://universe.roboflow.com/final-enlye/corn-disease and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset.
Declarations
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.
Jayakrushna Sahoo, Sivaiah Bellamkonda and Sumit Kumar contributOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:355-376Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して植物形質を推定する統計手法として開発・検証され、Rパッケージも提供されているため、形質取得・推定手法が中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
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1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワークの開発が研究の中心であり、植物生理形質の推定手法として適格。
abstractHere we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes
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1件を確認しました。Code · publicAll codes and data to ensure reproducibility of
the results can be accessed at: https://github.com/Rudan-X/KineticGPOpen asset ↗GitHub · Rudan-X/KineticGPpdf-page:19 lines:1-43Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice and corn hold significant importance due to their daily consumption worldwide. Naked-eye observations are not accurate. Therefore, we need an autonomous system that can accurately detect and classify diseases in both plants. We trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy. We added a new dense layer to the known DCNN-based deep learning models and achieved improved accuracy. The best results were observed when our invariants of the InceptionV3, ResNet152, and MobileNetV2 deep learning models were used on corn plant leaves (98.09%, 98.51%, and 89.73%, respectively). These models also performed well on rice plant leaves (98.51%, 93.59%, and 98.57%, respectively). Because InceptionV3 performed well for both plants, we implemented it in NVIDIA Jetson Nano as an end device for the detection and classification of diseases from both plant leaves. Received: 4 January 2025 | Revised: 26 May 2025 | Accepted: 13 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset. Author Contribution Statement Zubair Saeed: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – original draft, Project administration. Uzma Nawaz: Validation, Formal analysis, Writing – review & editing. Ali Raza: Conceptualization, Validation, Formal analysis, Writing – review & editing, Visualization. Kamran Javed: Validation, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法を開発・検証し、エッジデバイスにも実装しており、植物状態の取得・推定が研究の中心です。
abstractWe trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy.
Reproduction assets foundThe paper's Data Availability Statement explicitly names two public Kaggle image datasets (rice leaf diseases and corn/maize leaf disease) that were used directly as the phenotyping inputs for this study's disease-classification experiments. No author code, models, or checkpoints are reported as publicly available.Dataset · public.
Ethical Statement
This study does not contain any studies with human or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
WritinOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:10 lines:1-81Dataset · publicuman or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
Writing – review & editing. Ali Raza: Conceptualization, Validation,
ForOpen asset ↗Kaggle · smaranjit-ghose/corn-or-maize-leaf-disease-datasetpdf-raw-page:10 lines:1-81Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements.
Why it matches plant phenotyping methodsトウモロコシの気孔を画像から検出するモデルとデータセットを開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractwe curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO
Reproduction assets foundThe paper's analysis code (StomaYOLO detector) is openly available on GitHub with an authors' URL; the phenotype image dataset itself is only available on request from the corresponding author.Code · publicThe code that support the findings of this study are openly available in GitHub at https://github.com/yangziqi2003/StomaYOLO (accessed on 5 May 2025).Open asset ↗https://github.com/yangziqi2003/StomaYOLO · StomaYOLOlines:426-440Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
Why it matches plant phenotyping methodsトウモロコシのハイパースペクトル反射データによる形質推定について、複数の機械学習モデル、未知遺伝子型・季節への汎化性能、データ統合の影響を系統的かつネスト化交差検証で評価しており、フェノタイピング手法の検証が中心である。
abstractWe use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance.
Reproduction assets foundThe paper's data availability statement explicitly provides all code and raw data (hyperspectral reflectance and trait measurements) for reproducibility via the authors' public GitHub repository.Code · publicidge, Cambridge, UK
9
†
These authors contributed equally.
10
*
Corresponding authors.
11
12
Email address:
13
rudan.xu@uni-potsdam.de
14
jfergu@essex.ac.uk
15
jk417@cam.ac.uk
16
nikoloski@mpimp-golm.mpg.de
17
18
Data availability statement
19
All code and raw data to ensure reproducibility of the results can be accessed at:
20
https://github.com/Rudan-X/HyperspectralML
21
22
Funding statement:
23
J.F. was supported by the European Union’s Horizon 2020 research and innovation program
24
grant 862201 (to J.K. and Z.N.). R.X. was supported by the International Max Planck Research
25
School "Molecular Plant Science" between the Max Planck Institute of Molecular Plant
26
Physiology and the UniveOpen asset ↗Rudan-X/HyperspectralMLpdf-raw-page:1 lines:1-71Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Maize crop productivity is significantly impacted by various foliar diseases, emphasizing the need for early, accurate, and automated disease detection methods to enable timely intervention and ensure optimal crop management. Traditional classification techniques often fall short in capturing the complex visual patterns inherent in disease-affected leaf imagery, resulting in limited diagnostic performance. To overcome these limitations, this study introduces a robust hybrid deep learning framework that synergistically combines convolutional neural networks (CNNs) and vision transformers (ViTs) for enhanced maize leaf disease classification. In the proposed architecture, the CNN module effectively extracts fine-grained local features, while the ViT module captures long-range contextual dependencies through self-attention mechanisms. The complementary features obtained from both branches are concatenated and passed through fully connected layers for final classification. Data from Mendeley and Kaggle were used to build and check the model, and the model did this by applying image resizing, data normalization, expanding its training data, and shuffling the data to increase generalization. Additional testing is done on the corn disease and severity (CD&S) dataset, which is separate from the main combined dataset. After validation, the accuracy of the proposed model was 99.15%, and each of its precision, recall, and F1-score equaled 99.13%. To confirm it is statistically reliable, 5-fold cross-validation was performed, reporting on the Kaggle + Mendeley set an average accuracy of 99.06% and on the CD&S dataset 95.93%. As both of these scores are high, it shows that the model works well across other datasets as well. Experiments have shown that Hybrid CNN-ViT works better than standalone CNNs. Dropout regularization and using the RAdam optimizer greatly improved both stability and performance. The model stood out as a reliable, high-accuracy method for discovering maize diseases correctly, which may be valuable in real agricultural settings.
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CNN-ViT手法の開発と、別データセットおよび交差検証による技術検証が中心であるため。
abstractthis study introduces a robust hybrid deep learning framework that synergistically combines convolutional neural networks (CNNs) and vision transformers (ViTs) for enhanced maize leaf disease classification.
Reproduction assets foundThe paper's maize leaf disease classification experiments directly use three public image datasets: the Kaggle corn/maize leaf disease dataset, the Mendeley maize leaf disease dataset, and the CD&S dataset (arXiv). All are explicitly named in the Data Availability Statement with public URLs matching allowed_urls. No作者-Dataset · publicining, validation, and test splits. These measures help the model focus on disease‐specific visual cues rather than dataset‐specific artifacts, improving generalizability in real‐world deployments.
3.1.1.
Corn/Maize Leaf Disease Dataset‐1
The publicly accessible corn or maize leaf disease Dataset, which can be found on Kaggle ( https://www.kaggle.com/datasets/smaranjitghose/corn‐or‐maize‐leaf‐disease‐dataset ), enables the classification of maize leaf diseases. The dataset contains digitally processed maize leaf image files classified into four disease categories which include three illnesses and one category of healthy leaves. The dataset serves as an optimal resource for DL model training Open asset ↗Kaggle · corn‐or‐maize‐leaf‐disease‐datasetlines:88-104Dataset · publicceived no specific funding for this work.
Contributor Information
Ateeq Ur Rehman, Email: 202411144@gachon.ac.kr.
Seada Hussen, Email: seada.hussen@aastu.edu.et.
Data Availability Statement
Datasets used in this study are publically available at https://www.kaggle.com/datasets/smaranjitghose/corn‐or‐maize‐leaf‐disease‐dataset , https://data.mendeley.com/datasets/tywbtsjrjv/1 and https://arxiv.org/abs/2110.12084 .
References
Ahmad, A.
, Saraswat D., Gamal A. E., and Johal G.. 2021. “CD&S Dataset: Handheld Imagery Dataset Acquired Under Field Conditions for Corn Disease Identification and Severity Estimation.”
https://arxiv.org/abs/2110.12084 .
Amin, H.
, Darwish A., Hassanien A. E., and SolimOpen asset ↗Mendeley · tywbtsjrjv/1lines:958-994Code / 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 confirmedarXiv · checked 15 Sept 2026
Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
Why it matches plant phenotyping methodsUAV画像から個体検出・地理空間的形質抽出を行うオープンソース基盤の開発と性能検証が中心であり、植物形質（草丈・NDVI）を抽出する再利用可能なワークフローを提供している。
abstractThis study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction.
Reproduction assets foundThe paper's MatchPlant pipeline code is publicly available on GitHub, and the maize case study training dataset and pre-trained model are publicly available on Zenodo.Dataset · publicinistration, 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
The public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025). The source code and documentation for MatchPlant are available on GitHub at https://github.com/JacobWashburn-USDA/MatchPlant (accessed on February 14, 2025).
Acknowledgments
This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation PrOpen asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVM VI ) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R 2 of 0.86, Mean Absolute Error (MAE) of 1.54 t/ha, and Root Mean Square Error (RMSE) of 2.06 t/ha. Meanwhile, the analysis of the accuracy of CVM VI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVM VI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.
Why it matches plant phenotyping methodsUAVのLiDAR・マルチスペクトル・熱赤外データを統合し、トウモロコシの地上部バイオマスを推定するモデルを開発・比較・評価しており、植物形質取得手法が中心である。
abstractThis study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits authors' model training code and test data at a public GitHub repository, which qualifies as a paper-specific public code asset for the AGB prediction analysis.Code · publicCode and test data for model training are available at https://github.com/Joker1xuan/model_training .Open asset ↗Joker1xuan/model_traininglines:323-356Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The GPS framework was rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP), and a recent phenotype-assisted prediction model (MAK). These models were applied to large datasets from four crop species, maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. Our results indicated that: (1) data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS model (LightGBM) and by 18.7% compared to the best PS model (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism (SNP) density variations, underscoring its adaptability to diverse data conditions. Moreover, the model's accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) Lasso_D demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. This enhancement resulted in only a 0.3% reduction in accuracy compared to predictions generated using data from the same environment, affirming the model's reliability in cross-environmental scenarios. This study provides groundbreaking insights, pushing the boundaries of predictive accuracy, robustness, and transferability in trait prediction. These findings represent a significant contribution to plant science, plant breeding, and the broader interdisciplinary fields of statistics and artificial intelligence.
Why it matches plant phenotyping methods植物の形質予測を目的とするGPSデータ融合フレームワークを開発し、複数作物・モデルで精度、頑健性、環境間移 transferability を評価しており、形質推定手法が研究の中心である。
abstractwe introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion.
Reproduction assets foundThe paper's authors publicly released their GPS analysis scripts on GitHub, and the study used public genomic+phenotypic datasets (rice, maize, wheat, SoyNAM) with explicit URLs. DNNGP and MAK repositories are cited third-party tools, not paper-specific assets.Code · publicThe GPS scripts are available in the release package on GitHub ( https://github.com/Jinlab-AiPhenomics/BioGPS ). All public datasets used in this study are listed in the main text ( Table 1 ).Open asset ↗Jinlab-AiPhenomics/BioGPSlines:244-249Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeSugarcaneTomatoLiDAR / point cloudLeafStem / branchSegmentation
Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 % and 87.15 %, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 %, 85.60 %, 87.94 %, and 81.46 %, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.
Why it matches plant phenotyping methods植物器官の3D点群から茎・葉のインスタンスを抽出する手法を開発・比較検証しており、器官レベル表現型推定のための中心的なフェノタイピング手法である。
abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.
Reproduction assets foundThe authors state their dataset and code were uploaded to a public GitHub repository, and the paper's maize/tomato point cloud inputs come from the public Pheno4D dataset. Both are paper-specific, public, and actionable.Code · publicThe dataset and the code have been uploaded to Github: https://github.com/ice3664/3d-plant-organ-segmentation/tree/master.Open asset ↗https://github.com/ice3664/3d-plant-organ-segmentation/tree/masterhtml-lines:555-579Dataset · publicthe point clouds of maize and tomato were selected from the Pheno4D dataset [25] which can be accessed via https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗html-lines:109-124Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSeed / grainObject detectionGrowth / development / phenology
The germination potential of corn seeds, a key index for assessing their quality and directly associated with the ultimate corn yield, is currently defined in a way that cannot effectively portray the seed germination rate, and the prevalent measurement methods are traditional, consuming substantial process resources. To tackle these issues, this paper employs a public corn seed germination dataset, adds noise to it to simulate real - world production conditions, and ultimately acquires a dataset comprising 8148 images. It then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination. Specifically, the ECA lightweight attention mechanism is introduced to decrease small - target feature loss, assist in accurate target recognition, and remove redundant features; simultaneously, the P2BiFPN multiscale feature fusion technique is utilized to boost the detection ability for small targets; furthermore, the ScConv convolution is adopted to enhance the feature - extraction capacity and improve detection accuracy. Combined with the improved model, this paper also proposed a mathematical modeling algorithmnew method for measuring seed germination potential and observing seed germination rate. The results indicate that the proposed model attains a mean average precision at 50% Intersection over Union (mAP50) value of 98.9%, a mean average precision in the range of 50% - 95% Intersection over Union (mAP50 - 95) value of 95.8%, an accuracy of 96.7%, and a recall of 96.3%. In comparison with the original model, the mAP50 has increased by 0.9% and the mAP50 - 95 value has witnessed a 3.7% increment. The experiments have demonstrated that the research method for germination potential put forward in this paper can effectively depict the rate variation of seeds during the germination process, thus offering a novel perspective for future research on seed germination potential.
Why it matches plant phenotyping methodsトウモロコシ種子の発芽状態・発芽率を画像から検出・定量する改良YOLOv8モデルと数理測定法を開発し、データセット上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。
abstractIt then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination.
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1件を確認しました。Dataset · publicThe dataset used in this experiment can be accessed at ‘ http://dx.doi.org/10.17332/4wkt6thgp6.2 ’.Open asset ↗10.17332/4wkt6thgp6.2lines:333-347Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Photosynthesis is fundamental to agricultural productivity, but its relatively low light-to-biomass conversion efficiency represents an opportunity for enhancement. High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. Methods: To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. Results: Night measurement significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II (Fv/Fm) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130, have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. Discussion: These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.
Why it matches plant phenotyping methods夜間の蛍光測定による光合成形質フェノタイピングを開発・評価し、昼間測定との比較で環境変動低減と遺伝率向上を検証しているため、手法が研究の中心である。
abstractHigh-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity.
Reproduction assets foundThe paper's fluorescence phenotype datasets and analysis outputs (trait QC cutoffs, BLUP/heritability model tables, GWAS significant SNP tables) are stated to be available via the article's online Supplementary Material hosted by Frontiers. No standalone author code repository or named data repository accession appearsSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1595339/full#supplementary-material
References
Ali W. Grzybowski M. Torres-Rodríguez J. V. Li F. Shrestha N. Mathivanan R. K.
. ( 2024 ).
Quantitative genetics of photosynthetic trait variation in maize
. bioRxiv , eraf198 . doi:
10.1101/2024.11.25.625283
PMC12448886
40365812
Alter P. Dreissen A. Luo F.-L. MatsubaOpen asset ↗lines:613-651Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO 2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize ( Zea mays , C4) and basil ( Ocimum basilicum , C3) under different photon densities and spectral compositions. Maize showed the highest CO 2 assimilation rate of 30.99 ± 1.54 µmol CO 2 /(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO 2 /(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO 2 assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.
Why it matches plant phenotyping methodsPAM蛍光データから光合成性能を推定する機械学習モデルを開発・比較評価しており、植物生理形質の取得・推定手法が研究の中心である。
abstractTo improve prediction accuracy, we applied a machine learning model.
Reproduction assets foundThe paper's data availability statement names a public GitHub repository (KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlation) hosting the study's datasets, which underpin the PAM fluorescence/gas-exchange measurements and the Random Forest/XGBoost analysis. The statement does not explicitly distinguish code vs. 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://github.com/KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-CorrelationOpen asset ↗KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlationlines:486-500Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The real-time monitoring and counting of maize seed germination at seedling stage is of great significance for seed quality detection, field management and yield estimation. Traditional manual monitoring and counting is very time-consuming, cumbersome and error-prone. In order to quickly and accurately identify and count maize seedlings in a complex field environment, this study proposes an end-to-end maize seedling plant detection model H-RT-DETR (Hierarchical-Real-Time DEtection TRansformer) based on hierarchical feature extraction and RT-DETR (Real-Time DEtection TRansformer). H-RT-DETR uses Hierarchical Feature Representation and Efficient Self-Attention as the backbone network for feature extraction, thereby improving the network's ability to extract features of maize seedling stage in UAV remote sensing images. Through experiments on the UAV remote sensing data set of maize seedling stage, the mean Average Precision mAP0.5-0.95, mAP0.5 and mAP0.75 of the improved H-RT-DETR model reached 51.2%, 94.7% and 48.1%, respectively, and the Average Recall (AR) reached 68.5%. In order to verify the efficiency of the proposed method, H-RT-DETR is compared with the widely used and advanced target recognition methods. The results show that the detection accuracy of H-RT-DETR is better than that of the comparison methods. In terms of detection speed, the H-RT-DETR model does not require Non-Maximum Suppression (NMS) post-processing operations, the Frames Per Second (FPS) on the test dataset reaches 84f/s, which is 19,12,11 and 21 higher than that of YOLOv5, YOLOv7, YOLOv8 and YOLOX, respectively, under the same hardware environment. This model can provide technical support for real-time detection of maize seedlings under UAV remote sensing images in terms of both detection accuracy and speed (see https://github.com/wylSUGAR/H-RT-DETR for model implementation and results).
Why it matches plant phenotyping methodsUAV画像からトウモロコシ幼苗を検出・計数する深層学習手法を開発し、既存手法と精度・速度を比較検証しているため、植物フェノタイピング手法が中心である。
abstractIn order to quickly and accurately identify and count maize seedlings in a complex field environment, this study proposes an end-to-end maize seedling plant detection model H-RT-DETR
Reproduction assets foundThe authors provide a public GitHub repository containing the H-RT-DETR model implementation and results for maize seedling detection in UAV images. The UAV image dataset itself is not stated as publicly available, and other linked repositories (labelme, YOLOv5, ultralytics) are generic third-party tools, not paper-Code · publicthe test dataset reaches 84f/s, which is 19,12,11 and 21 higher than that of YOLOv5, YOLOv7, YOLOv8 and YOLOX, respectively, under the same hardware environment. This model can provide technical support for real-time detection of maize seedlings under UAV remote sensing images in terms of both detection accuracy and speed (see https://github.com/wylSUGAR/H-RT-DETR for model implementation and results).
Keywords
Maize seedling stage UAV remote sensing RT-DETR Target recognition Real-time detection Plant counting the National Key Research and Development Program of China 2023YFD1900704 2023YFD1900704 pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmOpen asset ↗wylSUGAR/H-RT-DETRlines:1-26Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detection of the intensity of an infection would enable the efficient allocation of treatment resources and prevent the infection from spreading across the entire area. In this study, deep saliency map segmentation-based CNN is utilized for the detection, multi-class classification, and severity assessment of maize crop leaf diseases has been proposed. The proposed model involves seven different maize crop diseases such as Northern Leaf Blight Exserohilum turcicum , Eye Spot Oculimacula yallundae , Common Rust Puccinia sorghi , Goss’s Bacterial Wilt Clavibacter michiganensis subsp. nebraskensis , Downy Mildew Pseudoperonospora , Phaeosphaeria leaf spot Phaeosphaeria maydis , Gray Leaf Spot Cercospora zeae-maydis , and Healthy are selected from publicly available datasets obtained from PlantVillage. After the disease-affected regions are identified, the features are extracted by using the EffiecientNet-B7. To classify the maize infection, a hybrid harris hawks’ optimization (HHHO) is utilized for feature selection. Finally, from the optimized features obtained, classification and severity assessment are carried out with the help of Fuzzy SVM. Experimental analysis has been carried out to demonstrate the effectiveness of the proposed approach in detecting maize crop leaf diseases and assessing their severity. The proposed strategy was able to obtain an accuracy rate of around 99.47% on average. The work contributes to advancing automated disease diagnosis in agriculture, thereby supporting efforts for sustainable crop yield improvement and food security.
Why it matches plant phenotyping methodsトウモロコシ葉の病変領域を画像から分割し、病害分類と感染重症度を推定する手法が研究の中心であり、植物の病害状態を直接評価しているため。
abstractIn this study, deep saliency map segmentation-based CNN is utilized for the detection, multi-class classification, and severity assessment of maize crop leaf diseases has been proposed.
Reproduction assets foundThe paper's Data Availability statement lists four public image datasets used directly for its maize leaf disease classification and severity assessment experiments. No author analysis code or trained model checkpoints are disclosed.Dataset · publiccy needs to be improved by cascading various deep learning approaches with advanced fusion aware techniques.
Data Availability
The dataset utilized for this work was compiled from publicly available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset
Funding Open asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:860-878Dataset · publicilability
The dataset utilized for this work was compiled from publicly available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset
Funding Statement
The author(s) received no specific funding for this work.
References
1. United Nations, Department of EconoOpen asset ↗GitHub · xtu502/maize-disease-identificationlines:860-878Dataset · publicy available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset
Funding Statement
The author(s) received no specific funding for this work.
References
1. United Nations, Department of Economic and Social Affairs, Population Division. World population prospectOpen asset ↗corn-leaf-disease/downy-mildewlines:860-878Code / dataset availability confirmedCrossref · checked 13 Sept 2026
To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep learning model size and the completion of the overall shape of the corn. In this work, the 3D corn models derived from segmentation are employed to systematically output the fragmented point cloud data in batches. The Shape Coding PointAttN (SCPAN) algorithm is also proposed, which is based on PointAttN. The model’s structure is simplified to output sparse point clouds and minimize computational complexity, and a gated multilayer perceptron (MLP) containing 3D position coding is introduced to enhance the model’s spatial awareness. In addition, the prior shape encoder module is initially trained and subsequently integrated into the model to enhance its focus on shape characteristics. Compared to the original model, PointAttN, SCPAN achieves a 34.2% reduction in the number of parameters, and the inference time is reduced by 30 ms while maintaining comparable accuracy. The experimental results show that the proposed method can complete the corn point cloud more effectively, using a small model to help estimate the pose and dimensions of corn accurately. This work supports the precise phenotypic analysis of corn and similar crops, such as citrus and tomatoes, and promotes the development of smart agricultural technology.
Why it matches plant phenotyping methodsトウモロコシの遮蔽点群を補完し、形状・姿勢・寸法を推定する計算手法の開発が中心であり、植物表現型取得への応用も明示されている。
abstractTo obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm
Reproduction assets foundThe paper's Data Availability Statement points to an authors' GitHub repository for the corn point cloud completion code (SCPAN). The phenotype dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publiccorresponding author upon reasonable request. The related code will be released at https://github.Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract The identification of plant diseases has become increasingly challenging due to the interference of complex backgrounds in images, which often hinders the accuracy of classification models. Recent studies have employed various Deep Learning (DL) techniques to overcome this issue, utilizing both publicly available and custom datasets. However, achieving high accuracy while managing background complexity remains a significant hurdle. This paper aims to address this challenge by introducing a two-step DL approach for plant disease classification. The approach begins with an enhanced Convolutional Neural Network (CNN), developed through a comparative analysis of several CNN architectures, including customized and cascaded versions of prominent DL models, achieving an accuracy of 93.3%. To further enhance accuracy, segmentation techniques such as DeepLabV3+, UNet, Iterative UNet, and UNet with Atrous Spatial Pyramid Pooling (ASPP) are integrated before applying customized CNN architectures. These segmentation methods effectively isolate diseased portions of leaf images, improving classification performance. The proposed methodology introduces model pruning to optimize performance and computational efficiency by removing redundant parameters and less significant features. The UNet with ASPP architecture, in combination with pruning strategies, significantly reduces time complexity and feature redundancy, leading to an impressive accuracy of 99.8%. This approach outperforms other existing models in terms of accuracy and efficiency. The model is trained on the Plant Village dataset, which includes 10 different diseases across plant species such as tomato, corn, and potato, offering a comprehensive solution for plant disease identification.
Why it matches plant phenotyping methods植物病葉画像から病変部を分離し、分類する深層学習手法の開発が中心であり、植物の病害状態を直接推定するため、方法論文として採用。
abstractThis paper aims to address this challenge by introducing a two-step DL approach for plant disease classification.
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1件を確認しました。Dataset · publicThe manuscript data set can be freely downloaded from
https://www.tensorflow.org/datasets/catalog/plant_villageOpen asset ↗plant_villagepdf-page:33 lines:1-34Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
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 the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping. Using genetic markers and data from high-throughput phenotyping of a maize multiparent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, we show that dynamicGP outperforms a baseline genomic prediction approach for the 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 environment over plant 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、植物形態・幾何・色彩形質の時系列を予測する計算手法dynamicGPが研究の中心であるため。
abstractHere we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their dynamicGP R implementation on GitHub and mirrored code plus genotyping data on Zenodo, and the maize HTP phenotype dataset is deposited on e!DAL (IPK). All are paper-specific, public, and directly actionable.Code · publicAn R implementation of algorithms 1 and 2 is available at https://github.com/dobby978/dynamicGP .Open asset ↗dobby978/dynamicGPlines:134-161Code · publicAll code that was used to generate the results of this study is available via GitHub at https://github.com/dobby978/dynamicGP and via Zenodo at https://doi.org/10.5281/zenodo.14959484 (ref. 32 ).Open asset ↗Zenodo · 10.5281/zenodo.14959484lines:181-276Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphological changes hinder precise posture analysis. To address these challenges, we propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data from a"keypoint-line"annotated phenotypic database which could mitigate the effects of uneven manual annotations and biases. PFLO also incorporates advanced architectural enhancements to optimize feature extraction and selection, enabling robust performance in complex conditions such as dense arrangements and severe occlusions. On a fivefold validation set of 1,862 images, PFLO achieved 72.2% pose estimation mean average precision (mAP50) and 91.6% object detection mean average precision (mAP50), outperforming current state-of-the-art models. The model demonstrates improved detection of occluded, edge, and small targets, accurately reconstructing skeletal poses of maize crops. PFLO provides a powerful tool for real-time phenotypic analysis, advancing automated crop monitoring in precision agriculture.
Why it matches plant phenotyping methodsトウモロコシの姿勢という植物表現型を、圃場画像から推定するモデルとデータ処理法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data
Corn seed breeding is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for corn kernel recognition and classification has great potential in terms of constructing environmentally friendly agriculture, and saving manpower. Existing segmentation methods that utilize U-shaped architectures typically operate by processing images in discrete pixel-based segments. This approach often overlooks the finer pixel-level structural details within these segments, leading to models that struggle to preserve the continuity of target edges effectively. In this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI, which aims to integrate MSADI module into the encoder and decoder of the VMUnet architecture. Our VMUnet-MSADI model benefits from self-attention computation in VMUnet and multi-scale coding to effectively model non-local dependencies and context relationships at the scale layer, thus improving the segmentation quality of different images. Unlike previous Unet-based improvement schemes, the proposed VMUnet-MSADI adopts a multiscale convolutional attention module coding mechanism at the depth level and an efficient multiscale deep convolutional decoder at the spatial level to extract coarse-grained features and fine-grained features at different semantic scales and effectively avoid the loss of information at the target boundary to improve the quality and accuracy of target segmentation. We introduce a Visual State Space (VSS) block to capture a wide range of contextual information and a Detail Infusion Block (DIB) to enhance the fusion of low-level and high-level features, which further fills in the remote contextual information during the up-sampling process. Comprehensive experiments were conducted on open-source datasets and the results demonstrate that the VMUnet-MSADI model excels in the task of corn kernel segmentation. The model achieved a segmentation accuracy of 95.96%, surpassing the leading method by 0.9%. Compared to other segmentation models, our method exhibits superior performance in both accuracy and loss metrics. Extensive comparative experiments conducted on various benchmark datasets further substantiate that our approach outperforms the state-of-the-art models. Code, pre-trained models and data processing protocols are available at https://github.com/corbining/VMUnet-MSADI .
Why it matches plant phenotyping methodsトウモロコシ種子画像から不健全カーネルを抽出する新規セグメンテーション手法の開発・ベンチマークが中心であり、画像ベースの植物器官状態の表現型取得に該当する。
abstractIn this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI
Reproduction assets foundThe paper reports corn kernel segmentation experiments and explicitly states that code, pre-trained models, and data processing protocols are publicly available at the authors' GitHub repository, which is a paper-specific actionable asset. The corn kernel dataset itself is described as open-source from GaoZhe Tech but,Code · publicCode, pre-trained models and data processing protocols are
available at https://github.com/corbining/VMUnet-MSADI.Open asset ↗corbining/VMUnet-MSADIpdf-page:1 lines:1-57Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
MaizeTomatoGreenhouseLiDAR / point cloudLeafStem / branchSegmentation
Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. Furthermore, their heavy reliance on high-quality annotated data restricts their use in high-throughput settings. To address these limitations, we propose a novel multi-stage region-growing algorithm based on an octree structure for efficient stem-leaf segmentation in maize point cloud data. The method first extracts key geometric features through octree voxelization, significantly improving segmentation efficiency. In the region-growing phase, a preliminary structural segmentation strategy using fitted cylinder parameters is applied. A refinement strategy is then applied to improve segmentation accuracy in complex regions. Finally, stem segmentation consistency is enhanced through central axis fitting and distance-based filtering. In this study, we utilize the Pheno4D dataset, which comprises three-dimensional point cloud data of maize plants at different growth stages, collected from greenhouse environments. Experimental results show that the proposed algorithm achieves an average precision of 98.15% and an IoU of 84.81% on the Pheno4D dataset, demonstrating strong robustness across various growth stages. Segmentation time per instance is reduced to 4.8 s, offering over a fourfold improvement compared to PointNet while maintaining high accuracy and efficiency. Additionally, validation experiments on tomato point cloud data confirm the proposed method’s strong generalization capability. In this paper, we present an algorithm that addresses the shortcomings of traditional methods in complex agricultural environments. Specifically, our approach improves efficiency and accuracy while reducing dependency on high-quality annotated data. This solution not only delivers high precision and faster computational performance but also lays a strong technical foundation for high-throughput crop management and precision breeding.
