Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests 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.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.
Why it matches plant phenotyping methodsUAV由来のフェノミクスを用いた疎な表現型取得と予測モデルを中心に、環境横断で評価しており、収量という植物形質の推定手法が主要な貢献である。
abstractadvances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance
Reproduction assets foundThe paper's grain yield BLUEs, spectral wavelength BLUEs, and genotypic data are publicly deposited in the CIMMYT data repository (https://doi.org/10.71682/10549399), directly reproducing this paper's phenotyping measurements. No author analysis code with a public URL is stated; other URLs are generic tools/services.Dataset · publicok.com.
Paolo Vitale, Email: p.vitale@cgiar.org.
DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during this study, including best linear unbiased estimates (BLUEs) for grain yield and spectral wavelengths, as well as the corresponding genotypic information, are publicly available in the CIMMYT data repository ( https://doi.org/10.71682/10549399 ).
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Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ , enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust ( R 2 = 0.58) and strong for leaf rust ( R 2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.
Why it matches plant phenotyping methods画像時系列から植物病害の進展と重症度を抽出する半自動・自動解析パイプラインを開発し、専門家評価との比較で検証しており、表現型取得手法が研究の中心です。
abstractHere, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images.
Reproduction assets foundThe paper's Code and Data Availability section explicitly states that software and datasets (the phenotyping pipeline and imaging datasets) are publicly available at the authors' GitHub repository and project website, both of which are in the allowed URL list.Code · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗USask-BINFO/greenskeye_analysislines:195-225Dataset · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗lines:195-225Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically composites spectral observations over fixed calendar windows, implicitly assuming phenological synchrony across fields. We present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction, for a quality trait whose physiology, senescence-linked nitrogen remobilization, contrasts with the season-integrating behavior of yield. Integrating Sentinel-2 imagery (31 vegetation indices, 10 spectral bands), ERA5-Land reanalysis, gSSURGO soil properties, and USGS 3DEP topography across 228 commercial winter wheat fields in western Kansas (2024–2025), we compared six temporal strategies (peakrelative vs. calendar × monthly, biweekly, growth-stage) using three ensemble tree models under nested cross-validation with Boruta feature selection. A single 30-day post-peak window (peak + [16,45] days) was the top-performing and most consistently selected window, chosen in 4 of 5 outer folds, reproducing prior accuracy under random cross-validation (R2 ≈ 0.28); though its advantage over the best calendar window was not statistically significant (paired bootstrap p = 0.08). Under leave-county spatial cross-validation, however, this skill did not transfer across counties (Sentinel-2–only R2 ≈ 0.01; per-county median R 2 = −0.23), indicating the satellite signal supports within-region interpolation but not spatial extrapolation to unseen counties; ablation shows that neither the spectral nor the static features transfer across counties on their own, and the residual crosscounty skill emerges only from their combination. A near-real-time application at ∼3 weeks before harvest retains most within-region skill at a modest accuracy cost. The results delineate where spectral-peak-relative alignment helps, concentrating a senescence-linked signal within region, and where it does not, providing an honest operational baseline for satellite-based grain-quality monitoring.
Why it matches plant phenotyping methods小麦の穀粒タンパク質濃度という植物形質を対象に、衛星時系列のスペクトルピーク相対アラインメントを開発・比較評価し、交差検証で性能と空間移 transfer 性を検証しているため、方法が中心的である。
abstractWe present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction
Reproduction assets foundThe preprint explicitly releases the authors' analysis code (data-acquisition pipeline, feature engineering, cross-validation/modeling, figure scripts) at a public GitHub repository, and a de-identified field-level GPC dataset released alongside the code repository. Both are paper-specific, public, and actionable. The Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗Ciampitti-Labpdf-page:48 lines:1-55Dataset · publica de-identified version of the dataset is released alongside the code repositoryOpen asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Accurate classification of unsound wheat kernels is important for automated grain quality assessment, but improved recognition performance often comes at the cost of increased model complexity. Methods This study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S. The architecture replaces two early-stage Fused-MBConv blocks with Mod-FusedMBConv blocks to introduce input-dependent local contextual modulation and replaces the squeeze-and-excitation modules in later stages with efficient channel attention to model local cross-channel interactions using fewer attention-related parameters. Experiments were conducted on the seven-class G600 wheat subset of the GrainSpace dataset. Results Across three independent runs, ECA-ModNet achieved an accuracy of 90.17 ± 0.21% and a macro-F1 score of 90.23 ± 0.21%, improving upon EfficientNetV2-S by 3.80 and 3.84 percentage points, respectively. The parameter count decreased from 20.19M to 16.49M, while FLOPs increased marginally from 2.90G to 2.95G. ECA-ModNet achieved accuracy statistically comparable to that of ConvNeXt-Tiny and InceptionNeXt-T while using substantially fewer parameters, and obtained 3.03-4.55 percentage points higher mean accuracy than six lightweight baselines. Discussion Ablation experiments identified two Stage 1 Mod-FusedMBConv blocks with a 3×3 context kernel as the configuration with the highest mean accuracy among those evaluated. These results indicate that ECA-ModNet offers a favorable accuracy-parameter trade-off for image-based classification of unsound wheat kernels.
Why it matches plant phenotyping methods小麦粒の状態（unsound kernel）を画像から分類するためのCNNを開発・比較・アブレーション評価しており、植物器官の状態推定手法が研究の中心である。
abstractThis study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S.
Reproduction assets foundThe paper analyzes the public GrainSpace dataset (G600 seven-class unsound wheat kernel subset) and provides an explicit data availability statement with a public GitHub URL. No author analysis code or trained model deposit is stated.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hellodfan/GrainSpace .Open asset ↗hellodfan/GrainSpacelines:1035-1076Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
The inflorescence is a key yield-determining organ, yet its complex morphology makes manual pixel-level annotation time-consuming, leading to a scarcity of high-quality segmentation datasets. To address this bottleneck, we propose ZCAT (Zero-shot Cross-crop Annotation Transfer), a novel paradigm for zero-annotation cross-crop pseudo-mask screening. ZCAT completely eliminates pixel-level manual annotation of the target crop, requiring only holistic quality assessment of model-generated pseudo-masks (5-10 s per image). Specifically, we train a SegFormer model on public rice panicle datasets (CVRP and RiceSEG) and transfer it across crops to the wheat spike segmentation task. The key innovation is the introduction of a human-defined quality function Q, which circumvents the fundamental challenge in self-learning algorithms: the inability of computers to autonomously distinguish good masks from bad ones. Through iterative human-in-the-loop pseudo-label screening with a curriculum learning strategy, each round adds only a few high-quality pseudo-masks to the training set, continuously improving model performance. After four iterations, ZCAT produces pseudo-masks with an average Spike IoU of 0.7003, evaluated against the GWFSS manual annotations as ground truth. Moreover, the pseudo-mask dataset exhibited higher benchmark performance than the GWFSS manual annotations (Spike IoU 0.7612 vs. 0.7027; mIoU 0.8627 vs. 0.8247), suggesting stronger self-consistency. A generalization test on a strictly held-out set of 100 manually annotated wheat spike images showed that the model trained on ZCAT-generated pseudo-masks achieved marginally better performance than that trained on the GWFSS manual annotations (Spike IoU: 0.5112 vs. 0.4927; mIoU: 0.5627 vs. 0.5247). The time budget of the ZCAT pipeline was substantially lower than that of manual annotation. ZCAT opens a new pathway for rapid annotation of plant reproductive structures or other organs and significantly reduces data preparation costs in plant phenomics. The generated wheat spike pseudo-mask dataset and the mask quality screening tool (Mask Quality Screener) are open-sourced.
Why it matches plant phenotyping methods植物器官セグメンテーションのためのゼロショット転移、擬似マスク品質評価、反復学習パイプラインを開発・検証しており、表現型取得基盤が中心である。
abstractThe key innovation is the introduction of a human-defined quality function Q
Reproduction assets foundThe paper explicitly open-sources two paper-specific assets: the ZCAT-generated wheat spike pseudo-mask dataset and the Mask Quality Screener tool, both with public GitHub URLs in the Data availability statement.Dataset · publicThe wheat spike pseudo-mask dataset and Mask Quality Screener are available at https://github.com/zyxyes1/MaskQualityScreener and https://github.com/zyxyes1/Wheat-Spike-Semantic-Segmentation , respectively.Open asset ↗Wheat-Spike-Semantic-Segmentationlines:415-440Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Code / 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 · 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。
abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出するパイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Why it matches plant phenotyping methods高スループットで14種類の気孔形質を抽出するパイプラインを開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production. Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation. Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment. Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.
Why it matches plant phenotyping methods小麦葉片の病斑特徴を画像から抽出し、植物の真菌病状態を分類する軽量深層学習手法を開発・検証しており、病害表現型の取得・推定が研究の中心である。
abstractThis paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification.
Reproduction assets foundThe paper's plant-image measurements are based entirely on publicly available wheat disease image datasets: a primary Kaggle dataset (Wheat Plant Diseases by Kushagra Agarwal) used for model development, and two additional public datasets (Alibaba Cloud Developer Community and CSDN Modelers) used for generalization andDataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases and was accessed on September 5, 2025.Open asset ↗Kaggle · kushagra3204/wheat-plant-diseaseslines:322-374Dataset · publicThe second dataset was contributed by blogger DL data set and released on December 25, 2025 via the CSDN Modelers platform ( https://modelers.csdn.net/69a67f4c7bbde9200b9c3240.html )Open asset ↗CSDN Modelerslines:644-669Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically relies on spectral observations composited over fixed calendar windows, implicitly assuming phenological synchrony across fields. This study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction. We integrated Sentinel-2 multispectral imagery (32 vegetation indices, 10 spectral bands), ERA5-Land meteorological reanalysis, gSSURGO soil properties, and USGS 3DEP topographic data, and systematically compared six temporal strategies, the factorial combination of two normalization approaches (peak-relative vs.\calendar) and three resolutions (monthly, biweekly, growth stages), across 228 commercial winter wheat fields in western Kansas (2024--2025). Three ensemble tree models (Random Forest, XGBoost, LightGBM) were trained under nested cross-validation with Boruta feature selection. Peak-relative monthly normalization achieved the highest accuracy (\((R^2 = 0.304 \pm 0.051)\), RMSE \((= 1.11)\)%), explaining an additional 5.1% of variance compared with the best calendar strategy (\((R^2 = 0.253)\)). A single 30-day post-peak window (M\((+)\)1, \((\sim)\)15--45 days after maximum canopy greenness) carried more predictive information than any broader aggregation. SHAP analysis identified topsoil organic matter, SWIR-based senescence indices (NBR2, MIRBI), and grain-filling temperature as the most influential predictors. Three-class quality classification reached 47--49% accuracy (versus 33.3% by chance), indicating practical utility for early grain segregation. While demonstrated for wheat GPC, the framework is transferable to other crop traits with temporally concentrated satellite signals, particularly those tied to specific developmental stages. The results highlight phenological alignment as a generalizable strategy for trait prediction from Earth observation data.
Why it matches plant phenotyping methods衛星リモートセンシング時系列を用いた小麦粒タンパク質濃度予測のため、フェノロジー整列と複数の時間集約戦略を体系的に比較・検証しており、植物形質推定手法が研究の中心である。
abstractThis study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction.
Reproduction assets foundThe paper's data availability statement releases a de-identified field-level GPC dataset alongside a public authors' code repository (Ciampitti-Lab WheatGPCPipeline) implementing the data-acquisition, feature-engineering, and modeling pipeline. Both are paper-specific, public, and actionable.Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat (Triticum aestivum L.) is a staple crop of paramount importance to global food security; however, its productivity is significantly compromised by foliar diseases. Conventional diagnostic approaches, relying on manual observation or laboratory analyses, are often labor-intensive and susceptible to inaccuracies. While recent advancements in deep learning present promising avenues for automated disease detection, persistent challenges such as limited annotated datasets, environmental heterogeneity, and model generalization continue to hinder optimal performance. This study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases. While ConvNeXt ensures local feature extraction and ViT provides global contextual understanding, CBAM dynamically highlights the most discriminative features. Additionally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is employed to enhance the visibility of disease symptoms in low-contrast leaf images. Unlike conventional hybrid CNN-Transformer approaches that rely on static feature concatenation, the proposed model employs an adaptive gated fusion mechanism to dynamically balance local and global feature representations. The fused features are further refined using a lightweight CBAM module to enhance discriminative capability. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to improve feature visibility under varying illumination conditions. Evaluated on three different datasets obtained under both controlled and field conditions, HybridViT achieved 100% accuracy on balanced datasets and 99.10% accuracy on complex images captured in real-world conditions, surpassing existing methods. Furthermore, a 5-fold cross-validation strategy yielded an average accuracy of 99.04% ± 0.22, demonstrating the model's robustness and stability across different data splits. The results demonstrate the model's robustness against environmental noise, lighting variations, and class imbalance. This approach, which enables early and accurate disease diagnosis, supports sustainable agricultural practices, reduces pesticide use, and contributes to global food security.
Why it matches plant phenotyping methods小麦葉の病徴を画像から分類する深層学習手法を開発・検証しており、植物病害状態の取得・推定が研究の中心です。
abstractThis study proposes a novel hybrid deep learning model called HybridViT, which combines ConvNeXt and Vision Transformer (ViT) architectures with the Convolutional Block Attention Module (CBAM) to improve the classification of wheat leaf diseases.
Reproduction assets foundThe paper evaluates HybridViT on three public wheat leaf disease image datasets from Kaggle, cited in the reference list with explicit URLs. These are the paper-specific image inputs used for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code deposit is披露dDataset · publicAvailable: https://www.kaggle.com/datasets/olyadgetch/wheat-leaf-datasetOpen asset ↗Kaggle · olyadgetch/wheat-leaf-datasetpdf-page:51 lines:1-64Dataset · public[78] J. Jayaprakash, “Wheat Leaf Disease,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/jayaprakashpondy/wheat-leaf-diseaseOpen asset ↗Kaggle · jayaprakashpondy/wheat-leaf-diseasepdf-page:51 lines:1-64Dataset · public[79] S. Kumar, “Multiple Plant Diseases Dataset,” Kaggle. Accessed: May 1, 2026.
[Online]. Available:
https://www.kaggle.com/datasets/samareshkumar/multipleplantdiseasesOpen asset ↗Kaggle · samareshkumar/multipleplantdiseasespdf-page:51 lines:1-64Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The global food security is severely threatened by various bacterial and fungal diseases that significantly degrade the quality, yield and productivity of wheat crop. This increases the need for an accurate and efficient system to improve wheat yield and mitigate these losses by enabling early intervention. The dataset used in this research comprises of 10,000 images from brown rust, yellow rust, powdery mildew, loose smut diseases and healthy wheat plants. The existing neural networks, ensembling and transformer-based models used for classifying wheat diseases are limited by high computational resource requirements that leads to inefficient feature extraction. These challenges are addressed by proposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer, a lightweight and optimized transformer model that enhances feature extraction while significantly reducing computational overhead. In particular, Swin-SHARP results in 82.5% reduction (48.9M to 8.5M parameters), making it an attractive solution for resource-constrained environments. The extracted features are further optimized by integrating the Swin-SHARP transformer with boosted Model-Agnostic Meta-Learning (MAML) and a weighted ensembling strategy to enhance generalization and classification accuracy. Our proposed model achieves a remarkable 98.1% accuracy, significantly outperforming existing CNN-based solutions, ensemble approaches, transformer, and deep learning models. We also cross-validated our proposed model on an unseen wheat plant diseases dataset, achieving 95.57% accuracy. Our proposed model is also compared against prominent models such as Inception-v3, ResNet-18, and VGG-16, which outperforms them by 1.6%, 1.7%, and 2.6%, respectively. The comparison with existing state-of-the-art models, including Sequential CNN, SGDR-S, Inception-v3, Cereal Conv, Darknet-53 CNN, EfficientNet B3, GLNet, CNN & SVM, Customized CNN, CaiT-YOLOv9 and MSFNet revealed that our method outperforms them by 0.6%, 5.8%, 5.3%, 0.75%, 2.8%, 2.68%, 1.42%, 1.3%, 3.31%, 3.29%, and 2.4% respectively. These results demonstrate the effectiveness and practicality of the Swin-SHARP transformer for wheat disease classification, particularly for real-time agricultural applications on mobile and embedded systems aimed at early disease detection and crop management.
Why it matches plant phenotyping methods小麦の病徴画像から植物の病害状態を推定する深層学習手法を開発し、別データセットで交差検証しており、植物表現型取得・判定が研究の中心である。
abstractproposing a customized, lightweight and optimized Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) transformer
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the authors' wheat disease dataset and analysis code are publicly available on GitHub (https://github.com/SWIN-SHARP/), which is a paper-specific, actionable asset. The paper also uses third-party public datasets (Zindi ICLR Workshop, Mundi, Watershed/GrabcCode · publicThe dataset and code used in this research have made publicly available on https://github.com/SWIN-SHARP/
SWIN-SHARP for reproducibility purposes.Open asset ↗SWIN-SHARPpdf-page:25 lines:1-104Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。
abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).
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 data can be accessed at: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:787-847Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Code / 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 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
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 · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenology
Disentangling genotype × environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology and environment. Here, we integrated time-resolved measurements of apical development, final leaf number (FLN), and expression of the flowering-time genes VRN1, VRN2 and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range of developmental sensitivities. By combining controlled-environment phenotyping with concurrent gene-expression profiling, we show that environmentally driven variation in FLN is coherently explained by shifts in the timing of key apical transitions and associated VRN gene-expression dynamics. These integrated datasets were used to parameterise and interrogate the Cereal Anthesis Molecular Phenology (CAMP) model, enabling direct comparison between observed foliar gene-expression time courses and modelled gene activity. While overall developmental responses were well captured by the model, systematic differences between observed and modelled gene-expression patterns highlight the importance of distinguishing foliar expression from apical regulatory activity, as well as differences in temporal scaling. Building on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Why it matches plant phenotyping methodsFLN応答に基づくフェノタイピングプロトコルを提示し、温度・光周期処理下で遺伝的に解釈可能な発育表現型を取得する方法が中心的に扱われている。
abstractBuilding on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Reproduction assets foundThe paper's CAMP model code and the analysis scripts producing its figures are explicitly stated as publicly available on the authors' GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicwere also validated and the best-performing sets selected. A
348 description of each of the primers used in this study is given in the supplementary material
349 (Table SA1).
350 2.9 Verification of CAMP predictions
351 2.9.1 Model set-up and operation.
352 The CAMP model was coded into a Python script which is available at
353 https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
354 description of the code and parameterisation scheme is given in the supplementary material.
355 The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
356 derive the Vrn expression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicpression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
358 Vrn gene expression could be compared with those observed. The script running the CAMP
359 code and producing the graphs displayed in this paper can be viewed at
360 https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.
14
UNOFFICIALOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicnd testing of the model in
690 broader contexts. EW contributed substantially to the improvement of model concepts and the
691 manuscript and all authors provided final checking.
692 8. Data Availability
693 All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
694 publicly available at https://github.com/HamishBrownPFR/CAMP/
695 9. References
696 Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
697 response of wheat vernalization to environmental variables indicates that vernalization is not
698 a response to cold temperature. Journal of Experimental Botany 63: 847–857.
699 Baumont M, Parent B, Manceau L, Brown HE,Open asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:31 lines:1-60Code / 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 · Crossref · checked 5 Sept 2026
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Ear density ( ) and ear surface area in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). was strongly correlated with grain dry mass per ear at harvest ( r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.
Why it matches plant phenotyping methodsRGB画像と地上ロボット、物体検出・セグメンテーション・Beer–Lambert法を組み合わせ、コムギ穂の密度と表面積を推定・検証する手法が研究の中心であるため。
abstractHere we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot.
Reproduction assets foundThe authors publicly release their ear surface area estimation algorithm with an example dataset on an INRAE forge repository, and the Phenomobile-derived ear density/ear surface area estimations used in the multi-environment analysis are included as supplemental material with the open-access article. The YOLOv5 GWC_SoDataset · publicThe algorithm developed to estimate the EAI and the average ear surface using binary images from ear segmentation are publicly available in the repository https://forge.inrae.fr/raul.lopez-lozano/wheat-ear-surface , jointly with an example dataset from the Mauguio 2023 trial (4 treatments, 1 replicate). The Phenomobile estimations of ear surface area and ear density used in the multi-environmental mixed model presented in Section 2.5 are included as supplemental material.Open asset ↗lines:614-652Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.
Why it matches plant phenotyping methodsコムギ穂の形態形質を対象に、画像に基づく機械学習フェノタイピングとガウス混合モデルを提案・適用しており、表現型の自動抽出・分類が研究の中心である。
abstractThe study compared the results of the F2 hybrids assessment - visually and using machine learning methods
Reproduction assets foundThe paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full textSupplement · publicof these traits
are controlled by oligogenes and have a clear phenotypic
manifestation, the hybridological method was an indispensable
and primary type of analysis for subsequent detailed
phenotyping spikes of wheat species using machine learning
methods.
Supplementary Materials are available in the online version of the paper:
https://vavilov.elpub.ru/jour/manager/files/Suppl_Kruch_Engl_30_3.pdf
Plant material. The object of study was interspecific
hybrids obtained by crossing two endemic tetraploid
wheat species ♀T. aethiopicum Jakubz. (k-19301/2) with
♂T. carthlicum
Nevski (k-32496). The experiment was produced
in spring sowing in the greenhouses of the Breeding
and Genetics Complex (BGC)Open asset ↗lines:111-200Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Why it matches plant phenotyping methods小麦穂の体積を画像・3D再構成・知識蒸留で推定する手法の開発と性能評価が中心であり、高スループット植物フェノタイピングへの応用も明示されている。
abstractWe therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Wheat kernel hardness, vitreousness, and creaseness are key determinants of milling performance, yet they reflect different physical scales of grain structure and are not necessarily coupled. Methods: We developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat. Pixel-level spectral unmixing resolved glassy, intermediate, and mealy endosperm components within individual kernels, enabling vitreousness to be expressed as a continuous spatial index. Results: The hyperspectral-derived vitreousness index showed moderate associations with kernel protein content and the protein-to-starch ratio, consistent with variation in endosperm packing density, but weak relationships with kernel hardness and crease geometry. Kernel hardness, primarily determined by puroindoline genotype, showed limited association with bulk protein and starch composition. Crease geometry, quantified using composite indices from RGB images, captured macroscopic grain features largely independent of both hardness and vitreousness. Discussion: These results demonstrate that hardness, vitreousness, and creaseness represent complementary but largely independent dimensions of grain quality, corresponding to molecular-scale adhesion, mesoscale packing, and macroscopic geometry, respectively. The proposed framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity, enabling improved digital phenotyping for wheat breeding and quality assessment.
Why it matches plant phenotyping methodsハイパースペクトル画像とスペクトルアンミキシングを用いて小麦粒の硝子質を定量するデジタル表現型解析フレームワークを開発しており、形質取得手法が中心的である。
abstractWe developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat.
Reproduction assets foundThe paper's data availability statement deposits full hyperspectral image cubes and RGB image datasets on Figshare, and the supplementary material includes Python analysis scripts (Supplementary Code S1–S2) and processed feature tables (Supplementary Table S3) directly reproducing the paper's phenotyping measurements.Dataset · publicfull hyperspectral image cubes and associated RGB imagedatasets are available via Research Datas 1 – 3 at Figshare: https://doi.org/10.6084/m9.figshare.31259530Open asset ↗Figshare · 10.6084/m9.figshare.31259530lines:151-201Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 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 the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
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 the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Motivation The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. Results We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. Availability and implementation The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). Supplementary information A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.
Why it matches plant phenotyping methods植物の表現型データを含む分散データを統合・提供するBrAPIプロキシの技術実装が中心であり、表現型データ基盤・再利用可能なソフトウェアとして対象に含める。
abstractWe discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI.
Reproduction assets foundThe paper's authors publicly released the BrAPI proxy software used to merge the AGENT project's phenotypic/genotypic BrAPI endpoints, available on PyPI and archived in Zenodo. The supplementary Jupyter Notebook for the marker-trait validation example is mentioned but no public URL is provided, so it is not listed as aCode · publicThe BrAPI proxy implementation and documentation is available at the Python Package Index ( https://pypi.org/project/brapi-proxy ) and archived in Zenodo (doi: 10.5281/zenodo.19436445).Open asset ↗brapi-proxylines:1-44Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Accurate and efficient grain quality assessment is critical for making informed decisions throughout the grain value chain. Early detection of disease enables actions to mitigate spread and further damage, and optimal batch mixing to fulfill specified quality requirements allows for maximizing value and minimizing scrapping. Vision based machine learning and deep learning approaches are gaining attention in the agricultural sector and are useful for the development of automated grain quality assessment. These techniques can reduce the current manual inspection load and are key for objective and precise analysis. Yet, the majority of prior studies are constrained to small or controlled and curated datasets. Practical challenges associated with real-world deployment and reliability are rarely addressed. That is the focus of this work. We present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification. The objective is to determine a CNN model that ensures high and robust classification accuracy, while elucidating and explaining how different image dataset characteristics and training parameters affect performance and reliability. We use a commercial mirror-based imaging system that captures over 90% of each kernel's surface and contrast and compare model architectures, robustness, the effect on pre-processing and image resolution. Our results show similar and high overall performance for ResNet50V2 and EfficientNetV2B0 ([Formula: see text]% accuracy), but per-class analysis indicate that the smaller classes suffer from lack of representative examples, and that most classes benefit from pre-processing including downsampling whereas others benefit from higher resolution. Interactive visualizations reveal that another contributing factor is dubious annotation and multi-class belongingness. Thus, our step-by-step analysis of CNN performance underscores the need for representative data, proper pre-processing, and class-aware evaluation to ensure trustworthy deployment in wheat grain quality assessment.
Why it matches plant phenotyping methods小麦粒画像から品質・病害クラスを推定するCNN画像解析手法の性能、頑健性、前処理、解像度、データ特性を体系的に評価しており、フェノタイピング手法が中心的である。
abstractWe present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification.
Reproduction assets foundThe paper's wheat grain image dataset has a publicly available subset deposited on Zenodo (DOI 10.5281/zenodo.17397123), explicitly stated in the Data Availability statement. The full dataset is proprietary; code is only available upon request, so no qualifying code asset.Dataset · publicA publicly available subset of the segmented wheat grain images used in this study has been deposited in Zenodo to support transparency and reproducibility. The dataset includes representative samples per class collected from instrument and can be accessed at https://doi.org/10.5281/zenodo.17397123.Open asset ↗Zenodo · 10.5281/zenodo.17397123html-lines:337-368Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate delineation of crop growth stages under real-world field conditions remains a long-standing challenge in computational phenotyping, particularly for wheat whose developmental phases are characterized by subtle, continuous morphological transitions and environmental noise. In this study, we propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery. Unlike conventional architectures that struggle with ambiguous inter-stage boundaries and rigid receptive structures, AMFR-Net leverages a ResNet-101 backbone augmented by a novel Adaptive Multi-Scale Attention Fusion (AMSAF) module-comprising cross-scale interaction blocks and confidence-weighted feature aggregation-to hierarchically recalibrate spatial-semantic representations. This design enables the network to adaptively amplify phenologically salient cues while suppressing irrelevant context, ensuring robust generalization under constrained annotation and deployment conditions. Evaluated on the expert-labeled CGIAR benchmark, AMFR-Net achieves state-of-the-art performance across all major metrics (Top-1 Accuracy: 89.10%; Macro-F1: 89.10%; AUC: 97.88%) and demonstrates superior discriminability in phenologically adjacent stages compared to lightweight and deep CNN baselines. Ablation studies validate the synergistic effect of multi-level attention and scale-aware refinement. The proposed framework offers a scalable, interpretable, and field-deployable solution for in-situ phenology monitoring, and sets a foundation for future integration of multimodal sensing, weak supervision, and cross-seasonal adaptation.
Why it matches plant phenotyping methods小麦の生育ステージを地上RGB画像から推定する新規深層学習手法を開発し、ベンチマーク、比較、アブレーションで検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery.
Reproduction assets foundThe paper's phenotyping analysis is built on the public CGIAR Wheat Growth Stage Challenge dataset (ground-level RGB wheat images with growth-stage labels), which the authors explicitly state is publicly available on Zindi with a direct link. No author analysis code, trained model checkpoints, or supplementary code/deeDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Dataset name: CGIAR Wheat Growth Stage Challenge Primary repository: Zindi (official competition page) Direct link: https://zindi.africa/competitions/cgiar-wheat-growth-stage-challengeAccession/Open asset ↗Zindilines:808-824Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R 2 > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R 2 up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.
Why it matches plant phenotyping methods高スループットの地上・UAVセンサーによる表現型取得と、機械学習による収量・安定性推定が研究の中心であり、育種選抜に用いる手法を実質的に評価・適用している。
abstractHigh-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data.
Reproduction assets foundThe authors explicitly state that the datasets and analysis scripts for all analyses (yield/stability modeling, VI extraction, Random Forest workflows) are publicly available in their Zenodo repository (DOI 10.5281/zenodo.17435708), referenced both in the statistical analysis section and the Data Availability statementCode · publicThe datasets and scripts for all the analyses conducted are available in our repository ( https://doi.org/10.5281/zenodo.17435708 ).Open asset ↗zenodo · 10.5281/zenodo.17435708lines:222-237Code / 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 confirmedbioRxiv · Europe PMC · checked 5 Sept 2026
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / tolerance
The Multiple Synthetic Derivatives (MSD) population is a unique hexaploid wheat resource that captures extensive genetic diversity from Aegilops tauschii and exhibits wide variation in agronomic traits. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype. A two-dimensional cultivation platform enabling continuous imaging of seedling root growth under controlled conditions was established to quantify RSA traits and their responses to high temperatures. MSD417 was compared with its recurrent parent, Norin 61 (N61). Under controlled conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61, indicating enhanced early root vigor. This genotype also exhibited a wider seminal root angle, suggesting improved horizontal soil exploration while maintaining root depth. High-temperature treatment reduced overall root growth and minimized genotypic differences, indicating that temperature stress constrains RSA expression. Microscopic observations further revealed a lower height-to-width ratio of coleorhiza tissue of MSD417, suggesting restricted downward expansion. Collectively, this study establishes a practical framework for RSA phenotyping and demonstrates the potential of Aegilops tauschii-derived germplasm to enhance wheat root-related adaptive traits.
Why it matches plant phenotyping methods根系構造を連続画像化して定量する2次元表現型解析プラットフォームを構築し、RSA形質の測定に実質的に適用しているため、方法が中心的である。
abstractHere, we established a practical phenotyping framework for RSA analysis and evaluated MSD417 as a representative genotype.
Reproduction assets foundThe paper deposits its paper-specific root images (N61 and MSD417) and coleorhiza microscopic images in Zenodo with explicit DOIs. The R analysis scripts are only in Supplementary Document S1 with no public URL, so they do not qualify as a public code asset.Dataset · publicThe microscopic images of coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131.Open asset ↗Zenodo · 10.5281/zenodo.18091131pdf-page:14 lines:1-71Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
1 Abstract Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.
Why it matches plant phenotyping methods高解像度画像とフォーカスブラケティングを用いて植物病害の重症度を定量化し、圃場プロット単位の推定精度と不確実性を評価する手法が研究の中心であるため。
abstractWe combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area.
Reproduction assets foundThe paper states that R code to reproduce the full analysis (Beta-distribution modeling, autocorrelation/AR(1) mixed models, effective sample size estimation for wheat disease severity phenotyping) is publicly available on the authors' GitHub repository. The repository name appears truncated in the supplied text ('plotCode · publicR-code to reproduce the full analysis is available at https://github.com/and-jonas/plot-spot-Open asset ↗and-jonas/plot-spot-pdf-page:9 lines:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface ('TillerCounter' program) allows adjusting the automatic detections interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting. The key contributions of our work include:1.An efficient method for imaging straw tillers based on bundle assembly.2.An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.3.Demonstration of the approach's high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).
Why it matches plant phenotyping methods小麦分げつ数という植物形態形質を、画像取得・古典的画像解析・専用ソフトウェアで自動推定し、手動計数を基準に精度検証しているため、フェノタイピング手法が中心です。
abstractWe present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples.
Reproduction assets foundThe paper's authors publicly released the TillerCounter GUI source code on GitHub, which implements the Hough-transform-based tiller counting analysis used in this study. The paper also cites original image/count data at Zenodo (10.5281/zenodo.14446564), but no Zenodo URL is present in the allowed URL list, so only theCode · publicThe source code of the TillerCounter GUI is given at https://github.com/agroscope-ch/TillerCounterGui. Original data is given at Zenodo repository: 10.5281/zenodo.14446564Open asset ↗agroscope-ch/TillerCounterGuihtml-lines:163-195Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat emergence rate and emergence uniformity are key indicators for evaluating seed vigor and sowing quality, and they play an important role in wheat growth and yield formation. Traditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data. In this study, RGB images and a two-stage deep learning algorithm were used to extract and analyze seedling traits of 420 wheat varieties under two nitrogen levels, and the results were applied to genome wide association studies to elucidate the genetic basis. The two-stage algorithm integrates a Bidirectional Feature Pyramid Network, small object detection layer, large size image input, and FasterNet to improve detection and instance segmentation speed and accuracy. The proposed method achieved an emergence rate accuracy of 0.929, with R 2 = 0.914 and RMSE = 2.448 compared to manual measurements, and required less than 0.2 s per image for analysis. By employing this two-stage algorithm for processing and analysis, varieties (e.g., Gao8901 and ShiYou20) that consistently exhibited high emergence rates and uniformity under multiple nitrogen treatments were identified. Furthermore, genome-wide association study identified the major loci qEmergence rate-3A and qUniformity-6B governing seedling emergence rate and uniformity, which likely enhance wheat seedling traits by modulating energy supply or related signaling molecules. The emergence-rate and uniformity data generated by the two-stage algorithm significantly accelerated the discovery of relevant genes and enabled the identification of wheat varieties with high emergence rate and uniformity, providing valuable insights and practical references for high-quality breeding and gene mining.
Why it matches plant phenotyping methodsRGB画像と二段階深層学習による出芽率・均一性の自動取得手法を開発し、手動測定との精度比較および大規模品種適用を行っており、表現型取得法が研究の中心である。
abstractTraditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data.
Reproduction assets foundThe authors openly provide test code, base models, and sample test data for the WS-YOLO two-stage wheat seedling phenotyping pipeline in a public GitHub repository. Raw phenotype datasets are only available upon request, so they do not qualify as public assets.Code · publicThe test code, base models, and sample test data are openly available in the GitHub repository: https://github.com/AIWheatLab/WheatSeedling.Open asset ↗AIWheatLab/WheatSeedlinghtml-lines:375-402Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
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 · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Time-resolved phenotyping of disease symptoms enables dissection of resistance mechanisms and improves diagnosis, but acquiring phenotypic data at satisfactory scale remains challenging. Advances in imaging and image processing have improved measurement precision, robustness, and throughput, but further improvements are needed for practical application. We present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms. All images are geometrically aligned with a median precision of 0.16 mm (≈5 pixels). The dataset includes transformation matrices, symptom segmentation masks, metadata on treatments, weather, crop phenology, and disease occurrence, and a lightweight Python toolkit for loading, aligning, inspecting, and editing image sequences. These resources enable detailed investigation of leaf-level disease dynamics such as lesion, pustule, and fruiting body emergence rates, lesion growth, and dynamic interactions of disease development with spatial and environmental contexts. They offer a broad basis for developing improved methods for image alignment and symptom detection, segmentation, and tracking, possibly by tackling these connected challenges within a single end-to-end framework.
Why it matches plant phenotyping methods葉の病徴を対象とした高解像度時系列画像データセットで、幾何位置合わせ、病徴セグメンテーション、追跡用ツールを提供しており、植物病害表現型の取得・解析基盤が中心である。
abstractWe present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms.
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 · publicWe provide a lightweight Python toolkit to facilitate loading, inspection, and curation of the image sequences and their associated processing products in the associated Git repository (https://github.com/and-jonas/sympathique-wheat).Open asset ↗github.com/and-jonas/sympathique-wheathtml-lines:317-337Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Wheat, being a major staple crop worldwide, is often attacked by rust diseases, which cause severe yield losses. The early detection and diagnosis of fungal infections, yellow rust, and brown rust are critical in minimizing their consequences. A web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases. The diseases that we examine in wheat plants are brown rust (BR) and yellow rust (YR), and healthy plants are classified in the third category. A dataset of labeled images of YR, BR, and healthy wheat plants was used to train the CNN. The model achieved a remarkable 96% classification accuracy. In addition to disease diagnosis, a recommendation module that gives advice on proper treatment based on disease names or symptoms is also provided. This twofold functionality allows for timely disease management and identification and facilitates the treatment of other wheat diseases besides rust diseases. Integrating the trained CNN model into an intuitive web application makes it user-friendly for end users, notably farmers, to have a practical tool in protecting wheat crops.