Why it matches plant phenotyping methodsトウモロコシの3D点群から茎・葉を分割し、表現型パラメータ抽出を可能にするアルゴリズムを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstract3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction.
Reproduction assets foundThe paper's stem-leaf segmentation experiments are performed on the public Pheno4D maize/tomato point cloud dataset, which the authors explicitly state is publicly available at the IPB Bonn URL. No author analysis code or trained models are reported as publicly released.Dataset · publicThe dataset is available at https://www.ipb.uni-bonn.de/data/pheno4d/ (accessed on
20 January 2025).Open asset ↗Pheno4Dpdf-page:4 lines:1-52Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Code / 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 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 confirmedCrossref · checked 14 Sept 2026
Published27 Feb 2025Journal of Information Systems Engineering and ManagementCited by 0 · OpenAlex ↗
The rapid advancement of artificial intelligence has paved the way for innovative solutions in agriculture, particularly in crop disease detection. Diagnosing plant diseases often rely on manual inspection and expert knowledge that time-consuming and prone to errors. As agriculture faces increasing challenges from pests, diseases and climate change, there is a pressing require for efficient, automated systems to monitor crop health. In this manuscript, Development of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images is (PCNN-ResNet-COA) proposed. Initially the data is collected from Plant Disease Classification Merged Dataset. This dataset includes both healthy and diseased leaves for various crop types. The collected images are organized into categories based on the crop type such as crop, grape and soybean and disease condition healthy or various types of diseases. Then the categorized images are fed to Pyramid Convolutional Neural Network with Residual Network (PCNN-ResNet), for identifying and classifying the Leaf Images as Corn_healthy, Corn_northern_leaf_blight, Corn_gray_leaf_spot, Grape_healthy, Grape_leaf_blight, Grape_black_rot, Grape_black_measles, corn_common_rust, soybean_bacterial_blight, Soybean_downy_mildew, Soybean_mosaic_virus, Soybean_powdery_mildew, Soybean_healty, Soybean_rust, and Soybean_southern_blight. In general, PCNN-ResNet does not express any adaption of optimization methods for determining optimal parameters to assure precise detection and classification of Leaf Images. Coati Optimization Algorithm (COA) is proposed for improving the weight parameter of PCNN-ResNet classifier that accurately predicts crop yield. The proposed PCNN-ResNet-COA method is implemented and analyzed with help of performance metrics like accuracy, precision, F1-score, computational time is evaluated. The proposed ResNet-COA approach attains 18.97%, 24.57% and 32.68% higher accuracy and 19.84%, 24.93% and 31.62% lower computational time with existing method respectively.
Why it matches plant phenotyping methods葉画像から植物病害状態を自動分類する新規CNN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す。
titleDevelopment of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images
Reproduction assets foundThe paper's leaf-image disease classification experiments are built entirely on the public Kaggle 'Plant Disease Classification Merged Dataset' (88 classes, >76,000 images), which is explicitly cited with its Kaggle URL in the references. No author code, models, or checkpoints are reported as available.Dataset · publicThe input data are obtained from Plant Disease Classification Merged Dataset [17]. A huge number of images, at
least one healthy plant and one disease per plant, the most prevalent diseases, annotated images, laboratory and
field photographs, significant staple foods and the plant species with the highest worldwide production were the
self-imposed conditions for the dataset.Open asset ↗Plant Disease Classification Merged Datasetpdf-layout-page:4 lines:1-64Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Published26 Feb 2025TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 8 · OpenAlex ↗
Key message Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.
Why it matches plant phenotyping methods単一種子NIRSを用いた非破壊フェノタイピングと予測モデルを開発・適用し、圃場形質および発芽を観測値で検証しているため、植物表現型取得・推定法が研究の中心です。
abstractHere, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits.
Reproduction assets foundThe paper explicitly states that all code and data used in the analyses are publicly available in the authors' GitHub repository (Resende-Lab/Graciano_skNIR_Phenomic_Seleciton), and additionally points to a second public repository (Resende-Lab/PLS_skNIR_Audrey) containing the kernel composition trait dataset derived/详Code · publicAll the codes and the data used in the analyses are available at https://github.com/Resende-Lab/Graciano_skNIR_Phenomic_Seleciton .Open asset ↗Resende-Lab/Graciano_skNIR_Phenomic_Selecitonlines:120-132Dataset · publicFor further information, the dataset is available at: https://github.com/Resende-Lab/PLS_skNIR_Audrey .Open asset ↗Resende-Lab/PLS_skNIR_Audreylines:78-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Corn ( Zea mays ), commonly referred to as Indian wheat, is a widely cultivated tropical annual herbaceous plant of the Poaceae family. It is primarily grown for its starch-rich grains and as a forage crop. In Cameroon, corn is the most consumed cereal, surpassing rice and sorghum, with an estimated production of 2.2 million tons annually. However, corn production is frequently threatened by insect infestations, which hinder crop development, reduce yields, and degrade its quality. Early detection of insect attacks is essential for farmers, as timely intervention can prevent widespread damage, reduce pesticide usage, and improve production yields. Insect infestations on corn manifest through various symptoms on leaves, stems, and seeds. Among these, foliar attacks are particularly detrimental, disrupting plant growth and significantly reducing yields. Symptoms of these attacks include leaf perforations, yellowing, and white spot deposits, ultimately altering the leaf texture. To address these challenges, machine learning models offer a promising solution for early detection of foliar attacks, enabling farmers to take timely and effective action. This paper introduces a dataset focused on three major pests: Spodoptera frugiperda (Fall Armyworm), Helminthosporium leaf blight, and Zonocerus variegatus (Variegated Grasshopper), which are among the most frequent and destructive agents affecting corn crops. The dataset comprises images of corn leaves captured in natural environments at various growth stages and field locations. Images were taken using smartphone cameras at different times of the day, providing diverse lighting conditions, and in various fields, which introduced several background contaminations, ensuring a realistic representation of field conditions. The dataset comprises eight directories: two containing healthy leaf images (1308 without augmentation and 11,772 with augmentation), two containing manually segmented backgrounds of healthy leaves (1308 without augmentation and 11,772 with augmentation), two containing healthy leaves with CNDVI algorithm-segmented backgrounds (1308 without augmentation and 11,772 with augmentation), one containing 848 infected images with manually segmented backgrounds and highlighted infected areas, and one containing 7632 augmented versions of the infected images. This dataset serves as a valuable resource for researchers and students, providing opportunities to develop machine learning and deep learning models for corn disease detection, classification, natural image segmentation, and model interpretability and explainability. By facilitating advancements in precision agriculture and automated pest detection, the dataset contributes to sustainable agricultural practices and the broader field of agroinformatics.
Why it matches plant phenotyping methodsトウモロコシ葉の病害・害虫症状を画像化し、セグメンテーション済みデータセットとして提供することが中心で、植物の病害状態を直接推定する画像ベースの表現型手法に該当する。
abstractThis paper introduces a dataset focused on three major pests: Spodoptera frugiperda (Fall Armyworm), Helminthosporium leaf blight, and Zonocerus variegatus (Variegated Grasshopper)
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository of corn leaf pest images (healthy and infected, with manual and CNDVI-based segmentation and annotations), directly reproducing this paper's phenotyping measurements. The dataset URL is explicitly given and matches an allowed URL.Dataset · publicogbessou PK17 (Latitude: 4.100316; Longitude: 9.802564)
•
Papas (Latitude: 4.057849; Longitude: 9.819752)
3.
On village of Moungo division in littoral region:
Edjocmoa (Latitude: 4.980205; Longitude: 9.946348)
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/ymvghfcww7.1
Direct URL to data: https://data.mendeley.com/datasets/ymvghfcww7/1
Related research article
A robust segmentation method combined with classification algorithms for field-based diagnosis of maize plant phytosanitary state [ 1 ] .
1.
Value of the Data
•
The data facilitate early detection and monitoring of major corn leaf diseases. The dataset enables the early identification and continuOpen asset ↗Mendeley Data · 10.17632/ymvghfcww7.1lines:38-76Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.
Why it matches plant phenotyping methods植物の病変を画像から検出・分類する深層学習モデルの開発とベンチマーク比較が中心であり、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。
abstractThis study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops.
Reproduction assets foundThe paper uses public leaf-image datasets (PlantVillage via TensorFlow, Kaggle rice leaf and wheat leaf datasets) as phenotyping inputs for its Slender-CNN disease classification. No author analysis code, trained model checkpoints, or deposited supplements are stated in the supplied blocks. The Kaggle rice and wheat-dsDataset · public6. Yang Y, Liu Z, Huang M, Zhu Q, Zhao X. Automatic detection of multi-type 28. Rice Leafs. Available: https://www.kaggle.com/datasets/shayanriyaz/RicelOpen asset ↗kagglepdf-page:21 lines:1-49Dataset · publicmodel. J Food Eng. 2023;336: 111213. 29. Wheat Leaf Dataset. Available: https://www.kaggle.com/datasets/olyadOpen asset ↗kagglepdf-page:21 lines:1-49Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISER’s potential to advance omics analyses and related research fields by capturing stronger genetic signals.
Why it matches plant phenotyping methodsWISERは集団構造を補正して表現型を推定する計算手法として開発され、複数作物・多数形質で検証されている。Rパッケージも提供され、表現型推定が研究の中心である。
abstractThis work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure.
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1件を確認しました。Code · publicThe R package wiser can be easily installed from GitHub at https://github.com/ljacquin/wiser.Open asset ↗ljacquin/wiserpdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The advancement of digital technologies has significantly impacted plant pest and disease management, yet gaps remain, especially in developing regions. This paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops. The dataset encompasses 25,844 raw images and over 12,000 labeled images, categorized into 30 classes (healthy crop, infested crop, and pest) across the three cropping systems. Acquired through meticulous fieldwork in Burkina Faso using high-resolution cameras, the dataset includes diverse environmental conditions and crop stages, ensuring a robust resource for AI model training and validation. The dataset is segmented into three categories: processed images (Category A), selected images with augmentation (Category B), and an online repository with over 25,000 raw images (Category C). Category A and B features images of crops affected by 21 distinct pests and diseases. This dataset addresses critical gaps in existing collections by offering extensive coverage and high-resolution imagery that can be used to developed AI models for automatic identification and classification of pests and diseases that affects crops. TOM2024's versatility extends to research, educational purposes, and the practical application of digital tools in agriculture thereby contributes to the advancement of precision agriculture, sustainable agricultural practices, and food security globally.
Why it matches plant phenotyping methods植物の健全・感染状態を含む画像データセットを構築し、病害・害虫状態の自動分類モデル開発用リソースとして提供することが中心であり、再利用可能な画像ベース表現型データセットに該当する。
abstractThis paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops.
Reproduction assets foundThe paper is a Data in Brief article describing the TOM2024 dataset of tomato, onion, and maize pest/disease images, publicly deposited on Mendeley Data with an explicit direct URL and DOI. This is a paper-specific public image dataset (phenotyping-style plant image asset) directly produced by this paper.Dataset · publicrce location
West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)
6 BP 9507
Ouagadougou, Burkina Faso
Tel: +226 25375423
Email: secretariat_cc@wascal.org
Website: www.wascal.org .
Data accessibility
Repository name: TOM2024
Data identification number: doi: 10.17632/3d4yg89rtr.1
Direct URL to data: https://data.mendeley.com/datasets/3d4yg89rtr/1
Related research articleOpen asset ↗10.17632/3d4yg89rtr.1lines:1-43Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro .
Why it matches plant phenotyping methods複数の作物表現型を予測するマルチタスク深層学習手法を開発し、複数データセットおよび既存モデルと比較検証しており、表現型推定手法が研究の中心である。
abstractwe introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space.
Reproduction assets foundThe paper's authors explicitly state that the MtCro analysis code is publicly available on GitHub, matching an allowed URL. No separate phenotype dataset deposit by the authors is stated (Wheat2000/Wheat599 data were provided by DNNGP; Maize8652 is cited prior work), so only the authors' code qualifies as a paper-assetCode · publicOur code is available on https://github.com/chaodian12/mtcro .Open asset ↗chaodian12/mtcrolines:1-67Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate counting of crop plants is essential for agricultural science, particularly for yield forecasting, field management, and experimental studies. Traditional methods are labor-intensive and prone to errors. Unmanned Aerial Vehicle (UAV) technology offers a promising alternative; however, varying UAV altitudes can impact image quality, leading to blurred features and reduced accuracy in early maize seedling counts. To address these challenges, we developed RC-Dino, a deep learning methodology based on DINO, specifically designed to enhance the precision of seedling counts from UAV-acquired images. RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. The RSCconv layer improves the representation of early maize seedlings compared to non-seedling elements within feature maps by calibrating spatial domain features. The ASCFF module enhances the discriminability of early maize seedlings by adaptively fusing feature maps extracted from different layers of the backbone network. Additionally, transfer learning was employed to integrate pre-trained weights with RSCconv, facilitating faster convergence and improved accuracy. The efficacy of our approach was validated using the Early Maize Seedlings Dataset (EMSD), comprising 1,233 annotated images of early maize seedlings, totaling 83,404 individual annotations. Testing on this dataset demonstrated that RC-Dino outperformed existing models, including DINO, Faster R-CNN, RetinaNet, YOLOX, and Deformable DETR. Specifically, RC-Dino achieved improvements of 16.29% in Average Precision (AP) and 8.19% in Recall compared to the DINO model. Our method also exhibited superior coefficient of determination (R²) values across different datasets for seedling counting. By integrating RSCconv and ASCFF into other detection frameworks such as Faster R-CNN, RetinaNet, and Deformable DETR, we observed enhanced detection and counting accuracy, further validating the effectiveness of our proposed method. These advancements make RC-Dino particularly suitable for accurate early maize seedling counting in the field. The source code for RSCconv and ASCFF is publicly available at https://github.com/collapser-AI/RC-Dino, promoting further research and practical applications.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ幼苗数を抽出する深層学習手法を開発し、公開データセット上で既存手法と比較検証しており、植物表現型取得法が研究の中心です。
abstractwe developed RC-Dino, a deep learning methodology based on DINO, specifically designed to enhance the precision of seedling counts from UAV-acquired images.
Reproduction assets foundThe paper's EMSD UAV image dataset is explicitly not publicly available, but the authors' RSCconv and ASCFF analysis code for the RC-Dino model is publicly released on GitHub.Code · publicHowever, our code is open to the public. The RSCconv and ASCFF code mentioned in this paper can be found here: https://github.com/collapser-AI/RC-Dino .Open asset ↗collapser-AI/RC-Dinolines:727-739Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.
Why it matches plant phenotyping methods遺伝・環境情報からトウモロコシ収量という植物形質を予測するモデルを競争形式で比較・評価しており、計算的形質推定とベンチマークが中心である。
titleGlobal genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates.
Reproduction assets foundThe paper is the G2F maize G×E prediction competition report. Its curated phenotype/genotype/weather/EC dataset is public (DOI 10.25739/tq5e-ak26), but that DOI is not among the allowed URLs, so it cannot be listed. However, the authors explicitly state that code from all participating teams is publicly available, and Code · publicour abilities to
solve critical, and technically challenging, problems.
Data availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EOpen asset ↗ksegaba/Genomes2Field_Competitionpdf-raw-page:13 lines:1-91Code · publicta availability
All data used in this manuscript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartyboiOpen asset ↗pdf-raw-page:13 lines:1-91Code · publicript are publicly available at https://
doi.org/10.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-202Open asset ↗mwylerCH/GxEcompetitionpdf-raw-page:13 lines:1-91Code · public0.25739/tq5e-ak26. Code from all teams is publicly avail
able as follows:
AgAdaptAR: https://github.com/EcoEvoInfo/maize-gxe-pre
diction-challenge-2023
AIMaize: https://github.com/ksegaba/Genomes2Field_Competition
All Models are Wrong: https://zenodo.org/record/7830071
arulrich: https://github.com/mwylerCH/GxEcompetition
CLAC: https://github.com/alenxav/Lectures/tree/master/MGC_2023
DataJanitors: https://github.com/qchen33/g2fcompetition2022
DeepCropVision: https://github.com/Ved-Piyush/DeepCrop
Vision_maizegxeprediction2022
EnBiSys: https://github.com/dperondi/maizegxeprediction2022
gartybois: https://github.com/Thyra/g2f-maize-challenge-2022
Kernel of Truth: https://github.com/robertkhu/mOpen asset ↗alenxav/Lecturespdf-raw-page:13 lines:1-91Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Published27 Jan 2025Journal of Information Systems Engineering and ManagementCited by 13 · OpenAlex ↗
Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual blocks for efficient feature extraction, attention mechanisms to enhance focus on disease-relevant regions, and lightweight layers for computational efficiency. These components collectively enable FourCropNet to achieve superior performance across varying datasets and class complexities, from single-crop datasets to combined datasets with 15 classes. The proposed model was evaluated on diverse datasets, demonstrating high accuracy, specificity, sensitivity, and F1 scores. Notably, FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset. Its scalability and ability to generalize across datasets underscore its robustness. Comparative analysis shows that FourCropNet consistently outperforms state-of-the-art models, such as MobileNet, VGG16, and EfficientNet, across various metrics. FourCropNet’s innovative design and consistent performance make it a reliable solution for real-time disease detection in agriculture. This model has the potential to assist farmers in timely disease diagnosis, reducing economic losses and promoting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉の病害状態を画像から推定するCNNモデルを開発し、複数作物データセットで性能評価しているため、植物フェノタイピング手法が中心です。
abstractThis study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn.
Reproduction assets foundThe paper evaluates FourCropNet on a public Kaggle multi-class crop disease image dataset (reference [28]), which is the plant image input used for the paper's phenotyping/disease-detection measurements. No author code or trained model checkpoints are reported as publicly available.Dataset · public[28] “20k+ Multi-Class Crop Disease Images.” Accessed: Jan. 28, 2024. [Online]. Available:
https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-imagesOpen asset ↗jawadali1045/20k-multi-class-crop-disease-imagespdf-page:10 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensive datasets for accurate prediction, which is impracticable to acquire and annotate. Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. As humans need a few examples to recognize things, Few-shot Learning mimics the same human brain process. The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %.•The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced.•By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications.•Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.
Why it matches plant phenotyping methodsトウモロコシ病害を植物画像から分類する深層学習手法の開発が中心であり、病害状態という植物表現型を直接推定している。
abstractThe proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset.
Reproduction assets foundThe paper's corn disease classification model was trained on two publicly available image datasets (Kaggle corn/maize leaf disease dataset and Roboflow Universe corn-disease dataset), explicitly cited in the Data Availability statement. No author analysis code or trained model checkpoint is publicly released.Dataset · publicsonal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The corn/maize dataset used in this study is taken from Roboflow Universe and Kaggle 1. https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 2. https://universe.roboflow.com/final-enlye/corn-disease .
References
1. Encyclopedia Britannica. Corn Plant: Uses and Products. Available at: https://www.britannica.com/plant/corn-plant/Uses-and-products . Accessed December 11, 2024.
2. United States Department of Agriculture (USDA) 20Open asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:225-274Dataset · publicaper.
Acknowledgments
This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The corn/maize dataset used in this study is taken from Roboflow Universe and Kaggle 1. https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 2. https://universe.roboflow.com/final-enlye/corn-disease .
References
1. Encyclopedia Britannica. Corn Plant: Uses and Products. Available at: https://www.britannica.com/plant/corn-plant/Uses-and-products . Accessed December 11, 2024.
2. United States Department of Agriculture (USDA) 2024. Corn: World Supply and Demand. https://fas.usda.gov/data/production/commodity/044Open asset ↗Roboflow Universe · final-enlye/corn-diseaselines:225-274Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Global climate change has driven breeding programs to develop abiotic stress‐resilient plant varieties. Traditionally, assessing drought resilience involves labor‐intensive and time‐consuming processes. This study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle. We grew transgenic maize hybrids in two trials, one irrigated and another subjected to drought stress, and used a drone equipped with red–green–blue (RGB) and multispectral sensors to capture images of the plots over time. Machine learning models and various prediction scenarios revealed significant correlations between vegetation indices over time. Interestingly, the RGB sensor outperformed the multispectral sensor in trait prediction. Prediction accuracy across scenarios with untested genotypes and environments ranged from 0.40 to 0.70 for grain yield, 0.43 to 0.69 for days to anthesis, 0.51 to 0.67 for days to silking, and 0.35 to 0.57 for plant height. Ridge and random forest models consistently delivered the most accurate predictions across traits and environments. The vegetation indices normalized green–red difference index, VARI, and RCC also effectively predicted and captured the plant response to drought. This study highlights the value of UAS phenotyping as a practical tool for assessing abiotic stress due to its straightforward implementation.
Why it matches plant phenotyping methodsUASによるRGB・マルチスペクトル画像と機械学習で、作物形質および干ばつ応答を予測するフェノタイピング手法を、複数環境・遺伝子型で検証しているため。
abstractThis study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle.
Reproduction assets foundThe paper's data availability statement says all codes and datasets (phenomic prediction scripts, folder 'Phenomic prediction', and described datasets) are publicly available at the authors' GCCRC publications page and on Dryad (doi:10.5061/dryad.0zpc8677b).Code · public14 of 16 PEREIRA ET AL.
in this work to perform phenomic prediction for all the eight
models and the four cross-validation scenarios were given as
examples in the folder “Phenomic prediction.” All the codes
and the datasets described are available at https://www.gccrc.unicamp.br/publications/ and https://doi.org/10.5061/dryad.0zpc8677b.O RC I D
HelcioDuartePereira https://orcid.org/0000-0002-2837-9396
Juliana Vieira Almeida Nonato https://orcid.org/0000-0003-4448-4652
Rafaela CarolineRangni MoltocaroDuarte https://orcid.org/0000-0003-2622-3758
Isabel Rodrigues Gerhardt https://orcid.org/0000-0003-1397-0199
RicardoAuOpen asset ↗GCCRCpdf-raw-page:14 lines:1-75Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG‐GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two‐step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG‐16) network to extract initial pixel‐based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition‐based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG‐GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG‐GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG‐GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.
Why it matches plant phenotyping methodsトウモロコシ葉画像から健全・感染状態を判定する画像解析モデルを開発し、他モデルとの性能比較で検証しているため、植物病害表現型の取得手法が中心である。
abstractThis study presents VG‐GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two‐step feature extraction process.
Reproduction assets foundThe paper's maize leaf image dataset is a public Kaggle dataset (Corn Leaf Infection by Acharya) explicitly cited as the source of the 4226 healthy/infected images used for phenotyping. No author analysis code or trained model is publicly deposited; the Data Availability Statement only offers data on request, so no作者代码Dataset · publicKashif Munir, Email: kashif.munir@kfueit.edu.pk.
Imran Ashraf, Email: imranashraf@ynu.ac.kr.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author.
References
Acharya, R.
. n.d. “Corn Leaf Infection.” Accessed October 03, 2024. https://www.kaggle.com/datasets/qramkrishna/corn‐leaf‐infection‐dataset?select=Corn+Disease+detection .
Blessing, D. J.
, Gu Y., Cao M., Cui Y., Wang X., and Asante‐Badu B.. 2022. “Overview of the Advantages and Limitations of Maize‐Soybean Intercropping in Sustainable Agriculture and Future Prospects: A Review.” Chilean Journal of Agricultural Research
82, no. 1: 177–188.
Bolatova, Z.
, andOpen asset ↗Kagglelines:530-598Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeRiceClassificationGrowth / development / phenologyStress response / tolerance
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether the expression levels of a subset of highly variable genes (HVGs) can be used to accurately identify the phenotypes of plants. Using maize ( Zea mays L.) as an example, we built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples. We showed that the ML models achieved excellent prediction accuracy using only the HVGs to identify different phenotypes, including tissue types, developmental stages, cultivars and stress conditions. By ML models, several important functional genes were found to be associated with different phenotypes. We performed a similar analysis in rice ( Orzya sativa L.) and found that the ML models could be generalized across species. However, the models trained from maize did not perform well in rice, probably because of the expression divergence of the conserved HVGs between the two species. Overall, our results provide an ML framework for phenotype prediction using gene expression profiles, which may contribute to precision management of crops in agricultural practices.
Why it matches plant phenotyping methods遺伝子発現データから植物の組織、発育段階、品種、ストレス状態を予測する機械学習フレームワークを開発しており、表現型推定手法が研究の中心である。
abstractwe built machine learning (ML) models to predict phenotypes using a gene expression dataset of 21 612 bulk RNA sequencing samples.
Reproduction assets foundThe paper's maize/rice gene expression datasets are publicly deposited on FigShare, and the authors' analysis source code is available on GitHub with a Zenodo DOI archive. The underlying expression profiles were originally downloaded from the PlantExp database (maize taxonId=4577, rice taxonId=39947).Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗GitHub · Zefeng2018/plant-phenotype-prediction-by-gene-expressionlines:100-176Code · publicSource code is available at https://github.com/Zefeng2018/plant-phenotype-prediction-by-gene-expression and https://doi.org/10.5281/zenodo.14358186 .Open asset ↗Zenodo · 10.5281/zenodo.14358186lines:100-176Dataset · publicthe maize gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=4577Open asset ↗PlantExp · taxonId=4577lines:29-36Dataset · publicthe rice gene expression data were downloaded from https://biotec.njau.edu.cn/plantExp/info.php?taxonId=39947Open asset ↗PlantExp · taxonId=39947lines:29-36Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employed a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrated superior modeling of environmental influences and out-performs PCA (principal component analysis), PLSR (Partial Least square regression) and vanilla autoencoders by 7 times for 'Days to Pollen' trait and 10 times improved predictive performance for 'Yield'. By disentangling latent features, the CAE provided a powerful tool for precision breeding and genetic research. This work has significantly enhanced trait prediction models, advancing agricultural and biological sciences.
Why it matches plant phenotyping methods植物形質（開花日数・収量）の予測を目的とする新規オートエンコーダを開発し、既存手法と比較評価しており、計算的な形質推定手法が研究の中心である。
abstractwe developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features.
Reproduction assets foundThe paper's data availability statement explicitly deposits the hyperspectral reflectance dataset and trained model weights on Figshare, and the authors' analysis code on a public Bitbucket repository (baskargroup/cae_hyperspectral). Both are paper-specific, public, and actionable.Dataset · publicte the technical advantages of disentanglement, it is not immediately clear how to connect these disentangled features to biological insights.
Data availability statement
The 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://figshare.com/articles/dataset/Hyperspectral_reflectance_data_molecular_and_weights_for_trained_model/24808491/4 ; https://bitbucket.org/baskargroup/cae_hyperspectral/src/main/ .
Author contributions
AP: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. TJ: Conceptualization, Open asset ↗figshare · 24808491lines:460-495Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models in GS lies in their low robustness and poor interpretability. To address these challenges, we developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks. This framework combines convolutional neural networks with multiple self-attention mechanisms to improve accuracy. The ability of Cropformer to predict complex phenotypic traits was extensively evaluated on more than 20 traits across five major crops: maize, rice, wheat, foxtail millet, and tomato. Evaluation results show that Cropformer outperforms other GS methods in both precision and robustness, achieving up to a 7.5% improvement in prediction accuracy compared to the runner-up model. Additionally, Cropformer enhances the analysis and mining of genes associated with traits. We identified numerous single nucleotide polymorphisms (SNPs) with potential effects on maize phenotypic traits and revealed key genetic variations underlying these differences. Cropformer represents a significant advancement in predictive performance and gene identification, providing a powerful general tool for improving genomic design in crop breeding. Cropformer is freely accessible at https://cgris.net/cropformer.
Why it matches plant phenotyping methods作物の表現型形質を予測する深層学習フレームワークを開発・評価しており、計算的な形質推定が研究の中心である。
abstractwe developed Cropformer, a deep learning framework for predicting crop phenotypes and exploring downstream tasks.