Why it matches plant phenotyping methods小麦植物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・推定が中心。治療推薦機能もあるが、画像ベース病害診断が主要な技術的貢献である。
abstractA web-based system based on a Convolutional Neural Network (CNN) was developed for the quick identification and classification of wheat plant diseases.
Reproduction assets foundThe paper's wheat disease image dataset (YR, BR, healthy; 3679 images) is a publicly available Kaggle dataset explicitly used for the CNN training, with an authors-provided URL matching an allowed URL.Dataset · publicThe images of YR and BR were taken from a Kaggle dataset, which is available at https://www.kaggle.com/datasets/sinadunk23/behzad‐safari‐jalal. The dataset includes 3679 images divided into three different categories, as shown in Table 2.Open asset ↗Kaggle · sinadunk23/behzad‐safari‐jalalhtml-lines:249-257Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトルセンサーによる植物表現型時系列データから潜在特徴を抽出し、収量予測に統合する解析手法が研究の中心であるため。
abstractfactor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction
Reproduction assets foundThe paper's Data and code statement provides public GitHub repositories containing the authors' analysis scripts for the hyperspectral latent-factor/Procrustes workflow and the glfBLUP R package implementing the genomic prediction methodology. The hyperspectral phenotype dataset itself is only available upon request, iCode · publicy of secondary trait data and successful integration in multivariate genomic prediction.
As such, this method can contribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning mOpen asset ↗KunstJF/glfBLUP-Procrusteslines:388-492Code · publicntribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning methods for genomic prediction .
Springer , Cham, Switzerland .
External Links: ISBN 978-3-030-89009-4 978-3-030-8901Open asset ↗KillianMelsen/glfBLUPlines:388-492Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Wheat, the third most widely consumed cereal crop worldwide, faces substantial yield and quality losses as a result of rust disease, notably leaf rust, stem rust, and stripe rust. These rust disease, caused by Puccinia triticina , Puccinia graminis , and Puccinia striiformis , respectively, are capable of causing significant yield losses in wheat in the absence of timely detection. Conventional disease identification relies heavily on manual visual inspection, which is time consuming, labor intensive, and prone to error, especially in large scale agricultural systems. To address these limitations, this study proposes a deep learning-based framework for the early detection and classification of wheat rust diseases. A real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data. The dataset comprises images of healthy leaves and those affected with the three major rust diseases. A modified convolutional neural network (CNN) architecture was employed for extract features and disease classification. Experimental results demonstrate that the proposed approach achieves high classification accuracy, highlighting its effectiveness as a reliable tool for automated wheat rust detection in precision agriculture. By enabling rapid and accurate disease identification, the system supports timely decision-making, reduces potential yield losses, and improves crop management practices, thereby contributing to food security and sustainable agricultural production.
Why it matches plant phenotyping methods小麦葉の画像からさび病の有無・種類を推定する深層学習手法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractA real-time dataset was developed using field images collected from various wheat-growing regions and augmented with publicly available data.
Reproduction assets foundThe paper's own wheat rust image dataset (field images from North Punjab, Pakistan plus Kaggle-sourced images, with disease severity, GPS, variety, and weather metadata) is publicly deposited on Kaggle via an explicit repository link in Table 1. No author analysis code or trained model checkpoint is publicly released.Dataset · publict, Stripe Rust
Collection Region North Punjab, Pakistan
Collection Period Feb–March 2025
Collection Method Field observation + Kaggle image samples
Plant Growth Stage Tillering to heading
Field Data Includes Disease severity, GPS, wheat variety, weather data
Usage Disease classification, model training, analysis
Repository Link https://www.kaggle.com/datasets/sabaunnisa/wheat-rust-disease
We have divided the datasets 1294 into 962 training
images and 332 testing images. In the current study, a 3:1
ratio was used to create the training, and validation sets for
the image dataset, meaning 75% of the data 722 used to
training and 25% 240 to validation. A fixed random seed
(seed = 42) was used toOpen asset ↗Kaggle · sabaunnisa/wheat-rust-diseasepdf-raw-page:4 lines:1-66Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
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 I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
The distribution of spikelets significantly affects wheat (Triticum aestivum L.) spike architecture. However, traditional methods lack the precision to study spikelet distribution effectively. We developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images. In addition to traditional spikelet number per spike (SNS), rachis length (RL), and spikelet density (SD, SNS/RL), we introduced spikelet distribution traits based on rachis internode lengths, providing quantitative insights into spike architecture. RachisSeg showed high consistency with manual measurements for SNS and RL, with the R2 values of 0.975 and 0.998, respectively. Using RachisSeg, we analyzed spikelet distribution patterns across wheat germplasm and found that traits such as spikelet distribution index (SDI) and apical-to-basal spikelet number ratio (AVB_SNS) were moderately correlated with grain yield per spike (GYPS) (r = 0.57 and 0.53, respectively), while internode width (IW) showed a strong positive correlation with GYPS (r = 0.75). Specifically, a denser spikelet arrangement in the upper spike negatively impacted grain number and weight in that section. Furthermore, comparative analysis revealed distinct spikelet distribution patterns among landraces, American cultivars, and Chinese cultivars. In a recombinant inbred line population, we identified 46 quantitative trait loci (QTLs) associated with rachis traits. A major QTL controlling SDI was detected on chromosome 6B, explaining up to 24.8% of the phenotypic variance. Candidate gene analysis suggested TraesCS6B02G417000 as a potential gene, whose mutant exhibited significant changes in RL and SDI. RachisSeg is a powerful tool for quantifying spikelet distribution, facilitating wheat genetic analysis, gene discovery, and breeding.
Why it matches plant phenotyping methodsRachisSegは、スキャン画像からコムギ穂軸・小穂分布形質を自動抽出する深層学習フェノタイピング手法として開発・検証されており、方法が研究の中心です。
abstractWe developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images.
Reproduction assets foundThe paper's authors publicly released the RachisSeg phenotyping pipeline (deep learning node detection and internode segmentation code) together with sample rachis images via their GitHub repository, explicitly stated in the Implementation and Data availability sections.Dataset · publicRachisSeg and sample rachis images is freely available online ( https://github.com/Jiang-Phenomics-Lab/RachisSeg ).Open asset ↗Jiang-Phenomics-Lab/RachisSeglines:514-549Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely and accurate in-season estimation of aboveground biomass (AGB) and yield in winter wheat is crucial for optimizing resources and ensuring food security. Light use efficiency (LUE) models have proven effective in estimating crop gross primary productivity and yield across sites and years due to their strong physiological and ecological mechanisms. However, existing studies are limited to satellite applications and have not utilized unmanned aerial vehicle (UAV) imagery. This study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments. Subsequently, the scalability of the LUE-ML yield prediction approach was assessed in farmer's fields from five counties of Jiangsu Province, China. The results demonstrated that while the AGB for the heading stage was estimated by combining the retrieved LAI and 20-day accumulated meteorological features, the AGB during the post-heading period could be estimated accurately using the stage-skipping or stage-progressive strategy, with the latter ( R val 2 = 0.93) outperforming the former ( R val 2 = 0.84). The combination of one spectral index, LUE-derived AGB, and three 20-day accumulated relative meteorological features (Comb. #6) performed the best ( R cal 2 = 0.89; R val 2 ≥ 0.79) for yield prediction among all combinations. When extended to farmer-field yield prediction across the province, Comb. #6 also achieved acceptable performance. This study suggests the use of LUE-ML models represents a significant step forward towards mechanistic estimation of AGB and yield for cereal crops from UAV imagery.
Why it matches plant phenotyping methodsUAV画像から冬コムギの地上部バイオマスと収量を推定するLUE-ML手法を開発・評価し、圃場で性能検証しているため、植物形質取得・推定が研究の中心である。
abstractThis study proposed a practical framework for accurate in-season estimation of AGB and yield in winter wheat from UAV imagery by combining a LUE model and machine learning (LUE-ML) across five plot experiments.
Reproduction assets foundThe paper's Data Availability statement explicitly hosts the core code for the two UAV-LUE AGB estimation strategies and related test data in a public GitHub repository; other data are only available upon request.Code · publicThe core code for the two strategies and related test data in the UAV-LUE method for estimating wheat AGB are hosted in a public repository: https://github.com/qtaocheng/agb-estimation-uav-lue-two-strategies . Other data that support the findings of this study are available from the corresponding author (T.C.) upon reasonable request.Open asset ↗qtaocheng/agb-estimation-uav-lue-two-strategieslines:507-519Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Agriculture is a cornerstone of the economies of many countries, and wheat is a staple cereal crop that sustains nearly half of the worldwide population. However, production of wheat is highly vulnerable to biotic stress such as pathogens and pests, as well as adverse environmental conditions. These factors significantly affect yield and quality, posing critical threats to food security and economic resilience. Conventional disease detection methods often involve intense human labor, prolonged procedures, and are predisposed to subjectivity. Therefore, the development of an automated, accurate, and real-time disease monitoring system is imperative for modern precision agriculture. We propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery. The framework leverages the pre-trained VGG-16 convolutional neural network for deep feature extraction via a transfer learning approach. These features are subsequently classified using Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), XGBoost, and Bernoulli Naïve Bayes (BNB) algorithms of machine learning. The model is trained and evaluated on a custom-curated dataset, containing wheat diseases: stripe rust, powdery mildew, scab (Fusarium head blight), and yellow dwarf. Evaluation of experiments demonstrates that the classification performance is enhanced significantly through our hybrid approach, with accuracy ranging from 74 to 97%, precision from 73 to 96%, and recall from 73 to 95.7%. The SVM-based variant of the model achieved the highest performance, yielding 96% precision, 95.7% recall, 96% F1-score, and 97% accuracy. The proposed two-phase fine-tuned system demonstrates its effectiveness and efficiency in detecting multiple wheat diseases. The MDDM-WD model offers a resource-efficient and scalable approach for early disease detection, supporting informed decision-making for farmers, agronomists, and policymakers in advancing sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像からコムギの病徴・病害状態を直接推定する深層学習フレームワークを開発・評価しており、植物表現型取得法が中心です。
abstractWe propose a hybrid deep learning based Multi-Disease Detection Framework for Wheat Diseases (MDDM-WD) for the identification of multiple wheat diseases using UAV imagery.
Reproduction assets foundThe paper's wheat disease image analysis is based on two public datasets (DAE-Mask GitHub dataset and a Kaggle multi-class crop disease image dataset), explicitly declared as open-source and publicly available in the Data Availability statement. No author code or trained model is released.Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://github.com/YcZhangSing/Dataset-of-DAE-Maskhtml-lines:699-732Dataset · publicThe two datasets analyzed during the current study are open-source and publicly available online in the repositories https://github.com/YcZhangSing/Dataset-of-DAE-Mask and https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-images.Open asset ↗https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-imageshtml-lines:699-732Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
WheatRootMorphology / geometry measurementRoot system architecture
Root anatomical phenotyping has become a demonstrably essential part of investigating root physiology and in acquiring a holistic understanding of plant development. However, accessible high throughput methods for root anatomical analysis are still lacking. Here, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging with a shallow learning curve for obtaining high quality images suitable for comparative analysis across a number of plant species. Its efficiency comes from combining blockface-like imaging and stain-free imaging using near-ultraviolet (nUV) autofluorescence utilising a combination of low-cost commercial equipment, readily available mechanical components, and custom designed and 3D printed tools. Using this platform, we investigated the anatomy of mature tissue along the axis of wheat crown roots, revealing a tendency of reduction in vascular complexity (expressed through a reduction in metaxylem number, area, and mean area per metaxylem file) from the basal to the distal region of the root. This study highlights the importance of thorough sampling strategies for investigating root anatomy in relation to organ function and introduces an accessible, relatively high-throughput platform to support such research.
Why it matches plant phenotyping methods根の解剖学的形質を高スループットに画像取得する低コスト基盤を開発しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present the Rapid Anatomics Tool (RAT), a novel, low-cost platform for high throughput root anatomical imaging
Reproduction assets foundThe paper's supplementary materials (hosted at the publisher DOI page) explicitly include the 3D design files (STL) for the RAT platform and the Python script used to control image acquisition, which are paper-specific phenotyping hardware/analysis assets. The phenotype datasets generated and analysed are only 'on the'Code · public3D design files (STL) are provided in the supplementary material. The Python script used to control image acquisition using the specific USB microscope used in this study is available in the supplementary materialsOpen asset ↗lines:229-267Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能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。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints 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: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Repeated occurrences of extreme weather events, such as low temperatures, due to global warming present a serious risk to the safety of wheat production. Quantitative assessment of frost damage can facilitate the analysis of key genetic factors related to wheat tolerance to abiotic stress. We collected 491 wheat accessions and selected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage. Image descriptors can complement the visual estimation of frost damage. Combined with genome-wide association study (GWAS), a total of 107 quantitative trait loci (QTL) (r 2 ranging from 0.75% to 9.48%) were identified, including the well-known frost-resistant locus Frost Resistance (FR)-A1/ Vernalization (VRN)-A1. Additionally, through quantitative gene expression data and mutation experience verification experiments, we identified two other frost tolerance candidate genes TraesCS2A03G1077800 and TraesCS5B03G1008500. Furthermore, when combined with genomic selection (GS), image-based descriptors can predict frost damage with high accuracy (r ≤ 0.84). In conclusion, our research confirms the accuracy of image-based high-throughput acquisition of frost damage, thereby supplementing the exploration of the genetic structure of frost tolerance in wheat within complex field environments.
Why it matches plant phenotyping methods小麦の霜害を画像記述子で定量評価し、その精度を検証しているため、画像ベース植物フェノタイピングが研究の中心です。
abstractselected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage.
Reproduction assets foundThe paper's data processing code is publicly available on GitHub. Genotype and phenotype data are only available on reasonable request, so they do not qualify as public assets.Code · publicThe data processing code presented in this study is available on the website https://github.com/yurui2024/Frost-tolerance .Open asset ↗yurui2024/Frost-tolerancelines:156-271Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurate monitoring of wheat phenology is critical for ensuring wheat production. Recent advances in deep learning have enabled the automated detection of wheat phenology in the field. In particular, deep learning models using multi-temporal image series have addressed the challenge of low accuracy in models that only use spatial features by incorporating dynamic aspects of the wheat growth process. However, utilizing multi-temporal image series introduces challenges such as model parameter redundancy, complex inference processes, and difficulties in real-time deployment. To address these issues, this study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI) that combines knowledge distillation and multi-layer attention transfer. The proposed approach employs knowledge distillation. In this framework, a teacher model extracts spatiotemporal features from multi-temporal image-series and generates soft labels to guide a student model trained on single-temporal images. This reduces model complexity and input data requirements. Multi-layer attention transfer allows the student model to inherit feature representations from multiple layers of the teacher model. This enhances its ability to capture key phenological characteristics and supports interpretability through attention mechanisms. The proposed method achieves an overall accuracy (OA) of 0.927, comparable to models trained on multi-temporal image series. Furthermore, the model demonstrates strong generalization on unseen datasets, enhancing real-time performance and computational efficiency while maintaining high accuracy, providing a practical solution for deriving wheat phenology in the field. The dataset is available at https://github.com/phenology-detection/WPDSI.
Why it matches plant phenotyping methods小麦の生育ステージを単一時点画像から推定する深層学習手法を開発・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。
abstractthis study presents an optimization method for deriving wheat phenology from single-temporal images (WPDSI)
Reproduction assets foundThe paper's wheat phenology image dataset is explicitly stated as publicly available at the authors' GitHub repository (https://github.com/phenology-detection/WPDSI), matching an allowed URL. No separate code availability is stated beyond this repository, so it is treated as the paper-specific public asset.Dataset · publicData availability
The dataset is publicly available at https://github.com/phenology-detection/WPDSI .Open asset ↗phenology-detection/WPDSIlines:270-275Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.
Why it matches plant phenotyping methods小麦葉の感染状態をハイパースペクトル画像と深層学習で分類する手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractHyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' training/deployment/testing code for the hyperspectral wheat disease classification models in a public GitHub repository. The 1447-image hyperspectral dataset itself has no stated public deposit, so it is not included as an asset.Code · publicCode for training, deploying and testing the models can be found at https://github.com/mc2295/hyperspectralplants .Open asset ↗mc2295/hyperspectralplantslines:140-218Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (
Why it matches plant phenotyping methods植物の病徴が現れる前の茎さび病状態を、ハイパースペクトル画像と機械学習で検出・推定する方法の開発および転移性検証が中心である。
abstractThis study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the hyperspectral imaging datasets (864 hyperspectral cubes of wheat and barley, control and stem-rust-inoculated) used for the phenotyping and machine learning analysis. No code or model checkpoints are explicitly deposited.Dataset · publicData is available via online download link https://drive.google.com/drive/folders/1qAgWEAH5BruTNmT0bmSm4HTbXe_iitap (accessed on 21 October 2025).Open asset ↗1qAgWEAH5BruTNmT0bmSm4HTbXe_iitaplines:233-282Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Background Wheat diseases significantly impair production efficiency and grain quality in the wheat industry. In recent research, deep learning techniques have been widely applied to plant disease detection. However, wheat disease images collected in field conditions often face complex backgrounds and diverse lesion shapes, making accurate disease classification difficult. In real-world applications, agricultural disease recognition systems must also deal with limited computational resources and edge device constraints, emphasizing the need for lightweight methods. Results To solve these challenges, this paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection. The framework adopts a unique global-local dual-stream architecture that combines global semantic understanding with local discriminative analysis. The global learning stream extracts comprehensive semantic features and generates saliency maps to highlight key regions, while the local learning stream performs fine-grained inspection of these regions using a novel dynamic-static dual attention (DSDA) mechanism. Additionally, a Kullback-Leibler (KL) divergence perturbation strategy is implemented during training to boost the LDSL framework's robustness in noisy and complex settings. Experimental results show that the proposed LDSL framework achieves an accuracy of 94.44%, a precision of 94.47%, a recall of 94.44%, and an F1-score of 94.45%, outperforming several mainstream classification models in wheat disease recognition, such as ConvNeXt-T (92.66% accuracy, 92.69% precision, 92.66% recall, and 92.63% F1). The proposed LDSL framework is lightweight, using only 4.41 M parameters and 1.71G FLOPs. On the NVIDIA Jetson Orin Nano, it requires just 15.99 MB of storage, 39.49 MB of peak memory, and achieves an inference latency of 234.76 ms/image, demonstrating good potential for real-world deployment. Conclusions This study provides a novel detection framework for wheat disease research, which significantly improves various classification metrics. With low parameter and computation costs, the framework demonstrates good potential for practical deployment.
Why it matches plant phenotyping methodsコムギ葉の画像から病害状態を推定する軽量な画像解析フレームワークを開発・評価しており、植物病害表現型の取得手法が中心である。
abstractthis paper introduces a lightweight dual-stream learning (LDSL) framework for wheat disease detection.
Reproduction assets foundThe paper's wheat disease image dataset (five classes: healthy, powdery mildew, smut, leaf rust, sharp eyespot) is explicitly stated to be publicly available via a Google Drive link, which matches an allowed URL. No code or model checkpoints are shared.Dataset · publicThe dataset used in this study originates from the “Smart Agriculture” Platform of Jilin Agricultural Science and Technology University. To facilitate further research, it has been made publicly available at: https://drive.google.com/file/d/1xK3NX7d2bccBDMQMp0qXp-2pG-Jb_kmx/view?usp=drive_linkOpen asset ↗lines:275-333Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Fusarium head blight (FHB), caused by the Fusarium species complex, significantly endangers wheat yield and safety. Accurate and timely assessment of FHB epidemic level in the field is crucial for effective disease management. However, the complex environment and indistinct edges of diseased areas present substantial challenges in distinguishing between healthy and diseased ears, thereby impacting the accuracy of FHB epidemic level detection. This study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level. The Focal-Edge Selection Module (FSM) within the backbone replaces original C2f module to enhance edge feature representation and facilitate multi-scale feature extraction. Furthermore, the Dual Spatial-Connection Feature Pyramid Network (DSCFPN), integrating Global-to-Local Spatial Aggregation (GLSA) with bidirectional pyramid interaction, balances global and local feature acquisition while optimizing the feature fusion mechanism. This design enables the model to effectively handle occlusions, scale variations, and complex environments. Experimental results demonstrate substantial improvements over eight comparative models in detecting healthy and diseased wheat ears, achieving mean Average Precision (mAP) of 86.1% and 82.9%, respectively. Notably, the model achieved a mean accuracy of 94.7% in detecting FHB epidemic levels through rigorous spatiotemporal validation using datasets collected from independent fields across different years, underscoring its robust generalization capability. Characterized by its low complexity and lightweight design, EBS-YOLO features a parameter count of 2.05 M, 7.4 GFLOPs, and a model size of 5.0 MB, making it an efficient approach for real-time FHB epidemic level detection.
Why it matches plant phenotyping methods小麦穂の健全・罹病状態と赤かび病の流行レベルを圃場画像から推定する深層学習手法を開発し、独立圃場・異なる年のデータで検証しているため、植物病害フェノタイピング手法が中心である。
abstractThis study proposes EBS-YOLO, a novel Edge-Optimized Bidirectional Spatial Feature Augmentation YOLO Network, specifically designed for the rapid and precise determination of FHB epidemic levels at the canopy level.
Reproduction assets foundThe paper's wheat FHB image dataset (1152 field images used for EBS-YOLO training/evaluation) is explicitly stated as publicly available on the authors' GitHub repository.Dataset · publicam Development Project [2025QCY-KXJ-070]; the Science and Technology Partnership Program, Ministry of Science and Technology of China [KY202002018] and the National Natural Science Foundation of China [32081330501].
Data availability
The datasets supporting the conclusions of this article are available in the GitHub repository, https://github.com/yuanYuan8686/wheat-FHB-dataset.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
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 affiliationOpen asset ↗yuanYuan8686/wheat-FHB-datasetlines:369-441Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Rapid prediction and control of flowering time is essential for breeding crops resilient to changing climates. Current models often fail to predict flowering time in new cultivars because molecular models lack integration of environmental signals, while physiological models inadequately capture the interactions of vernalization, photoperiod and temperature. This leads to mischaracterized genotypes and inaccurate forecasts. A new Cereal Anthesis Molecular Phenology (CAMP) model was developed for wheat. It explicitly integrates the regulatory roles of three major 'virtual' flowering genes (Vrn1, Vrn2, and Vrn3) with environmental cues. A novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration. CAMP predicted flowering time within 4-7 d across 64 genetically diverse wheat cultivars grown under contrasting environments. The leaf-number phenotyping method reduced phenotyping time by more than 80%, offering a practical alternative to resource-intensive field trials. Together, these advances enable accurate cultivar characterization and scalable prediction of flowering behaviour. CAMP enables the ability to predict flowering time directly from genotypic data (e.g. SNPs), eliminating the need for costly controlled-environment experiments. This represents a step change in molecular-physiological modelling, supporting faster deployment of new cultivars and more effective design of wheat for future climates.
Why it matches plant phenotyping methods主茎葉数に基づく新規フェノタイピング手法とCAMPモデルを開発し、多様なコムギ品種・環境で開花期予測を検証している。表現型取得の効率化が中心的貢献である。
abstractA novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration.
Reproduction assets foundThe paper's Data availability statement explicitly provides public repositories containing the CAMP model source code and analysis scripts used for the phenotyping data analysis and flowering-time prediction: the APSIM Next Generation framework repository, the standalone Python CAMP model and analysis scripts, and theCCode · publicAll the data and the source code of the model are freely accessible for research use through the APSIM General Use License at: https://github.com/apsimInitiative/apsimxOpen asset ↗apsimInitiative/apsimxlines:295-475Code · publicPython code and analysis scripts can be found at https://github.com/HamishBrownPFR/CAMPOpen asset ↗HamishBrownPFR/CAMPlines:295-475Code · publicC# implementation is available at https://github.com/APSIMInitiative/ApsimX/tree/master/Models/PMF/Phenology/CAMPOpen asset ↗APSIMInitiative/ApsimXlines:295-475Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R 2 of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、データセット上で精度・計算効率・堅牢性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices.
Reproduction assets foundThe paper uses the public GWHD2021 wheat spike detection dataset (available on Kaggle) as part of its hybrid dataset, with an explicit availability statement and URL. The self-collected Xinjiang field dataset is private and available only on request. No author analysis code, trained model checkpoints, or other paper-特定Dataset · publiciting, W.W., S.L. and Y.L.; supervision, J.C.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The public part of the dataset used in this study is available from the Global Wheat Head Detection (GWHD2021) dataset at https://www.kaggle.com/competitions/global-wheat-detection , accessed on 30 September 2025. The remaining part of the dataset is private and cannot be shared due to institutional or privacy restrictions. Requests for access to the private dataset may be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest. The funderOpen asset ↗GWHD2021lines:854-929Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Ethyl methanesulfonate (EMS) mutants are widely used for genetic analysis; however, EMS-derived mutant populations are not amenable to traditional genome-wide association studies (GWAS) because the EMS mutations are present at extremely low frequencies. To address this challenge, this work develops the GeneHunter-Gene-Level Association (GH-GLA) pipeline using an EMS-generated population of wheat (Triticum aestivum) mutants and an image-based phenotyping platform. GH-GLA enables comprehensive exploration of phenotypic variation induced by genome-wide saturation mutagenesis. Using GH-GLA to quantify 83 traits in the wheat population reveals that variation in spikelet geometry is significantly associated with key agronomic traits, including thousand-kernel weight. Using this indexed wheat EMS population and phenotype data, GH-GLA identified 5905 genes that are significantly associated with specific traits. Analysis of knockouts generated by gene editing, together with haplotypes affected by selection during breeding and genetic variation in 262 wheat accessions, confirm the roles of TaAN-1, TaBAM5L, and TaXTH28L in regulating thousand-kernel weight and spikelet angle. Furthermore, this work establishes an epistatic interaction network between gene pairs to elucidate their combined effects on the phenotype. Overall, GH-GLA provides a powerful strategy for functional gene identification, and the alleles discovered here offer valuable genetic resources for crop improvement.
Why it matches plant phenotyping methods画像ベースの表現型解析プラットフォームとGH-GLAパイプラインを開発・適用し、多数の小麦形質を定量して遺伝子同定に用いた研究であり、表現型取得・解析法が中心的です。
abstractthis work develops the GeneHunter-Gene-Level Association (GH-GLA) pipeline using an EMS-generated population of wheat (Triticum aestivum) mutants and an image-based phenotyping platform.
Reproduction assets foundThe paper's GH-GLA analysis code is publicly available on GitHub with explicit availability language. The phenotypic data (OMIX010498) and VCF data (GVM000963) are deposited in repositories whose URLs are not in the allowed list, so they cannot be cited as assets here.Code · publicAll scripts and codes associated with this project are available via GitHub at https://github.com/gaze‐abyss/GH‐GLA.Open asset ↗gaze‐abyss/GH‐GLAhtml-lines:434-491Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Wheat is a vital global staple crop, where accurate ear detection and counting are essential for yield prediction and field management. However, the complexity of field environments poses significant challenges to achieving lightweight yet high-precision detection. Methods This study proposes YOLOv8-FDA, a lightweight detection and counting method based on YOLOv8. The approach integrates RFAConv for enhanced feature extraction, DySample for efficient multi-scale upsampling, HWD for compressed and accelerated model training, and the SDL loss for improved bounding box regression. Results Experimental results on the GWHD dataset show that YOLOv8-FDA achieves a precision of 86.3%, recall of 77.5%, and mAP@0.5 of 84.9%, outperforming the original YOLOv8n by significant margins. The model size is 2.96MB with a computational cost of 8.3 GFLOPs, and it operates at 19.2 FPS, enabling real-time counting with over 97.5% accuracy using cross-row segmentation. Discussion The proposed YOLOv8-FDA model demonstrates strong detection performance, lightweight characteristics, and efficient real-time capability, indicating its high practicality and suitability for deployment in real-world agricultural applications.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物器官形質の画像ベース抽出法をYOLOv8改良モデルとして開発し、データセット上で性能検証しているため、方法が研究の中心である。
abstractThis study proposes YOLOv8-FDA, a lightweight detection and counting method based on YOLOv8.
Reproduction assets foundThe paper's wheat ear detection/counting experiments were run on the public 2021 GWHD dataset, which the authors explicitly link via a Zenodo DOI in the data availability statement. No author-specific code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Zenodo at https://doi.org/10.5281/zenodo.5092309 .Open asset ↗Zenodo · 10.5281/zenodo.5092309lines:698-734Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Protecting crops from diseases is vital for the sustainable agricultural systems that are needed for food security. Introducing functional resistance genes to enhance the plant immune system is highly effective for disease resistance, but identifying new immune receptors is resource intensive. We observed that functional immune receptors of the nucleotide-binding domain leucine-rich repeat (NLR) class show a signature of high expression in uninfected plants across both monocot and dicot species. Here, by exploiting this signature combined with high-throughput transformation, we generated a wheat transgenic array of 995 NLRs from diverse grass species to identify new resistance genes for wheat. Confirming this proof of concept, we identified new resistance genes against the stem rust pathogen Puccinia graminis f. sp. tritici and the leaf rust pathogen Puccinia triticina, both major threats to wheat production. This pipeline facilitates the rapid identification of candidate NLRs and provides in planta gene validation of resistance. The accelerated discovery of new NLRs from a large gene pool of diverse and non-domesticated plant species will enhance the development of disease-resistant crops.
Why it matches plant phenotyping methods995個のNLRを対象とする高スループット形質評価パイプラインを構築し、植物体内で病害抵抗性表現型を検証することが研究の中心であるため、単なる生物学的測定ではない。
abstractHere, by exploiting this signature combined with high-throughput transformation, we generated a wheat transgenic array of 995 NLRs from diverse grass species to identify new resistance genes for wheat.
Reproduction assets foundThe authors deposited the paper's raw phenotyping data, uncropped images, and analysis/figure scripts in a public figshare repository, explicitly linked in the Data availability and Code availability sections. Other URLs (TGRC, NASC, FAT-CAT, QKbusco, iTOL, HMMER) are stock centers or third-party tools, not paper-qualiDataset · publicThe raw data and uncropped images are available via figshare at https://doi.org/10.6084/m9.figshare.28680800.v1 (ref. 139 ).Open asset ↗figshare · 10.6084/m9.figshare.28680800.v1lines:159-171Code · publicThe scripts used for data analysis and figure preparation are available via figshare at https://doi.org/10.6084/m9.figshare.28680800.v1 (ref. 139 ).Open asset ↗figshare · 10.6084/m9.figshare.28680800.v1lines:159-171Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafPhysiological trait estimationGrowth / development / phenologyLeaf traits
Summary Disentangling genotype × environment (G×E) effects is critical to understand the performance of wheat across different environments. A framework for doing this was previously presented in a model that integrated knowledge of crop physiology and the Vrn gene feedback loop to explain and predict the time of anthesis. The aims of this study were: 1) provide an updated description of the Cereal Anthesis Molecular Phenology (CAMP) model; 2) to verify the model’s assumptions regarding the relationship between Vrn gene expression and the timing of phenological stages in a set of diverse genotypes and environments; 3) to use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation. Six wheat genotypes with a range of cool temperature and photoperiod sensitivities were evaluated. Apical development, final leaf number (FLN) and temporal expression of Vrn1, Vrn2 and Vrn3 were compared with model predictions. There was a clear relationship between FLN responses to cool temperature and photoperiod, the timing of phenological events and the patterns of Vrn gene expression for all genotypes. There was general agreement between the temporal patterns of foliar gene expression observed with those assumed by CAMP, but some obvious discrepancies. These may be related to differences between gene expression in foliar (observed) and apical (assumed by the model) parts of the plant, or differences in the way observed and modelled gene expression are scaled. Overall, the model described all the observed development responses to environment and provides a basis for building quantitative predictions of field-based development from genotypic and environmental data. A protocol is presented for phenotyping wheat using FLN measured in specific combinations of temperature and photoperiod. It allows easy and unconfounded measure of key developmental phenotypes that clearly relate to the genetic make-up of the plants and underlying gene expression profiles.
Why it matches plant phenotyping methodsCAMPモデルの更新・検証と、FLNを用いた小麦発育形質のフェノタイピングプロトコル提示が研究の中心であり、単なる生物学的測定ではない。
abstractto use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation.
Reproduction assets foundThe paper's CAMP model code, analysis scripts, and data are explicitly stated as publicly available on the authors' GitHub repository, with specific URLs for the model notebook and the test/plotting script.Code · publicwere also validated and the best-performing sets selected. A
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description of each of the primers used in this study is given in the supplementary material
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(Table SA1).
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2.9 Verification of CAMP predictions
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2.9.1 Model set-up and operation.
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The CAMP model was coded into a Python script which is available at
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https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
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description of the code and parameterisation scheme is given in the supplementary material.
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The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
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derive the Vrn expression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicpression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
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Vrn gene expression could be compared with those observed. The script running the CAMP
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code and producing the graphs displayed in this paper can be viewed at
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https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.360
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CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 12, 2025.
;
https://doiOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicnd testing of the model in
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broader contexts. EW contributed substantially to the improvement of model concepts and the
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manuscript and all authors provided final checking.
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8. Data Availability
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All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
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publicly available at https://github.com/HamishBrownPFR/CAMP/694
9. References
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Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
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response of wheat vernalization to environmental variables indicates that vernalization is not
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a response to cold temperature. Journal of Experimental Botany 63: 847–857.
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Baumont M, Parent B, Manceau L, Brown HE, DOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:31 lines:1-68Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.
Why it matches plant phenotyping methodsコムギ穂の罹病小穂率という植物病害形質を画像から自動推定する手法を開発し、データセット構築と専門家評価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。
abstractrequires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding.
Reproduction assets foundThe paper's Data availability statement explicitly deposits both the spikelet-level annotated wheat spike image dataset (620 RGB images, 5,222 annotations) and the FHBDSR-Net analysis code in a public GitHub repository under the authors' account, matching an allowed URL.Dataset · publicThe dataset and code generated in this study are available at https://github.com/WeizhenLiuBioinform/Wheat-FHB-DSR-Measurement .Open asset ↗WeizhenLiuBioinform/Wheat-FHB-DSR-Measurementlines:901-961Code / 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 6 Sept 2026
Wheat spike morphology plays a critical role in determining grain yield and has garnered significant interest in genetics and breeding research. However, traditional measurement methods are limited to simple traits and fail to capture complex spike phenotypes with high precision, thus limiting progress in yield-related trait analysis. In this study, a deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed. Our pipeline achieved a mean intersection over union (mIoU) of 0.948 in spike segmentation. Additionally, the spike traits measured by our method strongly agreed with the manually measured values, with Pearson correlation coefficients of 0.9865 for spike length, 0.9753 for the number of spikelets per spike, and 0.9635 for fertile spikelets. Using experimental data of 221 wheat cultivars from various regions of Zhao County, Hebei Province, China, our pipeline extracted 45 phenotypes and analyzed their correlations with thousand-grain weight (TGW) and spike yield. Our findings indicate that precise measurements of spike area, spikelet area, and other phenotypic traits clarify the correlation between spike morphology and wheat yield. Through hierarchical clustering on the basis of spike morphology, we categorized wheat spikes into six classes and identified the phenotypic differences among these classes and their effects on TGW and yield. Furthermore, phenotypic differences among wheat cultivars from different geographical regions and over decades were revealed in this study, with an increase in the number of large-spike cultivars over time, especially in southern China. This research may help breeders understand the relationship between wheat spike morphology and yield, thus providing an important basis for future wheat breeding efforts.
Why it matches plant phenotyping methods小麦穂の画像から形態形質を抽出する深層学習パイプラインを開発し、セグメンテーション性能と手動測定との一致を検証しているため、フェノタイピング手法が研究の中心です。
abstracta deep learning pipeline, called Speakerphone, for acquiring precise wheat spike phenotypes was developed.
Reproduction assets foundThe paper's SpikePheno phenotyping pipeline (deep learning segmentation and trait extraction for wheat spikes) is explicitly stated to be publicly available on GitHub. No public dataset of the 2198 spike images or annotations is stated; the labelme link is a generic third-party tool, not a paper-specific asset.Code · publicThe full implementation of the spikePheno pipeline is available in GitHub at the following URL: https://github.com/Jiang-Phenomics-Lab/spikePheno .Open asset ↗Jiang-Phenomics-Lab/spikePhenolines:210-330Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Spike morphology is crucial for wheat (Triticum aestivum L.) yield and environmental adaptation. We developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions. These 54 spike morphology traits exhibited clear geographical differences among 306 worldwide accessions and breeding selection trend across different time windows for 1,053 accessions released from 1900 to 2020 in China. Based on geographical distribution and breeding selection of haplotypes, we attribute the differences in spike morphology to variable haplotype combinations. Wheat breeding breaks the trade-off between spike length and width/thickness, resulting in increased spike volume. A large proportion of genomic regions has been identified across wheat varieties and utilized as a fixed group to facilitate the targeted improvement and selection of desirable traits during wheat breeding programs. Overall, we provide a resource for the molecular design of spike morphology to facilitate future wheat breeding.
Why it matches plant phenotyping methodsコムギ穂の形態形質を多数個体から取得するハイスループット表現型解析プラットフォームの開発と適用が研究の中心である。
abstractWe developed a high-throughput phenotyping platform to dissect spike morphology traits based on 54 traits in 1,359 wheat accessions.
Reproduction assets foundThe paper's high-resolution spike phenotyping platform software is explicitly released as public code by the authors on GitHub. The genotype datasets (GVM000272/GVM000720) are molecular omics deposits and do not qualify as phenotype/trait data; other listed tools are generic third-party libraries.Code · publicn/gvm) under accession number GVM00027239
or GVM000720.
• The genotype data for 1053 Chinese accessions (1900–2020) are pub
licly available at the Genome Variation Map (https://bigd.big.ac.cn/gvm)
under accession number GVM000720.
• The software for the high-resolution phenotyping platform is publicly avail
able with the link https://github.com/ShenKC-hub/wheat_platform1.0.
• Any additional information required to reanalyze the data reported in this
paper is available from the lead contact upon request.
ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation
of China (32272122, 32401876, and 32225038)，the Strategic Priority
Research Program of Chinese Academy of Open asset ↗ShenKC-hub/wheat_platform1.0 · wheat_platform1.0pdf-raw-page:15 lines:1-81Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 %. However, the precision for stems with 54 % was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
Why it matches plant phenotyping methods小麦器官の画素レベルセグメンテーション用データセットを構築し、モデル性能を検証する研究であり、植物形質抽出のための画像解析手法が中心です。
abstractThe labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.
Reproduction assets foundThe paper's GWFSS wheat organ segmentation dataset (1096 pixel-labelled images plus 52,078 unlabelled images, subset/imaging-setup metadata) and the benchmark segmentation model are publicly deposited in the ETH Research Collection and mirrored on Hugging Face, with links also listed on the Global Wheat site.Dataset · publicThe full dataset (GWFSS_v1.0_full) including the 1096 ground-truth labelled images (GWFSS_v1.0_labelled), the descriptions of the datasets (GWFSS_v1.0_subsets.csv) and imaging setups (GWFSS_v1.0_imaging_setups.csv) is available in the ETH research collection (https://doi.org/10.3929/ethz-b-000734546)Open asset ↗ETH research collection · 10.3929/ethz-b-000734546html-lines:1006-1041Dataset · publicTo facilitate access, the labelled data and the benchmark model will also be available at (https://huggingface.co/datasets/GlobalWheat/GWFSS_v1.0).Open asset ↗huggingface · GlobalWheat/GWFSS_v1.0html-lines:1129-1192Dataset · publicLinks to these datasets can be found at: https://www.global-wheat.com/gwfss.html.Open asset ↗html-lines:1006-1041Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
A novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits, revealing latent patterns related to dense root clusters in digital images. Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm, 27 ARTs were extracted reflecting dense root cluster size and spatial location. These ARTs were then used independently and in combination with Traditional Root Traits (TRTs) to classify wheat genotypes differing in drought tolerance. ART-based models outperformed TRT-only models in drought classification (e.g., 96.3 % vs. 85.6 % accuracy). Combining ARTs and TRTs further improved accuracy to 97.4 %. Notably, 4 selected ARTs matched the performance of all 23 TRTs, offering 5.8 × higher information density (0.213 vs. 0.037 accuracy/feature). This superiority reflects the ability of ARTs to capture richer, more complex architectural information, evidenced by higher internal variability (35.59 ± 11.41 vs. 28.91 ± 14.28 for TRTs) and distinct data structures in multivariate analyses; PERMANOVA confirmed that ARTs and TRTs provide complementary insights. Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes, ART offers a scalable, customisable toolset for high-throughput phenotyping of plant roots. By bridging conventional, visually derived traits with autonomous computational analyses, this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception. ART thus emerges as a promising framework for revealing hidden features in plant imaging, with broader applications across plant science to deepen our understanding of crop adaptation and resilience.
Why it matches plant phenotyping methodsデジタル画像から根の潜在形質を抽出する計算法を開発し、圃場・制御環境で検証した、植物フェノタイピング手法が中心の研究。
abstractA novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits
Reproduction assets foundThe authors explicitly state that all code, data, and segmented root images from this study are publicly available in their GitHub repository (shoaibms/ART), which directly reproduces the paper's root phenotyping measurements and analysis.Code · publicAll code, data and segmented images are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:277-403Dataset · publicAll code and data are available for download at https://github.com/shoaibms/ART .Open asset ↗shoaibms/ARTlines:120-154Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This study presents a comprehensive analysis of winter wheat phenological variations in China's Huang-Huai-Hai Plain (HHHP) from 1981 to 2021, leveraging data from 62 national agrometeorological observation stations. As the world's largest winter wheat production region, the HHHP contributes over 60% of China's total output, playing a pivotal role in national food security. Using kernel density estimation (KDE) and univariate linear regression, the dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations. Results reveal significant shifts in phenological timings and growth stages under climate change, such as advanced heading stages and altered phase lengths, which correlate with temperature increases and extreme weather events. The dataset, comprising 1,120 figures generated via Origin Lab, is publicly available on ScienceDB, providing critical insights for climate adaptation strategies, cultivation optimization, and yield stability. Technical validation confirms the reliability of the data, sourced from standardized, long-term manual observations by trained professionals under China Meteorological Administration protocols. This work offers a foundational resource for understanding climate-crop interactions and guiding sustainable agricultural practices in a warming world.
Why it matches plant phenotyping methods冬小麦の複数生育ステージという植物形質を長期・標準化観測で収録した公開データセットであり、データの技術的検証も含むため、フェノタイピングデータセットとして中心的です。
abstractthe dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations
Reproduction assets foundThe paper describes a public dataset of winter wheat phenology (1,120 KDE and linear-trend figures from 62 agrometeorological stations, 1981–2021) deposited on ScienceDB under DOI 10.57760/sciencedb.23011, freely downloadable. No custom analysis code exists ('No custom code was created for the production of this dataetDataset · publicThe Variation of winter wheat phenology dataset in Huang Huai Hai Plain of China from 1981 to 2021 is available at ScienceDB 35 . The dataset is provided in JPG format estimated and plotted by Origin Lab. All the diagrams can be downloaded directly for free.Open asset ↗lines:47-83Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Background Fungal diseases are among the most significant threats to global crop production, often leading to substantial yield losses. Early detection of crop infection by fungus is the very first step to deploying a timely and effective treatment. Early and reliable detection is thus key to improving yields, sustainability, and achieving food security. Conventional diagnostic methods are however often destructive, slow, or requiring visible symptoms which appear late in the infection process. To overcome these challenges, we propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time. Results We demonstrate the use of low-cost OCT to monitoring wheat (cultivar AxC 169) when infected by Septoria tritici . We show that OCT analysis can effectively detect signs of infection before any external symptoms appear. Although OCT cannot directly visualize fungal hyphae, OCT reveals apparent morphological changes of the mesophyll where the fungal filaments are expected to develop. This study thus focuses on monitoring and correlating changes within the mesophyll structural organisation with the state of infection. It results in distinct statistical difference between intact and infected wheat plants two days only after infection. We then demonstrate the use of machine learning (ML) for high throughput segmentation of OCT scans, providing a foundation for future automated fungus-detection analysis. Conclusions This work highlights the potential of OCT, combined with ML tools, to enable rapid, non-invasive, and early diagnosis of crop fungal infections, opening new avenues for precision agriculture and sustainable disease management.
Why it matches plant phenotyping methodsOCTによる植物内部構造の非侵襲的画像化と、機械学習によるセグメンテーションを用いて、感染植物の形態変化・感染状態を推定する手法が研究の中心である。
abstractwe propose using optical coherence tomography (OCT) as an innovative imaging tool to provide cross-sectional and three-dimensional images of the plant internal microstructure non-invasively, in vivo, and in real-time.
Reproduction assets foundThe paper's authors publicly released their bespoke ML-based OCT segmentation software (PyQt5 GUI with U-Net model for segmenting mesophyll gaps in wheat OCT scans) via a Google Drive link, stated in both the Methods and Data availability sections. Raw OCT B-scans are only available upon request, so no public phenotypeCode · publicuses OpenCV, TensorFlow, NumPy, and Pandas for image processing and ML-based analysis. After training, the U-Net model (unet_masking3.keras) is used for generating segmentation masks via MaskThread class. The code is provided in supplementary information (SI), and the software is made available for download following this link:
https://drive.google.com/drive/folders/1DJm3OZHfK-P-XSRXGMtpxgSx51WnVNsF?usp=sharing
In both the manual and the automated procedure, the analysis focuses on the thickness of these apparent gaps between the second and third upper layers of the mesophyll.
ResultsOpen asset ↗lines:42-51Code / 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 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Freeze injury during the seedling stage significantly impacts wheat growth and yield, making the development of freeze-tolerant varieties crucial for ensuring stable yields. To identify key genetic factors for wheat freeze tolerance, an accurate assessment of freeze tolerance is necessary. However, traditional methods, such as visual inspection, are subjective and can vary significantly among observers. In this study, we developed FreezeNet, a lightweight deep learning model designed to accurately quantify freeze injury using an image-based phenotyping method. Freeze tolerance traits, including vegetation area (VA), green vegetation area (GVA), yellow vegetation fraction (YVF), and mean hue value (mHue), were extracted for freeze tolerance assessment. We captured standardized images with a smartphone and used FreezeNet to extract the freeze tolerance traits for 220 wheat accessions. These traits were strongly correlated with traditional injury scores estimated through visual inspection. Moreover, they presented relatively high heritability. Using these traits, we conducted genome-wide association studies (GWASs) to identify genetic loci associated with freeze tolerance. Eleven significant QTLs associated with freeze tolerance were identified, including 8 novel loci. By integrating four of these loci into a wheat germplasm that lacked any of the 11 QTLs, we significantly enhanced its freeze resistance, demonstrating the practical application of these genetic loci in breeding for improved freeze tolerance. Our results highlight FreezeNet as an advanced tool for assessing wheat freeze injury and identifying the genetic factors responsible for freeze tolerance, with the potential to guide breeding efforts toward the development of more resilient wheat varieties.
Why it matches plant phenotyping methodsFreezeNetは画像ベースでコムギの凍害形質を定量化する深層学習手法として開発・検証されており、植物フェノタイピング手法が研究の中心です。
abstractwe developed FreezeNet, a lightweight deep learning model designed to accurately quantify freeze injury using an image-based phenotyping method.
Reproduction assets foundThe paper's data availability statement explicitly deposits the full FreezeNet implementation and trained model on the authors' public GitHub repository, which directly reproduces the paper's image-based freeze-injury phenotyping analysis. The 430 field images and trait tables are not stated as separately deposited (noCode · publicThe full implementation and the trained FreezeNet model are available in GitHub at the following URL: https://github.com/Jiang-Phenomics-Lab/FreezeNet .Open asset ↗Jiang-Phenomics-Lab/FreezeNetlines:199-214Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows measurements to be collected in p @0.5 of 97.1% for stomatal detection. When validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress.
Why it matches plant phenotyping methodsFieldDinoは、圃場で気孔の生理・解剖形質を高スループットに取得するフェノタイピング手法として開発され、大規模圃場試験で検証されているため含める。
abstractWe present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public GitHub repository containing the 3D-printed leaf clip STL files, the Python stomatal annotation/measurement script, and the FieldDino app, plus a public Roboflow dataset hosting the training/validation stomatal image set used to train the YOLOv8-M模型.Code · publicrse.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtsalter/FieldDinoMicroscopy . Validation datasets for the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x .
References
Baloch , M. J.
,
J.
Dunwell
,
K.
DrN
, et al. 2013 . “
Morpho‐Physiological Characterization of Spring Wheat Genotypes Under Drought Stress
.” InterOpen asset ↗williamtsalter/FieldDinoMicroscopylines:337-498Dataset · publicResearch Infrastructure Strategy (NCRIS). Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Data Availability Statement
The data that support the findings of this study are openly available in Roboflow at https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtOpen asset ↗narrabri-plant-physiology-hclvi/fielddino-training-set-200xlines:337-498Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R 2 and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R 2 and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.
Why it matches plant phenotyping methods作物生育情報を定量化するUAVマルチスペクトルセンサー、補正法、処理システムを開発し、LAIと地上部バイオマス推定を検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS)
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 figures, tables and data mentioned in the article can be downloaded from https://github.com/ikjkj2/Plant-Phenomics .Open asset ↗ikjkj2/Plant-Phenomicslines:395-413Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30-5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN . caas-4A2, QSN . caas-4D and QSN . caas-5B2 , were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN . caas-4A2 and QSN . caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.
Why it matches plant phenotyping methodsRGB画像とコンピュータビジョンによりコムギの穂数を自動推定する手法を開発・比較し、精度を評価しているため、植物フェノタイピング手法が中心である。
abstractHence, there is an urgent need to develop efficient and accurate methodologies for SN counting.
Reproduction assets foundThe paper publicly releases two paper-specific assets: (1) a wheat spike number image dataset (subsets CD&DD&XX) on GitHub, and (2) the YOLOX-P analysis code on Google Drive. Both have explicit availability statements with author-provided URLs.Dataset · publicThe image set for CD&DD&XX is publicly available on GitHub ( https://github.com/lileimax/YOLOXP-wheat-spike-identification ).Open asset ↗https://github.com/lileimax/YOLOXP-wheat-spike-identificationlines:223-246Code · publicThe code for YOLOX-P is publicly available on Google Drive ( https://drive.google.com/drive/folders/1urCDUdyrq14FuwG2I3YwCZraUGEEl_8X?usp=sharing ).Open asset ↗lines:247-250Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In complex farmland environments, wheat canopy coverage is insufficient at the tillering stage, posing a considerable challenge to the accurate extraction of its canopy using UAV(unmanned air vehicle) remote sensing images. In this paper, an end-to-end semantic segmentation method based on visible light (RGB) and thermal infrared (TIR) images, Tiff-SegFormer, which fused spectral features and temperature features effectively, is proposed for accurate pixel-level classification of winter wheat at the tillering stage images taken by UAV to segment the wheat canopy and background. Tiff-SegFormer utilizes hierarchical feature representation and efficient self-attention in the encoder stage to extract features of detail contours of RGB images and temperature changes of TIFF images, respectively. In the decoder stage, the features are concatenated and then the channel and spatial attention mechanisms are superimposed, aiming to further improve the segmentation accuracy and efficiency of winter wheat at the tillering stage in UAV remote sensing images. The results show that Tiff-SegFormer can achieve accurate segmentation of wheat canopy and background from UAV images of winter wheat at the tillering stage (mIoU = 84.28%, mPA = 88.97%, accuracy = 94.55%). In order to verify the efficiency of the proposed method, Tiff-SegFormer is compared with four widely used semantic segmentation methods, all of which show better performance. The four methods are UNet, DeepLabv3+, HRNet, SegFormer and four-channel (RGB + TIFF) Segformer. The generalization test shows that the proposed Tiff-SegFormer also achieves better performance than other comparison methods (mIoU = 84.94%, mPA = 91.46%, accuracy = 94.71%). Tiff-SegFormer provides a robust and efficient tool for segmenting winter wheat canopy from UAV remote sensing images of winter wheat at the tillering stage, and has great potential in applications (model implementation and results can be found at https://github.com/wylSUGAR/Tiff-SegFormer ).
Why it matches plant phenotyping methodsUAVのRGB・熱赤外画像から冬コムギのキャノピーを抽出するセマンティックセグメンテーション手法を開発し、複数手法との比較および汎化性能検証を行っており、植物状態の取得方法が中心である。
abstractan end-to-end semantic segmentation method based on visible light (RGB) and thermal infrared (TIR) images, Tiff-SegFormer, which fused spectral features and temperature features effectively, is proposed for accurate pixel-level classification of winter wheat at the tillering stage images taken by UAV to segment the wheat canopy and background.
Reproduction assets foundThe paper publicly releases the UAV RGB/TIR winter wheat tillering-stage image dataset and the TIR-to-TIFF conversion code via two author GitHub repositories. The Tiff-SegFormer model repository is referenced but its URL is not among the allowed URLs, and labelme is a generic third-party tool, so neither is included.Dataset · publicThe image can be found at
https://github.com/wylSUGAR/wheat_tillering_stage.Open asset ↗wylSUGAR/wheat_tillering_stagepdf-page:2 lines:56-74Code · publicthe TIR image was
converted into a TIFF image (the code can be found at https://github.com/wylSUGAR/TIR_DJ_tiff)Open asset ↗wylSUGAR/TIR_DJ_tiffpdf-page:2 lines:56-74Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made to assess nitrogen content (NC) at the organ level and its association with nitrogen use efficiency (NUE). Also, little is known about the performance of UAV-based image texture features of different spectral bands in monitoring crop nitrogen and NUE. In this study, multi-spectral images were collected throughout different stages of winter wheat in two independent field trials - a single-variety field trial and a multi-variety trial in 2021 and 2022, respectively in China and Germany. Forty-three multispectral vegetation indices (VIs) and forty texture features (TFs) were calculated from images and fed into the partial least squares regression (PLSR) and random forest (RF) regression models for predicting nitrogen-related indicators. Our main objectives were to (1) assess the potential of UAV-based multispectral imagery for predicting NC in different organs of winter wheat, (2) explore the transferability of different image features (VI and TF) and trained machine learning models in predicting NC, and (3) propose a technical workflow for mapping NUE using UAV imagery. The results showed that the correlation between different features (VIs and TFs) and NC in different organs varied between the pre-anthesis and post-anthesis stages. PLSR latent variables extracted from those VIs and TFs could be a great predictor for nitrogen agronomic efficiency (NAE). While adding TFs to VI-based models enhanced the model performance in predicting NC, inconsistency arose when applying the TF-based models trained based on one dataset to the other independent dataset that involved different varieties, UAVs, and cameras. Unsurprisingly, models trained with the multi-variety dataset show better transferability than the models trained with the single-variety dataset. This study not only demonstrates the promise of applying UAV-based imaging to estimate NC in different organs and map NUE in winter wheat but also highlights the importance of conducting model evaluations based on independent datasets.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から小麦の器官別窒素含量と窒素利用効率を推定する技術ワークフローを開発・検証しており、特徴量、機械学習モデル、独立データセットでの転移性評価が中心である。
abstractOur main objectives were to (1) assess the potential of UAV-based multispectral imagery for predicting NC in different organs of winter wheat, (2) explore the transferability of different image features (VI and TF) and trained machine learning models in predicting NC, and (3) propose a technical workflow for mapping NUE using UAV imagery.
Reproduction assets foundThe authors deposited the study's datasets (multi-temporal nitrogen content measurements and associated UAV multispectral image-derived features) on Zenodo, with an explicit data availability statement and a reference-list dataset entry. This is a paper-specific, publicly accessible asset. No author analysis code or TrDataset · publicThe datasets generated for this study are available on Zenodo ( https://doi.org/10.5281/zenodo.13732404 ).Open asset ↗Zenodo · 10.5281/zenodo.13732404lines:212-240Code / 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 · checked 6 Sept 2026
The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R2) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.
Why it matches plant phenotyping methods植物病害画像から病斑割合と病害重症度を推定する画像セグメンテーション手法を開発し、野外データセット、精度、速度を評価しており、植物表現型取得法が中心である。
abstractThe segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity.
Reproduction assets foundThe paper's field-scenario plant disease image dataset (SBB, WSR, CAR with three-color pixel labels) is publicly released on Kaggle via DOI, as stated in the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicData Availability Statement: The original data presented in this study are openly available in Kaggle
at https://doi.org/10.34740/kaggle/ds/6620728, accessed on 9 March 2025.Open asset ↗Kaggle · 10.34740/kaggle/ds/6620728pdf-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions. The most comprehensive parametric model tested, which included SNPs, HSI, and environmental covariates data, consistently achieved the best results, closely followed by machine learning (ML) approaches when considering the same omics data. For example, the most comprehensive model (M9), under the forward prediction cross-validation scheme, predicted the GY of the 2023 growing season using data from 2021 and 2022 for a correlation between predicted and observed values of 0.53. This model demonstrated superior performance compared to less complex models, emphasizing the advantage of integrating numerous data sources and their interactive effects. Furthermore, when comparing the top 25% of the predicted lines versus the corresponding observed lines with the highest GY, the M9 model returned a coincide index (CI) of 55% (i.e., in both sets, 55% of the top 25% values were common), whereas for the highest performing ML model (gradient boosting regression), the CI was of 46%. This study highlights the potential of multi-data source approaches to accelerate the selection of heat-tolerant wheat genotypes.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像を用いた小麦収量形質の推定を、ゲノム・環境データとの統合モデルで検証しており、形質予測性能の比較が研究の中心である。
abstractIntegrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) 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2件を確認しました。Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDglines:343-470Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpklines:343-470Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
In quantitative genomic analysis of wheat plant height (PH), the average height of a few representative plants is typically used to represent the PH of the entire plot, which overlooks the variation in height among other plants. Extracting different height quantiles from canopy point clouds can address this limitation. For this purpose, low-cost UAV cross-circling oblique (CCO) imaging, combined with structure-from-motion (SfM) and multi-view stereopsis (MVS), was employed to generate precise canopy point clouds for 262 F5 recombinant inbred lines (Zhongmai 578 × Jimai 22) across seven environments. Multi-level 3D-PH measurements were extracted from six height quantiles, revealing a strong correlation (mean r = 0.95) between 3D-PH and field-measured PH (FM-PH) across environments. The 90 % and 92 % height quantiles showed the closest agreement with FM-PH compared to other quantiles. Eleven stable quantitative trait loci (QTLs) associated with multi-level 3D-PH were identified using a 50K single nucleotide polymorphism array. Among these, QPhzj.caas-3A.2 (detected by 3D-PH) and QPhzj.caas-7A.1 (detected by both FM-PH and 3D-PH) represented potential novel loci. KASP markers for these QTLs were developed and validated. Furthermore, within the intervals of QPhzj.caas-5A and QPhzj.caas-3B (both were detected by 3D-PH), two candidate genes associated with PH regulation were identified: TaGL3-5A and Rht5 , respectively. Corresponding KASP markers for these genes were also developed and validated. This study highlighted the advantages of 3D model and multi-level 3D-PH in elucidating the genetic basis of crop height, and provided a precise and objective basis for advancing wheat breeding programs.
Why it matches plant phenotyping methodsUAV画像からSfM/MVSで3Dキャノピーモデルを構築し、複数の高さ分位点として植物高を抽出・検証することが研究の中心であるため、画像ベースの植物フェノタイピング手法として適格。
abstractExtracting different height quantiles from canopy point clouds can address this limitation.
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 plant height data for various environments and the detailed information of the genetic map can be downloaded from https://github.com/ILIKEWIND123/Plant-Phenomics .Open asset ↗ILIKEWIND123/Plant-Phenomicslines:364-399Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differences until the onset of symptoms. Hyperspectral image acquisitions were purchased with two cameras in visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges on eighteen dates between one day before inoculation and twenty days after inoculation. For each wavelength range studied, a total of 1175 images provided information on 3326 leaves measured throughout the experiment. These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.
Why it matches plant phenotyping methodsコムギ葉の病害状態をハイパースペクトル画像で取得したデータベースを構築し、感染状態分類・症状出現予測や画像解析手法の評価基盤として提供しており、表現型取得法が中心である。
abstractThis article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral imaging dataset of healthy and Zymoseptoria tritici-infected durum wheat leaves, deposited on Data INRAE with DOI 10.57745/WVP0FJ. This is the paper's own plant-phenotyping measurement data (VNIR/SWIR hyperspectral images, pixel coordinates, and CSVDataset · publicand HySpex SWIR-384 (Norsk Elektro Optikk, Norway).
Data source location
Institution: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE)
City: Montpellier
Country: France
Data accessibility
Repository name: Data INRAE
Data identification number: doi: 10.57745/WVP0FJ
Direct URL to data: https://doi.org/10.57745/WVP0FJ
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Value of the Data
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This dataset depicts the visual appearance and spectral information related to the onset kinetics of Lb disease symptoms on wheat leaves using hyperspectral images acquired post-inoculation.
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The images captured are valuable to monitor the evolution of the Lb disease on wheat leaves through the developmenOpen asset ↗Data INRAE · 10.57745/WVP0FJlines:1-60Code / 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 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 confirmedOpenAlex · arXiv · checked 6 Sept 2026
Yield is one of the core goals of crop breeding. By predicting the potential yield of different breeding materials, breeders can screen these materials at various growth stages to select the best performing. Based on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding areas is collected to provide data support for the breeding decisions of breeders. However, the accuracy of current yield predictions still requires improvement, and the usability and user-friendliness of yield forecasting tools remain suboptimal. To address these challenges, this study introduces a hybrid method and tool for crop yield prediction, designed to allow breeders to interactively and accurately predict wheat yield by chatting with a large language model (LLM). First, the newly designed data assimilation algorithm is used to assimilate the leaf area index into the WOFOST model. Then, selected outputs from the assimilation process, along with remote sensing inversion results, are used to drive the time-series temporal fusion transformer model for wheat yield prediction. Finally, based on this hybrid method and leveraging an LLM with retrieval augmented generation technology, we developed an interactive yield prediction Web tool that is user-friendly and supports sustainable data updates. This tool integrates multi-source data to assist breeding decision-making. This study aims to accelerate the identification of high-yield materials in the breeding process, enhance breeding efficiency, and enable more scientific and smart breeding decisions.
Why it matches plant phenotyping methodsUAVリモートセンシングによる作物フェノタイピングデータを基盤に、収量推定手法と対話型Webツールを開発しており、植物形質（小麦収量）の取得・推定が研究の中心である。
abstractBased on unmanned aerial vehicle remote sensing technology, high-throughput crop phenotyping data in breeding areas is collected to provide data support for the breeding decisions of breeders.
Reproduction assets foundThe article states that all study data (UAV remote sensing, LAI/CH phenotyping, yield, meteorological and soil data) are publicly available via a Zenodo deposit, which directly reproduces this paper's plant-phenotyping measurements. No author analysis code or trained model checkpoint URL is explicitly provided; other LDataset · publicAll data in this study are publicly available (https://doi.org/10.5281/zenodo.14376799).Open asset ↗zenodo · 10.5281/zenodo.14376799pdf-page:6 lines:1-52Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analysing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping is demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and it is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality, MRI, opens up new perspectives in seed phenotyping and crop improvement.
Why it matches plant phenotyping methodsMRIと深層学習を統合した種子表現型解析ツールを開発し、多数の種子形質を自動・非破壊・高スループットに定量化する中心的な方法論研究である。
abstractHere, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI.
Reproduction assets foundThe paper's MRI-Seed-Wizard segmentation/phenotyping pipeline (Python/PyTorch scripts, nnU-Net/U-Net models) and demonstration data are explicitly published online by the authors at the GitHub repository akvilonBrown/mri-wizard, matching an allowed URL.Code · publicCode and demonstration data are available at: https://github.com/akvilonBrown/mri-wizard .Open asset ↗akvilonBrown/mri-wizardlines:227-303Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noisy representations of plants using software or are expensive to capture in hardware. Recently, view synthesis models have been developed that can generate detailed 3D scenes, and even 3D models, from only RGB images and camera poses. These models offer unparalleled accuracy but are currently data hungry, requiring large numbers of views with very accurate camera calibration. RESULTS: In this study, we present a view synthesis dataset comprising 20 individual wheat plants captured across 6 different time frames over a 15-week growth period. We develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework. We trained each plant instance using two recent view synthesis models: 3D Gaussian splatting (3DGS) and neural radiance fields (NeRF). Our results show that both 3DGS and NeRF produce high-fidelity reconstructed images of a plant subject from views not captured in the initial training sets. We also show that these approaches can be used to generate accurate 3D representations of these plants as point clouds, with 0.74-mm and 1.43-mm average accuracy compared with a handheld scanner for 3DGS and NeRF, respectively. CONCLUSION: We believe that these new methods will be transformative in the field of 3D plant phenotyping, plant reconstruction, and active vision. To further this cause, we release all robot configuration and control software, alongside our extensive multiview dataset. We also release all scripts necessary to train both 3DGS and NeRF, all trained models data, and final 3D point cloud representations. Our dataset can be accessed via https://plantimages.nottingham.ac.uk/ or https://https://doi.org/10.5524/102661. Our software can be accessed via https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis.
Why it matches plant phenotyping methods3D植物表現型取得のための撮影システム、再構成手法、データセットを開発し、スキャナとの精度比較で検証しているため、方法が中心的である。
abstractWe develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework.
Reproduction assets foundThe paper releases its wheat plant multiview image dataset (via plantimages.nottingham.ac.uk and GigaDB DOI 10.5524/102661), its authors' analysis/capture codebase on GitHub (3D-Plant-View-Synthesis), a Software Heritage archive of that code, and a DOME-ML registry annotation. All are paper-specific, public, and have作者Code · publicruction output across all plants. We hope that our study will provide opportunities for researchers exploring new and improved 3D phenotyping algorithms, 3D reconstruction and view synthesis research, and active vision systems.
Availability of Source Code and Requirements
Project name: 3D Plant View Synthesis:
Project homepage: https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis [ 13 ]
Operating system(s): Windows, Ubuntu
Programming language: Python (>=3.8)
License: Apache 2.0
Any restrictions to use by nonacademics: None
Our code has also been archived in Software Heritage [ 66 ]. Functionality, such as Robotic View Capturing, 3DGS to Point Cloud, and our UR5 Configs files, are storOpen asset ↗GitHub · Lewis-Stuart-11/3D-Plant-View-Synthesislines:663-695Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The selection and promotion of high-yielding and nitrogen-efficient wheat varieties can reduce nitrogen fertilizer application while ensuring wheat yield and quality and contribute to the sustainable development of agriculture; thus, the mining and localization of nitrogen use efficiency (NUE) genes is particularly important, but the localization of NUE genes requires a large amount of phenotypic data support. In view of this, we propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots, propose a wheat 3D plot segmentation dataset, quantify the plot canopy height via combination with PointNet++, and generate 4 nitrogen utilization-related vegetation indices via index calculations. Six height-related and 24 vegetation-index-related dynamic digital phenotypes were extracted from the digital phenotypes collected at different time points and fitted to generate dynamic curves. We applied height-derived dynamic numerical phenotypes to genome-wide association studies of 160 wheat cultivars (660,000 single-nucleotide polymorphisms) and found that we were able to locate reliable loci associated with height and NUE, some of which were consistent with published studies. Finally, dynamic phenotypes derived from plant indices can also be applied to genome-wide association studies and ultimately locate NUE- and growth-related loci. In conclusion, we believe that our work demonstrates valuable advances in 3D digital dynamic phenotyping for locating genes for NUE in wheat and provides breeders with accurate phenotypic data for the selection and breeding of nitrogen-efficient wheat varieties.
Why it matches plant phenotyping methods航空画像・3D点群・マルチスペクトル画像から小麦区画の草冠高と植生指数を抽出するデジタルフェノタイピング手法を開発・適用しており、表現型取得が研究の中心である。
abstractwe propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' source code, testing data, and supporting datasets (including the W3DPS 3D plot segmentation dataset and phenotyping/GWAS data) at two public Quark pan links under CC BY 4.0. These are paper-specific, publicly actionable assets. Other allowed URLsCode · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Dataset · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Code / dataset availability 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 15 Sept 2026
This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues.
Why it matches plant phenotyping methods小麦葉の画像からさび病の有無・種類・重症度を推定するVQA、データセット、連合学習モデルを開発・評価しており、植物病害状態の取得が中心的な方法貢献である。
abstractThis dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections.
Reproduction assets foundThe paper's data availability statement explicitly releases the authors' FL/VQA code on GitHub and the paper-specific wheat rust image datasets (WheatRustDL2024, WheatRustVQA images and question-answer text) via public SharePoint/Google Drive/Docs links.Dataset · public• This study introduces a Federated Learning and a Visual Question-Answering model. These models are available online on this study’s GitHub (https://github.com/aknnvt/FL-VQA-in-Wheat-Rust). • The custom datasets curated for this study, WheatRustDL2024 (https://bitspilaniac-my.sharepoint.com/:f:/g/personal/f20212378_pilani_bits-pilani_ac_in/EvwjsY_JT4FIu7ZTU8zyXOMB8Ywk4OXgO6LYwTk8dOiN_Q? e=45eTOD) and WheatRustVQA (https://docs.google.com/document/d/1EvVdrMi7W-JZkeeePEkNmVIEJ1dn7eln/edit? usp=sharing&ouid=114090611032812705334&rtpof=true&sd=true), are available for public use. Additionally, the images in WheatRustVQA (https://drive.google.com/drive/folders/1izs5ZVmi9V__ixk4ODiJAyachAulP3RL? Open asset ↗WheatRustDL2024lines:292-351Dataset · publicmodels are available online on this study’s GitHub (https://github.com/aknnvt/FL-VQA-in-Wheat-Rust). • The custom datasets curated for this study, WheatRustDL2024 (https://bitspilaniac-my.sharepoint.com/:f:/g/personal/f20212378_pilani_bits-pilani_ac_in/EvwjsY_JT4FIu7ZTU8zyXOMB8Ywk4OXgO6LYwTk8dOiN_Q? e=45eTOD) and WheatRustVQA (https://docs.google.com/document/d/1EvVdrMi7W-JZkeeePEkNmVIEJ1dn7eln/edit? usp=sharing&ouid=114090611032812705334&rtpof=true&sd=true), are available for public use. Additionally, the images in WheatRustVQA (https://drive.google.com/drive/folders/1izs5ZVmi9V__ixk4ODiJAyachAulP3RL? usp=drive_link). The raw version of the answers can be found on the GitHub repository.
DecOpen asset ↗WheatRustVQAlines:292-351Dataset · public/g/personal/f20212378_pilani_bits-pilani_ac_in/EvwjsY_JT4FIu7ZTU8zyXOMB8Ywk4OXgO6LYwTk8dOiN_Q? e=45eTOD) and WheatRustVQA (https://docs.google.com/document/d/1EvVdrMi7W-JZkeeePEkNmVIEJ1dn7eln/edit? usp=sharing&ouid=114090611032812705334&rtpof=true&sd=true), are available for public use. Additionally, the images in WheatRustVQA (https://drive.google.com/drive/folders/1izs5ZVmi9V__ixk4ODiJAyachAulP3RL? usp=drive_link). The raw version of the answers can be found on the GitHub repository.
Declarations
Competing interests
The authors declare no competing interests.
References
1.
Abebe W
Wheat Leaf Rust Disease Management: a review
J. Plant. Pathol. Microbiol. 2021 12 1 8 Abebe, W. Wheat Leaf RusOpen asset ↗WheatRustVQAlines:292-351Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での植物病徴を画像から取得・追跡・定量する撮像および画像解析手法を開発・検証しており、植物表現型測定が中心である。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is publicly available on the authors' GitHub repository, and that a sample dataset plus the trained reference mark detection model are downloadable from the ETH Research Collection. Both are paper-specific, public, and actionable.Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:98-107Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:98-107Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recent research has shown that optimizing photosynthetic and stomatal traits holds promise for improved crop performance. However, standard phenotyping tools such as gas exchange systems have limited throughput. In this work, a novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes. Using the dual-imaging methods and traditional approaches, we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance, and Fq'/Fm', highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combinations of preferable traits (i.e. inherently high gsmax, low Ki, and maintained leaf evaporative cooling) are present in wheat. This work highlights the effectiveness of thermal imaging in screening dynamic gs in a panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimization within future breeding programmes.
Why it matches plant phenotyping methods特注ガス交換チャンバーと熱画像を組み合わせた動的な気孔コンダクタンス・光合成形質の取得手法を開発し、複数のコムギ遺伝子型で実証しているため、植物フェノタイピング手法が中心です。
abstracta novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes.
Reproduction assets foundThe paper's Data Availability statement points to a public Dryad repository containing the raw phenotyping data (photosynthesis and stomatal kinetics measurements) for this study, matching an allowed URL. Supplementary datasets S1–S2 are calculation spreadsheets but no standalone public URL is given for them beyond theDataset · publicRaw data can be accessed from the Dryad Digital Repository ( Faralli et al. , 2024 ) ( https://doi.org/10.5061/dryad.79cnp5j4d ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.79cnp5j4dlines:117-171Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Wheat ( Triticum aestivum ) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.