Reproduction assets foundThe paper's authors publicly released the Cropformer analysis code on GitHub, and the paper's phenotypic/genotypic analysis datasets (wheat, foxtail millet, tomato, rice, maize) are publicly available at author-cited URLs, directly reproducing this paper's genomic-prediction measurements and analysis.Code · publicThe Cropformer software, including documentation and tutorials, is available on GitHub ( https://github.com/jiekesen/Cropformer ).Open asset ↗jiekesen/Cropformerlines:131-156Dataset · publicThe wheat dataset was derived from 2403 Iranian bread wheat ( Triticum aestivum ) landrace accessions in the CIMMYT wheat gene bank ( https://hdl.handle.net/11529/10548918 ).Open asset ↗lines:90-98Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Corn seed breeding is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for corn kernel recognition and classification has great potential in terms of constructing environment friendly agriculture, and saving manpower. Existing segmentation methods that utilize U-shaped architectures typically operate by processing images in discrete pixel-based segments. This approach often overlooks the finer pixel-level structural details within these segments, leading to models that struggle to preserve the continuity of target edges effectively. In this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI, which aims to integrate MSADI module into the encoder and decoder of the VMUnet architecture. Our VMUnet-MSADI model benefits from self-attention computation in VMUnet and multiscale coding to efficiently model non-local dependencies and multiscale contexts to improve the segmentation quality of different images. Unlike previous Unet-based improvement schemes, the proposed VMUnet-MSADI adopts a multiscale convolutional attention module coding mechanism at the depth level and an efficient multiscale deep convolutional decoder at the spatial level to extract coarse-grained features and fine-grained features at different semantic scales and effectively avoid the loss of information at the target boundary to improve the quality and accuracy of target segmentation. In addition, we introduce a Visual State Space (VSS) block to capture a wide range of contextual information and a Detail Infusion Block (DIB) to enhance the fusion of low-level and high-level features, which further fills in the remote contextual information during the up-sampling process. Comprehensive experiments were conducted on open-source datasets and the results demonstrate that the VMUnet-MSADI model excels in the task of corn kernel segmentation. The model achieved a segmentation accuracy of 95.96%, surpassing the leading method by 0.9%. Compared to other segmentation models, our method exhibits superior performance in both accuracy and loss metrics. Extensive comparative experiments conducted on various benchmark datasets further substantiate that our approach outperforms the state-of-the-art models. Code, pre-trained models and data processing protocols are available at https://github.com/corbining/VMUnet-MSADI
Why it matches plant phenotyping methodsトウモロコシ種子の健全性状態を画像から分割・認識する新規深層学習手法を中心に開発し、公開データセットで比較検証しているため、植物フェノタイピング手法として収録する。
abstractIn this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI
Reproduction assets foundThe paper states that code, pre-trained models, data processing protocols, and supporting data are openly available at the authors' GitHub repository, which is an allowed URL. The repository is paper-specific (named after the model) and covers the corn kernel segmentation analysis.Code · publicCode, pre-trained models and data processing protocols are available at
https://github.com/corbining/VMUnet-MSADIOpen asset ↗VMUnet-MSADIpdf-page:2 lines:1-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Maize stands out as a versatile commodity, finding applications in food and animal feed industries. Notably, half of the total demand for maize is met through its utilization as animal feed. Despite its importance, maize cultivation often grapples with crop failures resulting from delayed disease management or insufficient knowledge about these diseases, impeding timely intervention. The advent of technological advancements, particularly in Machine Learning, presents solutions to address these challenges. This research focuses on employing a Convolutional Neural Network (CNN) to classify maize plant diseases. Two datasets form the foundation of this study. The first dataset encompasses 4144 images distributed across 4 classes, while the second dataset comprises 5155 images distributed among 7 to 8 classes. The second dataset encounters issues related to imbalanced class distribution, where certain classes possess substantially more data than others. To mitigate this imbalance, the weighted cross-entropy loss method is employed. During experimentation, three distinct architectural models-ResNet-18, VGG16, and EfficientNet-are rigorously tested. Additionally, various optimizers are explored, with noteworthy results indicating that both datasets achieve peak accuracy through the use of the SGD (Stochastic Gradient Descent) optimization. For the first dataset, optimal results are obtained with the VGG16 architecture, leveraging a frozen layer in the classification stage and achieving an impressive accuracy of 97.146 %. Shifting the focus to the second dataset, the most favorable outcome is realized by employing the EfficientNet architecture without a frozen layer, coupled with the implementation of weighted loss to address the class imbalance, resulting in an accuracy of 94.798 %.
Why it matches plant phenotyping methodsCNNによる植物画像からの病害状態分類が研究の中心であり、植物病害の画像ベース表現型推定手法の評価に該当する。
abstractThis research focuses on employing a Convolutional Neural Network (CNN) to classify maize plant diseases.
Reproduction assets foundThe paper's first dataset (4188 maize leaf disease images, 4 classes) is a public Kaggle dataset explicitly used for the paper's classification experiments and named in the data availability statement. The second dataset (5155 images, 7-8 classes) is only available from the corresponding author upon request, and no作者分析Dataset · public.
3.
Methodology
3.1.
Dataset
To classify diseases in maize plants, an appropriate dataset is required, which is a dataset that contains images of both diseased and healthy maize plants. In this research, two datasets will be used.
3.1.1.
First dataset
The first dataset is obtained from Kaggle, and the link to it is as follows: https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset . This dataset is a combination of PlantVillage and PlantDoc. The first dataset consists of 4188 images of Maize plants, which are divided into four classes. There are Healthy, Blight, Common Rust, and Gray Leaf Spot.
1
Common Rust
Common rust typically occurs at moist and cold environmeOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:39-84Code / 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 confirmedEurope PMC · Crossref · checked 7 Sept 2026
PURPOSE: This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location. METHODS: The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution. RESULTS: The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88. CONCLUSION: The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの健全・損傷状態をセグメンテーションし、損傷面積率と位置を推定する手法が研究の中心であり、植物状態の定量的フェノタイピングに該当する。
abstractThis paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV).
Reproduction assets foundThe authors publicly share processing code, trained models, and a data sample for their corn damage segmentation at the PUTvision GitHub repository; the full training dataset is not public due to commercial restrictions. The QGIS deepness plugin is a generic third-party inference tool, not a paper-specific asset.Code · publicThe processing code, trained models and data sample can be found at https://github.com/PUTvision/corn-field-damage , accessed 17.01.2024.Open asset ↗PUTvision/corn-field-damagelines:226-252Code / 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 confirmedCrossref · checked 14 Sept 2026
Abstract: Preserving plant health and early detection of diseases are crucial in modern agriculture. Artificial intelligence techniques, particularly deep learning networks, are employed for this purpose. In this study, disease recognition was conducted using leaf images from various plant species. The study encompassed important agricultural products such as apples, strawberries, grapes, corn, peppers, and potatoes among the plant species considered. Among the deep learning networks, popular architectures like AlexNet, Vgg16, MobileNetV2, and Inception were compared. The Inception V3 model achieved the highest success rate of 92%, followed by the AlexNet architecture with a success rate of 91%. Among these networks, the InceptionV3 model yielded the best results. The InceptionV3 model effectively learned from plant leaf images and accurately distinguished between diseased and healthy leaves. These findings demonstrate that AI-based systems can be efficiently utilized for disease recognition and prevention in the agriculture sector. In this study, the performance of the InceptionV3 model in disease recognition on plant leaves was analyzed in detail, emphasizing the role of deep learning networks in agricultural applications.
Why it matches plant phenotyping methods植物葉画像から健全・罹病状態を推定する深層学習手法を比較・評価しており、植物病害表現型の取得・分類が研究の中心です。
abstractIn this study, disease recognition was conducted using leaf images from various plant species.
Reproduction assets foundThe paper's plant-disease classification experiments were performed on the public New Plant Diseases Dataset (Kaggle), which the authors explicitly state is openly accessible via a Kaggle URL. This is a paper-specific, public, actionable phenotype image dataset. No author analysis code or trained models are reported asDataset · publicsector. Suggestions for
future research include the use of larger and more
diverse datasets and the application of federated learning
techniques, which can improve the performance of the
model and provide security.
Dataset Access: The dataset used in this study is open
and can be accessed from the relevant source link.
Access:
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetpdf-raw-page:11 lines:97-114Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.
Why it matches plant phenotyping methodsトウモロコシの葉巻きという植物形質を画像から検出する深層学習法を開発・評価しており、表現型取得手法が研究の中心である。
abstractIn this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model.
Reproduction assets foundThe authors explicitly state that source code for the LRD-YOLO leaf rolling detection method is publicly available on GitHub. The maize leaf rolling image dataset itself is only available from the corresponding author upon reasonable request, so it does not qualify as a public asset.Code · public00501 and no.32300239), Shenzhen Science and Technology Program (Grant No. RCBS20210609103819020), the Innovation Program of Chinese Academy of Agricultural Sciences, National Key R&D Program of China (Grant No. 2023ZD04076).
Data availability
Some of the data, source codes and more details about our project are in the GitHub ( https://github.com/WangYH1740/LRD-YOLO ). In addition, the original datasets are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors declare no conflicts of interest.
References
Bänziger M Edmeades GO Beck D Bellon M
Breeding for drought and nitrogen stress tolerance in maize: from theory to practice 2000
MeOpen asset ↗WangYH1740/LRD-YOLOlines:662-712Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Background Plant height is a significant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms. Method This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales. Results The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm’s 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm’s results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.
Why it matches plant phenotyping methodsトウモロコシの草丈という植物形質を画像・物体検出で自動推定する手法を開発し、既存モデルとの比較および手動測定による検証を行っており、フェノタイピング手法が中心である。
abstractTherefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms.
Reproduction assets foundThe authors deposited the paper's maize plant height measuring scale image dataset on figshare, explicitly linked in the Data Availability statement. Supplemental detection code and model configuration exist but their URLs are not among the allowed URLs, so only the figshare dataset is reported.Dataset · publicThe data is available at figshare: Li, Joish (2023). Maize plant height measuring scale data set. figshare. Figure. https://doi.org/10.6084/m9.figshare.24547165.v1 .Open asset ↗figshare · 10.6084/m9.figshare.24547165.v1lines:445-552Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.
Why it matches plant phenotyping methods植物花粉のSEM観察用試料調製法そのものを開発し、複数植物で適用・比較検証しているため、形態計測に関する中心的な方法論研究である。
abstractThe article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM).
Reproduction assets foundThe paper's quantitative pollen shrinkage measurements (Table S1) and light microscopy images (Figures S3–S4) are contained in the publicly downloadable MDPI Supplementary Materials, which directly reproduce this paper's phenotyping measurements. No author analysis code or trained models are mentioned.Supplement · publicoly Bogdanov—at the department of electron microscopy, Lomonosov Moscow State University.
Abbreviations
The following abbreviations are used in this manuscript:
SEM
Scanning Electron Microscopy
HMDS
Hexamethyldisilazane
SA
Short axis
LA
Long axis
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13152140/s1 , Figure S1: The order of steps for pollen preparation according to the developed protocol; Figure S2: Scheme of measured pollen grain diameters; Figure S3: Light microscopy of pollen grains of insect-pollinated species; Figure S4: Light microscopy of pollen grains of wind-pollinated species; Table S1: Comparison Open asset ↗lines:98-127Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.
Why it matches plant phenotyping methods植物病害を画像等の観察から識別する深層学習・SVM手法の構築と性能比較が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当します。
abstractThis study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn.
Reproduction assets foundThe paper's crop disease image dataset is publicly available on Kaggle (Bangladeshi Crops Disease Dataset), explicitly cited as the open-source image source used for the study's phenotyping/classification experiments. The authors' additionally collected 1334 field images and their analysis code/trained model are not公开;Dataset · publicung-EffNet: Lung Cancer Classification Using EfficientNet from CT-Scan Images
Eng. Appl. Artif. Intell. 2023 126 106902
10.1016/j.engappai.2023.106902
23.
Huang Z.
Su L.
Wu J.
Chen Y.
Rock Image Classification Based on EfficientNet and Triplet Attention Mechanism
Appl. Sci. 2023 13 3180
10.3390/app13053180
24. Available online: https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset (accessed on 11 June 2023)
25.
Atila U.
Uçar M.
Akyol K.
Uçar E.
Plant leaf disease classification using EfficientNet deep learning model
Ecol. Inform. 2021 61 101182
10.1016/j.ecoinf.2020.101182
26.
Padol P.B.
Yadav A.A.
SVM classifier-based grape leaf disease detection
Proceedings of the 2Open asset ↗Kaggle · bangladeshi-crops-disease-datasetlines:139-314Code / 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 confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors, which has important implications for crop breeding. Among these phenotypic characteristics, the number of leaves and growth trajectory of the plant are most accessible. Nonetheless, obtaining these phenotypes is labor intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding. However, it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments. To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture, in this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks. The experimental results demonstrate that the object detection accuracy (mAP50) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and stalks, we introduced a new lightweight "keypoint" detection branch that achieved a magnitude of 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for datasets with dot and line annotations.
Why it matches plant phenotyping methodsトウモロコシの葉・茎の数と成長軌跡をRGB画像から抽出する深層学習手法を開発し、精度評価も行っており、植物表現型取得が研究の中心である。
abstractin this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.
Reproduction assets foundThe authors explicitly deposit the code (and data) supporting this maize leaf/stalk trajectory phenotyping study in a public GitHub repository, matching an allowed URL.Code · publicThe computer code and data that support the findings of this study are deposited in a GitHub repository at https://github.com/VEGETALOADING/Point-Line-Net .Open asset ↗VEGETALOADING/Point-Line-Netlines:319-524Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.
Why it matches plant phenotyping methodsドローン画像由来の時系列植生指数をFPCAで要約し、穀粒収量・草丈予測へ統合するフェノタイピング手法と予測モデルを複数年・環境で評価しており、表現型取得・抽出と技術的評価が中心である。
titleField-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Reproduction assets foundThe authors deposited a public figshare archive ('Data.zip') containing the paper's phenomic FPCA result files, plant height and grain yield BLUEs, genomic numerical files, and the R prediction/FPCA code needed to reproduce the analysis.Dataset · publicData are available at figshare: https://doi.org/10.25387/g3.24657666 .Open asset ↗figshare · 10.25387/g3.24657666lines:273-291Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.
Why it matches plant phenotyping methodsUAV画像から植物高と成長率を抽出し、時系列形質による圃場内収量変動予測を評価しており、植物表現型の取得・解析手法が研究の中心です。
abstractTemporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield
Reproduction assets foundThe paper explicitly states that all analysis scripts are on GitHub and all UAV-derived phenotypic data (plot heights, vegetative indices, orthomosaics, DEMs, plot boundaries, masks, manual heights, yield, weather) are deposited in DRUM with a DOI.Code · publicAll of the scripts and files used to generate and analyze data are available on GitHub at
https://github.com/HirschLabUMN/Production_Drone_Height.git.Open asset ↗HirschLabUMN/Production_Drone_Heightpdf-page:17 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.
Why it matches plant phenotyping methodsトウモロコシ葉の画像から病害状態を分類し、VGG16とLRPによる説明可能な解析手法を中心的に開発・評価しているため、植物病害フェノタイピング手法に該当する。
abstractThis study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust.
Reproduction assets foundThe paper's corn leaf disease classification (VGG16 + LRP) is built entirely on a public Kaggle image dataset of 4,188 corn leaf images across four classes, explicitly cited as the data source. No author analysis code or trained model is stated as publicly available; the data availability statement only offers raw dataDataset · publicThe data were acquired from the Kaggle repository ( Smaranjit Ghose, 2024 ).Open asset ↗Kaggle · Smaranjit Ghoselines:93-166Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類するCNN手法の開発と性能比較が研究の中心であり、植物の病害状態を直接推定しているため。
abstractMultiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters.
Reproduction assets foundThe paper trains and evaluates its E-CNN plant disease detection models on the public PlantVillage Kaggle dataset (apple, corn, potato leaf images). No author-generated datasets, code, or models are released; the data availability statement says no datasets were generated or analysed. The only qualifying paper-specificDataset · publicPlantVillage Dataset (2023) [Online]. Available: https://www.kaggle.com/datasets/abdallahalidev/plantOpen asset ↗Kaggle · PlantVillage Datasetpdf-page:28 lines:1-67Code / dataset availability confirmedOpenAlex · checked 7 Sept 2026
Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
Why it matches plant phenotyping methodsマルチスペクトル画像とVision Transformerを用いて、トウモロコシ個体の干ばつストレス状態を推定する解析パイプラインを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform.
Reproduction assets foundThe paper's raw maize drought-stress imaging dataset (three trials, downsampled NGB/NIR images) is openly deposited on Zenodo with DOI 10.5281/zenodo.10991581. No author analysis code, trained models, or annotations are stated as publicly available.Dataset · publicData Availability Statement: The original data presented in the study are openly available on
the data sharing platform Zenodo https://zenodo.org/records/10991581 ( accessed on 18 April
2024) with DOI 10.5281/zenodo.10991581. The repository contains raw images before any of the
pre-processing steps mentioned in Section 3.Open asset ↗Zenodo · 10.5281/zenodo.10991581pdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Currently, foliar diseases of chili have significantly impacted both yield and quality. Despite effective advancements in deep learning techniques for the classification of chili leaf diseases, most existing classification models still face challenges in terms of accuracy and practical application in disease identification. Therefore, in this study, an optimized and enhanced convolutional neural network model named MCCM (MCSAM-ConvNeXt-MSFFM) is proposed by introducing ConvNeXt. The model incorporates a Multi-Scale Feature Fusion Module (MSFFM) aimed at better capturing disease features of various sizes and positions within the images. Moreover, adjustments are made to the positioning, activation functions, and normalization operations of the MSFFM module to further optimize the overall model. Additionally, a proposed Mixed Channel Spatial Attention Mechanism (MCSAM) strengthens the correlation between non-local channels and spatial features, enhancing the model's extraction of fundamental characteristics of chili leaf diseases. During the training process, pre-trained weights are obtained from the Plant Village dataset using transfer learning to accelerate the model's convergence. Regarding model evaluation, the MCCM model is compared with existing CNN models (Vgg16, ResNet34, GoogLeNet, MobileNetV2, ShuffleNet, EfficientNetV2, ConvNeXt), and Swin-Transformer. The results demonstrate that the MCCM model achieves average improvements of 3.38%, 2.62%, 2.48%, and 2.53% in accuracy, precision, recall, and F1 score, respectively. Particularly noteworthy is that compared to the original ConvNeXt model, the MCCM model exhibits significant enhancements across all performance metrics. Furthermore, classification experiments conducted on rice and maize disease datasets showcase the MCCM model's strong generalization performance. Finally, in terms of application, a chili leaf disease classification website is successfully developed using the Flask framework. This website accurately identifies uploaded chili leaf disease images, demonstrating the practical utility of the model.
Why it matches plant phenotyping methodsトウガラシ葉の病徴を画像から分類する深層学習手法を開発・比較評価しており、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractan optimized and enhanced convolutional neural network model named MCCM (MCSAM-ConvNeXt-MSFFM) is proposed
Reproduction assets foundThe paper's phenotyping inputs are public Kaggle image datasets: the chili leaf disease dataset used as the primary experimental data, plus rice and maize leaf disease datasets used for generalization tests. No author analysis code, trained model checkpoints, or dataset deposits are stated; the Flask website is a live-Dataset · publicn of experiments. Section 4 analyzes all research findings, proposes possible improvements, and suggests future research directions. Finally, Section 5 summarizes the main contributions of this study.
2
Materials and methods
2.1
Datasets
The chili leaf disease dataset is sourced from publicly available datasets on the internet( https://www.kaggle.com/datasets/dhenyd/chili-plant-disease/ ). The initial dataset consists of 500 images, encompassing five categories: healthy chili leaves, leaf curl disease, leaf spot disease, whitefly, and yellowing disease. Samples of each category are illustrated in
Figure 1
.
Figure 1
Chili samples. (A) Healthy chili leaves. (B) Leaf curl disease. (C) Leaf spoOpen asset ↗Kaggle · dhenyd/chili-plant-diseaselines:59-78Dataset · publicthe corresponding accuracy rate of each cross-validation.
3.10.2
Rice and maize disease test results
To validate the generalization ability of the MCCM model on different crop diseases, this study conducted performance tests using publicly available datasets, including rice leaf disease dataset and maize leaf disease dataset ( https://www.kaggle.com/datasets/nirmalsankalana/rice-leaf-disease-image and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset ). The rice leaf disease dataset covers four types: rice blast, bacterial leaf blight, brown spot, and rice tungro disease, while the maize leaf disease dataset includes healthy states and three types of diseases:Open asset ↗Kaggle · nirmalsankalana/rice-leaf-disease-imagelines:456-477Dataset · publicaize disease test results
To validate the generalization ability of the MCCM model on different crop diseases, this study conducted performance tests using publicly available datasets, including rice leaf disease dataset and maize leaf disease dataset ( https://www.kaggle.com/datasets/nirmalsankalana/rice-leaf-disease-image and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset ). The rice leaf disease dataset covers four types: rice blast, bacterial leaf blight, brown spot, and rice tungro disease, while the maize leaf disease dataset includes healthy states and three types of diseases: gray leaf spot, rust, and leaf blight. To ensure dataset diversity, data aOpen asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetlines:456-477Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴・感染程度という植物状態を、深紫外蛍光分光とオートエンコーダで非破壊的に定量する手法が研究の中心であり、視覚評価との相関による技術評価も行っている。
abstractwe demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer
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1件を確認しました。Dataset · publicData Availability: The data can be downloaded from here: https://datadryad.org/stash/share/NW062Bv9Cpe5VslBiiA52nweUJEQCHC2yzAqKlQYx6w .Open asset ↗Dryadlines:138-151Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.
Why it matches plant phenotyping methods作物の遺伝型・表現型データから収量を予測する移植学習フレームワークを開発し、複数作物と既存手法で精度比較しているため、植物表現型推定手法が中心である。
abstractWe therefore developed TrG2P, a transfer-learning-based framework.
Reproduction assets foundThe paper's authors publicly released the Python implementation of TrG2P (the transfer-learning G2P analysis tool used to produce the paper's yield-prediction results) on GitHub, with an explicit availability statement, plus a web-based tool. The phenotype/genotype datasets themselves are from previously published, citCode · publicThe Python implementation of TrG2P along with the demo files is available at https://github.com/lijinlong1991/TrG2P . The web-based tool is available at http://trg2p.ebreed.cn:81 .Open asset ↗https://github.com/lijinlong1991/TrG2Plines:224-294Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F 2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.
Why it matches plant phenotyping methodsトウモロコシ粒の高スループットNIRS測定と回帰モデルを用いて収量・千粒重を予測し、ゲノム予測との比較検証を行っており、表現型取得・推定法が研究の中心である。
titleNear-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the annotated R analysis code and all files needed to reproduce the NIRS phenomic and genomic prediction results. The NIRS/phenotype data themselves are from prior works (Lane et al. 2020, Farfan et al. 2015) and no separate public,Code · publicAn annotated script of the R code used in this research can be accessed via GitHub (DeSalvio, 2023) [https://github.com/ajdesalvio/Maize‐NIRS‐GBS.git]. All files needed to reproduce the results are provided in the GitHub repository for users to access.Open asset ↗ajdesalvio/Maize‐NIRS‐GBShtml-lines:240-391Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から得た縦断的NDVIを植物・圃場プロットの表現型として用い、空間・時系列モデリング手法を提案・評価しており、表現型取得と解析ワークフローが研究の中心です。
abstractour study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery.
Reproduction assets foundThe paper's maize field phenotype datasets (2015, 2017, 2019, 2020 G2F hybrid experiments) are publicly available via G2F DOIs. The genotypic SNP dataset DOI was excluded as molecular omics data; image data are said to be in the supplement but without a public URL.Dataset · publicn Johnson, Seth Murray, Jacob Washburn, Filipe I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected imageOpen asset ↗10.25739/erxg-yn49lines:427-461Dataset · publice I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are avaOpen asset ↗10.25739/w560-2114lines:427-461Dataset · publicadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this maOpen asset ↗10.25739/t651-yy97lines:427-461Dataset · publicprocessing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this manuscript.
Supplemental material available at GENEOpen asset ↗10.25739/hzzs-a865lines:427-461Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Why it matches plant phenotyping methodsU-Net画像解析を用いてトウモロコシ葉の病害症状の発生量を定量化し、学習データ量と性能を評価する方法開発・検証研究であるため。
abstractHere, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models.
Reproduction assets foundThe paper's Data availability section states that the full training and testing datasets (raw TIF leaf images, CSV annotation files, and all 140 model analysis CSV files) are publicly available at the Iowa State University Figshare repository (DOI 10.25380/iastate.23669328), which is an allowed URL. This directly hostsDataset · publicat: https://doi.org/10.25380/iastate.23669328. This data repository Plant Pathol. 43:S352-S357.Open asset ↗iastate · 10.25380/iastate.23669328pdf-page:9 lines:44-82Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error ( MAPE ) of 3.01%, a Mean Absolute Error ( MAE ) of 0.75 mm, a Root Mean Square Error ( RMSE ) of 1.07 mm, and a coefficient of determination ( R ²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
Why it matches plant phenotyping methodsRGB-Dカメラと画像・3D処理によりトウモロコシ茎径を非破壊測定する手法を開発し、誤差指標で精度検証しており、フェノタイピング手法が中心である。
abstractthis study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter
Reproduction assets foundThe paper's data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.25450039) containing the study's datasets (RGB/depth imagery and stem diameter measurements used for the maize stem diameter phenotyping analysis). No author analysis code or trained models are explicitly deposited.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: http://dx.doi.org/10.6084/m9.figshare.25450039 .Open asset ↗figshare · 10.6084/m9.figshare.25450039lines:909-917Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p 0.22, p
Why it matches plant phenotyping methods作物を対象に、塔載観測によるPRI・SIFを用いたGPP推定関係の改善と検証を主題としており、植物の生理状態推定手法が中心である。
titleAssessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean
Reproduction assets foundThe paper's tower-based SIF, PRI, and GPP measurements for corn and soybean at US-Ne2/US-Ne3 derive from an openly downloadable ORNL DAAC dataset (ds_id=2136), explicitly cited in the Data Availability Statement. No author analysis code or models are disclosed.Dataset · publicResearch and Development Program of
China, grant number 2021YFC2600501; the National Natural Science Foundation of China, grant
number SKLNBC2023-01.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset used in this study can be downloaded at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2136 (accessed on 1 January 2024).
Acknowledgments: We appreciate the open-access dataset supported by Wu from the Agroecosystem
Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at
Urbana-Champaign, Urbana, IL, USA.
Conflicts of Interest: The authors declare no conflictsOpen asset ↗daac.ornl.gov · ds_id=2136pdf-raw-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Projected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India’s Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.
Why it matches plant phenotyping methods衛星SIFを用いて作物収量という植物形質を推定する機構ガイド型フレームワークを開発し、複数地域・機械学習手法と比較検証しており、形質取得・推定法が中心である。
titleA scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)
Reproduction assets foundThe paper's yield estimation analysis relies on publicly available datasets explicitly named in the data availability statement: the OCO-2 SIF product (SIF_oco2_005) at ORNL DAAC, USDA NASS QuickStats corn yields, and ICRISAT DLD wheat yields. No author code, models, or paper-specific image/annotation assets are sharedDataset · public2 SIF available
from previous work (details below). Yield estimation
in US-CB was conducted for five years from 2015
to 2020 (when corn-specific OCO-2 SIF is available)
except 2017 (when OCO-2 had an instrument fail-
ure in August). The district-level wheat yield in IGP-
WB came from the District Level Database (DLD)
for India (http://data.icrisat.org/dld/), including 55
districts for the states of Bihar, Uttar Pradesh, and
Haryana. Yield estimation in IGP-WB was carried out
from 2015 to 2017 (the maximum overlap between
OCO-2 SIF and yield data).
2.2. The MLR-SIF yield estimation framework
The MLR-SIF based framework for yield estimation
consists of three steps. First, it estimaOpen asset ↗pdf-raw-page:4 lines:1-131Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy ( r 2 ) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.
Why it matches plant phenotyping methodsトウモロコシ雄穂を画像から自動計数し、画像セグメンテーションと2種類の機械学習手法の精度を検証する研究であり、植物表現型取得法が中心である。
abstractwe developed machine learning methods for obtaining automated tassel counts at the plot level.
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1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24072172/s1 , Data S1 containing training images and annotations, Figures S1–S7.Open asset ↗10.3390/s24072172/s1lines:127-146Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.
Why it matches plant phenotyping methodsハプロタイプ構築法を用いたゲノム予測の精度比較・評価が研究の中心であり、複数のトウモロコシ形質の予測という計算的な形質推定法を検証している。
abstractIn this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces.
Reproduction assets foundThe paper's data availability statement explicitly points to a public figshare dataset (Data_from_HoelkerMayer_et_al) containing the phenotypic/genotypic data analyzed, and a public GitHub repository (TUMplantbreeding/HaplotypeGP) with the authors' analysis code for haplotype-based genomic prediction.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://figshare.com/articles/dataset/Data_from_HoelkerMayer_et_al/17014421 and https://github.com/TUMplantbreeding/HaplotypeGP .Open asset ↗figshare · Data_from_HoelkerMayer_et_al/17014421lines:428-438Code / dataset availability confirmedCrossref · OpenAlex · checked 7 Sept 2026
Abstract Optical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectance dataset. We propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval. A standard (nadir) and a multi-angular, 10-band multispectral dataset was collected for maize using a UAV on two different days. Reflectance data was grouped by VZA and VAA (on average 2594 spectra/plot/day for the multi-angular data and 890 spectra/plot/day for nadir flights only, 13 spectra/plot/day for a standard orthomosaic), serving as predictor variables for leaf chlorophyll content (LCC), leaf area index (LAI), green leaf area index (GLAI), and nitrogen balanced index (NBI) classification. Results consistently showed higher accuracy using grouped VZA/VAA reflectance compared to the standard orthomosaic data. Pooling all reflectance values across viewing directions did not yield satisfactory results. Performing multiple flights to obtain a multi-angular dataset did not improve performance over a multi-angular dataset obtained from a single nadir flight, highlighting its sufficiency. Our openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groups, benefiting cross-disciplinary and agriculture scientists in harnessing the potential of multi-angular datasets. Graphical abstract
Why it matches plant phenotyping methodsUAVマルチアングル反射データから植物形質を抽出・分類する方法を提案し、標準オルソモザイクと精度比較しているため、表現型取得手法が中心である。
abstractWe propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval.