Why it matches plant phenotyping methodsドローン画像と気象データからコムギの生育ステージおよび開花日を推定するモデルを開発・検証し、公開データセットも構築しており、表現型取得・推定手法が研究の中心である。
abstractwe present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' GSP-AI source code and the open WGSP dataset (70,410 annotated drone/smartphone canopy images with climatic data) on GitHub, plus the AirMeasurer plot-segmentation platform code. Both are paper-specific, public, and actionable.Code · publicSource code, WGSP, and other datasets supporting the results presented in this article are available at https://Github.com/The-Zhou-Lab/GSP-AI/releases .Open asset ↗The-Zhou-Lab/GSP-AIlines:308-308Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods高解像度画像時系列と複数の植物形質を含む大規模な公開圃場フェノタイピングデータセットであり、再利用可能なフェノタイピング基盤・ベンチマークとして中心的です。
abstractThis data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Reproduction assets foundThis data note directly publishes its own phenotyping measurements and image time series: the FIP 1.0 dataset (images, aligned image sequences, eight wheat traits, environmental and marker data) is publicly available on the ETH Research Collection and Hugging Face, and the authors' analysis/processing code is publicly,Dataset · publicn
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and Hugging Face Data set): https://doi.org/10.5061/dryad.n02v6wwzc
• Private Agroscope marker data repository: Confidential (Con-
tact: Boulos Chalhoub, boulos.chalhoub@agroscope.admin.ch).
This repository contains marker data (Illumina InfiniumOpen asset ↗mikeboss/FIP1pdf-raw-page:7 lines:1-110Dataset · publicn with FAIR principles [26]:
• Findable: This publication and the Hugging Face data set card
(https://doi.org/10.57967/hf/3191) provide detailed meta-
data and a comprehensive description of the data set’s contents,
making it discoverable to researchers.
• Accessible: The data is hosted on the Research Collection of ETH
Zurich (https://doi.org/20.500.11850/697773), a reliable and
openly accessible data storage.
• Interoperable: The use of the open-source Hugging Face
datasets [27] package makes it easy to use and export to differ-
ent formats. The data is fully MIAPPE v1.1 [28] conform. Given
the shared genotypes the data set can be used to enhance the
data by Gogna et al. [13] by 6 envOpen asset ↗20.500.11850/697773pdf-raw-page:2 lines:84-130Code · publiclly aggregating the derived data into the final
data set using the fip1-dataset repository.
In addition, the data set can be recreated using the fip1-dataset
repository from the derived data that is freely available in the ETH
research collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL Open asset ↗fip-1.0-data-set-traitspdf-raw-page:7 lines:1-110Code · publicresearch collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data AvailabiOpen asset ↗fip1-alignmentpdf-raw-page:7 lines:1-110Code · publicramming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and HOpen asset ↗fip1-datasetpdf-raw-page:7 lines:1-110Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2 n = 6 x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.
Why it matches plant phenotyping methods小麦穂の2D画像解析による形態計測法を実際に適用し、手動測定との相関検証とデジタル形質の有用性評価を行っており、植物フェノタイピング手法が中心である。
abstractRecently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis.
Reproduction assets foundThe paper's spike image dataset (the 2D images used for quadrangle-model phenotyping of 190 wheat plants) is publicly deposited on Zenodo, explicitly linked in the Data Availability Statement. The supplementary files contain statistical results (normality tests, ANOVA tables, confusion matrices, specimen descriptions)衍Dataset · publicThe spike image dataset is available at https://zenodo.org/records/13837454 , accessed on 27 September 2024.Open asset ↗Zenodo · 13837454lines:895-912Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published13 Sept 2024Tenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024)Cited by 1 · OpenAlex ↗
Precision agriculture is the application of correct amount of fertilizers and water pesticide to achieve higher agricultural productivity. Furthermore, under the framework of precision agriculture is the automated estimation of yield with advanced technologies including Artificial Intelligence (AI) and Remote Sensing (RS). The use of RS has advanced crop yield estimations and predictions in recent years. However, to validate RS-based models it is important to perform in-situ exercises such as fruit counting, which is a time-consuming task that increases the production costs. Drones, robots, and in-situ cameras in combination with AI algorithms are widely used to efficiently address these issues. The recent advancement in computational resources and power available has enabled the utilization of Deep Learning AI models. One of the best-performing models for object detection is the You-Only-Look-Once (YOLO). In this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML. The first dataset consists of 1730 images of mango trees in Australia during night, and the second dataset consists of 6512 images of wheat heads collected from different regions around the world. The main objective of this work is to demonstrate the capabilities of light AI models for object detection and to evaluate their performance, which will serve as a benchmark for future comparison with the on-board environment.
Why it matches plant phenotyping methods植物器官の検出・カウントによる収量推定を対象とし、YOLOv5sの性能評価とベンチマーク化が主目的であるため、計算画像フェノタイピング手法として採用。
abstractIn this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML.
Reproduction assets foundThe paper evaluates YOLOv5s on two public benchmark datasets. The MangoYOLO dataset is explicitly cited with public access URLs and was directly used for the paper's mango yield-estimation experiments, qualifying as a paper-specific public asset. The Global Wheat Head Detection dataset is also used but its Zenodo URL (Dataset · publicAnand Koirala, C McCarthy, Kerry Walsh, and Z Wang, ‘MangoYOLO data set’. Central Queensland University,
2021. Accessed: May 23, 2024. [Online]. Available: http://hdl.handle.net/10018/1261224,
https://researchdata.edu.au/mangoyolo-setOpen asset ↗pdf-page:7 lines:1-50Code / 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 confirmedCrossref · checked 15 Sept 2026
Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small-grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment, which are typically laborious and time consuming. In this work, we developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index. Such tools are an important step toward the creation of automated and efficient phenotyping methods. The data used to generate the results are 3D point clouds consisting of four colour channels—red, green, blue (RGB), and near-infrared (NIR)—collected using a multispectral 3D scanner. Our 3D CNN models for FHB detection achieved 100% accuracy. The influence of the multispectral information on performance was evaluated; the results showed the dominance of the RGB channels over both the NIR (720 nm peak wavelength) and the NIR plus RGB channels combined. Our best 3D CNN models for estimation of total and infected number of spikelets achieved mean absolute errors (MAEs) of 1.13 and 1.56, respectively. Our best 3D CNN models for FHB severity estimation achieved 8.6 MAE. A linear regression analysis between the visual FHB severity assessment and the FHB severity predicted by our 3D CNN showed a significant correlation.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャンと3D CNNを用いて、コムギのFHB症状、穂の小穂数、感染小穂数、病害重症度を自動推定する手法を開発・評価しており、植物表現型取得が中心である。
abstractwe developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index.
Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R² value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R² ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R² = 0.74) and with the Sentinel-2 (R² = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R² = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像とSentinel画像、複数の推定モデルを用いて小麦の収量・タンパク質濃度・窒素出力を定量化し、センサー間の性能を比較しているため、表現型取得・推定法が研究の中心である。
abstractThis study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery.
Reproduction assets foundThe paper's Data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.21865410.v1) containing the data supporting the study's winter wheat trait estimations from airborne hyperspectral and Sentinel imagery. This is a paper-specific, publicly accessible dataset with an authors' URL. No作者分析Dataset · publicatory work,
and QuantaLab-IAS-CSIC staff members A. Hornero, A. Vera, D. Notario, and R. Romero for airborne and
laboratory assistance.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Data availability The data that support the findings presented in this study are available online at https://doi.org/10.6084/m9.figshare.21865410.v1.Declarations
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the oOpen asset ↗figshare · 10.6084/m9.figshare.21865410.v1pdf-raw-page:20 lines:1-46Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Wheat stripe rust poses a marked threat to global wheat production. Accurate and effective disease severity assessments are crucial for disease resistance breeding and timely management of field diseases. In this study, we propose a practical solution using mobile-based deep learning and model-assisted labeling. StripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds. Additionally, StripeRust-Pocket facilitates image acquisition, result storage, organization, and sharing. The underlying model employed by StripeRust-Pocket, called StripeRustNet, is a balanced lightweight 2-stage model. The first stage utilizes MobileNetV2-DeepLabV3+ for leaf segmentation, followed by ResNet50-DeepLabV3+ in the second stage for lesion segmentation. Disease severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel area. StripeRustNet achieves 98.65% mean intersection over union (MIoU) for leaf segmentation and 86.08% MIoU for lesion segmentation. Validation using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual scores. To address the challenges in manual labeling, we introduce a 2-stage labeling pipeline that combines model-assisted labeling, manual correction, and spatial complementarity. We apply this pipeline to our self-collected dataset, reducing the annotation time from 20 min to 3 min per image. Our method provides an efficient and practical solution for wheat stripe rust severity assessments, empowering wheat breeders and pathologists to implement timely disease management. It also demonstrates how to address the "last mile" challenge of applying computer vision technology to plant phenomics.
Why it matches plant phenotyping methodsコムギ赤さび病の葉画像から病斑面積比として病害重症度を推定する深層学習モデル、モバイルアプリ、アノテーション手順を開発・検証しており、植物表現型取得法が中心である。
abstractStripeRust-Pocket, a user-friendly mobile application developed based on deep learning models, accurately quantifies disease severity in wheat stripe rust leaf images, even under complex backgrounds.
Reproduction assets foundThe paper's Data Availability section explicitly links public GitHub repositories containing the authors' self-collected wheat stripe rust leaf image dataset, the StripeRust-Pocket application, and the application source code used for the disease severity phenotyping analysis.Dataset · publicThe wheat stripe rust leaf image dataset collected by smartphones is available at https://github.com/WeizhenLiuBioinform/StripeRustNet/tree/master/Dataset .Open asset ↗WeizhenLiuBioinform/StripeRustNet · Datasetlines:385-435Code · publicThe source code: https://github.com/WeizhenLiuBioinform/StripeRust-Pocket/tree/master/Application_source_code .Open asset ↗WeizhenLiuBioinform/StripeRust-Pocket · Application_source_codelines:385-435Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate wheat ear counting is one of the key indicators for wheat phenotyping. Convolutional neural network (CNN) algorithms for counting wheat have evolved into sophisticated tools, however because of the limitations of sensory fields, CNN is unable to simulate global context information, which has an impact on counting performance. In this study, we present a hybrid attention network (CTHNet) for wheat ear counting from RGB images that combines local features and global context information. On the one hand, to extract multi-scale local features, a convolutional neural network is built using the Cross Stage Partial framework. On the other hand, to acquire better global context information, tokenized image patches from convolutional neural network feature maps are encoded as input sequences using Pyramid Pooling Transformer. Then, the feature fusion module merges the local features with the global context information to significantly enhance the feature representation. The Global Wheat Head Detection Dataset and Wheat Ear Detection Dataset are used to assess the proposed model. There were 3.40 and 5.21 average absolute errors, respectively. The performance of the proposed model was significantly better than previous studies.
Why it matches plant phenotyping methods小麦穂数という植物形質をRGB画像から推定する深層学習手法を開発し、複数データセットで性能評価しており、表現型取得・抽出法が中心である。
abstractAccurate wheat ear counting is one of the key indicators for wheat phenotyping.
Reproduction assets foundThe paper uses two publicly available wheat ear image datasets (GWHD and WEDD) as its phenotyping inputs, with explicit public URLs in the data availability statement. No authors' analysis code or trained model is deposited.Dataset · publics generalization ability. This will provide real-time and accurate information for agricultural production, help farmers make scientific decisions, and improve crop management and yield.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: http://www.global-wheat.com/
https://github.com/simonMadec .
Author contributions
QH: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – review & editing. WL: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. YZ: Software, Writing – review & editing. TR: SoftwaOpen asset ↗https://github.com/simonMadeclines:388-410Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery.
Why it matches plant phenotyping methodsコムギ穂の画像セグメンテーション手法と合成データセットを開発し、内部・外部データセットで性能検証しており、植物フェノタイピング手法が中心である。
abstractwe developed a realistic annotated synthetic dataset for wheat head segmentation.
Reproduction assets foundThe paper's wheat head segmentation datasets (synthetic, GAN-generated, and evaluation sets) are publicly available at the authors' stated URL; the analysis code is only available on request.Dataset · publicPublicly available datasets were utilized in this study. These data can be found here: https://www.cs.usask.ca/ftp/pub/whs/ (accessed on 1 June 2023). The code used to generate synthetic data presented in this study are available on request.Open asset ↗lines:74-261Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Code / 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.
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The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
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Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
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details of models’ weights, hyperparameters, training scripts and datasets can be found at
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https://github.com/William-Yao0993/FD_detection.128
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Model evaluation
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Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
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calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
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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 confirmedOpenAlex · checked 7 Sept 2026
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.
Why it matches plant phenotyping methods小麦の形質・表現型抽出とエンティティ linking のための手動アノテーションコーパスを構築し、言語モデルの訓練・評価に用いており、表現型情報の取得手法とデータセットが研究の中心である。
titleA manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature
Reproduction assets foundThe paper's core assets are publicly available: the TaeC annotated corpus (trait/phenotype/species annotations of 528 PubMed wheat references) on Recherche Data Gouv, the Wheat Trait and Phenotype Ontology on AgroPortal, the AlvisNLP bread wheat workflow on Forgemia, and the ToMap method code on GitHub, all with authorDataset · publicThe corpus dataset TaeC is available under CC-BY-ND License at: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3QOpen asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qlines:142-152Code · publicThe code of the ToMap method is available under Apache License at https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-bibliome/src/main/java/fr/inra/maiage/bibliome/alvisnlp/bibliomefactory/modules/tomapOpen asset ↗github.comlines:142-152Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.
Why it matches plant phenotyping methods小麦の葉・穂・茎・根の病害や損傷を画像から認識するAIモバイルアプリの開発・性能評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。害虫・雑草識別も含むが、病害認識の技術的評価が明示されている。
abstractThis paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region.
Reproduction assets foundThe article's data availability statement explicitly states that the authors' weed phenotyping image dataset is publicly available on Zenodo (DOI 10.5281/zenodo.7598372), a paper-specific public asset. No author analysis code or trained model checkpoints are described with a public URL.Dataset · publicThe original contributions presented in the study are publicly available (see the weed phenotyping image dataset). This data can be found here: https://doi.org/10.5281/zenodo.7598372 .Open asset ↗Zenodo · 10.5281/zenodo.7598372lines:460-508Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods高解像度画像・深層学習による器官レベル老化モニタリングと熱画像による連続的なキャノピー温度測定を開発・適用し、stay-green機能の表現型評価法として検証しているため、方法が中心的である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Reproduction assets foundThe paper publicly deposits its manually annotated segmentation datasets (target-domain patches for the off-nadir stem/ear segmentation model) via the ETH Zurich research repository. All other raw phenotyping data (thermal images, physiological measurements) is only available on request from the authors. Generic tools/Dataset · publicd through logical operations to obtain the fractions of green, chlorotic, and necrotic tissues for each vegetation component. For details, refer to ( Anderegg et al., 2023 ). The annotated data sets representing the target domain will be made freely available via the Repository for Publications and Research data of ETH Zürich ( https://doi.org/10.3929/ethz-b-000668219 ).
Figure 2
Effects of canopy shading on agronomic traits and canopy characteristics. Effects of shading on (A) grain yield, (B) above ground vegetative dry biomass (total above ground biomass after threshing), (C) peduncle length, (D) plant height, (E) spike volume, (F) thousand kernel weight, (G) grain protein concentration.Open asset ↗Repository for Publications and Research data of ETH Zürich · 10.3929/ethz-b-000668219lines:58-67Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.
Why it matches plant phenotyping methods小麦の感染状態をハイパースペクトル画像から推定する分類手法を開発し、実験室データで学習したモデルを圃場データ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。
abstractA machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes.
Reproduction assets foundThe paper's hyperspectral lab/field wheat scan datasets are publicly deposited in OSU Scholars Archive (DOI 10.7267/z316q855z). Code is only available upon request, so it does not qualify as a public asset.Dataset · publicntal Monitoring Programs
(CTEMPs); and Collaborative Research; CompSustNet: Expanding the Horizons of Computational Sustain-
ability, respectively).
Availability of data and material The datasets generated during and/or analyzed during the current study are
available in the Oregon State University’s Scholars Archive repository, https://doi.org/10.7267/z316q855z.Code availability Code will be made available upon request.
Declarations
Conflicts of interest/competing interests The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which
permits use, sharing, adaptation, distribution aOpen asset ↗Oregon State University’s Scholars Archive · 10.7267/z316q855zpdf-raw-page:17 lines:1-41Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.
Why it matches plant phenotyping methods遺伝マーカーから作物病害表現型を予測するモデルを中心的に適用・評価しており、植物の病害状態を推定する計算的フェノタイピング手法に該当する。
abstractOur use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases
Reproduction assets foundThe article's data availability statement points to a public CIMMYT repository deposit containing the paper's own phenotypic (tan spot, septoria nodorum blotch, spot blotch) and genotypic datasets, matching the allowed handle URL. No author analysis code or trained model deposit is stated.Dataset · publicAgreement Research Fund (JA) through the Research Council of Norway for grants 301835 (Sustainable Management of Rust Diseases in Wheat) and 320090 (Phenotyping for Healthier and more Productive Wheat Crops).
DATA AVAILABILITY STATEMENT
The phenotypic (TS, SNB and SB) and genotypic data sets can be found in the following link: https://hdl.handle.net/11529/10548948 .
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De Los CamposOpen asset ↗lines:705-933Code / 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 15 Sept 2026
Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.
Why it matches plant phenotyping methods冬小麦の干ばつストレスという植物状態を、画像・土壌水分・気象センサーを統合した深層学習モデルで非破壊推定する手法が研究の中心であり、技術性能も比較評価している。
abstractthis paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods.
Reproduction assets foundThe authors deposited the minimal multimodal deep learning dataset (winter wheat drought stress images with soil moisture/meteorological data) in a public Kaggle repository, explicitly stated in the Data Availability section.Dataset · publicnned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All relevant data supporting the findings of this study are available within the article and its supplementary information files. The minimal dataset for multimodal deep learning is available in the Kaggle repository, accessible at https://www.kaggle.com/datasets/jianbinyao/minimum-dataset/data .
Data AvailabilityOpen asset ↗Kaggle · jianbinyao/minimum-datasetlines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100 , TracesCS6B03G0356400 , and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
Why it matches plant phenotyping methodsUAV時系列スペクトル指標を用いてコムギのstay-green（老化動態）を定量化し、RSGS指標と解析パイプラインを開発・適用しており、表現型取得法が研究の中心である。
abstractSpectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's genotype and phenotype data (the RSGS stay-green phenotypes and SNP genotypes for the 565-accession wheat panel) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code or raw UAV imagery depositDataset · publicThe genotype and phenotype data presented in this study are available at the website https://github.com/zengqd/PopulationGenetics/tree/main/Wheat/StayGreen .Open asset ↗zengqd/PopulationGenetics · Wheat/StayGreenlines:298-318Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Breeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.
Why it matches plant phenotyping methodsUASによる大規模なスペクトル形質取得を用い、SRIの予測性能を年次交差検証しており、植物フェノタイピング手法の実質的な適用・評価が中心である。
abstractUnoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology
Reproduction assets foundThe paper's data availability statement explicitly deposits all code and data (including UAS-derived SRI/NDVI phenotyping data and genomic prediction analysis) in a public GitHub repository whose URL matches an allowed URL.Code · publicNational Institute of Food and Agriculture, Hatch project
1014919, and the O.A. Vogel Research Endowment at
Washington State University.
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
All code and data used in the study can be found
at https://github.com/AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-prediction.O RC I D
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Appels, R., Eversole, K., Stein, N., Feuillet, C., Keller, B., Rogers, J.,
Pozniak, C. J., Choulet, F., Distelfeld, A., Poland, J., Ronen, G.Open asset ↗AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-predictionpdf-raw-page:10 lines:1-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wheat head detection and counting using deep learning techniques has gained considerable attention in precision agriculture applications such as wheat growth monitoring, yield estimation, and resource allocation. However, the accurate detection of small and dense wheat heads remains challenging due to the inherent variations in their size, orientation, appearance, aspect ratios, density, and the complexity of imaging conditions. To address these challenges, we propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes. In order to facilitate the development and evaluation of our proposed method, we introduce a novel dataset named the Rotated Global Wheat Head Dataset (RGWHD). This dataset is constructed by manually annotating images from the Global Wheat Head Detection (GWHD) dataset with oriented bounding boxes. Furthermore, we incorporate a Path-aggregation and Balanced Feature Pyramid Network into our architecture to effectively extract both semantic and positional information from the input images. This is achieved by leveraging feature fusion techniques at multiple scales, enhancing the detection capabilities for small wheat heads. To improve the localization and detection accuracy of dense and overlapping wheat heads, we employ the Soft-NMS algorithm to filter the proposed bounding boxes. Experimental results indicate the superior performance of the OFPN model, achieving a remarkable mean average precision of 85.77% in oriented wheat head detection, surpassing six other state-of-the-art models. Moreover, we observe a substantial improvement in the accuracy of wheat head counting, with an accuracy of 93.97%. This represents an increase of 3.12% compared to the Faster R-CNN method. Both qualitative and quantitative results demonstrate the effectiveness of the proposed OFPN model in accurately localizing and counting wheat heads within various challenging scenarios.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、専用データセットを構築して性能評価しているため、方法が中心的である。
abstractwe propose a novel approach called the Oriented Feature Pyramid Network (OFPN) that focuses on detecting rotated wheat heads by utilizing oriented bounding boxes.
Reproduction assets foundThe paper introduces the RGWHD dataset (oriented-bounding-box annotations of GWHD wheat images), publicly released via Baidu pan with extraction code, and makes its experiment scripts publicly available on GitHub. The underlying GWHD image dataset (public on Kaggle) is the image source used for the paper's phenotyping.Dataset · publiction, Validation, Writing—original draft preparation. N.L.: Formal analysis, Resources, Project ad-ministration.C .F.: Investigation, Data curation. All authors have read and agreed to the published version of the manuscript.
Data availability
The datasets generated and analysed during the current study are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors deOpen asset ↗RGWHDlines:445-520Code · publicdy are available in RGWHD ( https://pan.baidu.com/s/1Fy3HpIfAeQhRef_ZuKu4iw ) and the extraction code is vbiy. The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request. The scripts to run all experiments are publicly available through our GitHub page https://github.com/cwr0821/OFPN .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Sharma, S., Kooner, R., Arora, R., Insect pests and crop losses. Breeding insect resistant crops for suOpen asset ↗cwr0821/OFPNlines:445-520Dataset · public. The GWHD dataset is a comprehensive collection of well-annotated wheat head images, compiled by nine research institutions across seven countries. It serves as a valuable resource for training robust models to accurately estimate the location and density of wheat heads in seven categories. The GWHD dataset can be accessed at https://www.kaggle.com/competitions/global-wheat-detection/data . The SPIKE dataset comprises 335 images captured at three distinct growth stages, covering ten different wheat varieties. The UWHD dataset consists of 550 images captured by a drone at an altitude of 10 m. The ACID dataset consists of 520 images taken in controlled greenhouse conditions, featuring 4158 laOpen asset ↗lines:52-149Code / dataset availability 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 confirmedCrossref · checked 15 Sept 2026
Abstract Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R 2 = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R 2 = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.
Why it matches plant phenotyping methods圃場レベルの小麦収量という植物形質を、リモートセンシング等とAutoMLで推定する手法を開発・検証しており、収量取得・予測ワークフローが中心である。
titleDeveloping automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset
Reproduction assets foundThe paper's authors developed an H2O AutoML workflow in R for wheat yield prediction and explicitly state the full script is publicly hosted on GitHub. This is a paper-specific, publicly available analysis code asset. The data availability statement only says data are within the article/supplements, so no separate phenCode · publicweb GUI, H2O AutoML is also accessible in Python, R,
Java, and Scala. The technique is entirely automated, but many of the settings are made available to the user as
parameters so that some parts of the modelling phases can be changed. In our case, we developed H2OAutoML
in R language and the full script is hosted on GitHub at https://github.com/DrAhmedKheir/H2O_
AutoML.git.
2.3.1. Dataset preprocessing and AutoML training
Currently, all H2O supervised learning algorithms offer the same kind of automatic data-preprocessing as H2O
AutoML. Categorical data can be handled natively because H2O tree-based models (Gradient Boosting
Machines, Random Forests) provide group-splits on categoricalOpen asset ↗DrAhmedKheir/H2O_pdf-layout-page:6 lines:1-35Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Abstract Background. Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results. We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, image registration, symptom tracking, and leaf- and symptom characterization. The average accuracy of the co-registration of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in lesion numbers and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions. The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under natural field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での画像取得、症状検出・追跡・セグメンテーション、葉および病斑形質の定量化手法を開発・検証しており、植物病害表現型の取得が研究の中心です。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is available at the authors' GitHub repository (and-jonas/sympathique-wheat), and that a sample data set plus the trained reference mark detection model can be downloaded from the ETH research collection (doi 10.3929/ethz-b-000659812). Both are paper-Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:80-87Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:80-87Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.
Why it matches plant phenotyping methods小麦の3D形態情報をMVS-Phenoと点群・深層学習で取得し、器官分割、形態パラメータ抽出、精度評価を行う手法研究であり、フェノタイピング手法が中心です。
abstractwe propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale.
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code used in the article are publicly available on GitHub at the authors' repository, which matches an allowed URL. This qualifies as a paper-specific public asset covering the wheat 3D reconstruction/phenotyping analysis.Code · publicThe data and code used in this article are available on GitHub, at https://github.com/lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatOpen asset ↗lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatlines:280-436Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our correlation results ranged from r 2 = 0.81 for protein in wheat and oats to r 2 = 0.99 for polyphenol content in lettuce in both the Reflectometer and laboratory spectrophotometer assessment, suggesting the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Repeatability evaluation demonstrated good reproducibility of the Reflectometer to assess crop phytochemical content. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物の植物化学成分量を測定する低コスト反射計を、実験室用分光光度計と比較して精度・再現性検証しており、植物形質の取得手法の技術的検証が中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all Bionutrient Institute data (reflectometer/spectrometer phytochemical measurements used in this study) are publicly available in the authors' GitLab repository, and the authors' data-processing pipeline code is also publicly hosted on GitLab.Dataset · publicAll data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset . The data used in this manuscript covers samples submitted up to 7/31/2022.Open asset ↗our-sci/bionutrient-institute/datasetlines:156-212Code · publicAn automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.Open asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:132-143Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. Newly desirable wheat variety traits include disease resistance to reduce pesticide use, adaptation to climate change, resistance to heat and drought stresses, or low gluten content of grains. Wheat breeding experiments are documented by a large body of scientific literature and observational data obtained in-field and under controlled conditions. The cross-referencing of complementary information from the literature and observational data is essential to the study of the genotype-phenotype relationship and to the improvement of wheat selection. The scientific literature on genetic marker-assisted selection describes much information about the genotype-phenotype relationship. However, the variety of expressions used to refer to traits and phenotype values in scientific articles is a hinder to finding information and cross-referencing it. When trained adequately by annotated examples, recent text mining methods perform highly in named entity recognition and linking in the scientific domain. While several corpora contain annotations of human and animal phenotypes, currently, no corpus is available for training and evaluating named entity recognition and entity-linking methods in plant phenotype literature. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 540 PubMed references fully annotated for trait, phenotype, and species named entities using the Wheat Trait and Phenotype Ontology and the species taxonomy of the National Center for Biotechnology Information. A study of the performance of tools trained on the Triticum aestivum trait Corpus shows that the corpus is suitable for the training and evaluation of named entity recognition and linking.
Why it matches plant phenotyping methods小麦の形質・表現型を文献から抽出・リンクするための注釈付きデータセットを開発し、ツール性能も評価しており、植物表現型情報の計算的抽出が中心である。
abstractThe Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat.
Reproduction assets foundThe paper's core asset, the TaeC annotated wheat trait/phenotype corpus, is publicly deposited on Recherche Data Gouv under CC-BY-ND. The authors' AlvisNLP wheat text-mining workflow and the ToMap method code are also publicly available. The WTO ontology used for annotation is public on AgroPortal.Dataset · publicTaeC is
available under CC-BY-ND License at:
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3Q.Open asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qpdf-page:9 lines:1-56Code · publicThe AlvisNLP bread wheat workflow is available at : https://forgemia.inra.fr/migale/wheat-tm.
It includes the wheat-specific lexica of ToMap.Open asset ↗forgemia.inra.frpdf-page:13 lines:1-53Code · publicThe code of the ToMap method is available at
https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-Open asset ↗github.com/Bibliome/alvisnlppdf-page:13 lines:1-53Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data. Our approach concatenates two pre-trained image classification deep learning-based models, DenseNet121 and MobileNetV2. We enhance DenseNet121 with an attention-based transition mechanism and global average pooling layers, while MobileNetV2 benefits from adding an attention module and global average pooling layers. We deepen the architecture with extra-dense layers featuring swish activation and batch normalization layers, resulting in a more robust and accurate model for diagnosing leaf-related plant diseases. LeafDoc-Net is evaluated on two distinct datasets, focused on cassava and wheat leaf diseases, demonstrating superior performance compared to existing models in accuracy, precision, recall, and AUC metrics. To gain deeper insights into the model's performance, we utilize Grad-CAM++.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を開発し、複数データセットで性能評価しており、植物フェノタイピング手法が研究の中心である。
abstractWe introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data.
Reproduction assets foundThe paper evaluates LeafDoc-Net on two publicly available Mendeley datasets (cassava leaf disease and wheat leaf disease) that constitute the paper's phenotyping image inputs. Both are explicitly linked in the data availability statement with URLs matching allowed_urls. No author analysis code or trained model deposit,Dataset · publicPublicly available datasets were analyzed in this study. This data
can be found here: https://data.mendeley.com/datasets/3832tx2cb2/1Open asset ↗3832tx2cb2pdf-page:21 lines:1-61Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Globally, wheat ( Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.
Why it matches plant phenotyping methods小麦のタンパク質・リジン含量という植物形質について、歴史的表現型データを整理し、複数のゲノム予測法を比較して大規模コレクションの予測表現型を生成しており、計算的な形質推定とデータセット活用が研究の中心です。
abstracta large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means.
Reproduction assets foundThe authors deposited the paper's curated historical protein/lysine phenotype data (ISA-Tab), the R code for BLUE calculation and genomic prediction with all input files, and key output files (BLUEs and predicted phenotypes) in the public e!DAL repository under DOI 10.5447/ipk/2023/20. This is a paper-specific, public,Dataset · publicn with all input files, and the most important output files of the analysis. The output files include BLUEs of protein and lysine content as well as the predictions of protein content, lysine content and adjusted lysine content. The aforementioned information is available via the e!DAL ( Arend et al., 2014 ) online repository ( https://dx.doi.org/10.5447/ipk/2023/20 ).
Author contributions
MB: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft. SW: Data curation, Writing – review & editing. JR: Conceptualization, Methodology, Supervision, Writing – review & editing. AS: Conceptualization, Methodology, Supervision, Validation, WOpen asset ↗e!DAL · 10.5447/ipk/2023/20lines:302-323Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
Why it matches plant phenotyping methods植物画像と画像由来形質を収録する高スループット表現型データアーカイブであり、データセット、事前学習モデル、オンライン解析ツールを提供することが中心的な方法論的貢献である。
abstractHere, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms.
Reproduction assets foundThe paper describes OPIA, an open archive of plant images, i-traits, and pre-trained models, freely available online with explicit download and trait pages. The archive itself is the paper-specific public asset containing the phenotyping images, i-trait values, and downloadable datasets.Dataset · publicdifferent types of imaging sensors (e.g. visible light, near-infrared, depth camera and chlorophyll fluorescence sensors) can be submitted via opia@big.ac.cn . Users can also submit a compiled dataset with relevant metadata ( Supplementary Figure S3 ). All image datasets can be freely downloaded in a compressed zip format from https://ngdc.cncb.ac.cn/opia/downloads , which contains label records of image data in diverse formats (e.g. RSML ( 30 ), JSON, TXT, XML, MAT, CSV or H5). Collectively, these online tools and data services are invaluable for plant phenotyping research and application.
Potential applications of datasets and i-traits
To highlight the potential applications of the in-hOpen asset ↗OPIAlines:148-156Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.
Why it matches plant phenotyping methodsドローン画像とAirMeasurerを用いた作物形態・スペクトル・テクスチャ形質の取得、および曲線フィッティングによる動的表現型抽出が研究の中心であり、実質的な植物フェノタイピング手法の応用・解析である。
abstractPowered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties.
Reproduction assets foundThe authors explicitly deposit their phenotyping analysis code, testing aerial images, trait analysis outputs, and Jupyter notebooks in a public GitHub repository, and separately release the AirMeasurer phenotyping platform used for the drone-based trait analysis. Both are paper-specific, public, and actionable via theCode · publicmade available in this paper. The source code, testing data, and other datasets supporting the results presented in this article are available at https://Github.com/The-Zhou-Lab/Nitrogen-response-traits/releases . Other data and user guides are openly available upon request. The latest AirMeasurer platform can be downloaded via https://github.com/The-Zhou-Lab/UAV/releases ).
Supplementary Materials
Supplementary 1
Figs. S1 to S6
Tables S1 to S14
Notes S1 to S3
Supplementary 2
Data S1 to S9
References
1. Seppelt R, Klotz S, Peiter E, Volk M.
Agriculture and food security under a changing climate: An underestimated challenge. iScience. 2022;25(12):105551.
2. Li S, Tian Y, Wu K, Ye Y, Yu J, ZhaOpen asset ↗The-Zhou-Lab/UAVlines:143-192Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
ABSTRACT Background The prediction of desirable traits in wheat from imaging data is an area of growing interest thanks to the increasing accessibility of remote sensing technology. However, as the amount of data generated continues to grow, it is important that the most appropriate models are used to make sense of this information. Here, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models. Results Neural networks had greater accuracies than partial least squares regression models and gaussian naïve Bayes models for prediction of grain asparagine content, yield, genotype, and fertiliser treatment. Genotype was also more accurately predicted from seed data than from canopy data. Conclusion Using wheat canopy spectral data and combinations of wheat seed morphology and spectral data, neural networks can provide improved accuracies over other models for the prediction of agronomically important traits.
Why it matches plant phenotyping methods画像・スペクトルデータから穀粒成分や収量などの植物形質を予測するニューラルネットワークを他手法と比較評価しており、形質推定法の性能検証が中心である。
abstractHere, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models.
Reproduction assets foundThe preprint states that the data and code used in this study (neural network/PLSR/GNB modelling of wheat canopy spectral and seed imaging data) are publicly available in the author's GitHub repository, which matches an allowed URL.Code · publicData and code used in this study are available at: https://github.com/JosephOddy/wheat-Open asset ↗JosephOddy/wheat-pdf-page:7 lines:1-50Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
WheatRGB / grayscaleSeed / grainClassificationGrowth / development / phenology
Grain filling is essential for wheat yield formation, but is very susceptible to environmental stresses, such as high temperatures, especially in the context of global climate change. Grain RGB images include rich color, shape, and texture information, which can explicitly reveal the dynamics of grain filling. However, it is still challenging to further quantitatively predict the days after anthesis (DAA) from grain RGB images to monitor grain development. Results The WheatGrain dataset revealed dynamic changes in color, shape, and texture traits during grain development. To predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset. The results showed that Random Forest (RF) had the best accuracy of the traditional machine learning algorithms, but it was far less accurate than all deep learning algorithms. The precision and recall of the deep learning classification model using Vision Transformer (ViT) were the highest, 99.03% and 99.00%, respectively. In addition, few-shot learning could realize fine-grained image recognition for wheat grains, and it had a higher accuracy and recall rate in the case of 5-shot, which were 96.86% and 96.67%, respectively. Materials and methods In this work, we proposed a complete wheat grain dataset, WheatGrain, which covers thousands of wheat grain images from 6 DAA to 39 DAA, which can characterize the complete dynamics of grain development. At the same time, we built different algorithms to predict the DAA, including traditional machine learning, deep learning, and few-shot learning, in this dataset, and evaluated the performance of all models. Conclusions To obtain wheat grain filling dynamics promptly, this study proposed an RGB dataset for the whole growth period of grain development. In addition, detailed comparisons were conducted between traditional machine learning, deep learning, and few-shot learning, which provided the possibility of recognizing the DAA of the grain timely. These results revealed that the ViT could improve the performance of deep learning in predicting the DAA, while few-shot learning could reduce the need for a number of datasets. This work provides a new approach to monitoring wheat grain filling dynamics, and it is beneficial for disaster prevention and improvement of wheat production.
Why it matches plant phenotyping methodsコムギ粒のRGB画像から登熟段階（日数）を推定する画像解析手法を開発・比較し、データセットとモデル性能を評価しており、表現型取得・推定が研究の中心である。
abstractTo predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThese traits were extracted via Python and OpenCV (a Python library), and the codes are available online at https://github.com/shem123456/wheat-grain-traits (accessed on 21 September 2023).Open asset ↗shem123456/wheat-grain-traitslines:61-116Code · publicFinally, the Siamese network with contrastive loss was built using PyTorch, and the configuration of its training was consistent with that of the deep learning model described above. The codes are available online at https://github.com/shem123456/grain-filling-classification (accessed on 21 September 2023).Open asset ↗shem123456/grain-filling-classificationlines:117-128Code / dataset availability confirmedCrossref · checked 14 Sept 2026
A sound understanding of plant growth is critical to maintaining future crop productivity under ongoing climate change. Remotely sensed time series of crop functional traits from optical satellite imagery are an invaluable tool for deriving appropriate management practices that facilitate risk mitigation and increase the resilience of agroecosystems. However, the availability of imagery is limited by atmospheric disturbances that cause large temporal gaps and noise in the trait time series. Therefore, time series reconstruction methods are required for accurate crop growth modelling. Physiological priors, such as the fact that plant growth is mainly controlled by a few environmental covariates, among which air temperature plays a prominent role, represent a promising approach to improve the representation of crop growth. Here, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat. A probabilistic ensemble Kalman filtering data assimilation scheme allows the combination of high temporal resolution air temperature data and satellite imagery, which also allows quantification of uncertainties. The proposed approach requires a smaller number of satellite observations compared to conventional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover, and is considerably less complex than a mechanistic crop growth model. Validation was carried out using in-situ data collected on winter wheat plots in Switzerland in two consecutive years. The validation results suggest that the proposed assimilation of Sentinel-2 GLAI and temperature-response-based growth rates allows the reconstruction of physiologically meaningful GLAI time series. In particular, the systematic underestimation of high in-situ GLAI values (> 5 m^2 m^-2) often prevalent in purely remote sensing driven GLAI time series reconstruction was reduced. Thus, the proposed approach is advantageous compared to state-of-the-art remote sensing approach based on wide-spread logistic functions by means of physiological plausibility, fitting requirements and representation of high in-situ GLAI values. This has great potential to increase the reliability of remotely sensed crop productivity assessment.
Why it matches plant phenotyping methods衛星光学データと生理モデルを統合して作物GLAI時系列を再構築する手法を提案し、圃場データで検証しており、植物形質推定が研究の中心である。
abstractHere, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat.
Reproduction assets foundThe authors explicitly state that code and data to reproduce the entire workflow (DRC fitting, Sentinel-2 GLAI assimilation, and validation) are publicly available on GitHub under GNU GPL v3.0. This is a paper-specific, public, actionable asset. Other URLs in the text are cited references or generic libraries (e.g., NLCode · publicCode and Data Availability
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Code to reproduce the entire workflow including calibration and validation data is
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available at https://github.com/EOA-team/sentinel2_crop_trait_timeseries
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under GNU General Public License v3.0.
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Credit Authorship Contribution Statement
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Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
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dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
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Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / 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 · OpenAlex · checked 7 Sept 2026
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
Why it matches plant phenotyping methods圃場の時系列高解像度画像からキャノピー被覆率の変化を定量化し、霜害指数（FDI）を開発・検証しており、画像ベース表現型取得が研究の中心である。
abstractImage-based field phenotyping methods allow quantifying not only plant growth but also plant senescence.
Reproduction assets foundThe authors state that all analysis code for the Frost Damage Index is publicly available on GitLab with example data; the full phenotype dataset is only available upon request.Code · publicAll code is available at https://gitlab.ethz.ch/ftschurr/fdi_example with example data. All data are available upon reasonable request.Open asset ↗gitlab.ethz.ch/ftschurr/fdi_examplelines:81-106Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.
Why it matches plant phenotyping methods複数の地上・UAS型HTPセンサープラットフォームを比較評価し、スペクトル形質の相関・遺伝率・標準化を検証しており、フェノタイピング手法が研究の中心です。
abstractwe evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor.
Reproduction assets foundThe article's data availability statement points to a public repository deposit (DOI 10.7273/000004802) containing the study's HTP spectral reflectance and grain yield datasets. The WSU weather station URL is a generic external resource, not a paper-specific asset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.7273/000004802 .Open asset ↗10.7273/000004802lines:481-522Code / dataset availability confirmedCrossref · checked 14 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Abstract Background and aims Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.
Why it matches plant phenotyping methods深根画像をCNNで解析して根長密度を抽出し、機械学習による根形質推定と技術的解析を行っており、表現型取得・抽出法が研究の中心である。
abstractUsing the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network.
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 · publicCodes and data are available on GitHub at https://github.com/satyasaran/CropML.git .Open asset ↗satyasaran/CropML · CropMLlines:182-216Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model's hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants.
Why it matches plant phenotyping methods小麦穂の圃場画像から穂をセグメンテーション・計数する画像解析手法と注釈付きデータセットを開発・評価しており、植物形質取得が研究の中心である。
abstractThis study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' codebase and the curated SPIKE-segm wheat spike segmentation dataset in a public Figshare project, which qualifies as a paper-specific public asset. The Roboflow URL is only a cited generic tool and does not qualify.Dataset · publicThe codebase developed and 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/projects/WheatSpikeNet_An_Improved_Wheat_Spike_Segmentation_Model_for_Accurate_Counting_from_Field_Imaging/163225 .Open asset ↗figshare · 163225lines:914-939Code / 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 · Crossref · checked 7 Sept 2026
Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.
Why it matches plant phenotyping methods病害植物画像のセマンティックセグメンテーションデータセットを構築し、罹病範囲の推定を評価することが研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractin this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset.
Reproduction assets foundThe authors explicitly make the NWRD segmentation dataset, implementation, and pretrained models publicly available on GitHub.Dataset · publicWe make our dataset, implementation, and pretrained models publicly available at https://github.com/dll-ncai/NUST-Wheat-Rust-Disease-NWRDOpen asset ↗dll-ncai/NUST-Wheat-Rust-Disease-NWRDlines:23-28Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.
Why it matches plant phenotyping methods植物の画像から種子・シュート・根を認識し、形態形質を自動抽出して統計評価するオープンソースツールの開発・精度検証が中心である。
abstractIn this study, we developed Morley, a free, open-source graphical user interface written in Python.
Reproduction assets foundThe paper's authors publicly released the Morley analysis code (GitHub repo dashabezik/Morley) and example data/user guide (dashabezik/plants), both explicitly stated in the Data Availability Statement and Methods. These directly support the paper's seedling image analysis and morphometric measurements.Code · publicths and plant surface areas, and figures characterizing
distributions of measured parameters, bar plots with mean values and standard deviations (95% CI),
and heatmaps visualizing the conclusions on statistical significance of the morphometric differences.
Code, graphical user interface, user guide and examples are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/, respectively.
Morley is available as a graphical user interface and a command line tool.
3. Results
3.1. Comparison of Morley with ImageJ and Manual Measurements Demonstrates Agreement between
Results
ImageJ [23] is widely applied for image analysis of plants and seedlings [24–28] andOpen asset ↗dashabezik/Morleypdf-layout-page:6 lines:1-47Dataset · publicon,
IAT; funding acquisition, IAT. All authors have read and agreed to the published version of the manuscript.
Funding: The study was supported by Russian Science Foundation, grant #22‐26‐00109.
Data Availability Statement: Program code, GUI, user guide and example data are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/.
Acknowledgments: The authors thank Dr. Olga M. Zhigalina and Dr. Dmitri N. Khmelenin (Shubnikov Institute
of Crystallography, FSRC “Crystallography and Photonics”, RAS) for collecting high‐quality TEM images of
iron nanoparticles and Dr. Nadezhda G. Berezkina (N.N. Semenov Federal Research Center for Chemical
Physics, RAS) forOpen asset ↗dashabezik/plantspdf-layout-page:13 lines:1-65Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen's kappa, κ ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the F1w score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year's data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.
Why it matches plant phenotyping methodsRGB画像からコムギ赤かび病の重症度を推定する分類モデルを開発し、複数年データで性能を検証した、中心的な画像ベース植物フェノタイピング研究です。
abstractThe present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images
Reproduction assets foundThe authors explicitly state that the FHB RGB image dataset with annotations and the source code are publicly available via their GitHub repository.Dataset · publicAll images and corresponding annotations can be downloaded from the link provided in our GitHub repository: https://github.com/cvims/FHB_classification .Open asset ↗cvims/FHB_classificationlines:663-678Code · publicThe source code and the dataset are available at https://github.com/cvims/FHB_classification .Open asset ↗cvims/FHB_classificationlines:1-28Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Quantitative, accurate, and high-throughput phenotyping of crop diseases is needed for breeding programs and plant-pathogen interaction investigations. However, difficulties in the transferability of available numerical tools encourage maintaining visual assessment of disease symptoms, although this is laborious, time-consuming, requires expertise, and rater dependent. Deep learning has produced interesting results for plant disease evaluation, but has not yet been used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici, a frequently occurring and damaging disease on wheat crops. Results We developed a Python-coded image analysis script, called SeptoSympto, in which deep learning models based on the U-net and YOLO architectures were used to quantify necrosis and pycnidia, respectively. Small datasets of different sizes (containing 50, 100, 200, and 300 leaves) were trained to create deep learning models and to facilitate the transferability of the tool, and five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms. The results revealed that (i) the amount of annotated data does not influence the good performance of the models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and that (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions. Conclusions Although running SeptoSympto takes longer than visual assessment to evaluate STB symptoms, it allows the data to be stored and evaluated by everyone in a more accurate and unbiased manner. Furthermore, the methods used in SeptoSympto were chosen to be not only powerful but also the most frugal, easy to use and adaptable. This study therefore demonstrates the potential of deep learning to assess complex plant disease symptoms such as STB.
Why it matches plant phenotyping methodsSeptoSymptoは小麦葉の壊死・ピクニディアを画像から定量する深層学習手法として開発・検証されており、植物病害表現型の取得が研究の中心です。
abstractWe developed a Python-coded image analysis script, called SeptoSympto, in which deep learning models based on the U-net and YOLO architectures were used to quantify necrosis and pycnidia, respectively.
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 · publicWe developed an image analysis script, named SeptoSympto ( https://github.com/maximereder/septo-sympto ), in which deep learning models based on the U-net and YOLO architectures were trained on small datasetsOpen asset ↗maximereder/septo-symptolines:61-99Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
Why it matches plant phenotyping methodsドローン画像から植物高・葉面積・分げつ密度を推定し、これらを用いたフェノミック選抜モデルを評価しており、形質取得と解析ワークフローが研究の中心である。
abstractThe dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (phenotypic/trait data from drone-based wheat phenotyping) in the ETH Research Collection and the phenomics data processing source code in a public ETH GitLab repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated and analyzed during the current study are openly available in the ETH Research Collection repository, http://doi.org/10.3929/ethz-b-000566864 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000566864lines:210-223Code · publicSource code for the phenomics data processing methods used in this study are openly available in the ETH gitlab repository, https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing .Open asset ↗ETH gitlab · crop_phenotyping/htfp_data_processinglines:210-223Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.
Why it matches plant phenotyping methods圃場画像からコムギ穂を検出・計数するモデルのチャレンジ設計と、異なるデータセットへの頑健性評価を中心に扱うため、画像ベースの表現型解析手法・ベンチマーク研究に該当します。
abstractWe developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.
Reproduction assets foundThe paper's wheat head detection analysis relies on two paper-specific public assets: the Global Wheat Head Dataset 2021 (annotated field RGB images used for training/testing the challenge solutions) openly deposited on Zenodo, and the winning challenge solutions' code made open-source on GitHub. Both have explicit, inDataset · publiccquisition. W.G., F.B., and I.S. participated in the design of the challenges and data analysis. All authors participated in the writing of the paper. Competing interes ts: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The GWHD 2021 can be downloaded here: https://zenodo.org/record/5092309#.YrvsTBXP2Uk
Supplementary Materials
Supplementary 1
Sections S1 to S4
Figs. S1 to S2
Tables S1 to S3
References [ 45 , 46 ]
Click here for additional data file.
References
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H , Barbier
G , Goolsby
R
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Harnessing the crowdsourcing power of social media for disaster relief
. IEEE Intell Syst . 2011 ; 26 ( 3 ): 10 – 14 .
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Prill
Open asset ↗Zenodo · 5092309lines:381-537Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat’s yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m2(SNpM2) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM2and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.
Why it matches plant phenotyping methodsドローン画像からコムギ穂数や収量関連形質を抽出・検証するAIソフトウェア／表現型解析システム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。
abstractwe present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones.
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 testing datasets, Jupyter notebook, and CropQuant-Air software used in this paper are available at the Zhou lab’s GitHub repository: https://github.com/The-Zhou-Lab/CropQuant-Air/releases/tag/v1.0Open asset ↗The-Zhou-Lab/CropQuant-Air · v1.0lines:469-479Code / 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 confirmedEurope PMC · Crossref · checked 7 Sept 2026
Motivation Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. Results We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties. Availability and implementation Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).
Why it matches plant phenotyping methodsUAS由来の植物表現型データを用いて収量を推定する深層学習手法を開発・評価しており、表現型の取得・統合・推定が研究の中心である。
abstractWe propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems.
Reproduction assets foundThe paper's availability statement explicitly links a public GitHub repository with the authors' analysis code (PheGeMIL) and a Dryad DOI deposit containing the paper's phenotyping data (UAV multispectral/thermal images, DEMs, genotypes, yield). Both are paper-specific, public, and actionable.Dataset · publicAvailable at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).Open asset ↗doi:10.5061/dryad.kprr4xh5plines:49-57Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m 2 m -2 ) (root mean square error (RMSE): 0.23 to 0.89 m 2 m -2 ), including for sparse canopy (LAI less than 0.3 m 2 m -2 ) grown on different soil types (RMSE: 0.02 to 0.25 m 2 m -2 ). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータから小麦LAIを推定する汎用モデルを開発し、異なる土壌・圃場試験で検証しており、植物形質取得手法が中心である。
abstractdevelop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data
Reproduction assets foundThe paper's Data Availability statement points to public source code and data at UQ eSpace (DOI 10.48610/ac9642c), covering the RFR model code and supporting data. Additionally, the BASE soil reflectance dataset used to generate test soil backgrounds is publicly available on Zenodo (record 6265730).Code · publicOther data and source code supporting this work are available at UQ eSpace, and a unique DOI (https://doi.org/10.48610/ac9642c) is provided for public access.Open asset ↗UQ eSpace · 10.48610/ac9642clines:298-372Dataset · publicThe BASE soil reflectance data are available online ( https://zenodo.org/record/6265730 ).Open asset ↗Zenodo · 6265730lines:176-179Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background: The cereal spike is the main harvested plant organ determining the grain yield and quality, and its dissection provides the basis to estimate yield- and quality-related traits, such as grain number per spike and kernel weight. Phenotypic detection of spike architecture has potential for genetic improvement of yield and quality. However, manual collection and analysis of phenotypic data is laborious, time-consuming, low-throughput and destructive. Results We used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits. We used an optimized 3D image processing methods by point cloud for analyzing internal structure and quantifying morphological traits of barley spikes. The volume and surface area of grains per spike can be determined efficiently, which is hard to be measured manually. The UNet model was trained based on two types of spikes (wheat cultivar D3 and two-row barley variety S17350), and the best model accurately predicted grain characteristics from CT images. The spikes of ten barley varieties were analyzed and classified into three categories, namely wild barley, barley cultivars and barley landraces. The results showed that modern cultivated barley has shorter but thicker grains with larger volume and higher yield compared to wild barley. The X-ray CT reconstruction and phenotype extraction pipeline needed only 5 minutes per spike for imaging and traits extracting. Conclusions The combination of X-ray CT scans and a deep learning model could be a useful tool in breeding for high yield in cereal crops, and optimized 3D image processing methods could be valuable means of phenotypic traits calculation.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の内部形態・粒形質を非破壊かつ高スループットに抽出する手法を開発しており、フェノタイピング手法が研究の中心である。
abstractWe used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits.
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the allowed URL.Code · publicAvailability of data and materials: All code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_detection_barley_spike_python.Open asset ↗zerosky010/CT_detection_barley_spike_pythonlines:99-131Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.
Why it matches plant phenotyping methods小麦葉画像から黄さび病の重症度を推定する画像解析手法を開発・比較しており、植物病害状態の表現型取得が中心である。
abstractThe disease causes deformations in the wheat leaf texture that reveals the severity of the disease.
Reproduction assets foundThe paper's wheat yellow rust phenotyping image dataset (Yellow-Rust-19, 15,000 labeled wheat leaf images across six infection-type classes) is publicly deposited on Kaggle by the authors, as stated in the Data Availability section. No author analysis code or trained model checkpoints are reported as publicly availableDataset · publicThe data have been deposited in the Kaggle database ( https://www.kaggle.com/datasets/tolgahayit/yellowrust19-yellow-rust-disease-in-wheat ).Open asset ↗Kaggle · yellowrust19-yellow-rust-disease-in-wheatlines:761-833Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum . In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R 2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.
Why it matches plant phenotyping methods小麦粒のFHB損傷・重症度をハイパースペクトル画像と機械学習で分類・定量する手法が研究の中心であり、Mask R-CNNによる抽出と精度評価も含むため、植物病害表現型の方法研究として適格。
abstractwe explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels.
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 · publicData Availability Statement: The codes and the data are available at Li lab GitHub repository at
https://github.com/LiLabAtVT/WheatHyperSpectral (accessed on 1 March 2023).Open asset ↗LiLabAtVT/WheatHyperSpectralpdf-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R 2 , 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.
Why it matches plant phenotyping methodsコムギ葉のハイパースペクトル画像と画像特徴からクロロフィル量を推定する手法を開発・評価しており、植物表現型の取得・推定が中心である。
abstractCompared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique.
Reproduction assets foundThe paper's phenotype data (335 wheat varieties, SPAD values, hyperspectral-derived traits) are stated to be contained in the article and its supplementary files (Table S1 variety list, Table S2 SPAD values), publicly downloadable from the MDPI supplementary link. No author analysis code, models, or raw hyperspectral/3Supplement · 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/ijms24065825/s1 .
Click here for additional data file.
Author Contributions
C.Z. and Y.Y. conceived and designed the study; Y.Y., R.N., T.M., Y.S. and F.S. (Fanghui Shi) collected the wheat samples; Y.Y., Y.W. and C.Z. analyzed the data; Y.Y. and X.L. wrote the manuscript; F.S. (Fengli Sun), Y.X. and C.Z. revised the manuscript. AlOpen asset ↗lines:70-112Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Phytoliths constitute microscopic SiO 2 -rich biominerals formed in the cellular system of many living plants and are often preserved in soils, sediments and artefacts. Their analysis contributes significantly to the identification and study of botanical remains in (paleo)ecological and archaeological contexts. Traditional identification and classification of phytoliths rely on human experience, and as such, an emerging challenge is to automatically classify them to enhance data homogeneity among researchers worldwide and facilitate reliable comparisons. In the present study, a deep artificial neural network (NN) is implemented under the objective to detect and classify phytoliths, extracted from modern wheat ( Triticum spp.). The proposed methodology is able to recognise four phytolith morphotypes: (a) Stoma, (b) Rondel, (c) Papillate, and (d) Elongate dendritic. For the learning process, a dataset of phytolith photomicrographs was created and allocated to training, validation and testing data groups. Due to the limited size and low diversity of the dataset, an end-to-end encoder-decoder NN architecture is proposed, based on a pre-trained MobileNetV2, utilised for the encoder part and U-net, used for the segmentation stage. After the parameterisation, training and fine-tuning of the proposed architecture, it is capable to classify and localise the four classes of phytoliths in unknown images with high unbiased accuracy, exceeding 90%. The proposed methodology and corresponding dataset are quite promising for building up the capacity of phytolith classification within unfamiliar (geo)archaeological datasets, demonstrating remarkable potential towards automatic phytolith analysis.
Why it matches plant phenotyping methods植物由来の植物珪酸体を画像から検出・分類する深層学習手法とデータセットの開発が研究の中心であり、植物形態情報の取得・抽出に該当する。
abstracta deep artificial neural network (NN) is implemented under the objective to detect and classify phytoliths, extracted from modern wheat
Reproduction assets foundThe paper's phytolith photomicrograph dataset (annotated images of four morphotypes from modern wheat) is explicitly stated to be publicly available on Kaggle. Code and trained NNs are mentioned as contributions but no public repository URL is provided, so only the dataset qualifies.Dataset · publicData availability
The dataset of the current study is publicly available in the Kaggle platform: https://www.kaggle.com/datasets/georgepetrakis/phytolith-photomicrographs .Open asset ↗Kaggle · georgepetrakis/phytolith-photomicrographslines:130-152Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Deep learning has shown potential in domains with large-scale annotated datasets. However, manual annotation is expensive, time-consuming, and tedious. Pixel-level annotations are particularly costly for semantic segmentation in images with dense irregular patterns of object instances, such as in plant images. In this work, we propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation. As a use case, we focus on wheat head segmentation. We synthesize a computationally annotated dataset-using a few annotated images, a short unannotated video clip of a wheat field, and several video clips with no wheat-to train a customized U-Net model. Considering the distribution shift between the synthesized and real images, we apply three domain adaptation steps to gradually bridge the domain gap. Only using two annotated images, we achieved a Dice score of 0.89 on the internal test set. When further evaluated on a diverse external dataset collected from 18 different domains across five countries, this model achieved a Dice score of 0.73. To expose the model to images from different growth stages and environmental conditions, we incorporated two annotated images from each of the 18 domains to further fine-tune the model. This increased the Dice score to 0.91. The result highlights the utility of the proposed approach in the absence of large-annotated datasets. Although our use case is wheat head segmentation, the proposed approach can be extended to other segmentation tasks with similar characteristics of irregularly repeating patterns of object instances.
Why it matches plant phenotyping methods植物画像から穂を抽出するセマンティックセグメンテーション手法を開発し、複数データセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractwe propose a method for developing high-performing deep learning models for semantic segmentation of such images utilizing little manual annotation.
Reproduction assets foundThe paper's wheat head segmentation study provides two paper-specific public assets: the dataset (video frames, synthesized images, manual annotations) hosted on the authors' USask FTP server, and the authors' image synthesis pipeline code on GitHub. Both have explicit availability statements with public URLs.Dataset · publicThe data used for this study is available at https://www.cs.usask.ca/ftp/pub/whs/ .Open asset ↗lines:52-63Code · publicThe code used for image synthesis is available at https://github.com/KeyhanNajafian/ImageSimulatorPipeline .Open asset ↗lines:64-77Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
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 development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A large amount of training data is usually lacking at the beginning of system development and labeling such a large number of RGB (red, green, blue) images is laborious. Interactive recurrent annotation is beneficial to incrementally gain training images in the stream of the system development and provides an opportunity to reduce human workload. We developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems. Predictions can be performed under both single-image mode and batch mode for multiple images. The prediction results can be used as the initial image labeling and manually adjusted under a single image mode. Human labeling and machine predictions are visualized on the same image. ROOSTER provides fully automatic labeling for abundantly available initial images of wheat stripe rust to gain essential predictability. The navigation of integrating prediction with labeling benefits human adjustment to iteratively improve predictability. The development of a detection system for wheat stripe rust was presented as a use case to demonstrate the efficiency of using interactive deep learning to develop machine vision systems.
Why it matches plant phenotyping methods植物病害（コムギ縞萎縮病）の画像検出を対象とした対話型画像ラベリング・分類ソフトウェアを開発しており、植物病害状態の取得手法が中心である。
abstractWe developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems.
Reproduction assets foundThe paper's wheat stripe rust use case is supported by a public Zenodo underlying dataset (400 author-captured training images and use case output files) and public author source code (zzlab.net, GitHub, archived Zenodo). The independent test data from Schirrmann et al. is only available on request.Dataset · publicilability
Underlying data
The independent data used to test ROOSTER was sourced from Schirrmann et al.,10
see here: https://doi.org/10.3389/fpls.2021.469689). Please contact the corresponding author of this article (mschirrmann@atb-potsdam.de) to request
access to the test data if interested.
Zenodo: ROOSTER underlying dataset. https://doi.org/10.5281/zenodo.7530460.11
This project contains the following underlying data:
- RawImages.zip (400 input training images used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software aOpen asset ↗Zenodo · 10.5281/zenodo.7530460pdf-raw-page:5 lines:1-44Code · publicmages used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗GitHub · 12HuYang/ROOSTERpdf-layout-page:5 lines:1-63Code · publicseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗Zenodo · 10.5281/zenodo.7320405pdf-layout-page:5 lines:1-63Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The characterization, analysis, and evaluation of morphology and structure are crucial in wheat research. Quantitative and fine characterization of wheat morphology and structure from a three-dimensional (3D) perspective has great theoretical significance and application value in plant architecture identification, high light efficiency breeding, and cultivation. This study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept. Specifically, 3D plant architecture parameters at the organ, phytomer, single stem, and individual plant scales were extracted based on the geometric models. Furthermore, plant architecture vector (PA) was proposed to comprehensively evaluate wheat plant architecture, including convergence index (C), leaf structure index (L), phytomer structure index (PHY), and stem structure index (S). The proposed method could quickly and efficiently achieve 3D wheat plant modeling by assembling 3D phytomers. In addition, the extracted PA quantifies the plant architecture differences in multi-scales among different cultivars, thus, realizing a shift from the traditional qualitative to quantitative analysis of plant architecture. Overall, this study promotes the application of the 3D phytomer concept to multi-tiller crops, thereby providing a theoretical and technical basis for 3D plant modeling and plant architecture quantification in wheat.
Why it matches plant phenotyping methods3D小麦モデルを開発し、複数スケールの植物構造形質を抽出・定量化することが研究の中心であるため。
abstractThis study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept.
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/plants12030445/s1 , Table S1 shows the detailed parameter definition and description of 3D phytomers of wheat. Table S2 shows the plant architecture data for all plants used in this study.Open asset ↗MDPI · 10.3390/plants12030445/s1lines:95-296Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch. This dataset was used to train a deep learning model. The resulting model, named CerealConv, reached a 97.05% classification accuracy. When tested against trained pathologists on a subset of images from the larger dataset, the model delivered an accuracy score 2% higher than the best-performing pathologist. Image masks were used to show that the model was using the correct information to drive its classifications. These results show that deep learning networks are a viable tool for disease detection and classification in the field, and disease quantification is a logical next step.
Why it matches plant phenotyping methodsコムギ葉の病害状態を画像から分類するデータセットと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・推定が研究の中心である。
abstractIn this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch.
Reproduction assets foundThe paper's 999-image pathologist-comparison subset of the wheat disease image dataset is publicly deposited on Zenodo with an explicit availability statement and URL. The full ~19,160-image dataset is only available on request from the authors, and no analysis code or trained model is stated as publicly available.Dataset · publicThe 999 selected images used in the experiment to test pathology experts are available at https://zenodo.org/record/7573133 .Open asset ↗Zenodo · 7573133lines:222-282Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.
Why it matches plant phenotyping methods風洞実験と有限要素シミュレーションを統合し、植物茎の曲げ挙動・強度という表現型を予測・説明する手法が研究の中心である。
abstractWe ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions.
Reproduction assets foundThe paper's wind tunnel plant phenotyping assets are publicly available: raw wind tunnel videos of the cereal plants (DRUM repository), the authors' video-analysis scripts (GitHub), and the multi-scale finite-element model code (Dryad). Supplementary material with sample video and analysis details is on Figshare.Code · publiche scripts used and location of the data analysed from the wind tunnel experiment. Multi-scale material model codes in Python, Abaqus model file and python script for automatized simulations at different wind speed levels pertaining to multi-scale finite-element model simulations are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.612jm644j [ 53 ].
Supplementary material is available online [ 54 ].
Authors' contributionsOpen asset ↗Dryad Digital Repository · 10.5061/dryad.612jm644jlines:229-239Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Context Mixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant- plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. Objectives The objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. Methods A new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. Results Compared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivar’s grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R 2 =0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant (∼56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R 2 =0.43 in 2019-2020 and 0.17 in 2020-2021). Conclusions Plasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. Implications Both the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures.
Why it matches plant phenotyping methods精密播種による個体レベルの生育形質フェノタイピング実験デザインと、形質可塑性の分解手法が明示的な技術的貢献であり、単なる収量測定にとどまらない。
abstractA new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth.
Reproduction assets foundThe paper's phenotyping data, supplementary material, and analysis code are openly deposited on Recherche Data Gouv at https://doi.org/10.57745/LZS8SU, as stated in the Reproducibility section. This is a paper-specific, public, actionable asset directly reproducing the wheat pure/mixed-stand phenotyping measurements (eDataset · publicSupplementary information, data
and code that support the findings of this study are openly available at the INRAE
space of the Recherche Data Gouv repository https://doi.org/10.57745/LZS8SU.Open asset ↗Recherche Data Gouv · 10.57745/LZS8SUpdf-page:15 lines:1-40Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R 2 , root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R 2 , RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R 2 , RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.
Why it matches plant phenotyping methods消費者向けUAVマルチスペクトル画像を用いて小麦の収量および窒素利用効率を推定・検証する方法が研究の中心であり、植物形質の取得と予測性能を評価している。
abstractThis study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat
Reproduction assets foundThe article's data availability statement points to a public figshare deposit containing the paper's Supplementary Material and Appendix (including e.g. Supplementary Table S1 with VARI-threshold background-removal accuracy results). No author analysis code, raw imagery, or trained models are explicitly deposited; DJI/Supplement · publicapplications should be thoroughly explored.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Materials
. Further inquiries can be directed to the corresponding author. The Supplementary material and Appendix document for this article can be found online at: https://figshare.com/s/fa258c55dd6bd9fc69b9 named as Supplementary Material.zip.
Author contributions
XL, JL, and YZ designed and developed the research idea. YZ, XC, and XT conducted the field data collection. JL and YZ performed the data analysis. JL wrote the manuscript. JL, YZ, XT, XC, and XL contributed to the results and data interpretation, discussion, and reviOpen asset ↗figsharelines:861-886Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U 2 -Net; and (4) leaf segmentation with U 2 -Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.
Why it matches plant phenotyping methods植物画像の物体検出・インスタンスセグメンテーションを行うソフトウェア自体が中心で、植物科学での検証例も含むため、植物フェノタイピング手法として含める。
abstractHere, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning.
Reproduction assets foundThe paper's authors publicly deposited their analysis software JustDeepIt (the tool used for all four plant phenotyping case studies) on GitHub under an MIT License, and the data availability statement confirms the original contributions are available there.Code · publicThe source code is deposited in GitHub at https://github.com/biunit/JustDeepIt under an MIT License.Open asset ↗biunit/JustDeepItlines:277-285Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット画像・機械学習手法の開発と適用が研究の中心であり、植物表現型取得法として明確に該当する。
abstractwe created a high-throughput phenotyping approach to quantify stigma and ovary morphology
Reproduction assets foundThe paper's authors publicly deposited both the analysis code (CNN training/implementation scripts and R scripts) on GitHub and the carpel image/training/validation datasets on Earlham OpenData, directly reproducing this paper's wheat carpel phenotyping measurements and analysis.Code · publicTraining codes used for the development of the CNNs, adapted stigma and ovary CNNs, and R scripts used for data curation and visualisation can be found at https://github.com/Uauy-Lab/ML-carpel_traitsOpen asset ↗Uauy-Lab/ML-carpel_traitslines:122-188Dataset · publicDatasets for the training and validation of the models and raw images used for the different experimental analyses are freely available at https://opendata.earlham.ac.uk/wheat/under_license/toronto/Millan-Blanquez_etal_2022_machine-learning-carpel-traits/Open asset ↗lines:122-188Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Why it matches plant phenotyping methodsUAVおよびSentinel-2マルチスペクトル画像から草丈、LAI、バイオマス、葉窒素などの植物形質を推定し、参照データとの相関比較・技術評価を行っているため、植物フェノタイピング手法の検証・応用が中心です。
abstractprospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated
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/rs14174426/s1, Table S1: Summary statistics of the plant trait
variables measured at 20 sample points in field A; Table S2: Summary statistics of the plant trait
variables measured at 20 sample points in field G; Table S3: Absolute correlation results of plant
maximum height and mean height in field AOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
WheatField / plotMicroscopyRootMorphology / geometry measurementRoot system architecture
Root hairs play an important role in absorbing water and nutrients in crop plants. Here we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope. A collection of 24 century wide spring wheat cultivars released between 1911 and 2016 were phenotyped for RHL and RHD. The results revealed significant variations for both traits with five and six-fold variation for RHL and RHD, respectively. RHL ranged from 1.01 mm to 1.77 mm with an average of 1.39 mm, and RHD ranged from 17.08 mm -2 to 20.8 mm -2 with an average of 19.6 mm -2 . Agronomic and physiological traits collected from five different environments and their best linear unbiased predictions (BLUPs) were correlated with RHL and RHD, and results revealed that relative-water contents (RWC), biomass and grain per spike (GpS) were positively correlated with RHL in both water-limited and well-watered conditions. While RHD was negatively correlated with grain yield (GY) in four environments and their BLUPs. Both RHL and RHD had positive correlation indicating the possibility of simultaneous selection of both phenotypes during wheat breeding. The expression pattern of TaRSL4 gene involved in regulation of root hair length was determined in all 24 wheat cultivars based on RNA-seq data, which indicated the differentially higher expression of the A- and D- homeologues of the gene in roots, while B-homeologue was consistently expressed in both leaf and roots. The results were validated by qRT-PCR and the expression of TaRSL4 was consistently high in rainfed cultivars such as Chakwal-50, Rawal-87, and Margallah-99. Overall, the new phenotyping method for RHL and RHD along with correlations with morphological and physiological traits in spring wheat cultivars improved our understanding for selection of these phenotypes in wheat breeding.
Why it matches plant phenotyping methods携帯型顕微鏡を用いたコムギ根毛長・密度のハイスループット表現型測定法を最適化し、品種で実証しているため、根形態フェノタイピング手法が中心である。
abstractHere we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope.
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 · publicTable S1: Name of the cultivars, pedigree, year of release and raw phenotypic data used in this study.Open asset ↗lines:71-196Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract The stomata on the leaf surface are mainly responsible for the material exchange between the internal and external environments of the plant, a large number of methods have been proposed to automatically measure the distribution position and number of stomatal, but few methods could achieve both stomatal count and open/closed‐state judgment. Therefore, this study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning. In order to obtain more stomatal feature information and send it to the network for learning, the proposed method adds a coordinate attention (CA) mechanism to the YOLOV5 backbone part. At the same time, in order to avoid the overfitting of the model during the training process, the authors added the training trick of label smoothing. Finally, the detection ability of the proposed method for stomata is verified on the broad bean leaves stomata dataset. The experimental results show that our method achieves a detection accuracy of 0.934 and an mAP of 0.968. By comparing with other state‐of‐the‐art algorithms, the detection capability of our method has been significantly improved. The generalization of the model is verified on the wheat leaf stomatal dataset. The experimental results show that our method can achieve a detection accuracy of 0.894 and an mAP of 0.907.
Why it matches plant phenotyping methods植物葉の気孔形態を自動検出し、数と開閉状態を推定する画像解析法を開発・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。
abstractthis study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning.
Reproduction assets foundThe paper's broad bean/wheat leaf stomata microscopy image dataset (951 broad bean + 160 wheat images with YOLO-format annotations) is openly deposited on Zenodo per the data availability statement. No code or trained model deposit is explicitly stated.Dataset · publicOF INTEREST
problems, and its indicators are better than the six comparison The authors declare that there are no conflict of interests, we do
algorithms above. not have any possible conflicts of interest.
DATA AVAILABILITY STATEMENT
5 CONCLUSIONS The data that support the findings of this study are openly
available in zendo at https://doi.org/10.5281/zenodo.6302925.
In order to better detect and count the position, number, and
open/closed-status of stomata in plant leaves, we introduce a AUTHOR CONTRIBUTIONS
modified end-to-end target detection model YOLOv5 in this Xin Li: Conceptualization; Data curation; Formal analysis;
study. In order to improve the ability of YOLOv5s model to InvestiOpen asset ↗zenodo · 10.5281/zenodo.6302925pdf-layout-page:9 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.