Reproduction assets foundThe authors explicitly share their custom Python workflow for extracting multi-angular VZA/VAA reflectance data and reproducing the maize trait classification analysis via a public GitHub repository, referenced multiple times in the article.Code · publicOur openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groupsOpen asset ↗ReneHeim/proj_on_uavlines:1-64Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.
Why it matches plant phenotyping methodsトウモロコシのさび病抵抗性を対象に、UAVマルチスペクトル・熱赤外データから植物状態を評価し、目視評価との比較や再利用可能なデータセットとして提示しており、表現型取得法が中心的です。
abstractFor phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS).
Reproduction assets foundThe paper deposits its own phenotype data (visual scores, UAV multispectral/thermal remote-sensing vegetation indices) and imputed SNP genotypes for three maize DH populations in the CIMMYT Research Data & Software Repository Network, with a direct public URL (hdl:11529/10548898). This is a paper-specific, publicly resDataset · publicData accessibility
Repository name: CIMMYT Research Data & Software Repository Network [2]
Data identification number: 10548898
Direct URL to data:Open asset ↗CIMMYT Research Data & Software Repository Networklines:1-57Dataset · public016/j.fcr.2024.109281.
2. Loladze A., Rodrigues F., Petroli C., Muñoz C., Macia Naranjo S., San Vicente F., Gerard B., Montesinos-López O.A., Crossa J., Martini J. CIMMYT Research Data & Software Repository Network, V1. 2023. Replication data for: use of remote sensing for genome-wide association studies and genomic prediction. https://hdl.handle.net/11529/10548898
Associated Data
Supplementary Materials
Image, application 1
Data Availability Statement
Replication Data for: Use of Remote Sensing for Genome-Wide Association Studies and Genomic Prediction (Original data) (Dataverse) [2] .Open asset ↗CIMMYT Research Data & Software Repository Networklines:219-242Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1 . Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.
Why it matches plant phenotyping methodsトウモロコシのさび病抵抗性を対象に、リモートセンシング由来指標を従来の視覚評点と比較し、遺伝解析・ゲノム予測における性能を評価している。病害表現型の取得法の比較検証が中心である。
abstractRemote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put.
Reproduction assets foundThe paper's RS-derived vegetation indices, visual scores, and related phenotyping data for the three maize DH populations are publicly deposited in CIMMYT's dataverse repository, explicitly linked in the Data availability section. No author analysis code or trained models are stated as available.Dataset · publicThe corresponding data can be found on CIMMYT’s dataverse repository ( https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548898 ).Open asset ↗hdl:11529/10548898lines:114-253Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSunflowerField / plotLaboratory / benchtopStem / branchPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
There is currently a need for inexpensive, continuous, non-destructive water potential measurements at high temporal resolution ( Helianthus annuus ) (petioles and stems) and a monocotyledon ( Zea mays ) species (stems) for 1 week during dehydration and re-watering treatments under laboratory conditions. We also demonstrated the ability of the device to record branch and trunk diameter variation of a woody dicotyledon ( Rhus typhina ) in the field. Under laboratory conditions, we compared our device (hereafter 'contact' dendrometer) with modified versions of another open-source dendrometer (the 'optical' dendrometer). Overall, contact and optical dendrometers were well aligned with one another, with Pearson correlation coefficients ranging from 0.77 to 0.97. Both dendrometer devices were well aligned with direct measurements of xylem water potential, with calibration curves exhibiting significant non-linearity, especially at water potentials near the point of incipient plasmolysis, with pseudo R 2 values (Efron) ranging from 0.89 to 0.99. Overall, both dendrometers were comparable and provided sufficient resolution to detect subtle differences in stem water potential (ca. 50 kPa) resulting from light-induced changes in transpiration, vapour pressure deficit and drying/wetting soils. All hardware designs, alternative configurations, software and build instructions for the contact dendrometers are provided.
Why it matches plant phenotyping methods安価なオープンソース樹幹径計を開発し、水ポテンシャルと茎径成長を高時間・空間分解能で測定する手法を比較検証しており、植物表現型取得が研究の中心である。
titleDevelopment and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all sensor software, 3D prints, photos, and schematics in a public GitHub repository, and the dendrometer measurement data are provided as a CSV in the supplementary information (no allowed URL for the SI itself). The GitHub repository is a paper-specific,公开,可Code · publicins, CO 80526, USA.
Data Availability
All data used in this study are available for download from the supplemental information as a CSV file (sup_data_all_dendro.csv). All software needed for operating sensors, 3D prints, photos, and schematics have been included in the SI materials, and can also be freely accessed via github ( https://github.com/sean-gl/dendrometer_water_potential_device ).
Sources of Funding
J.J.S. was supported by an NSF Postdoctoral Research Fellowship in Biology, Grant No. IOS-1907338.
Contributions by the Authors
All authors contributed meaningfully to the manuscript. S.M.G., J.J.S., B.A., S.K.P. and J.M. designed the experiment and collected the data. S.M.G. wrote theOpen asset ↗https://github.com/sean-gl/dendrometer_water_potential_devicelines:224-283Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF 760 ) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.
Why it matches plant phenotyping methods作物キャノピーの光合成状態を推定する地上SIFについて、取得データセットと処理・校正・フットプリント拡大プロトコルが中心的に提示されており、植物フェノタイピング手法およびデータセットに該当する。
abstractWe introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint.
Reproduction assets foundThe paper's ground far-red SIF760 and vegetation indices dataset (15 site-years, US Corn Belt) is explicitly deposited in a public repository (ORNL DAAC) with a DOI matching an allowed URL.Dataset · publicThe processed half-hourly
SIF760 and VIs are available at the on Oak Ridge National Laboratory Distributed Active Archive Center (ORNL
DAAC) data repository https://doi.org/10.3334/ORNLDAAC/213653.Open asset ↗10.3334/ORNLDAAC/213653pdf-page:4 lines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Why it matches plant phenotyping methodsトウモロコシ種子の発芽・活力をRGB画像で推定するデータセットであり、植物表現型の画像取得・解析基盤が中心と判断できる。
titleAn RGB image dataset for seed germination prediction and vigor detection - maize.
Reproduction assets foundThe authors publicly deposited the paper's maize seed germination RGB image dataset (19,800 annotated images, PASCAL VOC XML labels) on Kaggle and IEEE DataPort, with explicit URLs in the text. No analysis code was deposited.Dataset · public. germinating:7042; 3. germinated:1936; 4. primary root:5087; 5. secondary root:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection Open asset ↗Kaggle · Seed Vigor Detection RGB Imagelines:59-108Dataset · publict:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection model Faster RCNN ( Girshick, 2015 ), the one-stage model SSD ( Liu et al., 20Open asset ↗rgb-image-dataset-seed-germination-prediction-and-seed-vigorlines:59-108Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
MaizeField / plotGreenhouseRootSegmentationRoot system architecture
Abstract Crop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.
Why it matches plant phenotyping methods根画像セグメンテーションモデルを開発・比較し、時間的および施設間ドメインシフト下での性能と根長推定精度を検証しており、植物表現型取得手法が研究の中心である。
abstractWe developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift.
Reproduction assets foundThe authors openly published the root images used to train their segmentation models, the trained models, and RhizoVision Explorer settings metadata in a Zenodo deposit (DOI 10.5281/zenodo.8224956), which is a paper-specific, publicly actionable asset.Dataset · publicBANET ET AL. 5 of 13
annotating for 4 h, and allowing model training to proceed
until 60/60 epochs without progress. Root images collected in
the field and the greenhouse that were used to train models are
openly published (https://doi.org/10.5281/zenodo.8224956).2.3 Image segmentation and trait
extraction
The trained models were used to generate segmentations
for the field images and greenhouse images using Root-
Painter’s “Segment folder” function from the “Network”
menu. These segmentations were then converted to binary
segmentations (i.e., black and white) using RootPainter’s
builtOpen asset ↗Zenodo · 10.5281/zenodo.8224956pdf-raw-page:5 lines:1-115Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
MaizePepper / chilliTomatoRootMorphology / geometry measurementRoot system architecture
Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination ( R 2 ), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.
Why it matches plant phenotyping methods画像から根長という植物形質を推定するCNN手法を開発し、複数データセットで精度・頑健性を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation.
Reproduction assets foundThe authors explicitly deposited the 4,015 minirhizotron root images with Rootfly TRL and point-coordinate annotations used to train and test their CNN models in a public Zenodo repository, making it a directly qualifying paper-specific public dataset.Dataset · publicThe datasets generated and analyzed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7482146 .Open asset ↗Zenodo · 10.5281/zenodo.7482146lines:373-471Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
MaizeRootMorphology / geometry measurementRoot system architecture
A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.
Why it matches plant phenotyping methods3Dトウモロコシ根系の構造を、persistent homologyによって従来の形質空間を拡張して解析する計算的フェノタイピング手法が中心であり、根系構造形質の抽出・評価に該当する。
abstractpersistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit of the 3D root models (paper-specific phenotyping inputs) and raw trait data in Supplementary Table 5 via the Frontiers supplementary material page. Matlab code is only referenced via a prior publication (Li et al., 2019) without an authors' deDataset · publicn this way, the positive value represents the QTL that have increases on the major allele, while negative values indicate QTL that have increases on minor allele.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Material
. All the 3D models can be found at: http://dx.doi.org/10.6084/m9.figshare.23692353 . Matlab Code can be found in Li et al., 2019 . Raw trait data can be found in
Supplementary Table 5
. Further inquiries can be directed to the corresponding authors.
Author contributions
ML: Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. ZL: ForOpen asset ↗figshare · 10.6084/m9.figshare.23692353lines:206-232Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1260005/full#supplementary-material
Supplementary Figure 1
Illustrations of persistent homology traits. (A) An example of persistence barcode. (B) The persistence diagram that is equivalent to the barcode in (A) . One example of corresponding bar-to-point is highlighted in pink color. (C) Gaussian density estimatoOpen asset ↗lines:206-232Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit.•Efficient methods to measure biomechanical traits and record metadata related to stalk lodging.•Can be used in studies with large sample sizes (i.e., > 1,000).
Why it matches plant phenotyping methodsトウモロコシの倒伏抵抗性に関わる生体力学的形質を高スループットに測定する、実験室・圃場対応の表現型解析パイプラインを開発・実装した研究であり、方法が中心的である。
abstractA phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented.
Reproduction assets foundThe paper's internode-length phenotyping workflow (YOLOv5m node detection, LabelImg verification, custom R scripts) is publicly available as the authors' InterMeas repository on GitHub, including scripts, example images, and a tutorial. Other assets (Instron methods file, DARLING data) are only supplementary or on-dataCode · publicodal annotations across individual stalks, convert pixel distances to physical distances using the known dimensions of the imaging background, and output internodal lengths as a single excel file of stalk/internode identifiers and lengths. All scripts, example images, and a tutorial on setup and usage are available on Github at https://github.com/nbo245/InterMeas and a Shiny dashboard can be run locally to implement the internode measurement workflow within an interactive GUI environment.
Measurements of minor diameter, rind thickness, rind penetration resistance and integrated puncture scoreOpen asset ↗nbo245/InterMeaslines:85-89Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
With the increasing integration of AI technology in the food industry, deep learning has demonstrated its immense potential in the domain of plant disease image recognition. However, there remains a gap in research between models capable of continual learning of new diseases and addressing the inherent catastrophic forgetting issue in neural networks. This study aims to comprehensively evaluate various learning strategies based on advanced computer vision models for multi-disease continual learning tasks in food disease recognition. To cater to the benchmark dataset requirements, we collected the PlantDiseaseCL dataset, sourced from the internet, encompassing diverse crop diseases from apples, corn, and more. Utilizing the Vision Transformer (ViT) model, we established a plant disease image recognition classifier, which, in joint learning, outperformed several comparative CNN architectures in accuracy (0.9538), precision (0.9532), recall (0.9528), and F1 score (0.9560). To further harness the potential of ViT in food disease defect recognition, we introduced a mathematical paradigm for crop disease recognition continual learning. For the first time, we proposed a novel ViT-TV architecture in the multi-disease image recognition scenario, incorporating a Total Variation (TV) distance-based loss (TV-Loss) to quantify the disparity between current and previous attention distributions, fostering attention consistency and mitigating the catastrophic forgetting inherent in ViT without prior task samples. In the incremental learning of the PlantDiseaseCL dataset across 3-Steps and 5-Steps, our strategy achieved average accuracies of 0.7077 and 0.5661, respectively, surpassing all compared Zero-Exemplar Approaches like LUCIR, SI, MAS, and even outperforming exemplar-based strategies like EEIL and ICaRL. In conclusion, the ViT-TV approach offers robust support for the long-term intelligent development of the agricultural and food industry, especially showcasing significant applicability in continual learning for crop disease image recognition.
Why it matches plant phenotyping methods植物病害画像から病害状態を推定する認識手法を開発・評価し、継続学習用データセットも構築しているため、植物フェノタイピング手法が中心である。
abstractwe collected the PlantDiseaseCL dataset, sourced from the internet, encompassing diverse crop diseases from apples, corn, and more.
Reproduction assets foundThe paper's PlantDiseaseCL dataset (30,863 crop disease images used for all phenotyping/recognition experiments) is explicitly made publicly available on Kaggle by the authors. No code or model checkpoint availability is stated in the supplied blocks.Dataset · publicFor the broader research community’s benefit, we have made the PlantDiseaseCL dataset publicly available on the Kaggle platform. It can be accessed at https://www.kaggle.com/datasets/gabrielwang01/leaf-disease-must (last accessed on 18 August 2023).Open asset ↗Kaggle · gabrielwang01/leaf-disease-mustlines:310-388Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MaizeLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsWater status / transpiration
Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R 2 =0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R 2 =0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R 2 =0.83) with actual canopy measurements. The iPhone’s surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R 2 =0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This Template The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com .
Why it matches plant phenotyping methodsiPhoneのLiDARと3D点群を用いてトウモロコシの葉・植物表面積を推定する手法を開発・検証しており、植物形質の取得が研究の中心である。
abstractThis study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays).
Reproduction assets foundThe paper states that all statistical code and data files for the maize 3D leaf phenotyping analysis are publicly available in the authors' GitHub repository.Code · publicconducted using the “scipy” package’s “f_oneway”
12 function [18]. The Python packages “pandas” [19] and “numpy” [20] were used to arrange the
13 data before plotting. The Python packages “matplotlib”, “seaborn” [21] were used for data
14 visualization. All statistical code and data files needed are available to download
15 at https://github.com/gavrielbs/3D_Corn_Phenotype.
16
17 PlantArray System by Plant-DiTech LTD
18 PlantArray is a high-throughput, multi-sensor physiological phenotyping gravimetric
19 platform. This plant phenotyping system performs quick plant screening based on precise
20 physiology traits measurements that are great indicators for yield potential with proven high
21 cOpen asset ↗gavrielbs/3D_Corn_Phenotypepdf-layout-page:4 lines:1-44Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Abstract Farmers face a daunting challenge in meeting the escalating demands of a rapidly growing population for agricultural products, while plant diseases continue to exact a devastating toll on food production. Despite investing billions of dollars in disease management, agriculturists often struggle to achieve effective disease control without the support of advanced technology. The article explores a deep learning-based approach for disease detection. Specifically, it employs a Convolutional Neural Network (CNN) architecture for the detection. For the automated detection of plant disease, using plant images. This paper presents a new model for the early detection of plant detection based on processing plant images. And compare the in-depth performance analysis of hyper parameters in the context of plant disease detection by focusing on three distinct crops: (Apple, Corn, and Potato). Moreover, the data augmentation impact is analyzed. To enhance accessibility for farmers, our model is seamlessly integrated with a mobile application. The experimental results show the efficiency of our fine-tuned enhanced CNN model (E-CNN) achieving 98.17% accuracy on fungal classes. This research endeavors to pave the way for more effective plant disease management and ultimately to improve agricultural productivity in the face of mounting global challenges.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNNを開発・評価し、ハイパーパラメータとデータ拡張の性能分析も行っているため、病害フェノタイピング手法が中心です。
abstractThe article explores a deep learning-based approach for disease detection.
Reproduction assets foundThe paper's plant disease detection experiments are built on the public PlantVillage image dataset, which the authors explicitly cite with a Kaggle URL. No author-generated code, trained model checkpoints, or supplementary data deposits are mentioned.Dataset · public[21] “PlantVillage Dataset.” Accessed: Dec. 05, 2023. [Online]. Available:
https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗Kaggle · plantvillage-datasetpdf-page:28 lines:1-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、ダウンサンプリングと深層学習による意味・個体セグメンテーション手法を開発しており、表現型抽出が中心的な技術貢献である。
abstractsegmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters.
Reproduction assets foundThe paper's plant-phenotyping measurements are based entirely on the public Pheno4D dataset of maize and tomato point clouds, which the authors explicitly state is openly available at the IPB Bonn URL. No author analysis code or trained model is disclosed.Dataset · publicervision, X.P.; project administration, X.P.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are openly available in https://www.ipb.uni-bonn.de/data/pheno4d/ (accessed on 15 November 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded by the Key Research and Development Program of Shaanxi (Grant No. 2019ZDLNY07-06-01).
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publicatioOpen asset ↗Pheno4Dlines:423-444Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング環境で、熱画像・ハイパースペクトル画像による干ばつストレス、水利用、蒸散速度の推定手法を評価・モデル比較しており、フェノタイピング手法が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe article's Availability of data and materials statement deposits the datasets generated and analyzed in this study (thermal/hyperspectral phenotyping data and analyses) in three Zenodo repositories with public DOIs. These are paper-specific, publicly accessible assets. No author analysis code with an explicit publicDataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.7807989lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8164473lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8033640lines:198-347Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images with experts' annotations. The grain kernels used in the study consist of four types of cereal grains including wheat, maize, sorghum and rice, and were collected from over 20 regions in 5 countries. The surface information of each kernel is captured by our custom-built device equipped with high-resolution optic sensor units, and corresponding sampling information and annotations include collection location and time, morphology, physical size, weight, and Damage & Unsound grain categories provided by senior inspectors. In addition, we employed a commonly used deep learning model to provide classification results as a benchmark. We believe that our GrainSet will facilitate future research in fields such as assisting inspectors in grain quality inspections, providing guidance for grain storage and trade, and contributing to applications of smart agriculture.
Why it matches plant phenotyping methods穀粒画像と形態・サイズ・重量・損傷状態の注釈を大規模に整備した再利用可能なデータセットで、カスタム撮像装置とベンチマーク分類も含むため、植物器官の表現型取得・解析が中心です。
abstractWe present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Reproduction assets foundThe paper's GrainSet database (annotated single-kernel grain images with DU-grain, weight, size, and mask annotations) is publicly deposited on Figshare under CC BY 4.0, split into four species sub-datasets plus tiny/raw previews, and the authors' validation code and trained models are released on GitHub.Code · publicThe validation code and models are released in the Github repository https://github.com/GrainSpace/GrainSet.Open asset ↗GitHub · GrainSpace/GrainSetpdf-page:10 lines:1-57Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.
Why it matches plant phenotyping methodsトウモロコシ葉画像からリン欠乏状態を分類する画像解析手法を構築・評価しており、植物の栄養状態という表現型の取得が研究の中心である。
abstractthis study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis.
Reproduction assets foundThe authors explicitly state that the complete generated maize-leaf phosphorus-deficiency image dataset (2,433 labeled images across three nutrition classes) is freely available on Zenodo, a paper-specific public asset directly reproducing this paper's phenotyping measurements.Dataset · publicwriting – original draft, M.R.; writing – review & editing, A.M., C.T.
and L.G. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Universidad EAFIT.
Data Availability Statement: The complete generated dataset presented in this work is freely available at Zenodo,
at https://zenodo.org/records/10041514.
Acknowledgments: M. Ramos-Ospina and A. Marulanda-Tobón acknowledge the support from María Isabel
Hernández-Pérez, head of the Undergraduate Program in Agricultural Engineering, from School of Applied
Sciences and Engineering, Universidad EAFIT, for the scientific assistance provided during the realization of this
work.
ConfliOpen asset ↗Zenodo · 10041514pdf-layout-page:23 lines:1-68Code / 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 · checked 7 Sept 2026
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
Why it matches plant phenotyping methodsロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・整合性評価が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractRobotized indoor phenotyping allows measuring such traits at high throughput for speed breeding
Reproduction assets foundThe paper's Data availability statement deposits its phenotypic/genotypic datasets (diversity panel, genetic progress panel, recent hybrids panel) on Recherche Data Gouv with three public DOIs. These are paper-specific public phenotype datasets directly reproducing the study's measurements. The PhenoArch platform page,Dataset · publicThe datasets for phenotypic and genotypic values for the diversity panel are available at https://doi.org/10.15454/IASSTN .Open asset ↗10.15454/IASSTNlines:186-234Dataset · publicThe datasets for phenotypic and genotypic values for the genetic progress panel are available at https://doi.org/10.15454/KLD0GH .Open asset ↗10.15454/KLD0GHlines:186-234Dataset · publicThe dataset for the ‘recent hybrid’ panel is available at https://doi.org/10.57745/NZY1KL .Open asset ↗10.57745/NZY1KLlines:186-234Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Introduction Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する深層学習手法を開発しており、植物の病徴・病害状態の取得と推定が研究の中心であるため。
abstractThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases.
Reproduction assets foundThe paper's plant leaf disease classification experiments are built entirely on two public Kaggle image datasets explicitly cited by the authors: the PlantVillage Dataset (eight-class experiment) and the Plant Disease Prediction Dataset (three-class experiment). No author code, trained model, or supplementary deposit (Dataset · publicWe verified the effectiveness and robustness of the DeepPlantNet model by using images from the publicly available Kaggle “PlantVillage Dataset” dataset ( Dataset : https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ).Open asset ↗Kaggle · abdallahalidev/plantvillage-datasetlines:355-366Dataset · publicWe validated our model using another common, publicly accessible Kaggle dataset, “Plant Disease Prediction Dataset,” to assess and estimate the generalizability and performance of the DeepPlantNet model ( Dataset : https://www.kaggle.com/datasets/shuvranshu/plant-disease-prediction-dataset ).Open asset ↗Kaggle · shuvranshu/plant-disease-prediction-datasetlines:729-756Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.
Why it matches plant phenotyping methods植物葉の気孔を対象に、回転を考慮した検出と気孔形質・コンダクタンス算出を一体化する手法を開発しており、フェノタイピング手法が中心である。
abstractThis paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time.
Reproduction assets foundThe paper's DeepRSD stomata detection and conductance calculation code is explicitly stated to be publicly hosted on GitHub at the authors' repository URL. No public dataset of the 2,192 maize stomata images is mentioned.Code · publicThe code of anchor-free stomata detection and stomata conductance calculation has been hosted to GitHub and is available at https://github.com/sswangbo159357/Rotating-stomata-detection .Open asset ↗sswangbo159357/Rotating-stomata-detectionlines:288-533Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Nitrogen (N) is one of the key inputs in maize production applied in the form of fertilizers. Nitrogen deficiency during the vegetation period leads to lower yields since N is utilized in proteins and enzymes that enable important biochemical processes such as photosynthesis. Nitrogen deficiency leads to specific symptoms that eventually become visible to the naked eye during vegetation. Our hypothesis was that N deficiency can be detected from maize RGB images in parametric process such as a deep neural network. The aim of the reported dataset is to optimize the usage of N in the farmer's fields and accordingly, reduce its environmental footprint. This dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution. The field trials included three levels of N fertilization: N0 without N fertilization, N75 with 75 kg of added N fertilizer, and NFull with 136 kg of added N fertilizer. For each fertilizer level, 400 plots were created with 238 different maize genotypes, resulting in a total of 1200 plots. Images were taken with a tripod mounted DSLR camera, aperture priority set to f/8 and sensor sensitivity set to ISO400. Images were taken at a 45° angle to each plot. This dataset can be useful to both researchers, data scientists and agronomists, especially in the context of emerging technologies in precision agriculture, such as robotics, 5G networks and unmanned aerial vehicle (UAV). The dataset is one of the first publicly accessible datasets of maize canopy images under different N fertilization levels and represents a valuable public resource for development of machine learning models for in-season detection of N deficiency in maize.
Why it matches plant phenotyping methodsトウモロコシの画像から窒素欠乏という植物状態を検出するための注釈付き公開画像データセットであり、機械学習による表現型抽出の基盤として方法論的に中心的です。
abstractThis dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution.
Reproduction assets foundThe paper is a Data in Brief describing a public Mendeley Data deposit of 1200 annotated maize canopy RGB images across three N fertilization levels, plus a preprocessing iPython notebook (TensorFlow_preprocessing.ipynb) included in the same repository. This is a paper-specific, publicly and freely downloadable phenopyDataset · publicers are not. Images at different field rows were taken randomly between 7:30 and 11:00 a.m.
Data source location
•
Institution: Agricultural Institute Osijek (AIO)
•
City/Town/Region: Osijek
•
Country: Croatia
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/g7xnn2bm4g.1
Direct URL to data: https://data.mendeley.com/datasets/g7xnn2bm4g/1
Instructions for accessing these data: Data are freely and anonymously downloadable from the link. Images are compressed into a single .zip file. Additionally, iPython notebook ‘TensorFlow_preprocessing.ipynb’ and ‘requirements.txt’ cover data preprocessing and required libraries to run the scripts.
1.
Value of the Data
Open asset ↗Mendeley Data · 10.17632/g7xnn2bm4g.1lines:1-58Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Stomata act as a pathway for air and water vapor during respiration, transpiration and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high throughput stoma is a key issue. However, current existing methods usually suffer from detection error or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired in a destructive way, and the maize stomatal data set acquired in a nondestructive way, enabling one-stop automatic collection of phenotypic such as the location, density, length and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. And the experimental results showed that the prediction results of the method are consistent with those of manual labeled. The test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).
Why it matches plant phenotyping methods気孔の検出と開度・密度・寸法などの表現型を自動抽出する画像解析手法を開発し、複数作物で精度検証しているため、植物フェノタイピング手法が中心である。
abstractThe proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time and intelligent phenotype analysis of stomata and apertures.
Reproduction assets foundThe paper explicitly states that the test sets, system code, and usage instructions are publicly available at the authors' GitHub repository (https://github.com/AITAhenu/RotatedStomataNet). This is a paper-specific asset: the RotatedStomataNet system code for rotated object detection of stomata and apertures, together,Code · publicThe test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).Open asset ↗AITAhenu/RotatedStomataNetpdf-page:3 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69 , ZCN8, sh1, wx1, du1 ) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.
Why it matches plant phenotyping methodsNIRSを用いた植物試料の表現型推定と、ゲノム予測との比較検証が研究の中心であり、複数のトウモロコシ農業形質・種子組成形質を対象としているため。
abstractOne phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition.
Reproduction assets foundThe paper's Data Availability section and Methods explicitly state that the annotated R analysis script, plus the data files (CSVs.zip, SNP60000.hmp.zip) needed to reproduce the prediction results, are publicly available in the authors' GitHub repository ajdesalvio/Maize-NIRS-GBS.Code · public52
1038
Data Availability
1039
An annotated script of the R code used in this research can be accessed via GitHub
1040
(https://github.com/ajdesalvio/Maize-NIRS-GBS.git). Supplementary Data 1
1041
(Supplementary_Data_1.xlsx) contains prediction results, GWAS results, and variable importance
1042
scores for NIRS bands. Files necessary to run the R script and reproduce the prediction results are
1043
available in the CSVs.zip folder and the SNP60000.hmp.zip folder. Supplementary figures are
1044Open asset ↗ajdesalvio/Maize-NIRS-GBSpdf-raw-page:52 lines:1-26Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Abstract Background Thermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. Results The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants’ water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. Conclusion Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング基盤で、熱画像・ハイパースペクトル画像から乾燥ストレス、蒸散速度、気孔コンダクタンスを推定する手法の評価・モデル開発が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe paper's declarations state that the datasets generated and analyzed during the study (thermal/hyperspectral imaging, environmental, and transpiration data from the maize drought phenotyping experiment) are publicly available in three Zenodo deposits with explicit DOIs. These are paper-specific, public, and directlyDataset · publicyield of photosystem II
ψ water potential
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. wOpen asset ↗zenodo · 10.5281/zenodo.7807989pdf-raw-page:30 lines:1-62Dataset · publicl
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
7Open asset ↗zenodo · 10.5281/zenodo.8164473pdf-raw-page:30 lines:1-62Dataset · publiconsent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
760
761
Funding
762
This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
In the agricultural sector, identifying plant diseases at their earliest possible stage of infestation still remains a huge challenge with respect to the maximization of crop production and farmers' income. In recent years, advanced computer vision techniques like Vision Transformers (ViTs) are being successfully applied to identify plant diseases automatically. However, the MLP module in existing ViTs is computationally expensive as well as inefficient in extracting promising features from diseased images. Therefore, this study proposes a comparatively lightweight and improved vision transformer network, also known as "TrIncNet" for plant disease identification. In the proposed network, we introduced a modified encoder architecture a.k.a. Trans-Inception block in which the MLP block of existing ViT was replaced by a custom inception block. Additionally, each Trans-Inception block is surrounded by a skip connection, making it much more resistant to the vanishing gradient problem. The applicability of the proposed network for identifying plant diseases was assessed using two plant disease image datasets viz: PlantVillage dataset and Maize disease dataset (contains in-field images of Maize diseases). The comparative performance analysis on both datasets reported that the proposed TrIncNet network outperformed the state-of-the-art CNN architectures viz: VGG-19, GoogLeNet, ResNet-50, Xception, InceptionV3, and MobileNet. Moreover, the experimental results also showed that the proposed network had achieved 5.38% and 2.87% higher testing accuracy than the existing ViT network on both datasets, respectively. Therefore, the lightweight nature and improved prediction performance make the proposed network suitable for being integrated with IoT devices to assist the stakeholders in identifying plant diseases at the field level.
Why it matches plant phenotyping methods植物の病徴画像から疾病状態を推定する新規画像解析モデルを開発し、複数データセットと既存モデルで性能比較しており、植物フェノタイピング手法が中心である。
abstractthis study proposes a comparatively lightweight and improved vision transformer network, also known as "TrIncNet" for plant disease identification.
Reproduction assets foundThe paper used two plant disease image datasets. The PlantVillage dataset is openly available at the authors' stated public URL (matching an allowed URL), qualifying as a public paper-specific asset. The in-field Maize dataset is only available from the corresponding author on request, so it is listed as request_only.Dataset · publicThe PlantVillage dataset, is openly available on https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw , and the Maize dataset will be available from the corresponding author on reasonable request.Open asset ↗https://github.com/spMohanty/PlantVillage-Datasetlines:743-770Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate ( A c ) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in A c between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in A c (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with A c and plant architecture traits were non-linearly related to A c . This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Why it matches plant phenotyping methods64台カメラのマルチビュー・ステレオ計測によるフェノミクスデータと、キャノピー光合成モデル・因子分析を統合した新規パイプラインの開発が中心であり、植物形態形質とキャノピー光合成の関係を定量化している。
titleDevelopment of a Novel 3D Canopy Modeling Pipeline
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' 3D canopy modeling pipeline source code and the FastTracer ray tracing software used for the canopy photosynthesis simulations, both on public GitHub repositories. No phenotype/image datasets are explicitly deposited.Code · publicThe source code used in this study is available for non-commercial use and the code can be downloaded from https://github.com/PlantSystemsBiology/3DCanopyModelOpen asset ↗PlantSystemsBiology/3DCanopyModellines:224-402Code · publicThe FastTracer software is available from https://github.com/PlantSystemsBiology/fastTracerPublicOpen asset ↗PlantSystemsBiology/fastTracerPubliclines:224-402Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background: Rapid and accurate detection of tassels is of great significance for maize breeding, seed production and the acquisition of key growth stage. To liberate manpower and improve the efficiency of production management, many automatic detection methods with acceptable accuracy have been proposed. However, images acquisition parameters of these methods were quite different, so they cannot provide an operable standard for practical applications. In this study, based on multi-temporal unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy from two aspects of efficient use of samples and data acquisition standards. Based on the detection results, we estimated tasseling date of different plots and analyzed varieties’ differences. Results: The results show that model performance would not be greatly affected before the amount of training data changed by orders of magnitude, but it can be improved effectively by adjusting sub-images’ sizes, and the final model was selected with AP@0.5IOU was 0.916; images obtained at 12 pm were more suitable for tassels detection, AP@0.5IOU, recall and precision were 3%, 2% and 6% higher than that at 8 am; optimal spatial resolution was around 1cm for tassels detection by considering the recognition effect and data acquisition efficiency. Conclusions: This study analyzed key factors affecting maize tassels detection and provided a reasonable reference for future applications, which is helpful to screen out varieties from large-scale breeding materials.
Why it matches plant phenotyping methodsUAV画像によるトウモロコシ雄穂検出のデータセット構築、検出精度の検証、撮影条件の最適化を中心とする植物フェノタイピング研究である。
abstractwe created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy
Reproduction assets foundThe paper's UAV maize tassel detection dataset (UAVMTD: 142 UAV RGB images, 28,182 labeled tassels) is explicitly stated to be publicly available on the authors' GitHub repository, which is an allowed URL. No separate analysis code or trained model checkpoints are explicitly deposited.Dataset · public1 Consent for publication
2 Not applicable.
3 Availability of data and materials
4 The dataset analyzed are available at: https://github.com/Xulizzz/UAVMTD
5 Competing interests
6 The authors declare that they have no known competing financial interests or personal
7 relationships that could have appeared to influence the work reported in this paper.
8 Funding
9 This work was supported by the National Key Research and Development Program of
10 China and Shandong Province, China（20Open asset ↗Xulizzz/UAVMTDpdf-layout-page:33 lines:1-49Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致を検証しているため、植物フェノタイピング手法が中心である。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Fusarium ear rot (FER) is a common disease in maize caused by the pathogen Fusarium verticillioides. Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify the damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of the infected area, a system of semi-automated image acquisition and subsequent image analysis was designed. The tool created for image acquisition, “The Ear Unwrapper”, successfully obtained images of the full exterior of maize ears. A set of images produced from The Ear Unwrapper was then used as an example of how machine learning could be used to estimate disease severity from unannotated images. A high correlation (0.74) was found between the methods estimating the area of disease, but low correlations (0.47 and 0.28) were found between the number of infected kernels and the area of disease, indicating how different methods can result in contrasting severity scores. This study provides an example of how a simplified image acquisition tool can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes such as Fusarium ear rot.
Why it matches plant phenotyping methodsトウモロコシ雌穂の病徴画像取得装置と画像解析・機械学習による病害重症度推定を開発しており、表現型取得法が研究の中心である。
abstracta system of semi-automated image acquisition and subsequent image analysis was designed
Reproduction assets foundThe authors publicly deposited the codebase for The Ear Unwrapper image acquisition pipeline on GitHub, which is the paper-specific computational/hardware asset for this phenotyping study. The raw phenotype data (Supplemental Table S1) is hosted as an MDPI supplement rather than a separate repository, and the Ilastik/GCode · publiciculture–National Institute of Food and Agriculture Tactical Sciences for Agricultural Biosecurity
Institute of Food and Agricultural Sciences Project 13117320.
Data Availability Statement: The raw dataset used to generate these results can be found in Sup-
plemental Table S1. The codebase for The Ear Unwrapper can be found at (https://github.com/ohudson1/The-Ear-Unwrapper-.git (accessed on 24 May 2023)). All code used is open source and
code bases for both Ilastik (https://github.com/ilastik (accessed on 1 January 2023)).
Acknowledgments: Marcio Resende and the Sweetcorn Lab for assistance with the field trials.
Conflicts of Interest: The authors declare no conflict of interest.
AbbreviatioOpen asset ↗ohudson1/The-Ear-Unwrapper-pdf-raw-page:9 lines:1-50Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
C 4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C 4 plants compared with the ancestral C 3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C 4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C 4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C 4 species demonstrating natural variation for this trait.
Why it matches plant phenotyping methodsC4植物の葉脈密度を高速・高スループットに定量する染色、画像取得、画像解析手法を開発しており、植物形態形質の抽出が研究の中心です。
abstractHere we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C 4 species.
Reproduction assets foundThe paper's Starch4Kranz vein-density analysis pipeline (MATLAB and Python scripts) is explicitly deposited publicly on GitHub by the authors. No separate phenotype dataset or image deposit is stated in the supplied text.Code · publicfunction, blur.m contains the blurring function, and running_Starch4Kranz.m contains a short script that runs Starch4Kranz.m while saving results to a table easily transferable to software for further data analysis. For Python, just two scripts are required, Starch4Kranz.py and running_Starch4Kranz.py. Scripts ara available at https://github.com/plycs5/Starch4Kranz . To run the script in MATLAB the user must have the ImageProcessingToolbox activated and in Python they must have the dependent libraries installed into their environment. There are 14 inputs the user can supply (Supporting Information: Table S1 ), five of which are necessary; filename, trim_factor (see Results), pixel_length_Open asset ↗plycs5/Starch4Kranzlines:94-103Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
Why it matches plant phenotyping methodsRGB・LiDAR・VNIRリモートセンシングによる作物形質抽出について、行選択とプロットトリミングを反復性・収量予測で評価しており、フェノタイピング手法の技術評価が中心である。
abstractThe objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data.
Reproduction assets foundThe paper states that remote sensing data, yield data, and the authors' R analysis code are publicly deposited in the Purdue University Research Repository under DOI 10.4231/PF9S-4G38. This is a paper-specific, publicly actionable asset covering both the phenotyping measurements (RGB/LiDAR/VNIR remote sensing traits, 4Dataset · publicRemote sensing data, yield data, and R code used for this study are available at the Purdue University Research Repository (10.4231/PF9S-4G38).Open asset ↗Purdue University Research Repository · 10.4231/PF9S-4G38lines:404-413Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Artificial Intelligence (AI) has been evident in the agricultural sector recently. The objective of AI in agriculture is to control crop pests/diseases, reduce cost, and improve crop yield. In developing countries, the agriculture sector faces numerous challenges in the form of knowledge gap between farmers and technology, disease and pest infestation, lack of storage facilities, among others. In order to resolve some of these challenges, this paper presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images (6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test sets. The latter consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.
Why it matches plant phenotyping methods植物の病害状態を画像で扱う再利用可能なデータセットの構築が主題であり、植物病害フェノタイピング用データセットとして中心的な方法貢献がある。
titleCCMT: Dataset for crop pest and disease detection.
Reproduction assets foundThe paper is a data descriptor for the CCMT crop pest/disease image dataset, with the authors' own images publicly deposited on Mendeley Data (DOI 10.17632/bwh3zbpkpv.1), explicitly stated as freely available.Dataset · publicw.uenr.edu.gh
African Technology Policy Society Network
8 th Floor – The Chancery – Valley Road - Nairobi
P.O. Box 10081-00100, Nairobi, Kenya
Website: http://www.atpsnet.org
Data accessibility
Repository name: Dataset for Crop Pest and Disease Detection
Data identification number(doi): 10.17632/bwh3zbpkpv.1
Direct URL to data: https://data.mendeley.com/datasets/bwh3zbpkpv
Value of the Data
•
The dataset is comprehensive and consists of 102,976 high-quality images of four crops with 22 different classes, respectively cashew (5 classes), cassava (5 classes), maize (7 classes), and tomato (5 classes).
•
The dataset consists of plant leaves, pests, fruits and images of sick parts of cashew, casOpen asset ↗10.17632/bwh3zbpkpv.1lines:1-60Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .
Why it matches plant phenotyping methods作物病害画像から病害状態を推定する画像拡張手法そのものを提案・評価しており、植物病害フェノタイピング手法が中心である。
abstractthis paper proposes a supervised image augmentation method Negative Contrast
Reproduction assets foundThe authors explicitly state that their Plant Real-World crop disease dataset (Kaggle) and their analysis/augmentation code (GitHub) are publicly available.Dataset · publiccy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows 9
that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, 10
and thus reflects better generalization ability in real-world disease classification. Our dataset and 11
codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean 12
and https://github.com/hiter0/contrastaug . 13
Keywords: Crop Disease Classification; Crop Disease Dataset; Image Augmentation 14
1. Introduction 15
Since AlexNet[1] first used deep learning to win the ImageNet[2] competition in 16
2012, deep learning-based approaches have comprehensively outperformOpen asset ↗kaggle.com/datasets/w970704112/corn-wheat-rice-soybean · corn-wheat-rice-soybeanpdf-raw-page:1 lines:1-66Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Understanding three‐dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field‐grown roots remains a challenge due to the unknown tradeoff between 3D root‐model quality and 3D root‐trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state‐of‐the‐art open‐source 3D model reconstruction pipelines on 12 contrasting genotypes of field‐grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch‐based Multi‐View Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi‐View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. In the second test, we compared the accuracy of 3D root‐trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP‐based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM‐based 3D reconstruction on the same dataset of 12 genotypes, with 5–10 replicates per genotype. The results revealed that (1) the average number of images needed to build a denser 3D model was reduced from 3000 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (DIRT/3D [COLMAP‐based 3D reconstruction]); (2) denser 3D models helped improve the accuracy of the 3D root‐trait measurement; (3) reducing the number of images can help resolve data storage problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・検証し、更新版DIRT/3Dによる根形質推定の精度と効率を評価しており、植物フェノタイピング手法が中心である。
titleComparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping
Reproduction assets foundThe paper publicly releases its analysis scripts on GitHub, demo workflows for reconstruction and trait computation, Docker/Singularity containers for DIRT/3D reconstruction and trait extraction, and manuscript data on CyVerse Data Commons via a permanent DOI.Code · publice computation of the software-
supported GPUs. The GPU model with the DELL workstation
was a GeForce RTX 2070 SUPER, NVIDIA Corporation
TU104, nvcc: NVIDIA (R) Cuda compiler driver. All the
pipelines were tested under the command-line interface to
generate related 3D root models in point cloud format. The
scripts are on GitHub (https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master, folder Compu-
tational_test_1).
25782703,
2023,
1,
Downloaded
from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.20068,
Wiley
Online
Library
on
[28/06/2023].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
Online
Library
for
rules
of
useOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:3 lines:1-114Code · publicm the University of Georgia to the University
of Arizona.
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
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
PeOpen asset ↗Computational-Plant-Science/3D_model_reconstruction_demopdf-raw-page:12 lines:1-88Code · publicF 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
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
Peter Pietrzyk https://orcid.org/0000-0002-6794-8133
Alexander Bucksch https:/Open asset ↗Computational-Plant-Science/3D_model_traits_demopdf-raw-page:12 lines:1-88Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m -2 ) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R 2 = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m -2 , 0.4 m row spacing) towards 8 (12 plants.m -2 , 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m -2 , 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.
Why it matches plant phenotyping methodsRGB画像からトウモロコシ葉の方位を推定するALAEMアルゴリズムを提案・検証し、葉の形態形質を抽出しているため、植物フェノタイピング手法が中心です。
abstractto propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level
Reproduction assets foundThe paper's ALAEM phenotyping algorithm (maize leaf azimuth estimation from RGB images) is publicly available on GitHub, along with data samples per date and GxR conditions and a reproducible example.Code · publicThe full ALAEM code is available at github.com/mserouar/ALAEM along with data samples of each date and GxR conditions with a reproducible example.Open asset ↗mserouar/ALAEMlines:74-83Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousands of points, which fails to take full advantage of the high-resolution of advanced scanning devices. To address this issue, we propose a feature-fusion-based method called FF-Net, which consists of two branches, namely the voxel-branch and the point-branch. In particular, the voxel-branch partitions a point cloud into voxels and then employs sparse 3D convolution to learn the context features, and the point-branch learns the point features within a voxel to preserve the detailed point information. Finally, an attention-based module was designed to fuse the two branch features to produce the final segmentation. We conducted extensive experiments on two large plant point clouds (maize and tomato), and the results showed that our method outperformed three commonly used models on both datasets and achieved the best mIoU of 80.95% on the maize dataset and 86.65% on the tomato dataset. Extensive cross-validation experiments were performed to evaluate the generalization ability of the models, and our method achieved promising segmentation results. In addition, the drawbacks of the proposed method were analyzed, and the directions for future works are given.
Why it matches plant phenotyping methods植物の3D点群を対象としたセマンティックセグメンテーション手法を開発し、トウモロコシとトマトのデータセットで比較・交差検証しているため、フェノタイピング用の画像解析手法が中心です。
abstractSemantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years.
Reproduction assets foundThe authors explicitly state that their analysis code and the relabeled Pheno4D-derived dataset used in this paper are publicly available on GitHub.Code · publicOur code and relabeled dataset are available at: https://github.com/daojianqingchou/FF-Net , accessed on 8 April 2023.Open asset ↗daojianqingchou/FF-Netlines:56-66Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Phenotypic plasticity is defined as the phenotypic variation of a trait when an organism is exposed to different environments, and it is closely related to genotype. Exploring the genetic basis behind the phenotypic plasticity of ear traits in maize is critical to achieve climate-stable yields, particularly given the unpredictable effects of climate change. Performing genetic field studies in maize requires development of a fast, reliable, and automated system for phenotyping large numbers of samples. Results Here, we develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field. Using this platform, we analyze 15 common ear phenotypes and their phenotypic plasticity variation in 3819 transgenic maize inbred lines targeting 717 genes, along with the wild type lines of the same genetic background, in multiple field environments in two consecutive years. Kernel number is chosen as the primary target phenotype because it is a key trait for improving the grain yield and ensuring yield stability. We analyze the phenotypic plasticity of the transgenic lines in different environments and identify 34 candidate genes that may regulate the phenotypic plasticity of kernel number. Conclusions Our results suggest that as an integrated and efficient phenotyping platform for measuring maize ear traits, MAIZTRO can help to explore new traits that are important for improving and stabilizing the yield. This study indicates that genes and alleles related with ear trait plasticity can be identified using transgenic maize inbred populations.
Why it matches plant phenotyping methodsMAIZTROという自動化・高スループットのトウモロコシ穂形質フェノタイピング基盤の開発と適用が研究の中心であり、複数の穂形質を測定する方法論的貢献が明示されている。
abstractwe develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field.
Reproduction assets foundThe paper deposits its maize ear phenotyping data and analysis code publicly: ear image data on Zenodo (7796696), R scripts on Zenodo (7792895), and scripts/data on GitHub (liumiguo/paper_ear_pp_code) under GPL-3.0.Code · publicThe scripts and data used in this study are available under a GPL-3.0 license in Github: https://github.com/liumiguo/paper_ear_pp_code.git [ 50 ]Open asset ↗GitHub · liumiguo/paper_ear_pp_codelines:145-221Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Methods Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels. Results and discussion We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.
Why it matches plant phenotyping methodsトウモロコシ穀粒画像から胚の向き分類と分割を行う自己教師あり画像解析手法を開発・評価しており、植物器官の表現型抽出が研究の中心である。
abstractwe implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels.
Reproduction assets foundThe paper's maize kernel image datasets (classification and segmentation with masks) are deposited on Zenodo and the authors' analysis code is on GitHub, both explicitly linked in the data availability statement.Dataset · publicat self-supervised learning provides a meaningful path forward in advancing agricultural efficiency with computer vision and machine learning.
Data availability statement
The 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/7577017 , https://github.com/ddavidd23/ssl_corn.git .
Author contributions
UF, TJ, TL, and BG conceived the project. RW, UF, and TJ conducted physical experiments, data collection and data curation. RW and UF annotated the ground truth images. DD, KN, and TJ developed the machine learning framework. DD performed computational experimenOpen asset ↗zenodo · record/7577017lines:221-235Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Predicting phenotypes accurately from genomic, environment, and management factors is key to accelerating the development of novel cultivars with desirable traits. Inclusion of management and environmental factors enables in silico studies to predict the effect of specific management interventions or future climates. Despite the value such models would confer, much work remains to improve the accuracy of phenotypic predictions. Rather than advocate for a single specific modeling strategy, here we demonstrate within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own. We investigated various ensemble combinations of different model types, model numbers, and model weighting schemes to determine the accuracy of each. We find that ensembling generally improves performance even when combining only two models. The number and type of models included alter accuracy with improvements diminishing as the number of models included increases. Using a genetic algorithm to optimize ensemble composition reveals that, when weighted by the inverse of each model’s expected error, using combinations of best linear unbiased predictors, linear fixed effects models, deep learning models, and select machine learning models perform best on our datasets.
Why it matches plant phenotyping methodsトウモロコシ収量という植物形質を対象に、BLUP・機械学習・深層学習のアンサンブル予測を比較・検証し、予測精度を評価することが中心であるため。
abstractPredicting phenotypes accurately from genomic, environment, and management factors is key to accelerating the development of novel cultivars with desirable traits.
Reproduction assets foundThe paper's maize yield, weather, soil, management, and genomic data come from the Genomes to Fields initiative's public releases (2014-2019), accessible via the G2F resources page. The authors also deposit cleaned data (Zenodo 10.5281/zenodo.6916775) and analysis scripts/model predictions (Zenodo 10.5281/zenodo.769738Dataset · publicinversely proportionate to expected model error was
131
optimal for these data.
132
133
Materials and Methods
134
Data Preparation
135
Maize yield, environmental, and management data came from the Genomes to Fields
136
(G2F) initiative’s data releases for 2014-2019 (McFarland et al. 2020). These data are publicly
137
available (https://www.genomes2fields.org/resources/) and provide weather and soil data for
138
fields in the continental United States, management information, and genomic data in addition to
139
phenotypic measurements. Weather data was supplemented with data from Daymet (Thornton
140
et al. 2020). Genomic, environmental, and management data was quality controlled using
141
cuOpen asset ↗Genomes to Fieldspdf-raw-page:6 lines:1-81Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Maize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.
Why it matches plant phenotyping methodsトウモロコシの健全・罹病葉画像を収録した公開データセットで、植物病徴の画像ベース判定モデル開発を直接支援するため、フェノタイピング用データセットが中心です。
abstractThis paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania.
Reproduction assets foundThe paper's core asset is its own maize leaf imagery dataset (18,148 images of healthy/MLN/MSV leaves), publicly deposited by the authors on Harvard Dataverse with an explicit DOI and direct URL. The annotation tools cited (VisiPics, LabelMe, Makerere web annotation tool) are generic third-party tools, not paper assetsDataset · publica source location
•
Institution: The Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania Agricultural Research Institute (TARI)
•
City/Town/Region: Arusha
•
Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/GDON8Q
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GDON8Q
Open in a new tab
Value of the Data
•Open asset ↗Harvard Dataverse · doi:10.7910/DVN/GDON8Qlines:1-98Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background: To successfully breed any plant for disease resistance, an accurate method of phenotyping disease severity is crucial. Fusarium ear rot (FER) is a common disease of maize ( Zea mays ) caused by the pathogen Fusarium verticillioides (Sacc.) Nirenberg (synonym F. moniliforme Sheldon, teleomorph Gibberella moniliformis Wineland). Because of the quantitative nature of the disease, scoring disease severity is difficult and nuanced, relying on various ways to quantify damage caused by the pathogen. Towards the goal of designing a system with greater objectivity, reproducibility, and accuracy than subjective scores or estimations of area damaged, a system of semi-automated image acquisition and subsequent image analysis was designed. Results The tool created for image acquisition, “The Ear Unwrapper”, successfully obtains images of the full exterior of maize ears in roughly 10 seconds. To “unwrap” each ear of maize, the approach was to rotate the ear around its axis (the cob) and using a camera, take a continuous set of images of a single row of pixels and merge them to form one image. A set of images produced from The Ear Unwrapper were used to produce a probabilistic pixel classification model for predicting disease severity from unannotated images. The system was deliberately constructed using open-source software and off-the-shelf parts so that the image acquisition and analysis pipeline is adaptable for quantifying other maize ear pathogens, morphologies, and phenotypes. The data obtained from The Ear Unwrapper was correlated with two other phenotyping methods for validation and comparison and showed that the output from the system was reasonably accurate to determine lesion size. Conclusions This study provides an example of how a simplified image acquisition machine can be built and incorporated into a machine learning pipeline to measure phenotypes of interest. Here, The Ear Unwrapper was built to image ears of maize, but other cylindrical objects can also be “unwrapped” to obtain a single image of the object’s exterior. We also present how the use of machine learning in image analysis can be adapted from open-source software to estimate complex phenotypes, here, disease severity of Fusarium Ear Rot.
Why it matches plant phenotyping methodsトウモロコシ雌穂の画像取得と機械学習による病害重症度推定を開発し、他の表現型測定法との検証・比較も行っており、植物フェノタイピング手法が研究の中心である。
abstracta system of semi-automated image acquisition and subsequent image analysis was designed.
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1件を確認しました。Code · publicThe software used to obtain the data used in this study can be found at the Github link: https://github.com/ohudson1/The-Ear-Unwrapper-.gitOpen asset ↗ohudson1/The-Ear-Unwrapper-lines:113-137Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many studies proposed the use of stable carbon isotope ratio (δ 13 C) as a predictor of abiotic stresses in plants, considering only drought and nitrogen deficiency without further investigating the impact of other nutrient deficiencies, that is, phosphorus (P) and/or iron (Fe) deficiencies. To fill this knowledge gap, we assessed the δ 13 C of barley ( Hordeum vulgare L.), cucumber ( Cucumis sativus L.), maize ( Zea mays L.), and tomato ( Solanum lycopersicon L.) plants suffering from P, Fe, and combined P/Fe deficiencies during a two-week period using an isotope-ratio mass spectrometer. Simultaneously, plant physiological status was monitored with an infra-red gas analyzer. Results show clear contrasting time-, treatment-, species-, and tissue-specific variations. Furthermore, physiological parameters showed limited correlation with δ 13 C shifts, highlighting that the plants' δ 13 C, does not depend solely on photosynthetic carbon isotope fractionation/discrimination (Δ). Hence, the use of δ 13 C as a predictor is highly discouraged due to its inability to detect and discern different nutrient stresses, especially when combined stresses are present.
Why it matches plant phenotyping methodsδ13Cを用いた栄養ストレス予測法の有効性を複数作物で評価・検証しており、植物状態の推定手法の技術的妥当性が中心である。
titleδ 13 C as a tool for iron and phosphorus deficiency prediction in crops.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the raw δ13C/physiology data and the analysis scripts used to generate figures, which is a paper-specific, publicly actionable asset.Code · publick Dr. Christian Ceccon for providing support for the isotope analysis.
DATA AVAILABILITY STATEMENT
The following information was supplied regarding data and code availability: the raw data, the version of the individual packages and scripts used to analyze the data and generate the figures of this study are available at GitHub: https://github.com/Fabio-Trevisan/13C-Experiment.git .
REFERENCES
Andaluz , S.
,
López‐Millán , A. F.
,
Peleato , M. L.
,
Abadía , J.
, &
Abadía , A.
( 2002 ).
Increases in phosphoenolpyruvate carboxylase activity in iron‐deficient sugar beet roots: Analysis of spatial localization and post‐translational modification
. Plant and Soil , 241 ( 1 ), 43 – 48 .
10.1023/A:1Open asset ↗Fabio-Trevisan/13C-Experiment · 13C-Experimentlines:309-505Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · checked 7 Sept 2026
Abstract C 4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C 4 plants compared with the ancestral C 3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intra-specific variation in traits such as vein density in C 4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species. Starch staining allowed high-contrast images to be acquired that permitted image analysis using a MATLAB-based program. The method works for the dicotyledon Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and the monocotyledon Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C 4 species demonstrating natural variation for this trait. One sentence summary Preferential accumulation of starch in bundle sheath cells of C 4 plants allows high-throughput phenotyping of vein density.
Why it matches plant phenotyping methodsC4植物の葉脈密度を定量する高スループット染色・画像解析法の開発と適用が研究の中心であり、植物形質の取得手法を扱っている。
abstractHere we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species.
Reproduction assets foundThe paper's Starch4Kranz MATLAB pipeline for quantifying vein density in C4 plants is explicitly stated to be publicly available on GitHub at the authors' repository.Code · publicScript is available at https://github.com/plycs5/Starch4Kranz.Open asset ↗plycs5/Starch4Kranzpdf-page:11 lines:1-28Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeTissueSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a novel ensemble segmentation model that integrates three different but promising networks, namely, SEResNet, InceptionV3, and VGG19, in the encoder part of its base model, which is the UNet model. EmergeNet can correctly detect the coleoptile at its first emergence when it is tiny and therefore barely visible on the soil surface. The performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). It contains top-view time-lapse images of maize coleoptiles starting before the occurrence of their emergence and continuing until they are about one inch tall. EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.
Why it matches plant phenotyping methodsコレオプタイルの出芽時期と成長を画像から抽出する深層学習セグメンテーション手法を開発し、ベンチマーク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。
abstractThe performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED).
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1件を確認しました。Dataset · publicThe dataset can be freely downloaded from https://plantvision.unl.edu/dataset .Open asset ↗lines:324-339Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Maize is the world's most produced cereal crop, and the selection of maize cultivars with a high stem elastic modulus is an effective method to prevent cereal crop lodging. We developed an ultra-compact sensor array inspired by earthquake engineering and proposed a method for the high-throughput evaluation of the elastic modulus of maize cultivars. A natural vibration analysis based on the obtained Young's modulus using finite element analysis (FEA) was performed and compared with the experimental results, which showed that the estimated Young's modulus is representative of the individual Young's modulus. FEA also showed the hotspot where the stalk was most deformed when the corn was vibrated by wind. The six tested cultivars were divided into two phenotypic groups based on the position and number of hotspots. In this study, we proposed a non-destructive high-throughput phenotyping technique for estimating the modulus of elasticity of maize stalks and successfully visualized which parts of the stalks should be improved for specific cultivars to prevent lodging.
Why it matches plant phenotyping methodsトウモロコシ茎の弾性率を非破壊・高スループットに推定するセンサーアレイと数値検証を開発しており、植物表現型取得法が研究の中心である。
abstractWe developed an ultra-compact sensor array inspired by earthquake engineering and proposed a method for the high-throughput evaluation of the elastic modulus of maize cultivars.
Reproduction assets foundThe paper states that all source code used (the plantFEM finite-element analysis software for the maize elastodynamics simulations) is publicly available on GitHub at the authors' URL. No phenotype dataset or raw sensor waveform deposit is explicitly stated.Code · publicThe authors declare no competing financial interests.