Why it matches plant phenotyping methods小麦葉・茎さび病という植物の病徴状態を画像から分類する手法を開発・比較しており、病害フェノタイピング手法が中心である。
abstractthis study proposes a method for identifying wheat rust based on ensemble learning (WR-EL).
Reproduction assets foundThe paper's wheat rust classification uses the public ICLR Workshop/CGIAR crop disease image dataset (healthy, stem rust, leaf rust wheat images from Ethiopia and Tanzania), which the authors explicitly state is downloadable from the Zindi competition page. No author code or trained models are stated as available (DataDataset · publict.
Figure 1
Workflow of this study.
Figure 2 is based on the dataset used in this research, which includes the three categories of healthy wheat, leaf rust wheat, and stem rust wheat, with image data from farm sites in Ethiopia and Tanzania, and from public images on Google Maps. The dataset can be obtain in the following link: https://zindi.africa/competitions/iclr-workshop-challenge-1-cgiar-computer-vision-for-crop-disease/data (accessed on 16 July 2022). Leaf rust occurs mainly on the leaf area, but it can also arise on the stem, and stem rust occurs mostly on the stem, but it may also appear on the leaf area. Therefore, it is important to judge not only the location of the disease but alOpen asset ↗Zindi · iclr-workshop-challenge-1-cgiar-computer-vision-for-crop-diseaselines:29-38Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Canopy photosynthesis is the sum of photosynthesis of all above-ground photosynthetic tissues. Quantitative roles of nonfoliar tissues in canopy photosynthesis remain elusive due to methodology limitations. Here, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities. The new model precisely predicts wheat canopy gas exchange rates at different growth stages, weather conditions, and canopy architectural perturbations. Using the model, we systematically study (1) the contribution of both foliar and nonfoliar tissues to wheat canopy photosynthesis and (2) the responses of wheat canopy photosynthesis to plant physiological and architectural changes. We found that (1) at tillering, heading, and milking stages, nonfoliar tissues can contribute ~4, ~32, and ~50% of daily gross canopy photosynthesis ( A cgross ; ~2, ~15, and ~-13% of daily net canopy photosynthesis, A cnet ) and absorb ~6, ~42, and ~60% of total light, respectively; (2) under favorable condition, increasing spike photosynthetic activity, rather than enlarging spike size or awn size, can enhance canopy photosynthesis; (3) covariation in tissue respiratory rate and photosynthetic rate may be a major factor responsible for less than expected increase in daily A cnet ; and (4) in general, erect leaves, lower spike position, shorter plant height, and proper plant densities can benefit daily A cnet . Overall, the model, together with the facilities for quantifying plant architecture and tissue gas exchange, provides an integrated platform to study canopy photosynthesis and support rational design of photosynthetically efficient wheat crops.
Why it matches plant phenotyping methods小麦の葉・非葉器官・群落の光合成と植物体構造を定量する統合モデルを開発し、ガス交換測定施設で検証しているため、植物フェノタイピング手法が研究の中心である。
abstractHere, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities.
Reproduction assets foundThe paper explicitly states that the source code and user manual for the 3dCAP-wheat framework (used for plant architecture extraction, 3D reconstruction, ray tracing, and canopy photosynthesis computation) are freely available on GitHub at the authors' public URL.Code · publicSource code used for this study, together with the user manual, are freely available for noncommercial use at https://github.com/rootchang/3dCAP-wheat .Open asset ↗rootchang/3dCAP-wheatlines:162-298Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance; and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resistance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211-0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other observed traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply genomic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.
Why it matches plant phenotyping methods圃場ハイスループット表現型解析によるコムギ病害重症度の評価が研究目的の中心であり、ゲノム解析・選抜にも応用しているため、植物フェノタイピング手法の実質的な適用研究に該当する。
abstractThe objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment
Reproduction assets foundThe authors state that raw and analyzed phenotypic data, genotypic data, and basic plot scripts are publicly available on GitHub (https://github.com/umngao/wsm1_bdv2) and Dryad (doi:10.5061/dryad.ncjsxkswd). The GitHub repository is an allowed URL and qualifies as authors' public analysis code for this paper's phenotypCode · publicSupplementary material , including raw and analyzed phenotypic data, genotypic data, and basic plot scripts are available at Dyrad doi:10.5061/dryad.ncjsxkswd and GitHub https://github.com/umngao/wsm1_bdv2 .Open asset ↗umngao/wsm1_bdv2lines:239-276Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second.
Why it matches plant phenotyping methodsコムギ穂数という植物器官形質を画像から検出・計数するモデルを開発し、性能比較とモバイルアプリ化まで行っており、表現型取得手法が研究の中心である。
abstracta high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure.
Reproduction assets foundThe paper's wheat head detection model was trained and evaluated on the public Global Wheat Head Detection dataset hosted on Kaggle, which directly provides the plant-phenotyping images and bounding-box annotations used in this study. No authors' code or trained model repository is disclosed; the Data Availability onlyDataset · publicThe data set used in this study was retrieved from the Global Wheat Head data set (Kaggle, 2020 ).Open asset ↗Kagglelines:40-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R 2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha -1 . Compared to county-level statistics during 2007-2015, the ranges of R 2 , RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha -1 , and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.
Why it matches plant phenotyping methods冬小麦の地上部バイオマスという明示的な植物形質を、作物モデルとMODIS LAIデータ同化で広域推定し、地上測定および統計値で検証したデータセット研究であり、形質推定手法と検証が中心です。
abstractwe assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015.
Reproduction assets foundThe paper's winter wheat AGB analysis code is publicly available on the authors' GitHub repository, and the MODIS LAI product assimilated into WOFOST is publicly accessible via the Land-Atmosphere Interaction Research Group website. The generated AGB dataset itself is on figshare (10.6084/m9.figshare.16680784.v3), but那Code · publicHuang and Jianxi Huang designed the research, performed the analysis, and wrote the paper; Xuecao Li, Wen Zhuo, Yantong Wu, Quandi Niu, Wei Su, and Wenping Yuan edited and revised the manuscript.
Code availability
Python scripts that implement model calibration, data assimilation, dataset generation, and mapping are available ( https://github.com/paperoses/CHN_Winter_Wheat_AGB ). Further questions can be directed towards Hai Huang (haihuang@cau.edu.cn).
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.
Supplementary information
The onOpen asset ↗paperoses/CHN_Winter_Wheat_AGBlines:117-143Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated, thanks to the automated image processing systems, MGW estimations have been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images including more than 72,000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area × Circularity, Perimeter × Circularity, and Area/Perimeter indices. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.
Why it matches plant phenotyping methods画像から穀粒形状指標を抽出し、平均穀粒重を推定する手法を開発・検証しており、表現型取得・推定が研究の中心である。
abstractthe present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations.
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 image archive used in this research (with the original resolution) along with the mean values of extracted quantities have been shared on Figshare, at [ 23 ]: https://figshare.com/articles/dataset/Images_of_wheat_grains/18480722Open asset ↗figsharelines:141-154Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Stripe rust (caused by Puccinia striiformis f. sp. tritici) is one of the most devastating diseases of wheat and causes large-scale epidemics and severe yield loss. Applying fungicides during early epidemic development is crucial to controlling the disease but is often challenged by resource-limited human visual scouting. Deep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust for timely application of fungicides and improve control efficiency. Here, we developed RustNet, a neural network-based image classifier, for efficiently monitoring fields for stripe rust. RustNet was built on a ResNet-18 architecture pre-trained with ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset using transfer learning. RGB images and videos of multiple wheat fields with different wheat types (winter and spring wheat), conditions (irrigated and non-irrigated), and locations were acquired using smartphones or unmanned aerial vehicles near the canopy. A semi-automated image labeling approach was conducted to improve labeling efficiency by combining automated machine labeling and human correction. Cross-validations across multiple categories (sensor platforms, wheat types, and locations) achieved Area Under Curve from 0.72 to 0.87. Independent validation on a published dataset from Germany achieved accuracies ranging from 0.79 to 0.86. The visualization of the last convolutional layer of RustNet demonstrated the identification of pixels with stripe rust. RustNet is freely available at https://zzlab.net/RustNet.
Why it matches plant phenotyping methods小麦のストライプさび病という植物状態を画像から検出する深層学習手法を開発し、複数条件で交差検証・独立検証しており、表現型取得手法が研究の中心です。
abstractDeep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust
Reproduction assets foundThe authors publicly released the RustNet trained model (integrated into Rooster) and the Rooster semi-automated image-labeling software used to produce this paper's wheat stripe rust phenotyping analysis, with explicit availability statements and URLs.Code · publicwere calculated based on its gradient to the disease prediction, which
was equal to the weights of the last fully connected layer. A ReLU function was applied to filter
negative input (Figure 2b). A python package was used to visualize the Grad-CAM
(https://github.com/jacobgil/pytorch-grad-cam).Image labeling
Rooster software (https://github.com/12HuYang/Rooster) was used to label tile images into
disease or non-disease classes by easily clicking it with a mouse. Rooster was developed with
python and can split raw images into tiles (e.g., 224 × 224 pixels) by defining column and row
numbers. A semi-automatic image labeling that combines machine- and human labeling was
implemented in RoOpen asset ↗12HuYang/Roosterpdf-raw-page:18 lines:1-30Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
The wheat grain three-dimensional (3D) phenotypic characters are of great significance for final yield and variety breeding, and the ventral sulcus traits are the important factors to the wheat flour yield. The wheat grain trait measurements are necessary; however, the traditional measurement method is still manual, which is inefficient, subjective, and labor intensive; moreover, the ventral sulcus traits can only be obtained by destructive measurement. In this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus traits. First, the 3D point cloud data of wheat grain were obtained by the structured light scanner, and then, the specified point cloud processing algorithms including single grain segmentation and ventral sulcus location have been designed; finally, 28 wheat grain 3D phenotypic characters and 4 ventral sulcus traits have been extracted. To evaluate the best experimental conditions, three-level orthogonal experiments, which include rotation angle, scanning angle, and stage color factors, were carried out on 125 grains of 5 wheat varieties, and the results demonstrated that optimum conditions of rotation angle, scanning angle, and stage color were 30°, 37°, black color individually. Additionally, the results also proved that the mean absolute percentage errors (MAPEs) of wheat grain length, width, thickness, and ventral sulcus depth were 1.83, 1.86, 2.19, and 4.81%. Moreover, the 500 wheat grains of five varieties were used to construct and validate the wheat grain weight model by 32 phenotypic traits, and the cross-validation results showed that the R 2 of the models ranged from 0.77 to 0.83. Finally, the wheat grain phenotype extraction and grain weight prediction were integrated into the specialized software. Therefore, this method was demonstrated to be an efficient and effective way for wheat breeding research.
Why it matches plant phenotyping methods構造化光画像から小麦粒の3D表現型と腹溝形質を抽出する手法を開発し、精度評価・条件最適化・ソフトウェア統合まで行っており、表現型取得が中心である。
abstractIn this paper, an intelligent analysis method based on the structured light imaging has been proposed to extract the 3D wheat grain phenotypes and ventral sulcus 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 original data of structured light imaging, X-ray CT, and manual measurements of 125 wheat grains.Open asset ↗lines:608-666Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract In the absence of pollination, female reproductive organs senesce leading to an irrevocable loss in the reproductive potential of the flower and directly affecting seed set. In self-pollinating crops like wheat ( Triticum aestivum ), the post-anthesis viability of the unpollinated carpel has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which is based on light microscopy imaging and machine learning, for the detailed study of floral organ traits in field grown plants using both fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase (in which stigma area reaches its maximum and the radial expansion of the ovary slows), and a final deterioration phase. These developmental dynamics were largely consistent across years and could be used to classify male sterile cultivars, however the absolute duration of each phase varied across years. This phenotyping approach provides a new tool for examining carpel morphology and development which we hope will help advance research into this field and increase our mechanistic understanding of female fertility in wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット表現型解析法を、光学顕微鏡画像と機械学習で開発・適用しており、表現型取得手法が研究の中心である。
abstractwe created a relatively high-throughput phenotyping approach to quantify stigma and ovary morphology.
Reproduction assets foundThe paper explicitly states that implementation scripts, data, and the trained stigma/ovary CNNs are publicly available at the authors' GitHub repository, which is an allowed URL.Code · publicImplementation scripts and data are available at
https://github.com/marina-millan/ML-carpel_traits.Open asset ↗marina-millan/ML-carpel_traits · ML-carpel_traitspdf-page:4 lines:1-61Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Abstract With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. Wheat is one of the most important food resources in the world, mainly because of the variety of wheat-based products. Wheat crops are threatened by three main types of diseases which cause large amounts of annual damage in crop yield. These diseases can be eliminated by using pesticides at the right time. While the task of manually spraying pesticides is burdensome and expensive, agricultural robotics can aid farmers by increasing the speed and decreasing the amount of chemicals. In this work, a smart autonomous system has been implemented on an unmanned aerial vehicle to automate the task of monitoring wheat fields. First, an image-based deep learning approach is used to detect and classify disease-infected wheat plants. To find the most optimal method, different approaches have been studied. Because of the lack of a public wheat-disease dataset, a custom dataset has been created and labeled. Second, an efficient mapping and navigation system is presented using a simulation in the robot operating system and Gazebo environments. A 2D simultaneous localization and mapping algorithm is used for mapping the workspace autonomously with the help of a frontier-based exploration method.
Why it matches plant phenotyping methods画像ベースの深層学習で感染したコムギ植物を検出・分類する手法を開発し、独自データセットも作成しており、植物病害状態の取得が中心的です。
abstractFirst, an image-based deep learning approach is used to detect and classify disease-infected wheat plants.
Reproduction assets foundThe authors explicitly state their custom wheat disease dataset (900 annotated field images plus 3672 cropped leaf images) is publicly available on Kaggle, matching an allowed URL.Dataset · publicThe dataset generated and analysed during the current study are available in the Kaggle repository via the following web link:
https://www.kaggle.com/sinadunk23/behzad-safari-jalalOpen asset ↗Kaggle · sinadunk23/behzad-safari-jalalpdf-page:9 lines:1-37Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Wheat ( Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400-1800 cm -1 ) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat.
Why it matches plant phenotyping methodsFTIRとケモメトリクスを用いて熱ストレス葉の化学的状態を識別・診断するプロトコルとバイオマーカーを開発しており、植物状態の取得・抽出が中心的です。
abstractFourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress.
Reproduction assets foundThe paper's custom R script for spectral biomarker (Fm) calculation was deposited as Supplementary File S1, publicly available at the MDPI supplementary URL. The raw FTIR spectral data (358 spectra) are not stated to be publicly deposited (Data Availability Statement: 'Not applicable').Code · publicThe R scripts were deposited in Supplementary File S1 .Open asset ↗lines:186-204Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The number of wheat spikes per unit area is one of the most important agronomic traits associated with wheat yield. However, quick and accurate detection for the counting of wheat spikes faces persistent challenges due to the complexity of wheat field conditions. This work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently. This RetinaNet consists of several improvements. First, a weighted bidirectional feature pyramid network (BiFPN) was introduced into the feature pyramid network (FPN) of RetinaNet, which could fuse multiscale features to recognize wheat spikes in different varieties and complicated environments. Then, to detect objects more efficiently, focal loss and attention modules were added. Finally, soft non-maximum suppression (Soft-NMS) was used to solve the occlusion problem. Based on these improvements, the new network detector was created and tested on the Global Wheat Head Detection (GWHD) dataset supplemented with wheat-wheatgrass spike detection (WSD) images. The WSD images were supplemented with new varieties of wheat, which makes the mixed dataset richer in species. The method of this study achieved 0.9262 for mAP50, which improved by 5.59, 49.06, 2.79, 1.35, and 7.26% compared to the state-of-the-art RetinaNet, single-shot multiBox detector (SSD), You Only Look Once version3 (Yolov3), You Only Look Once version4 (Yolov4), and faster region-based convolutional neural network (Faster-RCNN), respectively. In addition, the counting accuracy reached 0.9288, which was improved from other methods as well. Our implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting.
Why it matches plant phenotyping methods小麦穂の検出・計数という植物形質を対象に、改良した画像解析モデルを開発し、データセット上で性能検証しているため、フェノタイピング手法が中心である。
abstractThis work has trained a RetinaNet (SpikeRetinaNet) based on several optimizations to detect and count wheat spikes efficiently.
Reproduction assets foundThe authors explicitly state that their implementation code and partial validation data for the SpikeRetinaNet wheat spike detection/counting method are publicly available on GitHub. Other referenced repositories (COCO Annotator, YOLOv5, LabelImg) are generic third-party tools, not paper-specific assets.Code · publicOur implementation code and partial validation data are available at https://github.com/wujians122/The-Wheat-Spikes-Detecting-and-Counting .Open asset ↗wujians122/The-Wheat-Spikes-Detecting-and-Countinglines:227-316Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception-attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception-attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.
Why it matches plant phenotyping methods小麦種子のFHB感染状態という植物状態を、ラマン分光と改良深層学習モデルで非侵襲的に判定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractRaman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain Figure S1, images of wheat kernels with varying degrees of FHB damage used in this study's phenotyping, plus parameter-setting tables for the classification models and networks. No separate spectral dataset or analysis code repository is stated; the Supplement · publicf key indicators induced by the complex composition of wheat kernels. In the future, we believe that the innovation of RS technology, accumulation of samples, refinement of analysis, and development of modeling methods will be used to help mitigate these limitations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/foods11040578/s1 , Figure S1: Images of wheat kernels with varying degree of damage, Table S1: Parameter setting of different classification models, Table S2: Parameter setting of different networks.
Click here for additional data file.
Author Contributions
Conceptualization, S.W.; methodology, S.W., M.Q. and L.T.; software, L.T.; Open asset ↗foods11040578/s1lines:275-296Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.
Why it matches plant phenotyping methodsSARデータとGPRモデルにより作物のPAI・VWC・湿重量バイオマスを推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThe purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the authors' GPR analysis code for crop biophysical parameter retrieval from RADARSAT-2 data. The in situ SMAPVEX16-MB measurements and RADARSAT-2 imagery themselves are not stated as publicly released by the authors.Code · publicData Availability Statement: The code for the present work is available at: https://github.com/
Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2, accessed 15 February 2022.Open asset ↗Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2pdf-page:24 lines:1-60Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Wheat head detection can measure wheat traits such as head density and head characteristics. Standard wheat breeding largely relies on manual observation to detect wheat heads, yielding a tedious and inefficient procedure. The emergence of affordable camera platforms provides opportunities for deploying computer vision (CV) algorithms in wheat head detection, enabling automated measurements of wheat traits. Accurate wheat head detection, however, is challenging due to the variability of observation circumstances and the uncertainty of wheat head appearances. In this work, we propose a simple but effective idea-dynamic color transform (DCT)-for accurate wheat head detection. This idea is based on an observation that modifying the color channel of an input image can significantly alleviate false negatives and therefore improve detection results. DCT follows a linear color transform and can be easily implemented as a dynamic network. A key property of DCT is that the transform parameters are data-dependent such that illumination variations can be corrected adaptively. The DCT network can be incorporated into any existing object detectors. Experimental results on the Global Wheat Detection Dataset (GWHD) 2021 show that DCT can achieve notable improvements with negligible overhead parameters. In addition, DCT plays an important role in our solution participating in the Global Wheat Challenge (GWC) 2021, where our solution ranks the first on the initial public leaderboard, with an Average Domain Accuracy (ADA) of 0.821, and obtains the runner-up reward on the final private testing set, with an ADA of 0.695.
Why it matches plant phenotyping methods小麦穂の画像検出による形質取得を目的とし、照明変動に対応する動的色変換ネットワークを開発・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。
abstractIn this work, we propose a simple but effective idea-dynamic color transform (DCT)-for accurate wheat head detection.
Reproduction assets foundThe paper's experiments are performed on the GWHD 2021 wheat head detection dataset, which the authors explicitly state is publicly available at the Zenodo record. This is the phenotyping image/annotation dataset directly used for the paper's measurements. No authors' analysis code or trained model checkpoints are madeDataset · publicThe GWHD 2021 dataset is available at https://zenodo.org/record/5092309 .Open asset ↗zenodo · 5092309lines:327-432Code / 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 · Europe PMC · checked 8 Sept 2026
Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.
Why it matches plant phenotyping methods植物の高スループット圃場フェノタイピングに向けた画像セグメンテーション手法、注釈付きデータセット、ベンチマーク評価を中心に扱っているため採用。
abstractRobust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping.
Reproduction assets foundThe paper's EWS wheat segmentation dataset (images, annotations, metadata) is publicly deposited on ETH research collection, and the authors' analysis code is publicly available on GitHub, both explicitly stated in the data availability statement.Code · publicThe code is available at: https://github.com/RadekZenkl/EWS .Open asset ↗github.com/RadekZenkl/EWSlines:807-879Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modelling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang–Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose–responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose–response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度温度データと反復草丈データから小麦の成長・温度応答形質を抽出するモデルを開発・評価しており、表現型抽出手法が研究の中心である。
titlePhenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the source code supporting its phenotyping analysis (dose–response extraction from wheat height and temperature data) in a public ETH GitLab repository, archived in the ETH Research Collection with a DOI. Both URLs are allowed and the repository/identifier verCode · publicting—original draft. H.-P.P.: Conceptualization,
methodology, writing—review & editing. A.H.: Conceptualization,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 20Open asset ↗gitlab.ethz.ch/crop_phenotyping/htfp_data_processingpdf-raw-page:13 lines:1-88Code · publication,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 2000. Bases and limits to using ‘degree.day’
units. European Journal of Agronomy 13:1–10. doi:10.1016/
SOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:13 lines:1-88Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field‐based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat (Triticum aestivum L.) lines planted in full plots and grid‐spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unoccupied aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from ‐0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from ‐0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plants, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early‐generation germplasm. Efficient use of early‐generation, affordable HTP data could improve overall genetic gain in plant breeding.
Why it matches plant phenotyping methods単一個体向けに圃場HTPパイプラインを拡張し、UAVによるNDVI取得・抽出と全区画収量との関係を評価しており、表現型取得法の応用・検証が中心です。
abstractwe extended field‐based HTP pipelines to evaluate and extract data from spaced single plants
Reproduction assets foundThe paper's data availability statement explicitly deposits phenotypic data, raw images, and analysis scripts in a public Zenodo repository (DOI 10.5281/zenodo.6515042), which directly reproduces this paper's plant-phenotyping measurements and computational analysis. The NCBI BioProject (PRJNA764168) contains DNA/genoyDataset · publicilized in this work should
be applicable to a range of different crops and plant breeding
programs and allow the development of crops that can meet
the world’s food, fiber, and fuel needs.
DATA AVA I L A B I L I T Y S TAT E M E N T
Phenotypic data, including raw images and analysis scripts
are available in the Zenodo Repository https://doi.org/10.5281/zenodo.6515042. DNA sequence data from genotypes
used in this study is available in NCBI Sequence Read
Archive (SRA) (https://www.ncbi.nlm.nih.gov/bioproject/) as
BioProject accession number PRJNA764168.
AC K N OW L E D G M E N T S
We thank Shuangye Wu and Ethan Faryna for assistance
in genotyping. This publication is supported by the EArly-Open asset ↗Zenodo · 10.5281/zenodo.6515042pdf-raw-page:12 lines:1-85Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Why it matches plant phenotyping methods植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。
abstractthe assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Reproduction assets foundThe paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather UndergDataset · publicThe field validation data
presented in this study are openly available in the P-band Radiometer Inferred Soil Moisture (PRISIM)
website at https://www.prism.monash.edu/index.htmlOpen asset ↗pdf-page:19 lines:1-59Code · publicThe APSIM-EnKF data assimilation framework used in this study was the version
developed and described by Zhang [28] (source code available on https://github.com/Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Why it matches plant phenotyping methods小麦のハイパースペクトル反射から生理・光合成形質を推定する深層学習モデルを開発・比較し、精度を検証した研究であり、表現型取得・推定法が中心である。
abstractHere we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.
Reproduction assets foundThe paper publicly releases its authors' model code (GitHub) and hosts the training data and pre-trained models via the Wheat Physiology Predictor web server. The SAMS repository is a generic third-party tool and is excluded.Code · publicThe full code of these models is located at https://github.com/ashwhall/hyperspec-trait-prediction .Open asset ↗ashwhall/hyperspec-trait-predictionlines:132-148Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
Why it matches plant phenotyping methods小麦育種試験で近接センシングによりNDVI・群落温度を取得し、統計モデルで収量を予測するワークフローを5年間検証しており、形質取得と予測手法が研究の中心である。
abstractThe objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh.
Reproduction assets foundThe paper's data availability statement explicitly deposits all phenotypic data (NDVI, CT, agronomic traits) and analysis code in the Dryad Digital Repository with a public DOI, making it a paper-specific, publicly actionable asset.Dataset · publicAll phenotypic data and code for analysis have been placed in the Dryad Digital Repository available at: https://doi.org/10.5061/dryad.vdncjsxrz .Open asset ↗Dryad Digital Repository · 10.5061/dryad.vdncjsxrzlines:724-739Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Spectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs. In disease resistance phenotyping, spectral signatures can be analysed to derive infection severity scores and to screen breeding lines. Hyperspectra of winter wheat spikes were acquired in a Fusarium head blight phenotyping trial at the milk- and wax-ripening phenological phases. Disease severity ratings were simultaneously performed by an expert on a 9-point visual scale. Ordinal support vector machine models were then trained to assign hill plots to the individual severity levels. The predictive models' performance was evaluated for data collection timing, spectral pre-processing and permitted rating-error tolerance. The models trained to spectra acquired at the milk-ripening phase were sufficiently accurate to reliably distinguish between low, medium and high symptom severity; with accuracy approaching 100% for two-point error tolerance. However, deterioration in prediction quality was noted for the wax-ripening campaign, presumably due to spike-drying. After aggregation of the spectra using the median function no gain could be associated with further pre-processing. Modest performance improvements obtained with two schemes do not justify the additional data acquisition costs involved, but standard normal variate could be advantageous for some scenarios with mean-aggregated spectra. In addition to phenotyping, the results are discussed in relation to large-scale farming applications. Elevated infection risk detection prior to anthesis is recommended for fungicide treatment, considering the pathogen biology. The study is accompanied by a publicly-available dataset and the computational scripts employed to obtain the results.
Why it matches plant phenotyping methodsスペクトル測定と機械学習によりコムギ赤かび病の感染重症度を推定し、収集時期・前処理・誤差許容度を評価しているため、植物表現型取得法の検証・応用が中心です。
abstractSpectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs.
Reproduction assets foundThe authors deposited the paper's spectral phenotyping dataset (hyperspectra of winter wheat spikes, visual symptom scores) together with the computational analysis scripts and a GNU Guix environment specification in a public Zenodo repository, explicitly excluding only the unused hyperspectral image cubes.Dataset · publicl., 2018), with the scheme, plant health deterioration is associated with less
_
pre-registration form (Zelazny et al., 2020) hosted by the pronounced features, except for the longest wavelengths,
Center of Open Science. The dataset is available from a Zen- where the relationship is reversed. This pre-processing
odo repository (https://doi.org/10.5281/zenodo.4536881), accentuated the effect of the infection on the left shoulder
excluding the hyperspectral data cubes because of their of the NIR plateau. All of these patterns occurred also after
excessive size and the fact that they were not analysed. transforming mean-aggregated spectra (Supplement S2).
The analysis was coded in the R languOpen asset ↗Zenodo · 10.5281/zenodo.4536881pdf-layout-page:6 lines:1-49Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔と表皮細胞を検出・計数し、気孔指数を自動推定する画像解析パイプラインの開発とベンチマーク検証が研究の中心である。
abstractThe main aim of this research is to develop an automated stomatal index measurement pipeline.
Reproduction assets foundThe authors explicitly state the stomatal index pipeline code is fully open-source on GitHub and the wheat microscopic image dataset is downloadable as a zip release from the same repository. Both are paper-specific, public, and directly actionable.Code · publicThe code is fully open-source for academic usage and can be downloaded at https://github.com/WeizhenLiuBioinform/stomatal_indexOpen asset ↗WeizhenLiuBioinform/stomatal_indexlines:361-376Dataset · publicThe wheat dataset is available for downloading at https://github.com/WeizhenLiuBioinform/stomatal_index/releases/download/wheat1.0/wheat_dataset.zipOpen asset ↗WeizhenLiuBioinform/stomatal_index · wheat1.0lines:361-376Supplement · publicSupplementary Table 2
Description of the cuticle dataset used for training and testing the stomatal index measurement model.Open asset ↗lines:651-703Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.
Why it matches plant phenotyping methodsUAV-LiDAR点群から実験区画を自動抽出し、植物高や区画バイオマスなどの生育形質を測定するPythonアルゴリズムとツールの開発・検証が中心である。
abstractOur tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial.
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 · publicThis software tool is open-source and is freely available
here: https://github.com/UPOL-Plant-phenotyping-research-group/UAV-crop-analyzer,
accessed on 1 June 2021.Open asset ↗UPOL-Plant-phenotyping-research-group/UAV-crop-analyzerpdf-page:2 lines:1-59Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Recent technical advances in the computer-vision domain have facilitated the development of various methods for achieving image-based quantification of stomata-related traits. However, the installation cost of such a system and the difficulties of operating it on-site have been hurdles for experimental biologists. Here, we present a platform that allows real-time stomata detection during microscopic observation. The proposed system consists of a deep neural network model-based stomata detector and an upright microscope connected to a USB camera and a graphics processing unit (GPU)-supported single-board computer. All the hardware components are commercially available at common electronic commerce stores at a reasonable price. Moreover, the machine-learning model is prepared based on freely available cloud services. This approach allows users to set up a phenotyping platform at low cost. As a proof of concept, we trained our model to detect dumbbell-shaped stomata from wheat leaf imprints. Using this platform, we collected a comprehensive range of stomatal phenotypes from wheat leaves. We confirmed notable differences in stomatal density ( SD ) between adaxial and abaxial surfaces and in stomatal size ( SS ) between wheat-related species of different ploidy. Utilizing such a platform is expected to accelerate research that involves all aspects of stomata phenotyping.
Why it matches plant phenotyping methods低コストの顕微鏡画像と深層学習による気孔検出・形質定量化プラットフォームの開発であり、植物フェノタイピング手法が研究の中心です。
abstractHere, we present a platform that allows real-time stomata detection during microscopic observation.
Reproduction assets foundThe paper's stomata-detection GUI, trained SSD model weights, and model-training workflow are publicly available in the authors' GitHub repository (onsite_stomata_platform) with an executable Colab training notebook. The raw phenotype/image datasets are only available on request per the Data Availability Statement.Code · publicDetailed codes and instructions to reproduce the regarding system as well as the stomata detection model is described in Google Colaboratory executable notebook 7 hosted at https://github.com/totti0223/onsite_stomata_platform .Open asset ↗totti0223/onsite_stomata_platformlines:155-166Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modeling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang-Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature-response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose-response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度の温度データと低解像度の草丈データから、作物の温度応答パラメータを抽出するモデル手法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。
abstractIn a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data.
Reproduction assets foundThe paper's data and source code (phenotyping analysis for temperature dose-response extraction) are openly available in the ETH GitLab repository and archived in the ETH research collection.Code · publiconceptualization, Methodology, Writing - Review & Editing. Andreas Hund: Con-
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ceptualization, Supervision, Project administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10Open asset ↗crop_phenotyping/htfp_data_processingpdf-raw-page:16 lines:1-23Code · publicProject administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
357
research collection (http://doi.org/10.5905/ethz-1007-385).358
16
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10.1101/2021.07.23.453040
doi:
bioRxiv preprintOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:16 lines:1-23Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
Why it matches plant phenotyping methodsLiDAR計測とCropQuant-3Dによる作物の3D形質抽出システムを開発・実証しており、植物表現型の取得・解析手法が研究の中心である。
abstractHere, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis.
Reproduction assets foundThe paper's authors publicly deposited CropQuant-3D source code, GUI software, and testing point cloud datasets on GitHub, directly supporting this paper's LiDAR-based wheat phenotyping analysis.Code · publicSource code: https://github.com/The-Zhou-Lab/LiDAR/releases/tag/V2.0Open asset ↗The-Zhou-Lab/LiDAR · V2.0lines:150-195Dataset · publicThe datasets supporting the results presented here are available at https://github.com/The-Zhou-Lab/LiDAR/releases/tag/V2.0Open asset ↗The-Zhou-Lab/LiDAR · V2.0lines:150-195Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Chlorophyll fluorescence (ChlF) is a powerful non‐invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high‐throughput field phenotyping capabilities. The light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( ) and the kinetics of electron transport measured by reoxidation rates ( and ). Short‐ and long‐term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, and were little affected, while was consistently accelerated in water‐limited compared to well‐watered plants, increasingly so with rising vapour pressure deficit. This high‐throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.
Why it matches plant phenotyping methodsLIFTセンサーを用いた高スループットな圃場キャノピー蛍光計測が研究の中心であり、光合成形質を定量するフェノタイピング手法を実質的に適用している。
abstractThe light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance.
Reproduction assets foundThe paper's raw and processed/cleaned LIFT chlorophyll fluorescence and spectral phenotyping datasets for both growing seasons are openly deposited on Zenodo (DOI 10.5281/zenodo.4305673), as stated in the methods and data availability statement. TERRA-REF is only cited as the meteorological data provider (infraction: aDataset · public) and 77,946 (97%) ChlF
transients in Y1 and Y2, respectively, were averaged, resulting in one
value per trait per plot per time of measurement (N = 5,544 data
points per trait in Y1; and N = 4,032 data points per trait in Y2). The
raw data and the processed and cleaned datasets for both growing
seasons are publicly accessible (https://doi.org/10.5281/zenodo.4305673).2.9 | Statistical analysis
A linear mixed model (LMM) approach was used to analyse the resolv-
able row-column designs with repeated measures for both Y1 and Y2.
Single-stage analysis models were applied to partition variance com-
ponents and to estimate genotypic effects for all traits based on “Best
Linear Unbiased PredictioOpen asset ↗Zenodo · 10.5281/zenodo.4305673pdf-raw-page:6 lines:1-96Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
Why it matches plant phenotyping methods植物器官の可視パーツ数を画像から検出・計数する深層学習手法を開発し、複数作物データセットで評価しているため、表現型取得・推定法が中心です。
abstractWe propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster.
Reproduction assets foundThe paper's grape experiments use the public Embrapa WGISD dataset, extended by the authors with berry dot annotations that they state were made publicly available as part of that dataset extension. The banana and wheat datasets (Israel Phenomics Consortium) and the authors' code/models have no stated public release,; Dataset · publicThe dot annotations were made
publicly available as part of Embrapa WGISD dataset extension.Open asset ↗Embrapa WGISDpdf-page:5 lines:1-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Phenotyping wheat (Triticum aestivum L.) is time-consuming and new methods are necessary to decrease labor. To develop a heterotic pool of male wheat lines for hybrid breeding, there must be an efficient way to measure both anther extrusion and the size of anthers. Five hundred and ninety-four soft red winter wheat lines in two replications of randomized complete block design were phenotyped for anther extrusion, a key trait for hybrid wheat production. A device was constructed to capture images using a mobile device. Four heads were sampled per line when anthesis was evident for half the heads in the plot. The extruded anthers were scraped onto a surface, their image was captured, and the area of the anthers was taken via ImageJ. The number of anthers extruded was estimated by counting the number of anthers per image and dividing by the number of heads sampled. The area per anther was taken by dividing the area of anthers per spike by the number of anthers per spike. A significant correlation (R=0.9, p
Why it matches plant phenotyping methods小麦の葯突出数と葯サイズを画像取得・ImageJ解析で測定する手法を開発し、大規模材料で適用・評価しており、表現型取得法が中心である。
abstractTo develop a heterotic pool of male wheat lines for hybrid breeding, there must be an efficient way to measure both anther extrusion and the size of anthers.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the paper's anther extrusion phenotyping data (HD, AD, AOAPS, NOAPS, APA for the HGAWN population), alongside request-based access via the corresponding author. The ImageJ macro and R analysis code are described but no separate codeDataset · publicof 7
Funding: This research was funded by USDA National Institute of Food and Agriculture, grant
number 2017-67007-25939.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is available upon request via contact with the corresponding
author and at <https://github.com/zjwinn/Phenotyping-Anther-Extrusion-of-Wheat-Using-Image-Analysis>.
Acknowledgments: This work is supported by the Agriculture and Food Research Initiative Competi-
tive Grant 2017-67007-25939 (Wheat-CAP) from the USDA National Institute of Food and Agriculture.
Conflicts of Interest: The author claims no conflict of interest.