502
503
Additional information
504
All source code used in this paper is available at Github
505
(https://github.com/kazulagi/plantFEM)
506
507
References
508
509
1. Berry, P. M. et al. Understanding and reducing lodging in cereals. Adv. Agron. 84, 217–271 (2004).
510
511
2. Tirado, S. B., Hirsch, C. N. & Springer, N. M. Utilizing temporal measurements from UAVs to
512
assess root lodging in maize and its impact on productivity. F. Crop. Res. 262, 108014 (2021Open asset ↗kazulagi/plantFEMpdf-raw-page:18 lines:1-63Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. Results This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. Conclusion In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.
Why it matches plant phenotyping methods与えられた表現型情報から作物画像を生成するGANベースの手法とソフトウェアを開発しており、植物表現型の可視化・再現が研究の中心である。
abstractThis study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information.
Reproduction assets foundThe paper explicitly states that supplementary files including datasets, trained models, software, and source code are publicly available at the authors' HZAU plant phenotyping download site and a GitHub repository. These directly reproduce the paper's phenotyping datasets and CropPainter analysis.Code · publicSupplementary files for this article, which include datasets, trained models, software as well as the source codes used in this study, are available on website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download and https://github.com/zhwang-hzau/CropPainter-master .Open asset ↗zhwang-hzau/CropPainter-masterlines:157-211Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The spatial morphological structure of plant leaves is an important index to evaluate crop ideotype. In this study, we characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib, of which 13 phenotypic traits were firstly proposed for featuring plant leaf spatial structure. Cluster analysis results showed that the 13 traits could be divided into four groups, Group I, -II, -III and -IV. Group I contains HorizontalLength, OutwardGrowthMeasure, LeafAngle and DeviationTip; Group II contains DeviationAngle, MaxCurvature and CurvaturePos; Group III contains LeafLength and ProjectionArea; Group IV contains TipTop, VerticalHeight, UpwardGrowthMeasure, and CurvatureRatio. To investigate the genetic basis of the ear leaf midrib curve, 13 traits with high repeatability were subjected to genome-wide association study (GWAS) analysis. A total of 828 significantly related SNPs were identified and 1365 candidate genes were annotated. Among these, 29 candidate genes with the highest significant and multi-method validation were regarded as the key findings. In addition, pathway enrichment analysis was performed on the candidate genes of traits to explore the potential genetic mechanism of leaf midrib curve phenotype formation. These results not only contribute to further understanding of maize leaf spatial structure traits but also provide new genetic loci for maize leaf spatial structure to improve the plant type of maize varieties.
Why it matches plant phenotyping methodsトウモロコシ葉脈の3Dデジタル化により空間形態の表現型形質を取得・定量化することが研究の中心であり、GWASだけでなく新規形質の抽出を扱っている。
abstractwe characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1
The leaf midrib phenotypic indexes and their calculation description.Open asset ↗lines:910-964Code / 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 14 Sept 2026
Achieving accurate and reliable maize disease identification in complex environments is a huge challenge. This is because disease images obtained from natural environments are often in complex contexts that may contain elements similar to disease characteristics or symptoms. Based on cascade network and two-stage transformation learning, the new method is proposed in this paper and applied the improved method to the task of identification and classification of four maize leaf types in a complex environment. The proposed method has a cascade structure which consists of a Faster R-CNN leaf detector (denoted as LS-RCNN) and a CNN disease classifier, named CENet(Complex Environment Network). The LS-RCNN detector with an attention mechanism was used to detect maize leaves from the image, and the CENet model further classified the leaf images detected in the first stage into four categories: Cercospora leaf spot, Common rust, Northern Leaf Blight, and Healthy, which allowed image features to be extracted more efficiently. The subsequent use of a two-stage transfer learning strategy to train CENet models of disease images in complex contexts allows for faster training of the models while ensuring accuracy. The experimental results show that the proposed method is used to identify four types of maize leaves with an F1-score of 99.70%, which is better than some popular CNN models and others' methods, and has a more obvious advantage in terms of training speed. The model proposed in this experiment has a positive significance for exploring other Crop variety identification and classification under complex backgrounds.
Why it matches plant phenotyping methods複雑環境下のトウモロコシ葉画像から病害症状を検出・分類するカスケード画像解析手法を開発し、性能評価しているため、植物病害表現型の取得が中心である。
abstractBased on cascade network and two-stage transformation learning, the new method is proposed in this paper and applied the improved method to the task of identification and classification of four maize leaf types in a complex environment.
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1件を確認しました。Dataset · publicIn this experiment, corresponding datasets were created for different types of maize leaves, which can be accessed at https://github.com/gitergo/Datasets/ .Open asset ↗gitergo/Datasetslines:74-105Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Hyperspectral imaging is a promising method to predict traits in a high-throughput manner with the potential to unlock quantitative genetic studies. Researchers have successfully modeled physiological traits such as vegetative Nitrogen content, but scope of methodology and lack of truly novel testing data hinder large scale trust in the process. Here, I explore the ability to model leaf Nitrogen content from hyperspectral reflectance data collected with a LeafSpec imaging device on 22 maize hybrids. Three broad strategies based on different input feature sets are undertaken. Strategy one mines data for the most informative hyperspectral channels and then constructs a normalized index similar to NDVI as input features. Strategy two considers all 364 channels of hyperspectral data and makes predictions using various machine learning techniques; partial least squares regression(PLSR), random forest regression, and a feed-forward neural net regression. Strategy three aims to take advantage of the spatial distribution of hyperspectral data on the leaf surface by training a convolutional neural net(CNN). A normalized visual index constructed from bands most correlated with nutrient content out-performed established NDVI. PLSR was the most accurate algorithm, followed by feed-forward neural net and then CNN, based on coefficient of determination score. PLSR is well established as a robust method for hyperspectral prediction which is further evidenced by this study. This is one of the first applications of CNN for hyperspectral data. Despite not being the most accurate algorithm there remains room for hyper-parameter optimization.
Why it matches plant phenotyping methodsトウモロコシ葉の窒素含量をハイパースペクトル画像から推定する特徴量設計・機械学習手法を比較評価しており、植物フェノタイピング手法が中心である。
abstractHyperspectral imaging is a promising method to predict traits in a high-throughput manner
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository containing CorNDVI tensors (phenotyping-derived image data) and analysis scripts used in this study.Code · publicard neural network. CNN – Convolutional neural network.
Strategy Prediction goodness Prediction mean square
of fit - R2 error (% Total Nitrogen)
NDVI_Regression 0.21 0.21
CorNDVI_Regression 0.41 0.16
PLSR 0.57 0.12
RF 0.39 0.16
FFNN 0.54 0.12
CNN 0.49 0.14
DATA AVAILABILITY STATEMENT
CorNDVI tensors and scripts are available at https://github.com/B-Webster-Bio/NuteNet
ACKNOWLEDGMENTS
This work was made possible thanks to AgSpectrum company and NRT-IMPACTs fellowship.
4Open asset ↗B-Webster-Bio/NuteNet · NuteNetpdf-layout-page:5 lines:1-29Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based imagery has become widely used in collecting agronomic traits, enabling a much greater volume of data to be generated in a time-series manner. As one of the cutting-edge imagery analysis tools, machine learning-based object detection provides automated techniques to analyze these imagery data. In our previous study, UAVs have been used to collect aerial photography for field trials of 233 diverse inbred lines, grown under different nitrogen treatments. Images were collected during different plant developmental stages throughout the growing season. This dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season. To improve detection accuracy, we have developed an image segmentation method to remove non-tassel pixels and then feed these filtered images into machine learning algorithms. As a result, our method showed a significant improvement in the accuracy of maize tassel detection. This method can be used in future research to produce time-series counts of tassels at the plot level, and will allow for accurate estimates of flowering-related traits, such as the earliest detected flowering date and the duration of each plot's flowering period. This phenotypic data and the trait-associated genes provide new opportunities for crop improvement and to facilitate future plant breeding.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの雄穂数と開花関連形質を自動推定する画像分割・機械学習手法の開発が中心であり、植物表現型測定法に該当する。
abstractThis dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season.
Reproduction assets foundThe paper's UAV imagery, plot-level images, and metadata are publicly deposited in CyVerse under DOI 10.25739/4t1v-ab64, per the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe original UAV images, the clipped plot-level images, and the associated metadata used in this study
have been deposited in CyVerse (10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-raw-page:5 lines:1-39Code / 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 confirmedEurope PMC · Crossref · checked 8 Sept 2026
Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.
Why it matches plant phenotyping methodsトウモロコシ葉の画像から病害状態を分類するCNN手法を開発・評価しており、植物病害表現型の取得・抽出が研究の中心である。
abstractwe presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases.
Reproduction assets foundThe paper evaluates its EANet maize disease classification model on publicly available image datasets, explicitly citing the public Kaggle 'Corn or maize leaf disease dataset' as one of its data sources. This is a paper-specific, publicly accessible plant image dataset used directly for the paper's classificationex perDataset · publicCorn or maize leaf disease dataset . Available at: https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (Accessed May 12, 2022 ).Open asset ↗Kaggle · corn-or-maize-leaf-disease-datasetlines:697-776Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, R2 was 0.73, RMSE was 0.12 m, and MAE was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.
Why it matches plant phenotyping methods圃場トウモロコシのLiDAR点群から個体・器官・葉を分割し、葉長を推定・検証する手法が研究の中心であり、再利用可能な植物表現型抽出ワークフローに該当する。
abstractwe established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' PCL/C++ segmentation and stratification code at a public GitHub repository matching an allowed URL. The maize point cloud dataset is also stated to be online at scidb.cn, but the full dataset URL does not exactly match any allowed_urls entry, so aCode · publicThe main PCL/C++ code in this paper are available online at
https://github.com/1117ismore/HZAU-Segmentation-Stratification-pcd.gitOpen asset ↗HZAU-Segmentation-Stratification-pcdpdf-page:18 lines:1-56Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Corn is an important human food crop and animal feed source. Purity of corn seed is critical to yield and marketing. Screening of corn ears is an important but time-consuming and labor-intensive task in seed production. In recent years, deep learning has made great achievements in tasks such as image classification, object detection, face recognition, etc. In this paper , a method combining convolutional neural network VGG16 and machine vision to quickly classify different varieties of corn using corn ear images is proposed. By collecting RGB images of corn ears with intact phenotypic traits of 5 varieties, a data set containing 1000 images was constructed, and divided into training set, validation set and test set according to the ratio of 7:2:1. By improving the fully connected layer structure of the VGG16 network, optimizing the training parameters, and using transfer learning and data enhancement techniques, the optimal performance model was obtained after training all layers of the VGG16, and the accuracy rate reached 98.00% on the test set. Under the same experimental conditions, comparing the three methods of training from scratch, pre-feature extraction and only training the fully connected layer, the accuracy rates obtained were 94.00%, 96.88%, and 94.00%, respectively. The improved model achieved the highest classification accuracy rate and stable performance. In the experiments, the effects of network parameters on the model classification results were also discussed. The experiment showed that the phenotypic characteristics of the corn ears could better realize the classification and identification of different varieties of corn, which provides a reference for the intelligent sorting of corn seed production.
Why it matches plant phenotyping methodsトウモロコシ穂の画像から品種を分類・識別する機械視覚とCNN手法の開発が中心で、植物器官の表現型特徴を用いた再利用可能な解析ワークフローである。
abstracta method combining convolutional neural network VGG16 and machine vision to quickly classify different varieties of corn using corn ear images is proposed.
Reproduction assets foundThe preprint's Data Availability Statement deposits the paper's own corn ear image dataset (1000 RGB images of 5 varieties used for VGG16 classification) on Mendeley Data with a public DOI, making it a paper-specific, publicly actionable phenotyping image dataset.Dataset · publicThe datasets generated and/or analysed during the current study are available in the [Xu, jinpu (2022), “five_corn_ears”, Mendeley Data, V2,] repository, http://dx.doi.org/10.17632/hb3hbsz6t9.1Open asset ↗Mendeley Data · 10.17632/hb3hbsz6t9.1lines:382-409Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
Why it matches plant phenotyping methods3D LiDARとRGBによる苗のスキャン基盤を開発し、植物フェノタイピング用データベースを構築・検証しているため、取得手法と再利用可能なデータセットが中心である。
abstractthis paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research.
Reproduction assets foundThe paper's maize seedling LiDAR phenotyping database (362 plants, 7 campaigns, raw/processed point clouds with stem/leaf labels, RGB images, rosbags) is publicly released on OSF across seven campaign-specific repositories, explicitly stated in the Data Availability Statement and Table 3. The GitHub links (sick_scan, uDataset · publicOur generated dataset is available online at: 1st campaign: https://osf.io/fcgwk/ ;Open asset ↗osf · fcgwklines:435-442Code / 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
160
overfitting the model to any location
161
Materials and Methods
162
Data Preparation
163
We used data from the Genomes to Fields (G2F) initiative for years 2014-2019
164
(McFarland et al. 2020), focusing on the sites within the continental United States. Each year’s
165
data are publicly available (https://www.genomes2fields.org/resources/), including weather and
166
soil data for field sites, genomic data, management schedules (e.g., application of fertilizer,
167
herbicides, irrigation) and yield (in addition to other phenotypic variables). We augmented this
168
through additional genomic and weather data. Weather data retrieved from Daymet (Thornton et
169
alOpen asset ↗Genomes to Fieldspdf-raw-page:7 lines:1-53Code · publicthe eigenvectors
400
resulting from the principal components analysis are provided to enable transformation of
401
provided genomes. Additional weather measurements were retrieved from Daymet (Thornton et
402
al. 2020). Custom python scripts for downloading, aggregating and processing these data are
403
available on bitbucket (https://bitbucket.org/washjake/maizemodel and
404
https://bitbucket.org/daniel_kick/maizemodel/ ) in the notebooks directory (files with the prefix
405
0.0 to 0.5).
406
407
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
173
aggregate and standardize terminology across years. Rather than itemizing each operation, we
174
restrict ourselves to those which are likely to be of interest to those working with similar data
175
sets. The scripts used are available through Bitbucket
176
(https://bitbucket.org/washjake/maizemodel and https://bitbucket.org/daniel_kick/maizemodel/ ).
177
Scripts were written in Python (Van Rossum and Drake 2009 p. 3) and rely on scientific and
178
common general libraries (Seabold and Perktold 2010; Pedregosa et al. 2011; fuzzywuzzy
179
2017; Virtanen et al. 2020; team 2020; Harris et al. 2020; Da Costa-Luis et al. 2022) along with
180
plotting libraries for exploraOpen asset ↗daniel_kick/maizemodelpdf-raw-page:8 lines:1-57Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
Why it matches plant phenotyping methodsトウモロコシ雌穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との比較で各形質の信頼性を検証しており、植物フェノタイピング手法が研究の中心です。
abstractWe developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears.
Reproduction assets foundThe authors' full analysis pipeline (MATLAB GUI plus Python deep-learning code for grain segmentation and phenotypic trait extraction) is explicitly stated to be fully available on a public GitHub repository. The phenotype datasets themselves are only available on request, so they do not qualify as public assets.Code · publicThe code for the analysis using a Graphical User Interface in MATLAB is fully available on a public repository ( https://github.com/Phymea-Systems/Earbox ). It is used in combination with a Python code applying the trained neural network to extract the DL2 images, also available in the same public repository.Open asset ↗Phymea-Systems/Earboxlines:104-113Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as "GLS_net" to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴画像から灰色斑点病を識別する深層学習手法を開発・比較し、背景分離や外部妥当性も評価しているため、植物病害フェノタイピング手法が中心です。
abstractDeep learning has the potential for automated disease classification from images of leaf symptoms.
Reproduction assets foundThe paper publicly releases its two paper-specific phenotyping image assets: the In-field (IF) dataset of 2332 field-captured, expert-labelled maize leaf images on Kaggle, and the IFL leaf-segmentation annotation dataset on segments.ai. The supplementary materials contain only performance figures/tables, not datasets, Dataset · publicThe IF dataset is available on Kaggle [ 39 ].Open asset ↗Kagglelines:142-207Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Background High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. Results We propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. PhenoTrack3D improves a former method limited to 3D reconstruction at a single time point [Artzet et al ., 2019] by (i) a novel stem detection method based on deep-learning and (ii) a new and original multiple sequence alignment method to perform the temporal tracking of ligulated leaves. Our method exploits both the consistent geometry of ligulated leaves over time and the unambiguous topology of the stem axis. Growing leaves are tracked afterwards with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants x 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10 to 355 plants. Conclusions We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise automatically and at a high-throughput the development of maize architecture at organ level. It has been validated for hundreds of plants during the entire development cycle, showing its applicability to the GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D再構成・時系列追跡による表現型抽出パイプラインを開発し、大規模データセットで技術検証しており、方法が研究の中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images.
Reproduction assets foundThe paper explicitly states that the PhenoTrack3D pipeline source code and examples are publicly available on GitHub under an Open Source licence (Cecill-C). This is the authors' analysis code for the paper's maize phenotyping pipeline. No public phenotype dataset or trained model checkpoint URL is stated in the blocksCode · publicThe source code and examples are available on Github
(https://github.com/openalea/phenotrack3d) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dpdf-page:28 lines:1-62Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・NDVIという植物キャノピー形質の推定を、APSIM制約のためのエミュレーションおよびベイズ最適化ワークフローとして技術的に評価しており、形質取得・抽出法が中心的です。
abstractWe leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest.
Reproduction assets foundThe paper uses a publicly available maize yield dataset from Beck's Hybrids covering 332 locations (2014-2019) with management, soil, and weather information as site-level inputs for APSIM simulations and yield validation. No author analysis code, trained models, or data deposit is disclosed (Data Availability and AcknDataset · public4 of 25
Figure 1. 2 Figures side by side
million ha from 2014-2019 [31]. To perform APSIM simulations at a series of randomly 143
selected locations, site-level information was acquired from the publicly available maize 144
yield dataset maintained by Beck’s Hybrids (https://www.beckshybrids.com/Research/ 145
Yield-Data). The dataset included information on management operations (i.e., planting 146
date, harvesting date, plant population, row spacing, and previous crop planted for residue 147
type), soil, and weather for 332 locations from 2014 to 2019 (Figure 2(a)). Information on 148
soil texture and soil organic carbon (SOC)Open asset ↗pdf-raw-page:4 lines:1-19Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage. Next, we analyzed the phenotypic variation of leaf sheaths of 418 maize inbred lines based on 87 leaf sheath-related phenotypic traits. In order to further analyze the mechanism of leaf sheath phenotype formation, 25 key traits (2 biomass-related, 19 morphology-related and 4 color-related traits) with heritability greater than 0.3 were analyzed by genome-wide association studies (GWAS). And 1816 candidate genes of 17 whole plant leaf sheath traits and 1,297 candidate genes of 8 sixth leaf sheath traits were obtained, respectively. Among them, 46 genes with clear functional descriptions were annotated by single nucleotide polymorphism (SNPs) that both Top1 and multi-method validated. Functional enrichment analysis results showed that candidate genes of leaf sheath traits were enriched into multiple pathways related to cellular component assembly and organization, cell proliferation and epidermal cell differentiation, and response to hunger, nutrition and extracellular stimulation. The results presented here are helpful to further understand phenotypic traits of maize leaf sheath and provide a reference for revealing the genetic mechanism of maize leaf sheath phenotype formation.
Why it matches plant phenotyping methods画像ベースのハイスループット表現型取得と葉鞘形質の定義・解析手法が明示され、418系統から多数の再利用可能な植物形質を取得しているため、手法が中心的である。
abstractwe proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage.
Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm 3 , and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized.
Why it matches plant phenotyping methodsトウモロコシ穀粒の組成・物理形質を高スループットに取得・推定するハイパースペクトル画像プラットフォームと解析手法の開発が研究の中心である。
abstractwe developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels
Reproduction assets foundThe paper's authors explicitly state that all analysis code for the hyperspectral phenotyping pipeline (PLSR trait prediction, PLS-DA kernel-side classification, image analysis) is publicly available in their GitHub repository.Code · publicgenerate a confusion
matrix, along with specificity and sensitivity rates (Supplemental
Table 1).
2.7. Complete pipeline
The processes, measurements, and analyses described in Sections
2.3-2.6 were combined to produce a pipeline shown in Fig. 1B-E. All of
the code created to execute the analyses is available in this repository,
https://github.com/jivarelao/Hyperspectral_Scanner.3. Results and discussion
3.1. Variability of maize kernel traits in ground-truth sets
Directly measured traits ranged widely across the kernel samples
(Table 1). Kernel volume displayed the largest range (5.6-fold). Kernel
weight was second at 5-fold, followed by vitreousness (2.7-fold), protein
(2.4-fold) and densitOpen asset ↗https://github.com/jivarelao/Hyperspectral_Scanner.3pdf-raw-page:5 lines:1-77Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
The vascular bundle of the shank is an important 'flow' organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality.
Why it matches plant phenotyping methods植物茎部のマイクロCT画像から36形質を自動抽出するパイプラインが研究の中心であり、ハイスループット表現型解析手法の実質的な適用に該当する。
abstractshank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm.
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1件を確認しました。Supplement · publicSupplementary Table S3: The BLUP values for 30-item phenotypic traits of the 202 inbred lines; Supplementary Table S4: The result data of GWASOpen asset ↗lines:712-726Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.
Why it matches plant phenotyping methodsUAS画像から植生指数を抽出し、機械学習でトウモロコシのさび病と老化を推定するフェノタイピング手法が研究の中心であるため。
abstractunoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery
Reproduction assets foundThe article explicitly states that the analysis code is publicly viewable on the first author's GitHub repository, which directly reproduces the paper's machine learning phenomic prediction analysis. Supplementary Datasets S1–S3 are mentioned but no public URLs for them are provided in the supplied blocks.Code · publicThe code used in this analysis is viewable at ( https://github.com/alperadak/phenomic-prediction-/blob/main/Phenomic%20prediction ).Open asset ↗alperadak/phenomic-prediction-lines:111-127Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から識別する深層学習手法を開発・比較し、複雑な背景での病害状態推定を中心的に評価しているため。
abstractThis article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention.
Reproduction assets foundThe authors explicitly state that their dataset, code, and all trained models are publicly available on their GitHub repository, which directly supports this paper's maize leaf disease image classification analysis.Code · publicWe have made our dataset and code, as well as all the trained models of this article, publicly available in the site: https://github.com/haiyang-qian/code-and-dataset .Open asset ↗haiyang-qian/code-and-datasetlines:371-417Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Vitamin A deficiency (VAD) is a public health issue worldwide. Provitamin A (PVA) biofortified maize serves as an alternative to help combat VAD. Breeding efforts to develop maize varieties with high PVA carotenoid content combine molecular and phenotypic selection strategies. The phenotypic assessment of carotenoids is currently done using liquid chromatography, a precise but time- and resource-consuming methodology. Using near-infrared spectroscopy (NIRS) could increase the breeding efficiency. This study used ultra-performance liquid chromatography (UPLC) data from 1857 tropical maize genotypes as a training set and NIRS data to do an independent test of a set of 650 genotypes to predict PVA carotenoids using Bayesian and modified partial least square (MPLS) regression models. Both regression methods produced similar prediction accuracies for the total carotenoids (r2 = 0.75), lutein (r2 = 0.55), zeaxanthin (r2= 0.61), β-carotene (r2 = 0.22) and β-cryptoxanthin (BCX) (r2 = 0.57). These results demonstrate that Bayesian and MPLS regression of BCX on NIRS data can be used to predict BCX content, the current focus on PVA enhancement, and thus offers opportunities for high-throughput phenotyping at a low cost, especially in the early stages of PVA maize breeding pipeline when many genotypes must be screened.
Why it matches plant phenotyping methodsNIRSと回帰モデルを用いてトウモロコシ種子のプロビタミンAカロテノイド含量を高スループット推定する手法が研究の中心であり、育種用フェノタイピングへの応用を技術的に評価している。
abstractUsing near-infrared spectroscopy (NIRS) could increase the breeding efficiency.
Reproduction assets foundThe paper's maize carotenoid/NIRS datasets are publicly deposited in a CIMMYT repository with an explicit download link; the analysis R code is only in the article appendix, not a separate public deposit.Dataset · publicn [INV-003439 BMGF/FCDO Accelerating Genetic Gains in
Maize and Wheat for Improved Livelihoods (AG2MW)] as well as USAID projects [Amend. No. 9
MTO 069033, USAID-CIMMYT Wheat/AGGMW, AGG-Maize Supplementary Project, AGG (Stress
Tolerant Maize for Africa)].
Data Availability Statement: The datasets can be downloaded from the link https://hdl.handle.net/11529/10548607 (accessed on 24 April 2022).
Acknowledgments: The authors kindly thank Andrea Susana Cruz, research assistant at CIMMYT,
for her field work and for facilitating the maize germplasm used in the study. They also thank the
Maize Quality Laboratory “Evangelina Villegas” at CIMMYT for support in analyzing the samples).
Conflicts ofOpen asset ↗hdl.handle.net · 11529/10548607pdf-raw-page:10 lines:1-51Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
Why it matches plant phenotyping methods衛星データによる作物収量推定について、7種類の圃場測定法を比較し、収量推定モデルの適合度・精度への影響を検証している。収量という植物形質の取得・推定法が研究の中心である。
abstractTo evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019.
Reproduction assets foundThe paper's field-measured maize yield dataset (seven sampling methods plus full-field harvest for 196 Ethiopian fields) is openly available via a Zenodo DOI in the Data Availability Statement. No code availability is stated.Dataset · publicData Availability Statement: The data presented in this study are openly available here:
https://doi.org/10.5281/zenodo.6471977.Open asset ↗zenodo · 10.5281/zenodo.6471977pdf-page:11 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
MaizeField / plotRootPhysiological trait estimationRoot system architectureStress response / tolerance
High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.
Why it matches plant phenotyping methods数百遺伝子型・数千区画を対象とする高スループットな圃場根系表現型測定を設計・適用し、根抜き力と根系構造を取得している。主目的は遺伝解析だが、表現型取得手法の大規模適用が実質的に記述されているため採録する。
abstractHigh-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 5 ); however, we saw no overlap in hits between our root traits and flowering, consistent with the lack of correlation in Figure 4 .Open asset ↗lines:330-340Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genetic variation is the basis of selection, evolution and breeding. Maize landraces represent a rich source of allelic diversity, but their efficient utilization in breeding and research has been hampered by their heterogeneous and heterozygous nature and insufficient information about most accessions. While molecular inventories of germplasm repositories are growing steadily, linking these data to meaningful phenotypes for quantitative traits is challenging. Here, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces. Due to their full homozygosity, the DH lines can be multiplied ad libitum and represent a powerful biological resource available to the community. The DH lines allow high-precision phenotyping in repeated experiments and reveal the full additive genetic variance of the population. The DH lines were evaluated for nine agronomically important, quantitative traits in multi-environment field trials comprising seven locations and two years. The DH populations revealed high genetic variance and high heritability for the analysed traits. The DH lines were genotyped with 600k SNP markers. After stringent quality filtering 500k markers remained for further analyses. This is the largest resource of landrace derived DH material in maize, unprecedented in its structure and dimension. The presented data are ideal for linking molecular variation to meaningful phenotypes. They can be used for genome-wide association studies, genomic prediction, and population genetic analyses as well as for developing and testing statistical methods. All plant material is available to the community for conducting additional experiments, extending the panel of traits and environments, and for testing the landrace-derived lines in combination with other genetic material.
Why it matches plant phenotyping methods大規模な植物表現型データセットを資源として提示し、再利用可能な高精度表現型解析基盤を提供しているため、表現型データセットとして中心的に該当する。
abstractHere, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces.
Reproduction assets foundThe paper is a Data in Brief article depositing its own phenotypic (raw plot-level data and BLUEs for nine traits across eleven environments) and genotypic datasets on figshare under DOI 10.6084/m9.figshare.12137142.v1, with a direct URL matching an allowed URL. This is a paper-specific, publicly accessible phenotype/Dataset · publicData accessibility
Repository name: figshare
Data identification number: 10.6084/m9.figshare.12137142.v1
Direct URL to data: https://doi.org/10.6084/m9.figshare.12137142.v1
Related research article
M. Mayer, A.C. Hölker, E. González-Segovia, E. Bauer, T. Presterl, M. Ouzunova, A.E. Melchinger, C.-C. Schön, Discovery of beneficial haplotypes for complex traits in maize landraces, Nat. Commun. 11 (2020) 4954. https://doi.org/10.1038/s41467-020-18683-3
Value of the Data
•
This is the largest resourceOpen asset ↗figshare · 10.6084/m9.figshare.12137142.v1lines:52-74Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
MaizeSorghumMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.
Why it matches plant phenotyping methodsハイスループット hyperspectral imaging と回帰モデルにより、植物の相対含水量・窒素含量という生理形質を非破壊推定する手法を開発・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。
abstractLack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants.
Reproduction assets foundThe paper's hyperspectral reflectance measurements, RWC/NC ground-reference trait data, and metadata are explicitly deposited in the Purdue University Research Repository (PURR) with a public URL stated in the Data Availability Statement. The MDPI supplement contains only stepwise regression tables, model evaluation, VDataset · publicData and meta-data are available at The Purdue University Research Repository (PURR), https://purr.purdue.edu/publications/3958/1 (accessed on 30 January 2022).Open asset ↗The Purdue University Research Repository (PURR)lines:222-238Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Why it matches plant phenotyping methods穀粒という植物器官のタンパク質含量をNIRセンサーと機械学習で推定する校正モデルを開発・評価しており、形質取得・抽出法が研究の中心である。
abstractWe explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra.