Abbreviations
NOAPS NuOpen asset ↗https://github.com/zjwinn/Phenotyping-Anther-Extrusion-of-Wheat-Using-Image-Analysispdf-raw-page:7 lines:1-53Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
Why it matches plant phenotyping methodsRGB画像とCNNによるコムギ穂の病害重症度推定が研究の中心であり、植物病害状態を直接定量化するフェノタイピング手法を開発・評価している。
abstractWe then trained CNN models to classify wheat spike blast severity.
Reproduction assets foundThe paper publicly deposits its wheat spike blast image datasets (Dataset 1 and Dataset 2) and the corresponding trained CNN models on the Purdue University Research Repository (PURR), with explicit availability statements and URLs.Dataset · publicDataset 1, included maturing and non-matured wheat spikes; and Dataset 2 included only non-matured spikes (data available at: https://purr.purdue.edu/publications/3772/1 ).Open asset ↗lines:341-377Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.
Why it matches plant phenotyping methods三次元キャノピー再構築から葉角度分布を抽出し、光遮断モデルを比較・検証することが研究の中心であり、植物体の構造形質を定量化している。
abstractanalyzed θ distribution in the canopies from three-dimensional canopy reconstructions
Reproduction assets foundThe paper's K and FIPAR light interception models were coded in Matlab and implemented as a BioMA component; the authors state the source code and standalone executable are freely available on Zenodo (record 3820386). The SiriusQuality GitHub link is a general model repository, not paper-specific analysis code.Code · publicAll K and FIPAR models presented here were coded in Matlab and we also developed an independent executable component in the BioMA software framework ( http://www.biomamodelling.org ), which can easily be extended and coupled with CGMs. The source code and the standalone executable of the BioMA component are freely available at https://zenodo.org/record/3820386 .Open asset ↗Zenodo · 3820386lines:906-951Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets. It has three steps: developing a model for predicting sample values from UAV imagery, field gridding and trait value prediction, and statistical testing of predicted values. A field cultivation experiment was conducted to examine the effectiveness of the GAUSS framework, using a soybean-wheat crop rotation as the model system Fourteen soybean cultivars and subsequently a single wheat cultivar were grown in the same field. The crop rotation benefits of the soybeans for wheat yield were examined using GAUSS. Combining manually sampled data ( n = 143) and pixel-based UAV imagery indices produced a large amount of high-spatial-resolution predicted wheat yields ( n = 8,756). Significant differences were detected among soybean cultivars in their effects on wheat yield, and soybean plant traits were associated with the increases. This is the first reported study that links traits of legume plants with rotational benefits to the subsequent crop. Although some limitations and challenges remain, the GAUSS approach can be applied to many types of field-based plant experimentation, and has potential for extensive use in future studies.
Why it matches plant phenotyping methodsUAV画像と手作業データを統合し、植物形質・収量を高解像度で推定するGAUSS解析フレームワークを開発・実証しており、フェノタイピング手法が研究の中心である。
abstractA simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets.
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 ) with different plant types and yield potentials were used ( Kaga et al.Open asset ↗lines:317-335Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 8 Sept 2026
Abstract Plant phenomics is widely recognised as a key area to bridge the gap between traits of agricultural importance and genomic information. A wide range of field-based phenotyping solutions have been developed, from aerial-based to ground-based fixed gantry platforms and handheld devices. Nevertheless, several disadvantages of these current systems have been identified by the research community concerning mobility, affordability, throughput, accuracy, scalability, as well as the ability to analyse big data collected. Here, we present a novel phenotyping solution that combines a commercial backpack LiDAR device and our graphical user interface (GUI) based software called CropQuant-3D, which has been applied to phenotyping of wheat and associated 3D trait analysis. To our knowledge, this is the first use of backpack LiDAR for field-based plant research, which can acquire millions of 3D points to represent spatial features of crops. A key feature of the innovation is the GUI software that can extract plot-based traits from large, complex point clouds with limited computing time and power. We describe how we combined backpack LiDAR and CropQuant-3D to accurately quantify crop height and complex 3D traits such as variation in canopy structure, which was not possible to measure through other approaches. Also, we demonstrate the methodological advance and biological relevance of our work in a case study that examines the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments. The results indicate that the combined solution can differentiate significant genotype and treatment effects on key morphological traits, with strong correlations with conventional manual measurements. Hence, we believe that the combined solution presented here could consistently quantify key traits at a larger scale and more quickly than heretofore possible, indicating the system could be used as a reliable research tool in large-scale and multi-location field phenotyping for crop research and breeding activities. We exhibit the system’s capability in addressing challenges in mobility, throughput, and scalability, contributing to the resolution of the phenotyping bottleneck. Furthermore, with the fast maturity of LiDAR technologies, technical advances in image analysis, and open software solutions, it is likely that the solution presented here has the potential for further development in accuracy and affordability, helping us fully exploit available genomic resources.
Why it matches plant phenotyping methodsバックパックLiDARとCropQuant-3Dを組み合わせ、点群から草高・キャノピー構造などの植物形質を抽出するフェノタイピング手法の開発・適用が中心である。
abstractHere, we present a novel phenotyping solution that combines a commercial backpack LiDAR device and our graphical user interface (GUI) based software called CropQuant-3D, which has been applied to phenotyping of wheat and associated 3D trait analysis.
Reproduction assets foundThe authors publicly release the CropQuant-3D source code, GUI software, and supporting datasets (including test LAS files) via their GitHub repository, directly supporting this paper's LiDAR-based wheat phenotyping analysis.Code · publicSource code: https://github.com/The-Zhou-Lab/LiDAR/releasesOpen asset ↗The-Zhou-Lab/LiDARpdf-page:43 lines:1-62Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be used for phenotypic analysis. In this paper, a method is proposed to reconstruct wheat 3D model based on semantic information. The method can generate the corresponding 3D point cloud model of wheat according to the semantic description. First, an object detection algorithm is used to detect the characteristics of some wheat phenotypes during the growth process. Second, the growth environment information and some phenotypic features of wheat are combined into semantic information. Third, text-to-image algorithm is used to generate the 2D image of wheat. Finally, the wheat in the 2D image is transformed into an abstract 3D point cloud and obtained a higher precision point cloud model using a deep learning algorithm. Extensive experiments indicate that the method reconstructs 3D models and has a heuristic effect on phenotypic analysis and breeding research by deep learning.
Why it matches plant phenotyping methods小麦の表現型解析を目的とした3D点群再構成手法の開発であり、表現型情報を用いた画像・深層学習ベースの形状復元が中心的な技術貢献である。
abstracta method is proposed to reconstruct wheat 3D model based on semantic information
Reproduction assets foundThe paper's Data Availability Statement links a public Google Drive folder containing the authors' wheat dataset (RGB images, object-detection labels, textual annotations, and point cloud markers) used for the phenotyping pipeline. No code or trained model deposit is stated.Dataset · publicData Availability Statement: The data are available online at https://drive.google.com/drive/
folders/1ko6rlE1LThkNG_fcm5C12LcBaUWwdsPc?usp=sharing.Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物のFVCという形態・生育形質を推定するモデルを開発し、複数作物・圃場実験で精度と頑健性を検証しており、表現型取得手法が研究の中心である。
abstractThis study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PROSAIL-GP model code and all datasets (UAV-derived canopy reflectance/FVC measurements) in a public GitHub repository, plus detailed protocols on protocols.io. The PROSAIL model itself is a generic prior tool and is excluded.Code · publicle.
Conflict of interest
The authors declare no conflict of interest.
Data availability
Data supporting this work,such as details and source code of the PROSAIL
model used in this study,are openly available at http://teledetection.ipgp.jussieu.fr/prosail/.The code of the PROSAIL-GP model and all of the
datasets are available at https://github.com/WanLiangZJU/Crop-FVC-retrieval. The detailed protocols can be found at protocols.io (https://
dx.doi.org/10.17504/protocols.io.btmynk7w).
References
Aballa A, Cen H, Wan L, Mehmood K, He Y. 2020. Nutrient status diag-
nosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE
Transactions on Industrial Informatics 17, 4379–4389.
BacOpen asset ↗WanLiangZJU/Crop-FVC-retrievalpdf-raw-page:15 lines:1-89Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO 2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Why it matches plant phenotyping methods顕微鏡動画から個々の気孔を追跡し、セグメンテーションで気孔開口面積を定量化する観測・解析システムを開発しており、植物表現型取得が研究の中心です。
abstracta stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods.
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 · public(2) The module is easy to install with the aid of an executable program (EXE) ( https://github.com/shem123456/Stomata-segmentation-with-GUI ).Open asset ↗https://github.com/shem123456/Stomata-segmentation-with-GUIlines:61-68Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とスペクトル植生指数を用いて作物バイオマスおよびN状態を推定し、その感度を2作期で検証しており、植物形質取得・検証が実質的な構成要素である。
abstractThe data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits.
Reproduction assets foundThe article includes an explicit data availability statement depositing the plant and spectral data supporting the study's phenotyping measurements in the public ETH Research Collection repository, making it a paper-specific, publicly actionable asset. Supplementary XLSX files also exist but the repository deposit is aDataset · publicThe plant and spectral data that support the findings of this study, as well as the supplementary material, are available in the online repository with the identifier, https://doi.org/10.3929/ethz-b-000380508 . At https://www.research-collection.ethz.ch/handle/20.500.11850/380508 last accessed [09/06/2020].Open asset ↗10.3929/ethz-b-000380508lines:160-271Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Phosphorus (P) is an important in ensuring plant morphogenesis and grain quality, therefore an efficient root system is crucial for P-uptake. Identification of useful loci for root morphological and P uptake related traits at seedling stage is important for wheat breeding. The aims of this study were to evaluate phenotypic diversity of Yangmai 16/Zhongmai 895 derived doubled haploid (DH) population for root system architecture (RSA) and biomass related traits (BRT) in different P treatments at seedling stage using hydroponic culture, and to identify QTL using 660 K SNP array based high-density genetic map. Results All traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r = 0.59 to 0.98) among all traits. Inclusive composite interval mapping (ICIM) identified 34 QTL with 4.64-20.41% of the phenotypic variances individually, and the log of odds (LOD) values ranging from 2.59 to 10.43. Seven QTL clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) with pleiotropic effect played key role in modulating root length (RL), root tips number (RTN) and root surface area (ROSA) under low P condition, with the favorable allele from Zhongmai 895. Conclusions This study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture. It is an efficient approach for screening of large populations under different nutrient conditions. Four QTL on chromosomes 6BL (2) and 7AL (2) identified in low P treatment showed positive additive effects contributed by Zhongmai 895, indicating that Zhongmai 895 could be used as parent for P-deficient breeding. The most stable QTL QRRS.caas-4DS for ratio of root to shoot dry weight (RRS) harbored the stable genetic region with high phenotypic effect, and QTL clusters on 7A might be used for speedy selection of genotypes for P-uptake. SNPs closely linked to QTLs and clusters could be used to improve nutrient-use efficiency.
Why it matches plant phenotyping methods水耕条件下の根系形態とバイオマスを画像パイプラインで迅速に測定し、大規模集団・異なる栄養条件のスクリーニングに用いる方法が明示されており、表現型取得が実質的な役割を持つ。
abstractThis study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture.
Reproduction assets foundThe paper deposits its phenotype dataset (root system architecture and biomass-related trait measurements of the Yangmai 16/Zhongmai 895 DH population under three phosphorus treatments) in a Dryad repository with an explicit public sharing link and DOI. No author analysis code or trained models are reported.Dataset · publicThe datasets are available in the “Dataset Yang et al.” repository at Dryad data bank. Data can be accessed using following link; https://datadryad.org/stash/share/BTR6YCbZX1mr-vH5QojHRYlPHe4uZ5vWSsGmVE2jbPkOpen asset ↗Dryadlines:146-205Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images. We uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates. Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と構造を対象に、CO2フラックスの新規ハイスループット法と画像解析ソフトウェアを用いた機能的フェノミクスを中心的に実施しており、植物形質取得法が研究の主要部分である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images.
Reproduction assets foundThe paper explicitly deposits its trait data, GEMMA GWAS output, and R analysis scripts at Zenodo (10.5281/zenodo.4247894), and separately deposits the root respiration measurement protocol and flux-calculation R scripts at Zenodo (10.5281/zenodo.4247873). Both are paper-specific, public, and actionable. The Triticeae-Dataset · publicAll trait data, gemma output, and R analysis scripts necessary for the statistical analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b ).Open asset ↗Zenodo · 10.5281/zenodo.4247894lines:608-654Code · publicThe protocol for the root respiration measurements and the R script for calculating total flux from a directory of text files are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al.,
2020a ).Open asset ↗Zenodo · 10.5281/zenodo.4247873lines:85-97Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Image-based plant phenotyping is the major approach to quantitative assessment of important plant properties. For automated analysis of a large amount of image data from high-throughput greenhouse measurements, efficient techniques for image segmentation are required. However, conventional approaches to whole plant and plant organ segmentation are hampered by high variability of plant and background illumination, and naturally occurring changes in geometry and colors of growing plants. Consequently, application of advanced machine learning techniques for automated image segmentation is required. Here, we investigate six advanced neural network (NN) methods for detection and segmentation of grain spikes in RGB images including three detection deep NNs (SSD, Faster-RCNN, YOLOv3/v4), two deep (U-Net, DeepLabv3+) and one shallow segmentation NNs. Our experimental results show superior performance of deep learning NNs that achieve in average more than 90% accuracy by detection and segmentation of wheat as well as barley and rye spikes. However, different methods demonstrate different performance on matured, emergent and occluded spikes. In addition to comprehensive comparison of six NN methods, a GUI-based tool (SpikeApp) provided with this work demonstrates the application of detection and segmentation NNs to fully automated spike phenotyping. Further improvements of evaluated NN approaches are discussed.
Why it matches plant phenotyping methods穀粒穂の画像検出・セグメンテーション手法を比較評価し、SpikeAppによる自動フェノタイピングを実証しており、植物表現型取得手法が中心である。
abstractImage-based plant phenotyping is the major approach to quantitative assessment of important plant properties.
Reproduction assets foundThe paper's data availability statement explicitly provides demo software (SpikeApp with pre-trained U-Net, YOLOv3, and shallow ANN models) and example spike images for public download from the authors' IPK page. The DeepLabv3+ GitHub link is a cited third-party library, and psi.cz is the imaging facility, not a paper-Code · publics work was also supported by the project
of specific research provided by the Masaryk University.
Conflict of interest statement
The authors declare no competing interests.
Data availability statement
In addition to data presented in the main text, demo software as well as examples of spike images are provided for download
from https://ag-ba.ipk-gatersleben.de/spikeapp.html.11/14Open asset ↗spikeapp.htmlpdf-raw-page:12 lines:1-30Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal. Better predictive performance of the multivariate analysis method can be obtained when the information from differently scatter corrected data is jointly used. To demonstrate it, an open-source NIR spectroscopy data set related to protein prediction in wheat kernels was used. Two different pre-processing fusion approaches i.e., sequential and parallel fusion, were used for fusing the complementary information from four different scatter correction techniques, namely standard normal variate (SNV), variable sorting for normalisation (VSN), 2nd derivative, and multiplicative scatter correction (MSC). As a comparison, partial least-squares regression (PLSR) was performed on the SNV pre-processed data. The results showed that fusion of scatter correction can improve the predictive performance of NIR spectroscopic models. The results revealed that both sequential and parallel fusion approaches improved the predictive performance compared to the PLSR performed using a single scatter correction technique. The R²ₚ was improved by up to 3% and the RMSEP was reduced by up to 13% compared to the results obtained with conventional PLSR model developed with a single scatter correction technique.
Why it matches plant phenotyping methods小麦粒のタンパク質含量という植物形質を対象に、NIRスペクトルの散乱補正融合と予測性能を検証しており、形質取得・推定手法が研究の中心である。
abstractThe study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal.
Reproduction assets foundThe paper's analysis is built entirely on an open NIR spectroscopy dataset of 523 wheat kernels with reference protein content, publicly deposited on Figshare and explicitly linked by the authors. No author analysis code is stated as publicly available.Dataset · publicntional (single) scatter
correction technique, partial least-squares regression (PLSR)
was performed individually pre-processed data.
2. Materials and methods
2.1. Data set
The wheat kernel data set used in this study was obtained
from the Mendeley repository of open data sets (Wenya, 2016).
The data set can also be accessed at https://figshare.com/articles/wheat_kernel_dataset/4252217/1. The data set con-
tains NIR spectra and reference protein concentration of 523
wheat kernels. The spectra were measured in the spectral
range of 850e1050 nm with a total of 100 wavelengths (nm). In
this analysis, the data set was divided into calibration (60%)
and test set (40%) using the Kennard-Stone (KS)Open asset ↗Figshare · 4252217pdf-raw-page:2 lines:1-87Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2 = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R2 = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.
Why it matches plant phenotyping methodsAutoMLと画像解析を用いた植物表現型測定手法を、コムギ倒伏評価で比較・検証しており、表現型取得・推定手法が研究の中心である。
titleAutomated Machine Learning for High-Throughput Image-Based Plant Phenotyping
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the authors' source code to replicate the AutoML/transfer-learning phenotyping analyses and the best AutoKeras models. A Zenodo deposit with the wheat plot images and lodging ground-truth CSV is also referenced, but no Zenodo URL isCode · publicSource codes required to replicate the analyses in this article
and the best performing models reported for AutoKeras are provided in a GitHub repository [58].Open asset ↗pdf-raw-page:16 lines:1-53Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.
Why it matches plant phenotyping methodsUAV-RGB画像と3D作物表面モデルによるコムギ草丈推定法を開発・検証し、地上測定との比較、精度評価、複数プラットフォーム間の検証を行っており、表現型取得手法が研究の中心である。
abstractThis study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years.
Reproduction assets foundThe paper's authors publicly deposited the R scripts used for UAV image analysis and plant-height trait extraction on GitHub. The raw phenotyping data are only available on request. Pix4D support articles and the R boot package are generic third-party resources, not paper-specific assets.Code · publicThe PHaerial scripts used to perform the image analyses and trait extract are available at https://github.com/volpatoo/HTP-via-drone-imagery/tree/master/UAV-HTP_PlantHeightOpen asset ↗volpatoo/HTP-via-drone-imagery · UAV-HTP_PlantHeightlines:519-573Code / 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Summary The root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. We uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. Combining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と形態を対象に、CO2フラックスの新規ハイスループット測定法と画像解析ソフトウェアを用いたフェノタイピングが研究の中心である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images.
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: (1) the root respiration measurement protocol and R script for computing CO2 flux from LI-850 text files (Zenodo 4247873), and (2) all trait data, GEMMA output, and R analysis scripts for the statistical analysis and plotting (Zenodo 4247894). Both are theCode · publicThe protocol for the root
respiration measurements and the R script for calculating total flux from a directory of text files
are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al., 2020a).Open asset ↗Zenodo · 10.5281/zenodo.4247873pdf-page:8 lines:1-41Dataset · publicAll trait data, GEMMA output, and R analysis scripts necessary for doing the statistical
analysis and plotting are available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b).Open asset ↗Zenodo · 10.5281/zenodo.4247894pdf-page:12 lines:1-35Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface designed and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistacia nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.
Why it matches plant phenotyping methodsCT画像取得法と3D解析ソフトウェアを開発し、種子・果実形態形質の自動抽出をベンチマークしており、フェノタイピング手法が中心である。
abstractHere, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed.
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 · public3DPheno-Seed&Fruit software and CT image datasets used in this manuscript are free for academic purpose and can be downloaded from http://www.wutbiolab.com/resources/39/info/29 and https://github.com/whut-biolab-liuchang/projectOpen asset ↗lines:304-314Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.
Why it matches plant phenotyping methodsNDVIおよび炭素同位体組成を用いたバイオマス・干ばつ適応形質の推定性能と測定時期の影響を評価しており、表現型取得法の応用・技術評価が中心である。
titleCarbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection
Reproduction assets foundThe paper's Supplementary Materials (hosted at the MDPI URL listed in allowed_urls) contain paper-specific phenotyping assets: Table S1 (flowering time/phenological stage data for the 248 accessions), Tables S2–S3 (linear regressions of biomass vs. NDVI and biomass vs. δ13C), and Figure S1 (image of the field trial at Supplement · publicAgronomy 2020, 10, 1679 16 of 19
these traits are evaluated and (2) genotypic variability in intrinsic characteristics, such as phenology,
is accounted for.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/10/11/1679/s1,
Table S1: Number of accessions at different flowering times and phenological stages, Table S2: Linear regression
of the relationship between the biomass at mid grain filling and the NDVI, Table S3: Linear regression of the
relationship between the biomass and δ13C in the flag leaf dry matter sampled before imposition of stressOpen asset ↗mdpi.compdf-raw-page:16 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
Why it matches plant phenotyping methodsUAS画像から植物形質を抽出する画像処理手法を導入し、オルソモザイク画像との比較で精度・遺伝率を検証しており、フェノタイピング手法が中心である。
abstractwe introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images.
Reproduction assets foundThe paper publicly deposits its plot-level orthomosaic and orthorectified images (the phenotyping inputs/outputs of this study) at a KSU repository. The authors' Python analysis code (bip, traitExtraction) is mentioned via GitHub footnotes, but those URLs are not in the allowed list, so they cannot be included as verifDataset · publicData associated with these experiments, including the cropped, plot-level orthomosaic images and corresponding orthorectified images, can be accessed at the public repository 7 .Open asset ↗lines:528-572Supplement · publicSupplementary Table 2 ), were used to extract two independent datasets for the CT trait.Open asset ↗lines:333-343Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.
Why it matches plant phenotyping methodsコムギ葉の病害重症度を画像から推定する深層学習手法を開発し、データセット上で比較評価しており、植物表現型取得が中心である。
abstractthis study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet).
Reproduction assets foundThe paper's WSRgrading dataset (5,242 field wheat leaf images with 6 stripe rust infection grades) is explicitly declared publicly available on the authors' GitHub repository.Dataset · publicThe raw data supporting the conclusions of this article are available at https://github.com/xingyu960/C-DenseNet-for-wheat-stripe-rust- .Open asset ↗C-DenseNet-for-wheat-stripe-rust-lines:541-596Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel -1 ) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count ( R 2 = 0.52), but strong for tiller count ( R 2 = 0.86) and GS30 ( R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.
Why it matches plant phenotyping methodsドローン多視点画像と画像解析により、コムギの出芽・分げつ数・茎伸長を推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractWe aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery.
Reproduction assets foundThe paper publicly releases its authors' phenotyping processing code (multiview image generation, segmentation, early growth trait extraction) on ETH GitLab and secondary plot-based phenotype data (BLUEs, BLUPs, repeatability, heritability) on the ETH Research Collection. Raw UAS images are only available upon request,Code · publicfor the field management and freezing damage ratings at site FIP (all persons ETH Zurich, Zürich, Switzerland). We thank the anonymous reviewers for the thorough evaluation and constructive suggestions.
Additional Points
Source Code . Maintained source code for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentatiOpen asset ↗PhenoFly_data_processing_toolslines:218-251Code · publiccode for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GiOpen asset ↗PhenoFly_data_processing_tools/ImageProjectionAgisoftlines:218-251Code · publictools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GitLab repository furthermore includes the following trait extraction method: (4) Early growth trait extraction ( https://gitlab.ethz.ch/crop_pOpen asset ↗PhenoFly_data_processing_tools/ActiveLearningSegmentationlines:218-251Code / dataset availability confirmedarXiv · checked 9 Sept 2026
Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenotyping falls within this category. Object counting in plant images is further challenged by having plant image datasets with significant domain shift due to different experimental conditions, e.g. applying an annotated dataset of indoor plant images for use on outdoor images, or on a different plant species. In this paper, we propose a domain-adversarial learning approach for domain adaptation of density map estimation for the purposes of object counting. The approach does not assume perfectly aligned distributions between the source and target datasets, which makes it more broadly applicable within general object counting and plant organ counting tasks. Evaluation on two diverse object counting tasks (wheat spikelets, leaves) demonstrates consistent performance on the target datasets across different classes of domain shift: from indoor-to-outdoor images and from species-to-species adaptation.
Why it matches plant phenotyping methods植物器官数を画像から推定するドメイン適応・密度マップ推定法を開発し、コムギ小穂と葉の計数で評価しており、表現型取得手法が中心である。
abstractCounting plant organs for image-based plant phenotyping falls within this category.
Reproduction assets foundThe paper provides two paper-specific public assets: the authors' implementation code for the domain-adversarial counting model on GitHub, and the authors' newly created GWHD dot annotations deposited on figshare. Other URLs are cited prior-work datasets, not paper-specific assets.Code · publicAll experiments were performed on a GeForce RTX 2070 GPU with 8GB memory using the Pytorch framework. The implementation is available at: https://github.com/p2irc/UDA4POCOpen asset ↗p2irc/UDA4POClines:81-104Dataset · publicTo evaluate our method, we created dot annotations for 67 images from the GWHD which are used as ground truth. These annotations are made publicly available at https://doi.org/10.6084/m9.figshare.12652973.v2 .Open asset ↗10.6084/m9.figshare.12652973.v2lines:105-155Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Future increases in yield potential will rely largely on improved photosynthesis. Whereas emphasis has traditionally been given to measuring leaf photosynthesis, wheat spikes have an important role in filling grains since they can intercept up to a third of incident light. In the present study, 196 genetically diverse spring wheat lines were evaluated for spike photosynthesis (SP) under temperate (yield potential) and heat stressed, irrigated conditions. Two different methods to estimate SP were used: (i) gas exchange measurements of SP rate and (ii) integrative measurements using a SP inhibition treatment (consisting of a permeable textile covering the spikes). Rate of SP was measured directly in 45 selected genotypes under yield potential conditions using a custom-made illuminating chamber. In these lines, a variation of 2.8-fold for spike photosynthetic rate is reported for the first time with good heritability estimates. Correlations between SP rate and yield, thousand grain weight, number of grains per spike and radiation use efficiency are reported across different panels. Genotypic variation in SP was independent from flag leaf photosynthesis suggesting that any strategy aiming to increase canopy photosynthesis should also consider SP. The SP inhibition treatments were applied on the 196 lines in both environments to estimate SP contribution to grain weight per spike, which was 30–40 % under both heat stressed and yield potential conditions averaged across lines. Positive correlations with grain yield were observed for spike photosynthesis contribution across all of the panels under heat stress and when combining heat and yield potential environments (P < 0.001, r = 0.401). These results indicate a highly significant genotypic variation of spike photosynthetic rate and spike photosynthesis contribution to grain yield among wheat lines and highlight its importance under irrigated and heat stressed conditions.
Why it matches plant phenotyping methods小麦穂の光合成速度・寄与を高スループットに取得する2種類の測定法を用い、カスタム照明チャンバーによる直接測定も実施しており、植物生理形質のフェノタイピング手法の実質的適用が研究の中心である。
titleSpike photosynthesis measured at high throughput indicates genetic variation independent of flag leaf photosynthesis
Reproduction assets foundThe article reports spike photosynthesis phenotyping of 196 wheat lines (gas-exchange rates and SP inhibition treatments) but contains no explicit public dataset or code deposit. The only paper-specific, publicly accessible asset indicated is the article's supplementary material (Supplementary Tables 4-5 and Fig. 1), 'Supplement · publicnical assistance with measurements, data
and trial management. A special thanks to J.M. Esquer who was re-
sponsible to design the spike illumination chamber used in these ex-
periments for the measurements.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.fcr.2020.107866.References
Abbad, H., El Jaafari, S., Bort, J., Araus, J.L., Jaafari, S.E., Bort, J., Araus, J.L., 2004.
Comparison of flag leaf and ear photosynthesis with biomass and grain yield of
durum wheat under various water conditions and genotypes. Agronomie 24, 19–28.
https://doi.org/10.1051/agro:2003056.Acreche, M.M., Slafer, G.A., Open asset ↗pdf-raw-page:11 lines:1-57Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
Why it matches plant phenotyping methods小麦穂の画像検出を対象とする大規模データセットを構築し、取得・ラベリング指針と検出手法の開発・ベンチマークを目的としており、植物表現型抽出法が中心である。
abstractwe have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset.
Reproduction assets foundThe paper's core asset is the GWHD dataset itself: 4700 high-resolution RGB wheat images with ~190,000 labelled wheat head bounding boxes, explicitly stated to be publicly available at the authors' website (global-wheat.com). This is a paper-specific, public, directly actionable plant phenotyping dataset. The coco-annuDataset · publicd from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2020 Apr 25; Accepted 2020 Jul 1; Collection date 2020.
1. IntroductionOpen asset ↗global-wheat.comlines:1-34Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.
Why it matches plant phenotyping methods小麦葉の窒素含量を推定するハイパースペクトル画像・非画像センサーを比較評価しており、植物形質取得法の技術的検証が研究の中心です。
abstractThis study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer.
Reproduction assets foundThe authors deposited the pre-processed hyperspectral reflectance data and demonstration Python code for this wheat nitrogen experiment on Adelaide Figshare, explicitly linked in the Supplementary Materials section.Dataset · publicThe pre-processed data and the Python codes for the demonstration are available at https://adelaide.figshare.com/articles/public_data_for_wheat_n_experiment/12502160 .Open asset ↗adelaide.figshare.com · 12502160lines:411-465Code · publicThe pre-processed data and python codes for demonstration is public available (refer to Supplementary Materials ).Open asset ↗lines:34-48Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model. Results The recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model R 2 reached 0.96, the root mean square error (RMSE) was 10.84 ears, the macro F1-score and micro F1-score both achieved 98.47%, and the best performance was observed during late grain-filling stage ( R 2 = 0.99, RMSE = 3.24 ears). The model could also be applied to the UAV platform ( R 2 = 0.97, RMSE = 9.47 ears). Conclusions The classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.
Why it matches plant phenotyping methods小麦穂数という植物形態・収量関連形質を、画像セグメンテーションとCNNで自動推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractTo realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices.
Reproduction assets foundThe authors publicly released their analysis code (K-means segmentation + CNN wheat ear counting pipeline) on GitHub. The image/phenotype datasets are only available on request from the corresponding author, so they do not qualify as public assets.Code · publicThe code can be found at https://github.com/xuxin468/earcouting .Open asset ↗xuxin468/earcoutinglines:190-213Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
To develop a stable estimation model and identify effective wavelengths that could explain the variations in leaf nitrogen (N) concentration with different N supplies, growing seasons, ecological locations, growth stages, and wheat cultivars. Four field experiments were performed during two consecutive years (2017-2019) at three sites (Yuanyang, Hebi, and Wenxian) in Henan, China. In situ canopy spectral reflectance data under the aforementioned N supply conditions were obtained over a range of 400-950 nm (visible and near-infrared region). On the basis of the canopy raw spectral reflectance data and their subsequent transformation by two different techniques, first-derivative reflectance (FDR) and continuum removal (CR), four multivariate regression methods were comparatively analyzed and used to develop predictive models for estimating leaf N concentration: multiple linear regression (MLR), principal component regression (PCR), partial least square (PLS), and support vector machine (SVM). Results showed that leaf N concentration and canopy reflectance significantly varied with the levels of N fertilization, and a good correlation was observed for all the spectral techniques. Seven wavelengths with relatively higher r values than the bands of the raw spectra centered at 508, 525, 572, 709, 780, 876, and 925 nm were specified using the FDR technique. Based on the full wavelengths, the FDR-SVM model exhibited a good performance for leaf N concentration estimation, with coefficients of determination ( r 2 val ) for the validation datasets and corresponding relative percent deviations (RPD val ) values of 0.842 and 2.383, respectively. However, the FDR-PLS yielded a more accurate assessment of the leaf N concentration than did the other methods, with r 2 val and RPD val values of 0.857 and 2.535, respectively. The variable importance in projection (VIP) scores from the FDR-PLS with the all canopy spectral region were used to screen the effective wavelengths of the spectral data. Therefore, six effective wavelengths centered at 525, 573, 710, 780, 875, and 924 nm were identified for leaf N concentration estimation. The SVM regression method with the effective wavelengths showed excellent performance for leaf N concentration estimation with r 2 val = 0.823 and RPD val = 2.280. These results demonstrated that the in situ canopy spectral technique is promising for the estimation of leaf N concentration in winter wheat based on the FDR-PLS regression model and the effective wavelengths identified.
Why it matches plant phenotyping methods冬コムギ葉N濃度という植物形質を対象に、キャノピー分光データ、波長変換、回帰モデルを用いた推定法を開発・比較・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。
abstractfour multivariate regression methods were comparatively analyzed and used to develop predictive models for estimating leaf N concentration
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 presented in this study are included in the article/ Supplementary Material .Open asset ↗lines:559-596Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat ( Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.
Why it matches plant phenotyping methodsUAVによる航空高スループット表現型計測を用いて植生指数を取得し、収量予測・選抜への有効性を評価しており、表現型取得法の適用と技術的評価が研究の中心である。
abstractWith the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield.
Reproduction assets foundThe paper's phenotypic and genotypic data (vegetation index BLUPs, grain yield, phenology, and SNP data for the yield trials and small plots) are publicly deposited on CIMMYT Dataverse. No author analysis code or trained models are explicitly deposited; QGIS is a generic library and excluded.Dataset · public18 study are available on CIMMYT Dataverse (http://hdl.handle.net/11529/10548379).Open asset ↗CIMMYT Dataverse · 11529/10548379pdf-page:22 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.
Why it matches plant phenotyping methodsコムギの老化動態をハイパースペクトル反射から推定する頑健な表現型計測法の開発が研究の中心であり、スペクトル指標と全スペクトルモデルを比較・評価している。
abstractThe aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance.
Reproduction assets foundThe paper's data availability statement points to an ETH research-collection deposit for the experimental phenotyping data (hyperspectral reflectance, visual senescence scorings, GY/GPC) and states that all analysis scripts are publicly available, with a GitHub development repository and an archived ETH version.Dataset · publicExperimental data supporting the conclusions of this article can be downloaded here: https://doi.org/10.3929/ethz-b-000365618 .Open asset ↗10.3929/ethz-b-000365618lines:853-864Code · publicAll analysis scripts required to reproduce the results published in this article are publicly available.Open asset ↗lines:853-864Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., s p i k e n u m b e r m - 2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. Results In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ( 91.01 % counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on 912 × 1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity , and improving efficiency without sacrificing accuracy . It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.
Why it matches plant phenotyping methodsコムギ穂数という植物形態形質を画像から自動計数する手法を開発し、精度・速度を評価するとともに大規模データセットを構築しており、植物フェノタイピング手法が中心である。
abstractWe thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2.
Reproduction assets foundThe paper explicitly states that the WSC dataset (1764 images, 675,322 annotated wheat spikes) and code/models are made available online at the authors' public URL https://tinyurl.com/TasselNetv2.Dataset · publicThe WSC dataset and other supporting materials are made available online at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:205-231Code · publicCode and models are made available at: https://tinyurl.com/TasselNetv2 .Open asset ↗lines:1-72Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Conventional methods for screening for stress-tolerant cereal varieties rely on expensive, labour-intensive field testing and molecular biology techniques. Here, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage. Wheat and barley seedlings had stress applied, and the response quantified in terms of programmed cell death (PCD), viability and necrosis. Heat shock experiments of seven barley varieties showed that winter and spring barley varieties could be partitioned into their two distinct seasonal groups based on their PCD susceptibility, allowing quick data-driven evaluation of their thermotolerance at an early seedling stage. In addition, evaluating the response of eight wheat varieties to heat and salt stress allowed identification of their PCD inflection points (35°C and 150 mM NaCl), where the largest differences in PCD levels arise. Using the PCD inflection points as a reference, we compared different stress effects and found that heat-susceptible wheat varieties displayed similar vulnerabilities to salt stress. Stress-induced PCD levels also facilitated the assessment of the basal, induced and cross-stress tolerance of wheat varieties using single, combined and multiple individual stress exposures by applying concurrent heat and salt stress in a time-course experiment. Two stress-susceptible varieties were found to have low constitutive resistance as illustrated by their high PCD levels in response to single and combined stress exposure. However, both varieties had a fast, adaptive response as PCD levels declined at the other time-points, showing that even with low constitutive resistance, the initial stress cue primes cross-stress tolerance adaptations for enhanced resistance even to a second, different stress type. Here, we demonstrate the RHA's suitability for high-throughput analysis (∼4 days from germination to data collection) of multiple cereal varieties and stress treatments. We also showed the versatility of using stress-induced PCD levels to investigate the role of constitutive and adaptive resistance by exploring the temporal progression of cross-stress tolerance. Our results show that by identifying suboptimal PCD levels in vivo in a laboratory setting, we can preliminarily identify stress-susceptible cereal varieties and this information can guide further, more efficiently targeted, field-scale experimental testing.
Why it matches plant phenotyping methods根毛アッセイ（RHA）を用いてストレス誘導性PCD・生存性・壊死を定量し、作物品種の耐性を迅速かつハイスループットにスクリーニングする手法を実証しており、表現型取得法が研究の中心である。
abstractHere, we use the root hair assay (RHA) as a rapid screening tool to identify stress-tolerant varieties at the early seedling stage.