Reproduction assets foundThe authors explicitly state that the custom CNN analysis code for this paper's NIR protein-prediction models is publicly available on GitHub at the authors' repository URL, which matches an allowed URL. Supplementary tables are only referenced via a placeholder (www.mdpi.com/xxx/s1) and are not actionable; the Video SCode · publicced by the quality
and size of the datasets used for training the model. To minimize the “over-fitting” error,
the large dataset was used and split carefully to include the different multi-cereal species
in both the calibration and validation dataset (as described in section 2.5.1). The code is
available on the GitHub platform (https://github.com/adamavip/nirs-protein-prediction)
and its particular parts can be now utilized to enhance and develop other pipelines and
products.
For our dataset, the algorithms built using ML-based methods (particularly, the
stacked ensemble model via Hone Create and custom-designed CNN; section 3.4)
achieved the better comparative metrics for both spectra typesOpen asset ↗adamavip/nirs-protein-predictionpdf-raw-page:14 lines:1-54Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
MaizeField / plotMesh / voxelLiDAR / point cloudRootWhole plant / canopy / plot / field2D/3D reconstructionRoot system architecture
Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・評価し、圃場トウモロコシ根の表現型取得性能を検証する研究であり、フェノタイピング手法が中心です。
titleComparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
Reproduction assets foundThe paper's data availability statement provides two public, paper-specific assets: a GitHub repository with the scripts used to run the 3D reconstruction pipeline comparison, and a Cyverse archive containing all 60 resulting 3D root point cloud models from the twelve field-grown maize genotypes.Code · publicDATA AVAILABILITY STATEMENT
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
Cyverse link to all the 3D model results:
https://data.cyverse.org/dav-anon/iplant/home/lsx1980/3D_model_compare.zip
ACKNOWLEDGMENTS
The research was supported by the NSF CAREER Award No. 1845760 and USDOE ARPA-E ROOTS
Award Number DE-AR0000821 to A.B. Any Opinions, findings, and conclusions or recommendations
expressed in thisOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:6 lines:1-40Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
Why it matches plant phenotyping methods植物表現型データのデジタル記録・取得を目的とするオープンソースアプリの開発であり、表現型測定ワークフローが中心的です。
abstractTherefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses.
Reproduction assets foundThe paper describes PhenoApp, an open-source Android phenotyping app. Authors provide the app's source code (Gitea, archived on Zenodo) and underlying example input/output phenotype data files on Zenodo under CC0. The SHAPE II project website is a project page, not a paper-specific data deposit, and is excluded.Code · publice ‘in’ folder of the app main directory and no additional source data is required).
-
Output_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants fOpen asset ↗gitea.julius-kuehn.de · JKI/pheno-applines:333-433Code · publicput_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants from the German Federal Ministry of Education and Research to FS (SelWineQ, FKZ 031B0889Open asset ↗Zenodo · 10.5281/zenodo.5525779lines:333-433Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Background Stalk lodging (mechanical failure of plant stems during windstorms) leads to global yield losses in cereal crops estimated to range from 5% to 25% annually. The cross-sectional morphology of plant stalks is a key determinant of stalk lodging resistance. However, previously developed techniques for quantifying cross-sectional morphology of plant stalks are relatively low-throughput, expensive and often require specialized equipment and expertise. There is need for a simple and cost-effective technique to quantify plant traits related to stalk lodging resistance in a high-throughput manner. Results A new phenotyping methodology was developed and applied to a range of plant samples including, maize ( Zea mays ), sorghum ( Sorghum bicolor ), wheat ( Triticum aestivum ), poison hemlock ( Conium maculatum ), and Arabidopsis (Arabis thaliana). The major diameter, minor diameter, rind thickness and number of vascular bundles were quantified for each of these plant types. Linear correlation analyses demonstrated strong agreement between the newly developed method and more time-consuming manual techniques (R 2 > 0.9). In addition, the new method was used to generate several specimen-specific finite element models of plant stalks. All the models compiled without issue and were successfully imported into finite element software for analysis. All the models demonstrated reasonable and stable solutions when subjected to realistic applied loads. Conclusions A rapid, low-cost, and user-friendly phenotyping methodology was developed to quantify two-dimensional plant cross-sections. The methodology offers reduced sample preparation time and cost as compared to previously developed techniques. The new methodology employs a stereoscope and a semi-automated image processing algorithm. The algorithm can be used to produce specimen-specific, dimensionally accurate computational models (including finite element models) of plant stalks.
Why it matches plant phenotyping methods植物茎の横断面形態を高スループットに定量する画像ベースの表現型計測法を開発し、手作業法との一致性検証と有限要素モデルへの応用を行っており、方法が研究の中心である。
abstractA new phenotyping methodology was developed and applied to a range of plant samples
Reproduction assets foundThe paper's MATLAB image-processing algorithm (authors' analysis code) and sample cross-sectional images are publicly available as supplementary files (Additional files 2 and 3) attached to this open-access article, along with standard operating protocols (Additional file 1). These directly reproduce the paper's phenotCode · publicThe code for the image-processing algorithm is also provided as Additional file 2 . Sample images and instructions are provided as Additional file 3 .Open asset ↗lines:110-119Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize ( Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.
Why it matches plant phenotyping methods低コストの温室HTPプラットフォームを構築し、画像から植物高・群落被覆・群落体積・バイオマス指標を抽出して手動測定と検証しており、表現型取得法が研究の中心である。
abstractPlant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development.
Reproduction assets foundThe paper's Data Availability section states that the image data underlying this greenhouse maize phenotyping study are publicly deposited on Mendeley Data (https://doi.org/10.17632/frsfpgnsyz.1), which is an allowed URL. The genotype data deposit (10.17632/5gvznd2b3n.3) is also mentioned but is genomic/omics data and,Dataset · publicestimate genomic heritability
using the following formula:
ℎ2
𝑔 =
σ2
𝑔
σ2
𝑔+
σ2
𝑒
𝑛𝑟
The estimates of genomic correlation were obtained from
the estimated variance-covariance matrix in the bivariate
Bayesian GBLUP model.
2.9 Data availability
The genotype and image data are available at https://doi.org/10.17632/5gvznd2b3n.3 and https://doi.org/10.17632/frsfpgnsyz.1, respectively.
3 RESULTS
3.1 Image processing and data extraction
A total of 756 plots (plants) in each replication across time
were evaluated during plant development. Each collection of
images took approximately 10 min. The ground resolution of
the orthomosaics was approximately 2.30 mm pix–1, and the
GCP error was approximatOpen asset ↗pdf-raw-page:6 lines:1-131Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran's I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.
Why it matches plant phenotyping methods衛星画像からトウモロコシの生育段階（植物状態）を抽出・分類する画像解析および機械学習手法が研究の中心であり、特徴量比較と分類精度評価も実施している。
abstractThe purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models.
Reproduction assets foundThe paper's supplement (Table S1) publicly lists the 41 Sentinel-2 satellite images analyzed for maize phenology classification, hosted on MDPI. No author analysis code or trained models are stated as publicly available; MATLAB scripts are described but no deposit/URL is given.Dataset · publicThe following are available online at https://www.mdpi.com/article/10.3390/s22010094/s1 , Table S1: The sentinel 2 satellite images analyzed in this research work.Open asset ↗MDPI · 10.3390/s22010094/s1lines:453-471Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Code / 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 confirmedOpenAlex · Crossref · checked 14 Sept 2026
High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging (LiDAR) provides a new way to characterize three-dimensional (3D) plant structure, there is a need to develop robust algorithms for extracting 3D phenotypic traits from LiDAR data to assist in gene identification and selection. Accurate 3D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial LiDAR data. We describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the PointCNN model and obtains stem instances by fitting 3D cylinders to the points. It then segments the field LiDAR point cloud into individual plants using local point densities and 3D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs (F-score =0.8207) and plants (F-score =0.9909). The effectiveness of terrestrial LiDAR for phenotyping at organ (including leaf area and stem position) and individual plant (including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position (position error =0.0141 m), plant height (R2 >0.99), crown width (R2 >0.90), and leaf area (R2 >0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.
Why it matches plant phenotyping methodsLiDARとCNNを用いてトウモロコシの器官・個体を分割し、葉面積、茎位置、草丈、樹冠幅などの表現型形質を抽出する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractWe describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments.
Reproduction assets foundThe authors explicitly state that the implementation code and test data for the maize LiDAR segmentation/phenotyping method are publicly available on GitHub at the sysu-xin-lab/Corn_segmentation repository, which is an allowed URL.Code · publicated that the proposed method extracts accurate
information for high-throughput phenotyping from terrestrial
LiDAR data and provides helpful information for potential analysis
of the relationship between genotypes, environmental conditions
and phenotypes. The implementation code and test data may be
publicly accessed in GitHub (https://github.com/sysu-xin-lab/Corn_segmentation). We welcome researchers and scholars to fur-
ther evaluate and improve the proposed method.
CRediT authorship contribution statement
Zurui Ao: Methodology, Investigation, Writing – original draft.
Fangfang Wu: Investigation. Saihan Hu: Investigation. Ying Sun:
Methodology. Yanjun Su: Methodology. Qinghua Guo: MethodolOpen asset ↗sysu-xin-lab/Corn_segmentationpdf-raw-page:10 lines:1-83Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.
Why it matches plant phenotyping methods植物画像から病害状態を分類するCNN手法の開発・比較・検証が研究の中心であり、植物病害という表現型状態を直接推定しているため採用。
abstractIn this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases.
Reproduction assets foundThe paper's corn disease classification experiments directly use the public Kaggle 'Corn or Maize Plant Leaf Diseases' dataset (4188 images), cited with an explicit public URL. The PlantVillage subset and authors' field images have no public deposit (data availability is on-request only), and no author analysis code orDataset · public39. Corn or Maize Plant Leaf Diseases. Available online: https://www.kaggle.com/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on 15 January 2021).
40. Nachtigall, L.G.; Araujo, R.M.; Nachtigall, G.R. Classification of apple tree disorders using convolutional neural networks.
In Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA,
6–8 November 2016; pp. 472–476.
41. Wang,Open asset ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-datasetpdf-raw-page:19 lines:53-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Current methods in measuring maize ( Zea mays L.) southern rust ( Puccinia polyspora Underw.) and subsequent crop senescence require expert observation which are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. Vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated the pedigree performances as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores. UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.
Why it matches plant phenotyping methodsUAS高スループット画像、植生指数、機械学習を用いてトウモロコシのさび病と老化を推定する方法が研究の中心であり、植物状態の取得・予測手法を実質的に評価している。
abstractunoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery
Reproduction assets foundThe preprint explicitly states that the authors' phenomic prediction analysis code (machine learning regressions in R caret) is publicly viewable on the first author's GitHub repository, matching an allowed URL. Supplementary Data 1 contains the phenomic data but no public URL is given for it, so only the code asset isCode · publicThe code used in this analysis is viewable at [ https://github.com/alperadak/phenomic-prediction-/blob/main/Phenomic%20prediction ].Open asset ↗alperadak/phenomic-prediction-lines:159-166Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.
Why it matches plant phenotyping methods葉・キャノピーの蛍光、反射、植生指数を用いて植物の光合成機能を測定・統合した再利用可能なデータセットであり、センサー計測系とデータ処理が研究の中心である。
abstractWith this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP).
Reproduction assets foundThis Data in Brief article explicitly deposits its maize leaf/canopy fluorescence, reflectance, VI and GEP time-series dataset (raw and collated files) in Mendeley Data under DOI 10.17632/b84jk376c3.1, and the FloX reflectance/SIF processing was performed with two author-maintained open-source R packages on GitHub (tomDataset · publicral Research Center (BARC)
City/Town/Region: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3Open asset ↗Mendeley Data · 10.17632/b84jk376c3.1lines:36-69Dataset · publicion: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3390/rs11050488 ]
Yang, P., C. van der TolOpen asset ↗Mendeley Data · b84jk376c3lines:36-69Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two portable microscopes with different resolutions (TipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomata in maize leaves. FPN model was used to detect stomata in the TipScope images and measure the stomata number and stomatal density. Faster RCNN model was used to detect opening and closing stomata in the ProScope HR2 images, and the number of opening and closing stomata was measured. An improved CV model was used to segment pores of opening stomata, and a total of 6 pore traits were measured. Compared to manual measurements, the square of the correlation coefficient (R 2 ) of the 6 pore traits was higher than 0.85, and the mean absolute percentage error (MAPE) of these traits was 0.02%-6.34%. The dynamic stomata changes between wild-type B73 and mutant Zmfab1a were explored under drought and re-watering condition. The results showed that Zmfab1a had a higher resilience than B73 on leaf stomata. In addition, the proposed method was tested to measure the leaf stomatal traits of other nine species. In conclusion, a portable and low-cost stomata phenotyping method that could accurately and dynamically measure the characteristic parameters of living stomata was developed. An open-access and user-friendly web portal was also developed which has the potential to be used in the stomata phenotyping of large populations in the future.
Why it matches plant phenotyping methods生きた葉の気孔形質を画像取得・深層学習・セグメンテーションで自動抽出する手法を開発し、手動測定との比較検証とWebポータル提供まで行っており、植物フェノタイピング手法が中心である。
abstracta new method for detecting stomata and extracting stomatal traits was proposed.
Reproduction assets foundThe paper's Data Availability Statement deposits the trained stomata detection/segmentation models and all labelled leaf stomata images at a public Huazhong Agricultural University plant phenomics download portal, which directly supports this paper's phenotyping measurements. The analysis source codes are only 'availabDataset · publical document of web portal.
File S2 Technical document of EXE software.
Data Availability Statement
The operating procedure for stomatal trait extraction is shown in Video S1 . The detailed technical documentation is given in Note S1 . The trained model, user guideline and all the labelled images of leaf stomata are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action . The source codes are available from the first/corresponding author. The web portal of extracting stomatal traits was available at http://x40833180q.zicp.vip .Open asset ↗plantphenomics.hzau.edu.cnlines:638-643Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系のX線CT画像から3Dモデルを構築し、71形質を抽出する計算パイプラインを開発・適用しており、表現型取得手法が研究の中心です。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper's custom image-processing and feature-extraction scripts are publicly available in the Topp-Roots-Lab GitHub repository, explicitly linked by the authors for reproducing the work. The phenotype data (Data File S1) is in supplements without a direct URL, and image volumes are only available upon request.Code · publicA more extensive description of trait implementations, all scripts used for image processing and feature extraction, and links to repositories required to reproduce the work are available at https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/Open asset ↗Topp-Roots-Lab/3d-root-crown-analysis-pipelinelines:42-50Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
Why it matches plant phenotyping methodsトウモロコシ葉の気孔を顕微鏡画像から自動検出・分類する画像解析手法を開発し、精度評価しており、植物表現型取得が中心である。
abstractwe propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN).
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all microscope images and analysis code in a public Zenodo record, directly reproducing this paper's maize stomata classification/detection experiments.Dataset · publicour findings can significantly benefit future research. As future work, we intend to develop a computational toolkit to support specialists in the biology area in their studies.
Supporting information
S1 File
(TXT)
Click here for additional data file.
Data Availability
All images and code are available from the ZENODO database. https://zenodo.org/record/3938047#.YE_l6v7Q85k .
Funding Statement
FAF received support of the Brazilian scientific funding agency CNPq through project #408919/2016-7 and São Paulo Research Foundation FAPESP grant #2018/23908-1. JPP received support of the Brazilian scientific funding agency CNPq through project #307066/2017-7. FAF received GPUs as donation from NVIDIOpen asset ↗ZENODO · zenodo.org/record/3938047lines:250-290Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks ( n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize "true stromata," but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.
Why it matches plant phenotyping methodsRGB画像からトウモロコシ葉のタースポット病徴（病斑・病害強度）を自動定量する輪郭検出アルゴリズムを開発・検証しており、植物病害表現型の取得法が中心である。
abstractHere, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves.
Reproduction assets foundThe paper's data availability statement explicitly states the original contributions (RGB leaf images, image blocks, and SCDA-related data) are publicly available at a Purdue PURR repository URL, which is an allowed URL.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://purr.purdue.edu/publications/3820/2Open asset ↗purr.purdue.edu · publications/3820/2lines:669-700Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを時系列抽出する高スループット植物表現型計測が研究の中心的基盤であり、取得データと解析ワークフローを大規模トウモロコシ集団に実質的に適用している。
abstractUnmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost.
Reproduction assets foundThe authors explicitly state that the data and code used in this maize UAV-NDVI phenotyping study are deposited in the Dryad Digital Repository, providing a public DOI. This is a paper-specific, publicly actionable asset containing the NDVI phenotype data and analysis code.Dataset · publicrces; Writing-review & editing. Kevin P. Price:
Conceptualization; Data curation; Methodology; Resources;
Writing-review & editing. Jianming Yu: Conceptualization;
Resources; Supervision; Writing-review & editing.
DATA A N D C O D E AVA I L A B I L I T Y
Data and code used in this study are uploaded in Dryad Digital
Repository: https://doi.org/10.5061/dryad.44j0zpcf0.C O N F L I C T O F I N T E R E S T
The authors declare no conflict of interest.
O RC I D
Jinyu Wang https://orcid.org/0000-0003-2880-5612
XianranLi https://orcid.org/0000-0002-4252-6911
Tingting Guo https://orcid.org/0000-0002-6647-6998
MatthewJ. Dzievit https://orcid.org/0000-0002-1437-1027
Xiaoqing Yu https://orcid.org/0000-Open asset ↗10.5061/dryad.44j0zpcf0pdf-raw-page:15 lines:1-84Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Summary Male fertility in maize involves complex genetic programming affected by environmental factors. Evaluating the presence and proportion of fertile anthers is crucial for agronomic purposes. Anthers in maize emerge from male-only florets, and quantifying anther exertion is a key indicator of male fertility; however, traditional manual scoring methods are subjective. To address this limitation, we developed an automated method, Tasselyzer , for large-scale analysis. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion, capturing regional differences within the tassel based on the distinct color of anthers. We successfully applied this method to diverse maize lines to demonstrate its utility for research and breeding programs. Significance Statement Tasselyzer is a novel image-based segmentation tool for automated, large-scale measurement of anther exertion and the impact of genetic and environmental variation on male fertility in maize.
Why it matches plant phenotyping methodsトウモロコシの葯突出を画像ベースで自動定量する手法とソフトウェアを開発し、複数系統への適用も実施しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed an automated method, Tasselyzer , for large-scale analysis.
Reproduction assets foundThe paper explicitly states that Tasselyzer code, original and pseudo-colored tassel images, and the full image sets are publicly available on GitHub and Zenodo, directly supporting the paper's maize anther exertion phenotyping analysis.Dataset · publicThe
full image sets were used in this study are available within Zenodo at
https://doi.org/10.5281/zenodo.5525073 (Teng et al., 2021).Open asset ↗10.5281/zenodo.5525073pdf-page:15 lines:1-61Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Purpose Root growth, respiration, water uptake as well as root exudation induce biogeochemical patterns in the rhizosphere that can change dynamically over time. Our aim is to develop a method that provides complementary information on 3D root system architecture and biogeochemical gradients around the roots needed for the quantitative description of rhizosphere processes. Methods We captured for the first time the root system architecture of maize plants grown in rectangular rhizotrons in 3D using neutron computed laminography (NCL). Simultaneously, we measured pH and oxygen concentration using fluorescent optodes and the 2D soil water distribution by means of neutron radiography. We co-registered the 3D laminography data with the 2D oxygen and pH maps to analyze the sensor signal as a function of the distance between the roots and the optode. Results The 3D root system architecture was successfully segmented from the laminographic data. We found that exudation of roots in up to 2 mm distance to the pH optode induced patterns of local acidification or alkalization. Over time, oxygen gradients in the rhizosphere emerged for roots up to a distance of 7.5 mm. Conclusion Neutron computed laminography allows for a three-dimensional investigation of root systems grown in laterally extended rhizotrons as the ones designed for 2D optode imaging studies. The 3D information on root position within the rhizotrons derived by NCL explained measured 2D oxygen and pH distribution. The presented new combination of 3D and 2D imaging methods facilitates systematical investigations of a wide range of dynamic processes in the rhizosphere.
Why it matches plant phenotyping methodsNCLを用いた3D根系構造の取得・セグメンテーションが研究の中心であり、根系アーキテクチャという植物表現型を抽出する新しい画像計測法を開発・適用している。
abstractOur aim is to develop a method that provides complementary information on 3D root system architecture
Reproduction assets foundThe paper's Data availability statement deposits the raw and reconstructed 3D neutron computed laminography dataset of one maize root sample in the datacite repository at Helmholtz-Zentrum Berlin (DOI 10.5442/ND000004), a public, paper-specific phenotyping asset. No author analysis code or trained models are disclosed;Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG) under Grant Numbers OS 351/8-1 and TO 949/2-1.
Data availability The raw data and reconstructed 3D dataset
from neutron computed laminography of one maize sample is
available at the datacite repository from Helmholtz Centre Ber-
lin under http://doi.org/10.5442/ND000004.Declarations
Conflicts of interest The authors have no conflicts of interest
to declare that are relevant to the content of this article.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, asOpen asset ↗datacite · 10.5442/ND000004pdf-raw-page:11 lines:1-94Code / 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
723
Declarations
724
Ethics approval and consent to participate
725
Not applicable
726
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 confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. RESULTS: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. CONCLUSIONS: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像セグメンテーションと8形質の自動定量を目的とする手法開発・比較検証および高スループット解析パイプラインの構築が中心である。
abstractWe aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping
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1件を確認しました。Code · publicDeep learning model and manuals with codes for custom detections and model updating: https://gitlab.com/kjschmidlab/deepcob .Open asset ↗gitlab · kjschmidlab/deepcoblines:182-229Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are required to detect the small plants present at the early stages. This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm. Data collected at high resolution (GSD ≈ 0.3 cm) over six contrasted sites were used for model training. Two additional sites with images acquired both at high and low (GSD ≈ 0.6 cm) resolutions were used to evaluate the model performances. Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE = 0.08) performances when native high-resolution images are used both for training and validation. Similarly, good performances were observed (rRMSE = 0.11) when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images. Conversely, poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution. Training on a mix of high- and low-resolution images allows to get very good performances on the native high-resolution (rRMSE = 0.06) and synthetic low-resolution (rRMSE = 0.10) images. However, very low performances are still observed over the native low-resolution images (rRMSE = 0.48), mainly due to the poor quality of the native low-resolution images. Finally, an advanced super resolution method based on GAN (generative adversarial network) that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images. Results show some significant improvement (rRMSE = 0.22) compared to bicubic upsampling approach, while still far below the performances achieved over the native high-resolution images.
Why it matches plant phenotyping methodsUAV画像とFaster-RCNNを用いてトウモロコシの個体密度を検出・計数し、空間解像度や超解像手法の性能を比較・検証しており、植物表現型取得法が研究の中心です。
abstractThe use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization.
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1件を確認しました。Code · publicThe Cycle-ESRGAN network was implemented using Keras [ 56 ] deep learning library in Python. The codes will be made available on Github at the following link: https://github.com/kaaviyave/Cycle-ESRGAN .Open asset ↗kaaviyave/Cycle-ESRGANlines:231-249Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
Why it matches plant phenotyping methods植物フェノタイピング用の時系列3D点群データセットを構築し、手動アノテーションと複数の解析ベースラインを提供することが中心であり、再利用可能なデータ基盤として適格です。
abstractwe present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks
Reproduction assets foundThe paper's core contribution is the Pheno4D dataset of labeled maize and tomato plant point clouds, explicitly stated to be freely and publicly available at the authors' Bonn repository. A companion public data-loader API (Python/C++) is also provided on GitHub. Both are paper-specific, public, and directly actionableDataset · publicThe dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗https://www.ipb.uni-bonn.de/data/pheno4d/pdf-page:1 lines:1-65Code · publicWe provide the
code and the examples for loading the data at https://github.com/AIS-Bonn/data_loaders.Open asset ↗https://github.com/AIS-Bonn/data_loaderspdf-page:10 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP-based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideas A low-cost greenhouse-based HTP platform was developed. Image-derived phenotypes presented moderate to high genomic heritabilities and correlations. Plant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions.
Why it matches plant phenotyping methods低コスト温室HTPプラットフォームを開発し、画像から植物高・キャノピー被覆・体積などの形質を抽出・検証しており、表現型取得手法が研究の中心である。
abstractIn this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes.
Reproduction assets foundThe article describes a low-cost greenhouse HTP platform and image-derived phenotyping of a 360-line tropical maize association panel under PGPB inoculation. The only paper-specific public asset explicitly referenced is a Mendeley Data deposit containing information about the maize panel used in the experiment. No phenDataset · publiclines was used to study the response
172 to PGPB. Of these, 179 inbred lines were from the Luiz de Queiroz College of Agriculture-
173 University of Sao Paulo (ESALQ-USP) and 181 were from the Instituto de Desenvolvimento
174 Rural do Paraná (IAPAR). More information about this panel is available on the Mendeley
175 platform (https://data.mendeley.com/datasets/5gvznd2b3n).
176 The inbred lines were evaluated under two managements: with (B+) and without (B-)
177 PGPB inoculation under nitrogen stress. The B+ management consisted of a synthetic pop-
178 ulation of four PGPB. Bacillus thuringiensis RZ2MS9, Delftia sp. RZ4MS18 (Batista et al.,
179 2018, 2021), Pantoea agglomerans 33.1 (Quecine Open asset ↗Mendeley · 5gvznd2b3npdf-layout-page:8 lines:1-37Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
ArabidopsisMaizeLiDAR / point cloudRGB / grayscaleLeafWhole plant / canopy / plot / fieldClassificationMorphology / geometry measurementSegmentationGrowth / development / phenology
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.
Why it matches plant phenotyping methodsRoADはロボット、RGBカメラ、レーザープロフィロメータ、画像処理による植物形質抽出を中核とする自動フェノタイピングシステムであり、方法開発と実証が主目的です。
abstractwe developed the Robotic Assay for Drought (RoAD) system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' Arabidopsis image-processing source code (the pipeline that produced the paper's phenotypic trait measurements) on GitHub, making it a paper-specific, publicly actionable analysis code asset. No public phenotype dataset or image deposit is stated;Code · publicMN, ME, YY, YB, LT, SHH, and JWW. Funding
acquisition, YY, LT, JWW, and SHH.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
All relevant data can be found within the manuscript and
its supporting materials. The source code for Arabidopsis
image processing is available on GitHub at https://github.com/lr-xiang/RoAD-image-processing.SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. PCZ and BRZ responses of Arabidopsis accessions.
Figure S2. Drought responses in Arabidopsis using RoAD end-
point drought mode.
Figure S3. Validation results for maize plants.
Figure S4. ComparisonOpen asset ↗lr-xiang/RoAD-image-processingpdf-raw-page:15 lines:80-150Code / 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 confirmedOpenAlex · arXiv · checked 15 Sept 2026
In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we notice a similarity between the current state-of-the-art for separating corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a number of novel, application-specific algorithms with the goal of producing a general and scalable field segmentation algorithm. The novel algorithms proposed in this work are shown to produce quantitatively better results than the current state-of-the-art while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithms should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.
Why it matches plant phenotyping methods農業ロボティクス向けの作物分離・フィールドスケール植物フェノタイピング用クラスタリング手法を開発し、既存手法と定量比較しているため、方法が中心である。
abstractusing agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions
Reproduction assets foundThe paper explicitly states that the corn field point cloud data and implementations of the proposed clustering algorithms (Non-random RAIN, Z-Quickshift, GD Quickshift, GD Quickshift++) are publicly available at the authors' GitHub repository to facilitate replication of results.Code · publicns in 𝒪 ( n log n ) \mathcal{O}(n\log n) so the final algorithm is also 𝒪 ( n log n ) \mathcal{O}(n\log n) .
IV Data
To evaluate the algorithms from Section III , both synthetic data and real-world sensor data are used. The corn field data and implementations of the algorithms have been made publicly available 1 1
1
https://github.com/hennels/CropPreClustering to facilitate the replication of results and continued development by the community. The non-corn data used in this work are already publicly available [ 14 ] .
Synthetic data was created by taking the individual plant reconstructions provided in [ 15 ] and organizing them into a large scale field of plants. This allows us Open asset ↗hennels/CropPreClustering · hennels/CropPreClusteringlines:63-72Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Root hydraulic properties play a central role in the global water cycle, in agricultural systems productivity, and in ecosystem survival as they impact the canopy water supply. However, the existing experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging, and their applicability to thin roots and small root segments is limited. Therefore, there is a gap in methods enabling easy estimations of root hydraulic conductivities in diverse root types. Here, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes. Shortly, free-hand root cross-sections were used to extract a selected number of key anatomical traits. We used these traits to parametrize the Generator of Root Anatomy in R (GRANAR) model to simulate root anatomical networks. Finally, we used these generated anatomical networks within the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities ( k x and k r , respectively). Using this combination of anatomical data and computational models, we were able to create a root hydraulic conductivity atlas at the root system level, for 14-day-old pot-grown Zea mays (maize) plants of the var. B73 . The altas highlights the significant functional variations along and between different root types. For instance, predicted variations of radial conductivity along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem vessels. Differences in anatomical traits along and across root types generated substantial variations in radial and axial conductivities estimated with our novel approach. Our methodological pipeline combines anatomical data and computational models to turn root cross-section images into a detailed hydraulic atlas. It is an inexpensive, fast, and easily applicable investigation tool for root hydraulics that complements existing complex experimental methods. It opens the way to high-throughput studies on the functional importance of root types in plant hydraulics, especially if combined with novel phenotyping techniques such as laser ablation tomography.
Why it matches plant phenotyping methods根の断面画像と計算モデルを組み合わせ、根の解剖形質から油圧伝導性を推定する新規パイプラインを開発しており、植物表現型の取得・推定手法が研究の中心である。
abstractHere, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes.
Reproduction assets foundThe paper provides two paper-specific public assets: the GRANAR–MECHA coupling workflow (Jupyter notebook via Binder, GitHub repo HeymansAdrien/GranarMecha, Zenodo DOI 10.5281/zenodo.4316762) and the Rmarkdown script plus all input/output data used to compute the B73 root hydraulic atlas (GitHub repo granar/B73_HydraulCode · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
Why it matches plant phenotyping methods画像ベースの3D根形態フェノタイピング基盤を開発し、18形質を算出して手動測定と検証しているため、フェノタイピング手法が研究の中心である。
abstractWe address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique.
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1件を確認しました。Code · publicOur open-source software is available to the whole plant science community on GitHub and can be deployed within a platform-agnostic Singularity/Docker container to be executed independently of the operating system ( Supplemental Data S D3 ; https://github.com/Computational-Plant-Science )Open asset ↗Computational-Plant-Sciencelines:184-191Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Published1 Jul 2021IEEE Transactions on Computational Biology and BioinformaticsCited by 2 · OpenAlex ↗
Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.
Why it matches plant phenotyping methods植物フェノミクスデータを探索・解析し、表現型特性を抽出するオープンソースソフトウェアの開発と評価が中心であるため。
abstractIn this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits.
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1件を確認しました。Code · publicThe tool is available as open source in the GitHub repository [ 18 ] .Open asset ↗lines:185-261Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A , which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.
Why it matches plant phenotyping methods高スループット光学フェノタイピングシステムの開発と、画像から植物の外部・内部形質を抽出する解析パイプラインが研究の中心であるため含める。
abstractHere we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days
Reproduction assets foundThe paper publicly deposits its maize RGB/HSI/CT images, i-trait phenotypic data, and genotype data on Figshare, and the authors' CT/HSI/RGB image-analysis pipeline code on GitHub and Zenodo, plus figures/supplemental files on Figshare.Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-programOpen asset ↗github · fenghuifh2006/Maize-RGB-CT-HSI-programlines:185-218Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-program and https://doi.org/10.5281/zenodo.4690730Open asset ↗zenodo · 10.5281/zenodo.4690730lines:185-218Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
569
fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
570
montage image was then created to quickly estimate segmentation quality. The imageJ macro
571
and sample images are available in the Zenodo repository,
572
http://doi.org/10.5281/zenodo.4809980.573
574
AI Segmentation
575
The Machine learning approach is based on the “Waikato Environment for Knowledge
576
Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
577
classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
578
middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study.
Why it matches plant phenotyping methodsトウモロコシの光遮断係数kとfPARiを推定する統計手法を比較・評価し、植物キャノピー形質の再現可能な推定ワークフローとして推奨手法を提示しているため、方法論が中心である。
abstractThe parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution.
Reproduction assets foundThe paper's authors explicitly state that the R code implementing the k-estimation analysis (LSE, LogTLM, MLE, Bayesian estimation) is freely available in a public GitHub repository. The underlying phenotype datasets (fPARi/LAI measurements for seven maize genotypes) are only available from the corresponding author on,Code · publicR code is freely available at https://github.com/jlacasa/k-estimation/blob/main/k_estimation_02182021.Rmd .Open asset ↗https://github.com/jlacasa/k-estimation · k_estimation_02182021.Rmdlines:172-210Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g., yield). Heritable variation of PHT is determined by many discovered quantitative trait loci; however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome-wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data show recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.
Why it matches plant phenotyping methodsUASを用いた週次測定により、従来法では見落とされるトウモロコシ草丈の時間変動を抽出し、遺伝子効果を検証しており、フェノタイピング手法の適用が研究上の中心的役割を果たしている。
abstractWeekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season.
Reproduction assets foundThe paper's Data Availability statement deposits its own UAS point cloud data, orthomosaic tif files, processing reports, ruler-based plant height measurements, UAV-derived canopy height measurements, field map, and experimental area files on figshare, plus supplementary material on the G3 figshare repository. TheseareDataset · publicRuler-based plant height measurements (Ruler measurement.xlsx), canopy height measurements derived from UAV-point cloud data (Uav-chm.xlsx), Field map (Field Map.xlsx), and Experimental area (Experimental area .pdf) are available at https://doi.org/10.6084/m9.figshare.13046306.v4 .Open asset ↗figshare · 10.6084/m9.figshare.13046306.v4lines:69-72Dataset · publicUAV-point cloud data (.laz files), processing reports (.pdf files), tif files, belonging to 06/19/19, 06/21/19, 06/28/19, 07/02/19, 07/09/19 and 07/12/19 flight dates, are available at https://doi.org/10.6084/m9.figshare.13269953.v1 .Open asset ↗figshare · 10.6084/m9.figshare.13269953.v1lines:69-72Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Why it matches plant phenotyping methodsUAS画像から植生指数と草冠高を抽出し、機械学習で収量・開花期を推定する高スループット表現型解析が研究の中心です。
abstractUnoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials
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1件を確認しました。Code · publicand PLSR regression. All R codes are available in Github repository (https://github.com/Open asset ↗pdf-page:8 lines:1-175Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
Why it matches plant phenotyping methods植物形質をハイパースペクトル反射データから推定する手法をレビューし、トウモロコシの新規サンプルで既存モデルを評価しており、表現型取得・推定法が中心である。
abstractHere, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' spectral reflectance data and ground truth phenotyping measurements in a public repository (Zenodo-style DOI 10.21232/y5TTxY3N), which is an allowed URL. This is a paper-specific, publicly actionable hyperspectral phenotyping dataset.Dataset · publicd the potential for reusable genotypic datasets, that makes the potential of hyperspectral reflectance phenotyping to both expand our current genetic knowledge and address the challenges of breeding for the 21st century so exciting.
Data availability
Spectral reflectance data and ground truth measurements have been deposited in https://doi.org/10.21232/y5TTxY3N .
Funding
This research was supported by the Office of Science (BER), 10.13039/100000015 U.S. Department of Energy , grant no. DE-SC0020355 to J.C.S. and Y.G., the 10.13039/100000001 National Science Foundation under grant OIA-1557417 to Y.G. and J.C.S. and OIA-1826781 to J.C.S. This project was completed utilizing the HollandOpen asset ↗10.21232/y5TTxY3Nlines:311-337Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area ( R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight – a yield component trait ( R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997 , a maize homolog of the Arabidopsis HCF244 ( high chlorophyll fluorescence 244 ), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.
Why it matches plant phenotyping methodsUAV画像から作物プロットの被覆率・緑色度を抽出するパイプラインを開発し、地上測定との相関で検証した研究であり、フェノタイピング手法が中心です。
abstractA pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed.
Reproduction assets foundThe paper's raw UAV RGB imagery used for the maize phenotyping pipeline is publicly deposited on CyVerse (DOI: 10.25739/4t1v-ab64), as stated in the supplied text. No author analysis code or trained models are described with public availability.Dataset · publicThe original UAV images
taken for this study are available at CyVerse (DOI: 10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-page:5 lines:1-38Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman’s rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key Message Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.
Why it matches plant phenotyping methodsNIRスペクトルとSVMを用いて、トウモロコシ種子の加工中水分含量を高スループットかつ定量的に推定する方法の開発・検証が研究の中心であり、育種規模への適用も示している。
abstractThis study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels.
Reproduction assets foundThe paper's Code Availability section states all analysis code is publicly available on GitHub at the HirschLabUMN ML_Moisture_Prediction repository, which is an allowed URL. This is the authors' code for the NIR/machine-learning moisture prediction analysis. No separate public phenotype dataset deposit is explicitly aCode · publicCode Availability
All code is publicly available on GitHub at https://github.com/HirschLabUMN/ML_Moisture_Prediction.Open asset ↗HirschLabUMN/ML_Moisture_Predictionpdf-page:15 lines:1-55Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.
Why it matches plant phenotyping methodsUASによる生育期間中の植物高の反復取得と、収量予測・遺伝解析への利用が研究の中心であり、実質的な植物表現型取得ワークフローを扱っている。
abstractWeekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year
Reproduction assets foundThe paper's UAS point-cloud/phenotype data are stated to be publicly available on CyVerse (Murray et al., 2019, DOI 10.25739/4ext-5e97), but that DOI is not among the allowed URLs, so it cannot be listed as an asset. Two qualifying paper-specific public assets are present in the allowed URLs: (1) the 2017 agronomic/wh�Dataset · publicAgronomic field data and weather data is available for 2017 (https://doi.org/10.25739/w560‐2114) (McFarland et al., 2020 ).Open asset ↗10.25739/w560‐2114lines:175-178Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.
Why it matches plant phenotyping methods画像ベース高スループット表現型測定の自動抽出値を手動測定（ground truth）と比較し、測定誤差と精度を遺伝的に評価しているため、表現型取得法の技術的検証が中心です。
abstractDuring automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects
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1件を確認しました。Code · publicThe codes used for automated tassel segmentation, skeleton construction and trait extraction are available at https://github.com/schnablelab/Tassel-Image-Trait-Extraction-Tool .Open asset ↗schnablelab/Tassel-Image-Trait-Extraction-Toollines:170-188Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4-10 minutes to segment a maize shoot and consumes 10-20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.
Why it matches plant phenotyping methodsトウモロコシの3D点群を器官レベルに分割・注釈するツールを開発し、植物形態の表現型推定と深層学習用データ構築を技術的に支援するため、フェノタイピング手法が中心です。
abstractWe propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots.
Reproduction assets foundThe paper's authors publicly released the Label3DMaize toolkit (MATLAB source code and executable) on GitHub, which implements the paper's point cloud segmentation/annotation analysis for maize shoots. A supporting data deposit (GigaScience Database, 10.5524/100884) is cited but its URL is not among the allowed URLs,soCode · publicgmented point clouds. The segmentation algorithm and this toolkit will be extended to other crops according to their morphological characteristics, which will promote the automatic 3D point cloud segmentation of plants.
Availability of Supporting Source Code and Requirements
Project name: Label3DMaize Toolkit
Project home page: https://github.com/syau-miao/Label3DMaize.git
Source code and executable program: [ 57 ]
Operating systems: Windows
Programming languages: MATLAB
License: GNU General Public License (GPL)
RRID:SCR_021029
biotools ID: label3dmaize
Data AvailabilityOpen asset ↗Label3DMaize · syau-miao/Label3DMaizelines:277-291Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION: The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出するソフトウェアを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype
Reproduction assets foundThe authors' Maize-IAS analysis software (the paper's phenotyping analysis code) is publicly available on GitHub. The maize image datasets are explicitly not public and require request.Code · publicl development prospects of visual phenotype detection using deep learning methods. The methods and workflow provided in this article can also be easily applied to other crops.
Availability and requirements
Project name: A Maize Image Analysis Software using Deep Learning for High-throughput Plant Phenotyping.
Project home page: https://github.com/surefyyq/Maize-IAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None.
Supplementary information
Additional file 1. Installation and debug guidelines.
Publisher’s Note
Springer Nature remains neutral with regard to juOpen asset ↗surefyyq/Maize-IASlines:403-475Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.
Why it matches plant phenotyping methodsUAV画像から個体を検出・計数し、作物密度を推定する画像解析手法を比較・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。
abstractThis study compares several automated detection of individual plants in the images from which the plant density can be estimated.
Reproduction assets foundThe paper's authors explicitly state that the deep-learning model architecture and data augmentation details are given in their public code repository on GitHub, which is an authors' public URL implementing the paper's plant detection/counting analysis.Code · public258 architectural details are given in the code (https://github.com/EtienneDavid/plants-counting-detection)Open asset ↗EtienneDavid/plants-counting-detectionpdf-page:9 lines:1-52Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.
Why it matches plant phenotyping methods植物の光合成・蛍光などの生理状態を推定する結合モデルを開発し、トウモロコシおよび草本キャノピーで評価しており、モデル開発と検証が研究の中心です。
abstractTo address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere.
Reproduction assets foundThe paper's Code and data availability section explicitly archives the exact STEMMUS–SCOPE model version on Zenodo and publishes the Yangling eddy-covariance validation dataset on 4TU, both with public DOIs.Dataset · publicdoi.org/10.5281/zenodo.3839092 , Wang et al., 2020). The original source of the SCOPE model and STEMMUS model was obtained from Van der Tol et al. (2009) and Zeng et al. (2011a, b), respectively. The tower-based eddy-covariance measurements used for model validation were provided by the authors for the Yangling station, China ( https://doi.org/10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7 , Wang et al., 2019), and were obtained from the FLUXNET2015 Dataset and PLUMBER2 program for the Vaira Ranch (US-Var) FLUXNET site.
Author contributions
YW, YZ, HC, and ZS designed the study. YW developed the code, conducted the analysis, and wrote the paper. YW and HC collected and shared their eddy-cOpen asset ↗10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7lines:657-684Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系を対象に、X線CTによる3Dモデル化と計算パイプラインで71形質を抽出し、根引抜き力との較正・解釈まで行う、中心的な表現型計測手法研究である。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper states that the authors' scripts for X-ray CT image processing and root feature extraction (batch-segmentation, batch-skeleton) are publicly available in the Topp-Roots-Lab GitHub repository. Raw phenotype data is said to be in Supplemental File 1, but no public URL for it is provided in the supplied blocks.Code · publicestimated by taking the 2D projection of the 3D volume, then
185 calculated using a similar approach to that described in Grift et al., 2011. DensityS features are
186 computationally similar to plant compactness traits described in Yang et al., 2014. Scripts used
187 for image processing and feature extraction are available at https://github.com/Topp-Roots-Lab/
188
189 Statistical Analysis
190
191 All downstream (i.e. post feature extraction) analysis was performed in the R statistical
192 computing environment. Initially, principal component analysis using all 71 3D roots traits was
193 used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3
19Open asset ↗Topp-Roots-Labpdf-layout-page:5 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.
Why it matches plant phenotyping methods近接ハイパースペクトル画像を用いた植物生理形質の非破壊フェノタイピング手法を扱い、照明変動補正、形質予測、プラットフォーム適用を技術的に検証しているため、方法が中心である。
abstractHyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
The relationship between relative reflectance and physiological traits.Open asset ↗lines:646-777Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Increased plant density markedly affects canopy morphophysiological activities and crop productivity. This study aims to model maize canopy final morphology under increased interplant competition by revising a functional-structural plant model, i.e., ADEL-Maize. A 2-year field experiment was conducted at Mengcheng, Anhui Province, China, in 2016 and 2018. A randomized complete block design of five plant densities (PDs), i.e., 4.5, 6, 7.5, 9, and 15 plants m -2 , with three replications was applied using a hybrid, i.e., Zhengdan 958. Canopy morphology at different PDs was measured with destructive samplings when maize canopy was fully expanded. The relationship of changes of organ morphology in relation to increased plant density was analyzed based on 2016 data. The ADEL-Maize was first calibrated for the hybrid at 4.5 plants m -2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field data. A heatmap visualization was shown to clearly illustrate the effects of increased plant density on final morphology of laminae, sheaths, and internodes. The logarithmic + linear equations were found to fit changes for the organ size versus increased plant density for phytomers excluding ear position or linear equations for the phytomer at ear position based on 2016 field data. The revision was then further tested independently by having achieved satisfactory agreements between the simulations and observations in canopy size under different PDs with 2018 field data. In conclusion, this study has characterized the relationship between canopy morphology and increased interplant competition for use in the ADEL-Maize and realized the simulations of final size of laminae, sheaths, and internodes, as affected by increased plant density, laying a foundation to test an ideotype for maize withstanding high interplant competition.
Why it matches plant phenotyping methodsADEL-Maizeを改訂してトウモロコシ群落形態をシミュレーションし、独立圃場データで検証しており、植物形態の取得・モデル化が研究の中心である。
abstractThe ADEL-Maize was first calibrated for the hybrid at 4.5 plants m -2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field 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1件を確認しました。Supplement · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:496-519Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Abstract Background : Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results : On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion : The Maize-PAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science. Keywords : Maize phenotyping; Instance segmentation; Computer vision; Deep learning; Convolutional neural network
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出する深層学習ソフトウェアを開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting.
Reproduction assets foundThe paper's maize phenotyping software (Maize-IAS), which implements the paper's image analysis functions (RoI extraction, color analysis, internode/height/stem diameter, leaf counting), is publicly available via the authors' GitHub project home page. The maize image datasets and annotations are not public and require Code · publicWe
reveal the potential development prospects of visual phenotype detection using deep
learning methods. The methods and workflow provided in this article can also be
easily applied to other crops.
Availability and requirements
Project name: Automated Maize Phenotyping Analysis Software using Deep Learn-
ing.
Project home page: https://github.com/sureatgithub/Maize- IAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The datasOpen asset ↗sureatgithub/Maize-pdf-raw-page:18 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Nutrient uptake is critical for crop growth and is determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively little is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.). Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate, and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed substantial genetic variation for multiple ion-uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion-uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition.
Why it matches plant phenotyping methods根の複数イオン吸収速度を高スループットで測定するRhizoFluxプラットフォームを開発し、性能・遺伝的変異を評価しており、植物表現型取得法が研究の中心です。
abstractA modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.).
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1件を確認しました。Code · publicthe statistical analysis R codes including the packages needed are available at https://doi.org/10.5281/zenodo.3893944 ( Griffiths and York, 2020b )Open asset ↗Zenodo · 10.5281/zenodo.3893944lines:83-89Code / 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 confirmedbioRxiv · checked 15 Sept 2026
Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Why it matches plant phenotyping methods根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。
abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
Reproduction assets foundThe paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.Code · publicection can be visualized through different figures that show the
186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes).
The whole script that was used to compute the root hydraulic maps from the root anatomical
188 measurement is presented as a Rmarkdown script stored in a GitHub repository
(https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same
190 repository are stored all input and output data of this study.
192
10 of 24
.
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 Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861pdf-raw-page:10 lines:1-20Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
Why it matches plant phenotyping methods3D画像再構成と葉・茎分離による植物表現型抽出アルゴリズムを開発し、ベンチマークデータセットも提示する、植物フェノタイピング手法が中心の研究。
abstractWe introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV).
Reproduction assets foundThe paper introduces the UNL-3DPPD benchmark dataset (multiview maize/cotton plant images and calibration checkerboards) and states it is publicly available at the authors' plantvision.unl.edu site. No separate analysis code deposit is explicitly stated.Dataset · publicThe datasets generated for this study are publicly available from https://plantvision.unl.edu/dataset .Open asset ↗plantvision.unl.edulines:481-518Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.
Why it matches plant phenotyping methods高速・高スループットな植物カウント手法を開発し、複数作物で検証した植物フェノタイピング手法の中心的研究。
abstractThe goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery.
Reproduction assets foundThe paper's authors publicly released their TasselNetV2+ PyTorch implementation (the paper's plant counting/phenotyping analysis code) online. The three plant counting datasets used are cited prior datasets, not paper-specific deposits.Code · publicTo encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plusOpen asset ↗TasselNetV2pluslines:225-304Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Common beanMaizeField / plotRootClassificationMorphology / geometry measurementRoot system architecture
A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T -test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.
Why it matches plant phenotyping methods根系長分布と根系構造を推定する土壌コア採取プロトコルを開発し、シミュレーション・圃場データ・トレンチで比較検証しており、植物表現型取得法が研究の中心である。
abstractA soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean.
Reproduction assets foundThe authors publicly deposited the field and simulation phenotype data, OpenSimRoot parameterizations/outputs, Voronoi R code, and analysis scripts on Zenodo (DOI 10.5281/zenodo.3952179), explicitly stated in the Data Availability section and Methods.Dataset · publicThe field and simulation data, model parameterization, R package to calculate Voronoi-adjusted root length distribution, and R scripts used to analyze data are available at Zenodo ( https://doi.org/10.5281/zenodo.3952179 ).Open asset ↗Zenodo · 10.5281/zenodo.3952179lines:95-122Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeField / plotRootStem / branchMorphology / geometry measurementGrowth / development / phenologyRoot system architectureStress response / tolerance
Mechanical failure, known as lodging, negatively impacts yield and grain quality in crops. Limiting crop loss from lodging requires an understanding of the plant traits that contribute to lodging-resistance. In maize, specialized aerial brace roots are reported to reduce root lodging. However, their direct contribution to plant biomechanics has not been measured. In this manuscript, we use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize. These measurements demonstrate that the more brace root whorls that contact the soil, the greater their overall contribution to anchorage, but that the contributions of each whorl to anchorage were not equal. Previous studies demonstrated that the number of nodes that produce brace roots is correlated with flowering time in maize. To determine if flowering time selection alters the brace root contribution to anchorage, a subset of the Hallauer's Tusón tropical population was analyzed. Despite significant variation in flowering time and anchorage, selection neither altered the number of brace root whorls in the soil nor the overall contribution of brace roots to anchorage. These results demonstrate that brace roots provide a rigid base in maize and that the contribution of brace roots to anchorage was not linearly related to flowering time.
Why it matches plant phenotyping methodsトウモロコシの茎基部アンカレッジという植物力学形質を、非破壊の野外機械試験で定量する測定法が研究の中心であり、単なるルーチン測定ではない。
abstractwe use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize.
Reproduction assets foundThe paper's data availability statement explicitly deposits all raw data, processing code, and analyzed data (DARLING force-deflection phenotyping measurements) in a public authors' GitHub repository.Code · publicAll raw data, the code used to process data, and the analyzed data are available at: https://github.com/EESparksL/ab/Reneau_et_al_2020 .Open asset ↗EESparksL/ab/Reneau_et_al_2020lines:132-295Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.
Why it matches plant phenotyping methodsLAIの連続測定システムと衛星画像によるLAI推定を構築し、実測値との比較で性能検証しており、植物形質取得法が中心である。
abstractfield LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology
Reproduction assets foundThe paper's continuous field LAI measurements (LAINet WSN datasets for 2018 and 2019 corn plots at the Heihe watershed) were explicitly released publicly via the National Science & Technology Infrastructure at the National Tibetan Plateau Data Center. Satellite imagery (Landsat/Sentinel-2) came from generic public USGSDataset · publicThe LAINet datasets of both years
have been released publicly via the National Science & Technology Infrastructure
(http://www.tpdc.ac.cn/zh-hans/, in Chinese).Open asset ↗National Science & Technology Infrastructurepdf-page:4 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The plant cell wall (PCW) is a pecto-cellulosic extracellular matrix that envelopes the plant cell. By integrating extra-and intra-cellular cues, PCW mediates a plethora of essential physiological functions. Notably, it permits controlled and oriented tissue growth by tuning its local mechano-chemical properties. To refine our knowledge of these essential properties of PCW, we need an appropriate tool for the accurate observation of the native ( in muro ) structure of the cell wall components. The label-free techniques, such as AFM, EM, FTIR, and Raman microscopy, are used; however, they either do not have the chemical or spatial resolution. Immunolabeling with electron microscopy allows observation of the cell wall nanostructure, however, it is mostly limited to single and, less frequently, multiple labeling. Immunohistochemistry (IHC) is a versatile tool to analyze the distribution and localization of multiple biomolecules in the tissue. The subcellular resolution of chemical changes in the cell wall component can be observed with standard diffraction-limited optical microscopy. Furthermore, novel chemical imaging tools such as multicolor 3D dSTORM (Three-dimensional, direct Stochastic Optical Reconstruction Microscopy) nanoscopy makes it possible to resolve the native structure of the cell wall polymers with nanometer precision and in three dimensions. Here we present a protocol for preparing multi-target immunostaining of the PCW components taking as example Arabidopsis thaliana , Star fruit ( Averrhoa carambola) , and Maize thin tissue sections. This protocol is compatible with the standard confocal microscope, dSTORM nanoscope, and can also be implemented for other optical nanoscopy such as STED (Stimulated Emission Depletion Microscopy). The protocol can be adapted for any other subcellular compartments, plasma membrane, cytoplasmic, and intracellular organelles.
Why it matches plant phenotyping methods植物細胞壁成分の多重免疫染色と共焦点・超解像イメージングのプロトコル開発が中心であり、細胞壁構造・局在という植物状態の取得法を扱う。
abstractHere we present a protocol for preparing multi-target immunostaining of the PCW components
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1件を確認しました。Code · publicGrafeo (Custom made software for dSTORM data analysis and visualization, https://github.com/inatamara/Grafeo-dSTORM-analysis- (Open asset ↗inatamara/Grafeo-dSTORM-analysis-lines:208-269Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.
Why it matches plant phenotyping methods葉・キャノピーのリモートセンシング手法を比較し、窒素含量や収量推定との相関を検証しており、植物表現型取得法が研究の中心である。
abstractThis study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe.
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1件を確認しました。Code · publicregions of interest corresponding to each plot were segmented and exported using the MosaicTool (Shawn C. Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/ , https://gitlab.com/sckefauver/MosaicTool , University of Barcelona, Barcelona, Spain) integrated as a plugin for FIJIOpen asset ↗sckefauver/MosaicToollines:152-165Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
BACKGROUND: The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. RESULTS: Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels-TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. CONCLUSIONS: error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC.
Why it matches plant phenotyping methodsトウモロコシ雄穂の検出・計数という植物形質の取得を対象に、複数の画像認識手法をベンチマークし、専用データセットも構築しているため、表現型計測手法が中心である。
abstractWe create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset
Reproduction assets foundThe paper's MTDC dataset (MTC maize tassel images with new bounding box annotations, 13,562 boxes) is explicitly released publicly at the authors' URL https://git.io/MTDC, stated in both the abstract and Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe MTDC dataset and other supporting materials are made available at https://git.io/MTDC .Open asset ↗lines:201-255Code / 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 confirmedOpenAlex · checked 15 Sept 2026
Abstract Background: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.Results: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.Conclusion: The Maize-PAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出するソフトウェアを開発し、精度評価も行っているため、植物フェノタイピング手法が中心である。
abstractThis paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype
Reproduction assets foundThe authors' Maize-PAS phenotyping analysis software is publicly available on GitHub with an explicit project home page. The maize image/annotation datasets are explicitly not public (available only on request), and Labelme is a generic third-party annotation tool, not a paper-specific asset.Code · publicWe
reveal the potential development prospects of visual phenotype detection using deep
learning methods. The methods and workflow provided in this article can also be
easily applied to other crops.
Availability and requirements
Project name: Automated Maize Phenotyping Analysis Software using Deep Learn-
ing.
Project home page: https://github.com/sureatgithub/MaizePAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The datasetsOpen asset ↗sureatgithub/MaizePASpdf-raw-page:16 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
MaizeRootPhysiological trait estimationGrowth / development / phenologyRoot system architecture
Nutrient uptake is critical for crop growth and determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively less is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays . Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed that substantial genetic variation exists for multiple ion uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition. Significance Statement Nutrient uptake is among the most limiting factors for plant growth and yet has not been used as a selection criterion in breeding. This is partly due to the lack of high-throughput phenotyping methods for measuring nutrient uptake. Here we describe a novel high-throughput phenotyping pipeline for quantification of multiple ion uptake rates. Using this new phenotyping system, our results demonstrate that specific ion uptake performance by maize plants is positively correlated among the macronutrients nitrogen, phosphorus, potassium and sulfur, and that substantial variation exists within a genetically diverse population. The findings reveal components of regulatory pathways possibly related with enhanced uptake, and confirm that nutrient uptake itself is a potential target for breeding of nutrient-efficient crops.
Why it matches plant phenotyping methods複数イオンの吸収速度を定量する高スループット植物表現型解析プラットフォーム自体の設計・記述が中心であり、植物の生理形質を測定する方法論研究に該当する。
abstractA new modular platform for high-throughput phenotyping of multiple ion uptake kinetics was designed to determine nutrient uptake rates in Zea mays .
Reproduction assets foundThe paper's RhizoFlux phenotyping analysis R scripts and statistical analysis code are explicitly deposited on Zenodo with an authors' public URL (https://doi.org/10.5281/zenodo.3893945), directly reproducing the paper's ion-uptake and trait analysis. No public phenotype dataset or image deposit is stated in the blocksCode · publics quantified based on the ∆ ∆Ct method using normal-
681
ized geo-metric means of the two reference genes (Zm00001d002944,
682
Zm00001d020826; (59)).
683
Statistical analysis. Statistical analyses were conducted using R ver-
684
sion 3.6.0 (60); the statistical analysis R codes including the pack-
685
ages needed are available (https://doi.org/10.5281/zenodo.3893945).686
The depletion rate of a nutrient from a solution is commonly
687
accepted as equal to the net uptake rate by roots (assuming both
688
influx and efflux). Therefore, the following equation was used
689
to determine the total net influx rates for nitrate, ammonium,
690
potassium, phosphate and sulfate:
691
In =
(Ct − C0)
(t0Open asset ↗zenodo · 10.5281/zenodo.3893945pdf-raw-page:9 lines:1-156Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV-based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV-based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.
Why it matches plant phenotyping methodsUAV画像とSfMによる圃場作物の草丈推定手法を開発し、手測定との比較で検証した研究であり、表現型取得パイプラインが中心です。
abstractWe have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' image analysis and trait extraction scripts in a public GitHub repository, which directly implements the UAV plant-height phenotyping pipeline described in the paper. No phenotype dataset deposit is stated; supporting information files are not URLCode · publicThe scripts and processes used to perform the image analyses and trait extraction are available at https://github.com/SBTirado/UAV_PH.git .Open asset ↗SBTirado/UAV_PHlines:182-204