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:703-766Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Nitrogen use efficiency (NUE) in crops is generally low, with more than 60% of applied nitrogen (N) being lost to the environment, which increases production costs and affects ecosystems and human habitats. To overcome these issues, the breeding of crop varieties with improved NUE is needed, requiring efficient phenotyping methods along with molecular and genetic approaches. To develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham. The results from the initial experiment showed that two relative N levels-5 mM and 20 mM, designated as low and optimum N, respectively-were ideal to screen a diverse range of wheat germplasm for NUE on the automated imaging phenotyping platform. In the second experiment, estimated plant parameters such as shoot biomass and top-view area, derived from digital images, showed high correlations with phenotypic traits such as shoot biomass and leaf area seven weeks after sowing, indicating that they could be used as surrogate measures of the latter. Plant growth analysis confirmed that the estimated plant parameters from the vegetative linear growth phase determined by the "broken-stick" model could effectively differentiate the performance of wheat varieties for NUE. Based on this study, vegetative phenotypic screens should focus on selecting wheat varieties under low N conditions, which were highly correlated with biomass and grain yield at harvest. Analysis indicated a relationship between controlled and field conditions for the same varieties, suggesting that greenhouse screens could be used to prioritise a higher value germplasm for subsequent field studies. Overall, our results showed that this phenotypic screening method is highly applicable and can be applied for the identification of N-efficient wheat germplasm at the vegetative growth phase.
Why it matches plant phenotyping methods自動画像フェノタイピング基盤を用い、デジタル画像から植物形質の推定値を抽出してN利用効率スクリーニング法を開発・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。
abstractTo develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham.
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 , was supplied depending on the crop growth stages (vegetative or reproductive).Open asset ↗lines:306-315Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on the GitHub repository located at https://github.com/hsd1121/PointCloudProcessing.
Why it matches plant phenotyping methodsUAV搭載3D LiDARによる作物高の抽出手法、シミュレーション用ツールチェーン、検証実験、データセットを中心に扱っており、植物表現型取得法が明確に中心である。
abstractWe present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV).
Reproduction assets foundThe authors explicitly release their point cloud processing tools, real-world wheat LiDAR datasets, and simulation farm-generation toolchain on their public GitHub repository. The Turbosquid URL only references the license for commercial third-party soybean 3D models, not a paper-specific asset.Code · publicgorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on our GitHub repository. 1 1
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https://github.com/hsd1121/PointCloudProcessing
I INTRODUCTION
The goal of precision agriculture is to optimize the growth, maintenance, and harvesting of crops using data-driven technologies [ 1 , 2 ] . This will become especially important as the population grows, leading to a higher demand of efficiency from farms [ 3 , 4 , 5 ] . One way of achieving higher efficieOpen asset ↗hsd1121/PointCloudProcessinglines:1-69Dataset · publicThe dataset released along with this paper has models for three representative environments, simulated 3D LiDAR scans, and ground truth information. This is released for the community-at-large to benchmark their algorithms against.Open asset ↗lines:70-87Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Why it matches plant phenotyping methods圃場ハイパースペクトル反射の時系列特徴量を開発・検証し、コムギの病害存在と重症度を定量化する手法が研究の中心であるため。
abstractwe collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.
Reproduction assets foundThe paper's data availability statement explicitly deposits the datasets generated and analyzed (canopy hyperspectral reflectance, STB scorings, PLACL leaf-scan measurements) in the ETH Zürich research repository with a DOI, and makes all analysis scripts (R/Python, including the stb_placl leaf-image analysis pipeline)Dataset · publicThe datasets generated and analyzed for this study can be found in the ETH Zürich publications and research data repository ( https://www.research-collection.ethz.ch/ ) and can be downloaded from the following link: https://doi.org/10.3929/ethz-b-000370027 .Open asset ↗ETH Zürich publications and research data repository · 10.3929/ethz-b-000370027lines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/Andereggetal2019blines:684-694Code · publicAll analysis scripts are publicly available. Development repositories: https://github.com/and-jonas/Andereggetal2019b and https://github.com/and-jonas/stb_placl. Programming language: R, Python. License: GNU General Public License, version 3 (GPL-3.0).Open asset ↗github.com/and-jonas/stb_placllines:684-694Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Grain nitrogen (N) uptake (GNup) in winter wheat (Triticum aestivum L.) is influenced by multiple components at the plant organ level and by pre- and post-flowering N uptake (Nup). Although spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N traits. Hence, 48 spectral vegetation indices (SVIs) were evaluated on 10 measurement days for the estimation of 34 N traits in four data subsets, representing the variation generated by six high-yielding cultivars, two N fertilization levels (N), two sowing dates (SD), and two fungicide (F) intensities. Close linear relationships (p < 0.001) were found for GNup both in response to cultivar differences (Cv; R2 = 0.52) and other agronomic treatments (R2 = 0.67 for Cv*F*N, R2 = 0.53 for Cv*SD*N and R2 = 0.57 for the combined treatments), notably during milk ripeness. Especially near-infrared (NIR)/red edge SVIs, such as the NDRE_770_750, outperformed NIR/visible light (VIS) indices. Index rankings and seasonal R2 values were similar for total Nup, while the N harvest index, which expresses the partitioning to the grain, was moderately estimated only during dough ripeness, primarily from indices detecting contrasting senescence between different fungicide intensities. Senescence-sensitive indices, including R787_R765 and TRCARI_OSAVI, performed best for N translocation efficiency and some organ-level N traits at maturity. Even though grain N concentration was best assessed by the red edge inflection point (REIP), the blue/green index (BGI) was more suited for leaf-level N traits at anthesis. When SVIs were quantitatively ranked by data subsets, a better agreement was found for GNup, total Nup, and grain N concentration than for several contributing N traits. The results suggest (i) a good general potential for estimating GNup and total Nup by (ii) red edge indices best used (iii) during milk and early dough ripeness. The estimation of contributing N traits differs according to the agronomic treatment.
Why it matches plant phenotyping methods小麦の窒素吸収・窒素形質を対象に、近接スペクトルセンシングと植生指数を季節・波長別に評価し、推定性能を比較している。植物形質の取得・推定法が中心的である。
abstractAlthough spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N 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。Supplement · publicThe following are available online at https://www.mdpi.com/1424-8220/19/21/4640/s1 . Table S1: List of plant traits considered in this study, grouped by trait groups. Figure S1: Field trial measurements on 21 June 2017, colored by the values of the simple ratio index R760/R730. Figure S2: Plot-level spectra acquired on 31 March (leaf development, left), 17 May (stem elongation; middle) and 4 July 2017 (soft dough; right).Open asset ↗MDPI · s1lines:1150-1158Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.
Why it matches plant phenotyping methods遺伝子型・環境情報からイネ・コムギ等の表現型を予測し、複数の機械学習・統計手法を比較評価する計算的表現型推定研究であり、方法比較が中心です。
abstractWe investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat).
Reproduction assets foundThe paper's data availability statement explicitly lists public URLs for the yeast, wheat, and rice phenotype datasets analyzed in the study, plus a public GitHub repository with the authors' custom R analysis code.Code · publicCustom R code used to analyse the datasets can be found at: https://github.com/stas-g/grinberg-et-al-evaluation-of-ML-codeOpen asset ↗grinberg-et-al-evaluation-of-ML-codelines:194-224Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Crop yield is an essential measure for breeders, researchers, and farmers and is composed of and may be calculated by the number of ears per square meter, grains per ear, and thousand grain weight. Manual wheat ear counting, required in breeding programs to evaluate crop yield potential, is labor-intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under natural field conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using simple linear iterative clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep convolutional neural network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs) and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables, such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap toward a portable and smartphone-assisted wheat ear counting systems, results in reducing the labor involved, and is suitable for high-throughput analysis. It may also be adapted to work on Red; Green; Blue (RGB) images acquired from unmanned aerial vehicle (UAVs).
Why it matches plant phenotyping methodsコムギ穂数という植物形質を圃場画像から自動抽出・計数する手法を開発し、比較検証・地上測定による妥当性確認まで行っており、フェノタイピング手法が研究の中心である。
abstractwe propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under natural field conditions.
Reproduction assets foundThe paper provides an authors' public code URL (GitHub profile of the first author) for the DeepCount wheat spike counting model, and a data availability statement directing to the public DFW repository (ckan.grassroots.tools) and supplementary files for the datasets generated in this study.Code · publicThe code can be found at https://github.com/pouriast .Open asset ↗https://github.com/pouriastlines:557-572Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published21 Sept 2019Plant science : an international journal of experimental plant biologyCited by 30 · OpenAlex ↗
This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ 13 C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ 13 C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ 13 C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed.
Why it matches plant phenotyping methods小麦収量を対象に、マルチスペクトル・RGB・熱赤外リモートセンシングと同位体指標を比較し、収量予測性能を評価することが中心であり、表現型取得・推定手法の検証に該当する。
titleRemote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance
Reproduction assets foundThe paper used the authors' MosaicTool software (a FIJI plugin) to crop and process UAV RGB/thermal/multispectral plot images and compute vegetation indices for this wheat phenotyping study. MosaicTool is publicly available via the authors' GitLab repository and project page, both listed in the article text and in the,Code · publiclater overlaps up to 30 images (with at least 80%
ro
overlap) and removes UAV flight effects to produce accurate ortho-mosaics. Afterwards, regions
of interest (plots) were cropped and processed using the MosaicTool software (Prof. Shawn C.
Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/,
-p
https://gitlab.com/sckefauver/MosaicTool/, University of Barcelona, Barcelona, Spain)
integrated as a plugin for the open source image analysis platform FIJI (Fiji is Just ImageJ;
http://fiji.sc/Fiji) [40].
re
Extracted RGB vegetation indices collected from both ground and aerial platforms were obtained
using an updated version of the original Breedpix 2.0 software [41Open asset ↗sckefauver/MosaicToolpdf-layout-page:7 lines:1-78Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background As yields of major crops such as wheat ( T. aestivum ) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions. Results Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences ( P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user-defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency ( F v '/ F m '- P F q '/ F m '- P = 0.04) under both low and high light. In addition, the rate of induction and relaxation of non-photochemical quenching (NPQ) was also cultivar specific. A specialised imaging chamber was designed and built in-house to maintain gaseous conditions around excised leaf sections. The purpose of this is to manipulate electron sinks such as photorespiration. The stability of carbon dioxide (CO 2 ) and oxygen (O 2 ) was monitored inside the chambers and found to be within ± 4.5% and ± 1% of the mean respectively. To test the chamber, T. aestivum 'Pavon76' leaf sections were measured under at 20 and 200 mmol mol -1 O 2 and ambient [CO 2 ] during a light response curve. The F v '/ F m 'was significantly higher ( P 2 ] for the majority of light intensities while values of NPQ and the proportion of open PSII reaction centers (qP) were significantly lower under > 130 μmol m -2 s -1 photosynthetic photon flux density (PPFD). Conclusions Here we demonstrate the development of a high-throughput (> 500 samples day -1 ) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes.
Why it matches plant phenotyping methods葉緑素蛍光イメージングと専用チャンバーを用いた高スループットな光合成・光防御形質の取得法を開発し、葉およびチャンバー条件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。
abstractHere we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions.
Reproduction assets foundThe paper's phenotyping pipeline (chlorophyll fluorescence imaging of excised wheat leaves in custom gas-controlled chambers) is supported by a paper-specific public asset: Additional file 2, a ZIP supplement containing the CAD files, printer settings, and construction notes for the custom imaging chambers, explicitly'Supplement · publicThe CAD files for the final chamber design are fully available with this manuscript (Additional file 2 ) including printer settings and additional notes, so that users can either print their own, outsource the printing or modify the designs.Open asset ↗lines:94-102Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat ( Triticum aestivum L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years' data with nine measurement dates from early stem elongation until flowering in eight N regimes (0-420 kg N ha -1 ) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R 2 = 0.47, p 2 = 0.59, p NC ≈ DM. Depending on the number (n = 1-3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high-excessive N fertilization, the range of the target traits was divided into two intervals with NNI values 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R 2 = 0.35, RMSE = 567 kg ha -1 ; NC: REIP: average R 2 = 0.40, RMSE = 0.25%; NNI: REIP: average R 2 = 0.46, RMSE = 0.10; Nup: REIP: average R 2 = 0.48, RMSE = 21 kg N ha -1 ). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.
Why it matches plant phenotyping methods冬コムギのスペクトル植生指数によるN状態・バイオマス等の非破壊推定性能を複数年・品種・施肥条件で批判的に評価しており、センサー型表現型計測と検証が中心である。
abstractPrecise sensor-based non-destructive estimation of crop nitrogen (N) status is essential
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 are available online at https://www.mdpi.com/1424-8220/19/17/3712/s1 , Supplementary Table S1: Treatment effects of main plot treatment factors; Supplementary Table S2: Treatment effects of N fertilization; Supplementary Table S3: RMSE and mean-normalized RMSE for regressions across main plots; Supplementary Table S4: Significant ( p < 0.05) coefficients of determination (R²) for the whole data and for data subsets; Supplementary Table S5: Index ranking by data and statistical approachOpen asset ↗lines:241-279Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
The extraction of desirable heritable traits for crop improvement from high-throughput phenotyping (HTP) observations remains challenging. We developed a modeling workflow named “Digital Plant Phenotyping Platform” (D3P), to access crop architectural traits from HTP observations. D3P couples the Architectural model of DEvelopment based on L-systems (ADEL) wheat (Triticum aestivum) model (ADEL-Wheat), which describes the time course of the three-dimensional architecture of wheat crops, with simulators of images acquired with HTP sensors. We demonstrated that a sequential assimilation of the green fraction derived from Red–Green–Blue images of the crop into D3P provides accurate estimates of five key parameters (phyllochron, lamina length of the first leaf, rate of elongation of leaf lamina, number of green leaves at the start of leaf senescence, and minimum number of green leaves) of the ADEL-Wheat model that drive the time course of green area index and the number of axes with more than three leaves at the end of the tillering period. However, leaf and tiller orientation and inclination characteristics were poorly estimated. D3P was also used to optimize the observational configuration. The results, obtained from in silico experiments conducted on wheat crops at several vegetative stages, showed that the accessible traits could be estimated accurately with observations made at 0° and 60° zenith view inclination with a temporal frequency of 100 °Cd (degree day). This illustrates the potential of the proposed holistic approach that integrates all the available information into a consistent system for interpretation. The potential benefits and limitations of the approach are further discussed.
Why it matches plant phenotyping methodsHTP画像から作物の建築形質を推定するD3Pワークフローを開発し、推定精度と観測配置を評価しており、フェノタイピング手法が研究の中心である。
abstractWe developed a modeling workflow named “Digital Plant Phenotyping Platform” (D3P), to access crop architectural traits from HTP observations.
Reproduction assets foundThe paper's D3P phenotyping platform code (coupling ADEL-Wheat with POV-Ray/PyProSAIL simulators used for the GF assimilation experiments) is explicitly stated to be freely available on GitHub under an MIT license. Other URLs (POV-Ray, OpenAlea, PyProSAIL, Python) are generic third-party dependencies, not paper assets.Code · publicThe code and user manual of D3P is freely available on GitHub ( https://github.com/lsymuyu/Digital-Plant-Phenotyping-Platform ). D3P is distributed under the free software open-source MIT license.Open asset ↗https://github.com/lsymuyu/Digital-Plant-Phenotyping-Platformlines:191-201Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world's growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future.
Why it matches plant phenotyping methodsコムギ画像から穂領域を抽出する深層学習パイプラインを開発・検証しており、収量関連形質のフェノタイピング手法が研究の中心である。
abstractwe have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds.
Reproduction assets foundThe paper's wheat spike segmentation study (CropQuant field images 2015-2017, FCN analysis) has an explicit public data/code release: the authors state the supporting dataset, including source code, is available in their GitHub repository. Other URLs (picamera docs, image labelling tool, arXiv) are generic third-party,Dataset · publicThe dataset supporting the results is available at https://github.com/tanh86/ws_seg/tree/master/CQ , which includes source code and other supporting data in the GitHub repository.Open asset ↗github.com/tanh86/ws_seglines:120-164Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Genebanks are valuable sources of genetic diversity, which can help to cope with future problems of global food security caused by a continuously growing population, stagnating yields and climate change. However, the scarcity of phenotypic and genotypic characterization of genebank accessions severely restricts their use in plant breeding. To warrant the seed integrity of individual accessions during periodical regeneration cycles in the field phenotypic characterizations are performed. This study provides non-orthogonal historical data of 12,754 spring and winter wheat accessions characterized for flowering time, plant height, and thousand grain weight during 70 years of seed regeneration at the German genebank. Supported by historical weather observations outliers were removed following a previously described quality assessment pipeline. In this way, ready-to-use processed phenotypic data across regeneration years were generated and further validated. We encourage international and national genebanks to increase their efforts to transform into bio-digital resource centers. A first important step could consist in unlocking their historical data treasures that allows an educated choice of accessions by scientists and breeders.
Why it matches plant phenotyping methods7 दशकにわたるコムギ表現型データを大規模に整理・品質評価・検証し、再利用可能な処理済みデータとして提供することが中心であり、植物フェノタイピングデータセットとして適格です。
abstractThis study provides non-orthogonal historical data of 12,754 spring and winter wheat accessions characterized for flowering time, plant height, and thousand grain weight during 70 years of seed regeneration at the German genebank.
Reproduction assets foundThe paper deposits its historical wheat phenotypic data (FT, PH, TGW for 12,754 accessions), outlier-corrected and BLUE-processed datasets, and example R analysis scripts in the e!DAL-PGP repository under DOI 10.5447/IPK/2019/11, which is an allowed URL and appears verbatim in the text.Dataset · publicPhilipp, N. et al. Historical phenotypic data from seven decades of seed regeneration in a wheat ex situ collection hosted at the
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). e!DAL - Plant Genomics and Phenomics Research Data Repository,
https://doi.org/10.5447/IPK/2019/11 (2019).Open asset ↗e!DAL - Plant Genomics and Phenomics Research Data Repository · 10.5447/IPK/2019/11pdf-page:9 lines:1-56Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Premise In crop breeding programs, breeders use yield performance in both optimal and stressful environments as a key indicator for screening the most tolerant genotypes. During the past four decades, several yield-based indices have been suggested for evaluating stress tolerance in crops. Despite the well-established use of these indices in agronomy and plant breeding, a user-friendly software that would provide access to these methods is still lacking. Methods and results The Plant Abiotic Stress Index Calculator ( i PASTIC) is an online program based on JavaScript and R that calculates common stress tolerance and susceptibility indices for various crop traits including the tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and yield index (YI). Along with these indices, this easily accessible tool can also calculate their ranking patterns, estimate the relative frequency for each index, and create heat maps based on Pearson's and Spearman's rank-order correlation analyses. In addition, it can also render three-dimensional plots based on both yield performances and each index to separate entry genotypes into Fernandez's groups (A, B, C, and D), and perform principal component analysis. The accuracy of the results calculated from our software was tested using two different data sets obtained from previous experiments testing the salinity and drought stress in wheat genotypes, respectively. Conclusions i PASTIC can be widely used in agronomy and plant breeding programs as a user-friendly interface for agronomists and breeders dealing with large volumes of data. The software is available at https://mohsenyousefian.com/ipastic/.
Why it matches plant phenotyping methods作物形質（収量など）から耐性・感受性指標を算出するオンラインソフトウェアが研究の中心であり、植物表現型データの解析ツールとして適格です。
abstractThe Plant Abiotic Stress Index Calculator ( i PASTIC) is an online program based on JavaScript and R that calculates common stress tolerance and susceptibility indices for various crop traits including the tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and yield index (YI).
Reproduction assets foundThe paper's iPASTIC analysis software (R source codes) and supporting phenotype data sets (wheat yield performance under control/stress conditions) are explicitly stated to be publicly available on GitHub, and the web application is hosted at the authors' site.Code · publicach index. iPASTIC is written in the JavaScript programming
language on the browser‐side and PHP on the server‐side, and is
available as a web application (https
://mohse
nyous
efian.com/ipast
ic/). Alternatively, users can access the source codes in R language
(R Development Core Team, 2014) and supporting data sets on
GitHub (https://github.com/pour-aboughadareh/iPASTIC/). In ad-
dition to the web application, iPASTIC is available in R language
for more advanced users. Figure 1 shows the information flow of
this software. The software reads standard Microsoft Excel for-
mats, hence it is easy and approachable even for users with lim-
ited knowledge of computer programming languages. As itsOpen asset ↗pour-aboughadareh/iPASTICpdf-raw-page:2 lines:1-81Code · publicEconomic Co‐operation and Development
(OECD) Co‐operative Research Programme (CRP) grant (to P.P.).
DATA ACCESSIBILITY
The R script source codes used to develop iPASTIC, as well as the
supporting data sets, are available on GitHub (https
://github.com/
pour-aboughadareh/iPASTIC/) and the iPASTIC web application is
available at https://mohsenyousefian.com/ipastic/.SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Label, GenBank accession number, and species of
the 90 wheat genotypes and accessions tested in Data Set 1.
APPENDIX S2. Yield performance of 90 wheat genotypes and ac-
cessions under control Open asset ↗pdf-raw-page:5 lines:1-87Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases whilst minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of >300 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Why it matches plant phenotyping methods時系列ハイパースペクトル反射からコムギ葉病害の存在・重症度を推定する特徴量と手法を開発し、多様な遺伝子型で検証しており、植物表現型取得が研究の中心である。
abstractwe collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThe developed python script with
detailed annotations can be retrieved from github (https://github.com/and-jonas/stb_placl).Open asset ↗and-jonas/stb_placlpdf-page:8 lines:1-43Code · publicThe analysis was
implemented using a custom-developed R-Script (https://github.com/and-jonas/rfe).Open asset ↗and-jonas/rfepdf-page:13 lines:1-41Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicles have an immense capacity for remote imaging of plants in agronomic field research trials. Traits extracted from the plots can explain development of the plants coverage, growth, flowering status, and related phenomenon. An important prerequisite step to obtain such information is to find the exact position of plots to extract them from an orthomosaic image. Extraction of plots using tools which assume a uniform spacing is often erroneous because the plots may neither be perfectly aligned nor equally distributed in a field. A novel approach is proposed which uses image-based optimization algorithm to find the alignment of plots. The method begins with a uniformly spaced grid of plots which is iteratively aligned with regions of high vegetation index, i.e., the underlying plots. The approach is validated and tested on two different orthomosaic images of fields containing wheat plots with simulated and real alignment problems, respectively. The result of alignment is compared to manually located ground truth position of plots and the errors are quantitatively analyzed. The effectiveness of the proposed method is confirmed in accurately estimating the phenotypic trait of canopy coverage compared to the common methods of extraction from uniform grids or trimmed grids. The software developed in this study is available from SourceForge, https://sourceforge.net/projects/phenalysis/.
Why it matches plant phenotyping methods圃場オルソモザイクから試験区を自動抽出し、キャノピー被覆率という植物形質を推定する画像解析手法を開発・検証しており、フェノタイピング手法が中心である。
abstractA novel approach is proposed which uses image-based optimization algorithm to find the alignment of plots.
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 developed in this study is available from SourceForge, https://sourceforge.net/projects/phenalysis/ .Open asset ↗phenalysislines:224-299Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.
Why it matches plant phenotyping methodsUAV・地上のRGB／マルチスペクトル／熱画像を用いた圃場表現型取得と、プラットフォーム間の性能比較が研究の中心であるため。
abstractRecent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly.
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)Open asset ↗sckefauver/MosaicToolpdf-page:4 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
In the present study we analyzed the responses of wheat to mild salinity and drought with special emphasis on the so far unclarified interaction of these important stress factors by using high-throughput phenotyping approaches. Measurements were performed on 14 genotypes of different geographic origin (Austria, Azerbaijan and Serbia). The data obtained by non-invasive digital RGB imaging of leaf/shoot area reflect well the differences in total biomass measured at the end of the cultivation period demonstrating that leaf/shoot imaging can be reliably used to predict biomass differences among different cultivars and stress conditions. On the other hand, the leaf/shoot area has only a limited potential to predict grain yield. Comparison of gas exchange parameters with biomass accumulation showed that suppression of CO2 fixation due to stomatal closure is the principal cause behind decreased biomass accumulation under drought, salt and drought plus salt stresses. Correlation between grain yield and dry biomass is tighter when salt- and drought stress occur simultaneously than in the well-watered control, or in the presence of only salinity or drought, showing that natural variation of biomass partitioning to grains is suppressed by severe stress conditions. Comparison of yield data show that higher biomass and grain yield can be expected under salt (and salt plus drought) stress from those cultivars which have high yield parameters when exposed to drought stress alone. However, relative yield tolerance under drought stress is not a good indicator of yield tolerance under salt (and salt plus drought) drought stress. Harvest index of the studied cultivars ranged between 0.38-0.57 under well watered conditions and decreased only to a small extent (0.37-0.55) even when total biomass was decreased by 90% under the combined salt plus drought stress. It is concluded that the co-occurrence of mild salinity and drought can induce large biomass and grain yield losses in wheat due to synergistic interaction of these important stress factors. We could also identify wheat cultivars, which show high yield parameters under the combined effects of salinity and drought demonstrating the potential of complex plant phenotyping in breeding for drought and salinity stress tolerance in crop plants.
Why it matches plant phenotyping methods非侵襲RGB画像による葉・シュート面積測定を用いてバイオマス予測の信頼性を評価しており、表現型取得法の応用・技術検証が研究の中心に含まれる。
Reproduction assets foundThe article reports wheat phenotyping measurements (RGB-imaged leaf/shoot area, biomass, grain yield, water use, gas exchange, ETR, proline) for 14 cultivars under four stress treatments. No author analysis code, images, or standalone dataset deposit is mentioned. The only paper-specific public asset is the article's在线Supplement · publicThe Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00501/full#supplementary-material
Click here for additional data file.
References
Addinsoft ( 2019 ). XLSTAT.
Available at: https://www.xlstat.com
Ahn C. H. Hossain M. A. Lee E. Kanth B. K. Park P. B.
( 2018 ).
Increased salt and drought tolerance by D-pinitol production in transgenic Arabidopsis thaliana .
Biochem. Biophys. Res. Commun.
504
315 – 320 .
10.1016Open asset ↗10.3389/fpls.2019.00501lines:178-342Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( G × E ) interactions across environments within a bread wheat ( Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトル画像でコムギ育種試験のキャノピー反射を取得し、収量予測のための関係行列として技術的に評価・適用しており、表現型取得法が中心的です。
abstractHyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield.
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 phenotypic and genotypic data required to confirm the results presented in this study are available on CIMMYT Dataverse link: hdl:11529/10548109. The “GID” column denotes the unique identifiers for the genotypes. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7653473 .Open asset ↗Figshare · 10.25387/g3.7653473lines:629-629Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 10 Sept 2026
Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here, we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of 2 years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.
Why it matches plant phenotyping methodsUASとデジタル標高モデルを用いてコムギの倒伏を大規模・定量評価し、目視評価との相関や再現性を検証しており、表現型取得法が研究の中心です。
abstractwe demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging
Reproduction assets foundThe paper explicitly states that all experimental data (raw UAS images, orthomosaics, polygons, DEMs) are deposited in a public figshare repository, and analysis scripts are available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll data associated with the experiments including raw images, orthomosaics, polygons, etc. can be accessed at the public repository 4 .Open asset ↗lines:386-410Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
SoybeanWheatRGB / grayscaleRootMorphology / geometry measurementRoot system architecture
ABSTRACT Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a back light and a monochrome machine vision camera to capture root crown silhouettes. RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. RhizoVision Analyzer was physically validated using copper wire and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean ( Glycine max ) root crowns of 187 lines and 1,753 wheat ( Triticum aestivum ) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns, and sets a standard by which open plant phenotyping platforms can be benchmarked.
Why it matches plant phenotyping methods根冠形質を高スループットに取得するハードウェア、画像取得・解析ソフトウェアを開発し、物理的・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。
abstractThe RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns.
Reproduction assets foundThe paper's data availability statement deposits the wire and root crown image sets, tabular phenotype data, and R analysis code on Zenodo (10.5281/zenodo.3380473), and the authors' RhizoVision Imager and Analyzer software are publicly available on Zenodo (10.5281/zenodo.2585882 and 10.5281/zenodo.2585892). These are直接Dataset · public6953), the
520 Department of Energy ARPA-E ROOTS program (DE-AR0000822), and the United Soybean
521 Board (1420-532-5613).
522 Competing interests: The authors declare no competing interests.
523 Data availability: The wire and root crown image sets, tabular data, and R code for statistics and
524 graphing are available online: http://doi.org/10.5281/zenodo.3380473. The simulated root images
30Open asset ↗Zenodo · 10.5281/zenodo.3380473pdf-layout-page:30 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background High-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales. Findings CropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted. Conclusions As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.
Why it matches plant phenotyping methods分散型植物フェノタイピングのデータ収集・管理プラットフォームを開発し、センサーと画像による作物成長評価を統合しているため、方法が中心的である。
abstractCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations.
Reproduction assets foundThe paper's authors publicly released the CropSight system source code (the software used for the paper's distributed plant phenotyping and IoT crop management) on GitHub under a BSD-3-Clause license, and Additional File 2 contains Python code to replicate the paper's plotted figures with datasets available in the sameCode · publicsimilar subsampling idea can be expanded to a larger and multi-site level, which can then truly help inform decision in crop research and agricultural practices across a country's arable land.
Availability of source code and requirements
Project name: CropSight for wheat prebreeding in Designing Future Wheat
Project home page: https://github.com/Crop-Phenomics-Group/cropsight/releases [ 35 ]
Operating system(s): Platform independent
Programming language: Python, PHP, JavaScript, SQL
Requirements: Apache (or other PHP5+) server, MySQL (or other SQL) server, a recent version of Chrome, Firefox, or Safari
License: BSD-3-Clause available at https://opensource.org/licenses/BSD-3-Clause
RRID:SCR_0Open asset ↗Crop-Phenomics-Group/cropsightlines:79-115Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest ( Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.
Why it matches plant phenotyping methodsNIRSを用いて植物組織から表現型関連情報を非破壊・高スループットに取得し、複雑形質を予測する手法自体が研究の中心である。
abstractusing near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits
Reproduction assets foundThe paper's NIRS spectra, phenotypic and SNP datasets are publicly deposited in the INRA Dataverse repository (DOI 10.15454/MB4G3T), and the authors' R functions for cross-validation prediction comparisons are on GitHub (visegura/PS). Supplemental material (including File S1 with variance-partition results) is on FigshDataset · publicThe datasets generated during and/or analyzed during the current study are available in the INRA Dataverse repository ( https://data.inra.fr/ ). They can be accessed with the following link http://dx.doi.org/10.15454/MB4G3T .Open asset ↗INRA Dataverse · 10.15454/MB4G3Tlines:66-74Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Background: High-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales. Findings: CropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted. Conclusions: As an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.
Why it matches plant phenotyping methods分散型植物フェノタイピング設備とIoTセンサーのデータ収集・管理・比較を中核とするオープンソース基盤であり、植物生育画像や微気候データを用いた再利用可能なワークフローを提供している。
abstractCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations.
Reproduction assets foundThe authors explicitly state that the CropSurveyor system code and the datasets supporting the paper's results (wheat phenotyping image/sensor data) are openly available on their public GitHub releases page, with a BSD-3-Clause license.Code · publicpanded to a larger and multi-site
397 level, which can then truly help inform decision in crop research and agricultural practices at the
398 national level, across a country’s arable land.
399
400 Availability and requirements
401 Project name: CropSurveyor for wheat prebreeding in Designing Future Wheat
402 Project home page: https://github.com/Crop-Phenomics-Group/cropsurveyor/releases
403 Operating system(s): Platform independent
404 Programming language: Python, PHP, JavaScript, SQL
405 Requirements: Apache (or other PHP5+) server, MySQL (or other SQL) server, a recent version of
406 Chrome, Firefox, or Safari
407 License: BSD-3-Clause available at: https://opensource.org/licenses/BSD-3Open asset ↗Crop-Phenomics-Group/cropsurveyorpdf-layout-page:16 lines:1-73Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment ( G × E ) interactions across environments within a bread wheat ( Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトル画像によるキャノピー反射率フェノタイピングを用い、穀粒収量予測への有効性と予測精度を評価しており、表現型取得法の応用が中心的です。
abstractHyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants.
Reproduction assets foundThe authors state that all phenotypic and genotypic data needed to reproduce the study's hyperspectral-reflectance genomic prediction results are publicly deposited on the CIMMYT Dataverse under handle hdl:11529/10548109. This is a paper-specific, publicly actionable phenotype dataset asset. No author analysis code or Dataset · publicbioRxiv preprint doi: https://doi.org/10.1101/389825; this version posted November 27, 2018. The copyright holder for this preprint (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 available
under a CC-BY-NC-ND 4.0 International license.
1 All phenotypic and genotypic data required tOpen asset ↗pdf-layout-page:19 lines:1-60Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1-23, 2017a. 10.1186/s13007-016-0154-2; Plant Methods 13(62):1-29, 2017b. 10.1186/s13007-017-0212-4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. RESULTS: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. CONCLUSIONS: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves.
Why it matches plant phenotyping methods高速表現型データの解析に用いるベイズ関数回帰を体系的に検討し、モデル比較、実装時間評価、GFRパッケージによる実装方法を提示しており、表現型解析手法が中心である。
abstractThe purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image 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。Dataset · publicThe phenotypic, genotypic, HTP data and other materials used in this study can be downloaded from the link: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm . The data used in this study can also be downloaded from the Additional file with the Genomic Functional Regression (GFR) R Package.Open asset ↗lines:236-324Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution-canopy shoot area as a function of height-which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R2 value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height.
Why it matches plant phenotyping methodsステレオ画像による作物キャノピー構造・高さ分布の自動取得法を開発し、手動測定と比較検証しているため、植物表現型取得が中心である。
abstractwe report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging
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 sub-dataset for land-based crop phenotyping using stereo images can be downloaded from https://sourceforge.net/projects/land-based-crop-phenotyping/ , which includes images of all 60 plots and their depth maps on 23 Sept 2016 and 11 Oct 2016. These data are sufficient to validate the results presented in this paper.Open asset ↗sourceforge · land-based-crop-phenotypinglines:135-142Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.
Why it matches plant phenotyping methods小麦葉の病害症状と病原菌繁殖を自動画像解析で定量化し、精度・客観性・再現性を評価するとともに、従来の目視評価と比較しているため、植物表現型取得法が研究の中心である。
abstractWe used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible.
Reproduction assets foundThe paper explicitly deposits its full automated image analysis phenotype dataset (PLACL, rlesion, rleaf, pycnidia counts, visual scores) in the Dryad Digital Repository with a public DOI, making it a paper-specific, publicly actionable asset. The scipy and agrometeo.ch URLs are generic libraries/external weather data,Dataset · publicm2 was recognized as damaged by STB. The mean analyzed
area of an individual leaf was 17 cm2. In total, 2.7 million pycnidia
were counted. The mean number of pycnidia within a leaf was 127.
A more detailed description of the overall dataset is given in Table 2.
The full dataset can be accessed from the Dryad Digital Repository:
https://doi.org/10.5061/dryad.171q4. Correlations between the two
biological replicates ranged from 0.23 to 0.66, with P values
ranging from 10_4 to 10_35 (Fig. A3; see Appendix, “Correlation
between replicates”, for more details).
Thedistributionsoftheraw datapointscorrespondingtoindividual
leaves withrespect toPLACL, rlesion, and rleaf are showninFigures 2
andOpen asset ↗Dryad Digital Repository · 10.5061/dryad.171q4pdf-raw-page:5 lines:80-131Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage ≤ 25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods複数作物の植物パラメータ、表現型、ハイパースペクトル反射を体系的に取得した地上基準データベースであり、取得設計と各パラメータの測定法を詳細に記述して、リモートセンシングモデルの校正・検証に用いる点が中心的です。
abstractwe describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations
Reproduction assets foundThis is a data descriptor paper whose plant-phenotyping measurements (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM, soil moisture, photos, survey data) are explicitly deposited as public PANGAEA datasets with DOIs listed in the text. Multiple paper-specific public assets are直接Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Why it matches plant phenotyping methods圃場型HTPシステムを用いたNDVI・群落温度の取得と、ゲノム選抜モデルへの統合を技術的に評価しており、表現型取得基盤の適用が中心的です。
abstractA portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates.
Reproduction assets foundThe authors explicitly state that all data sets and analysis scripts for this wheat HTP/genomic-selection study are publicly deposited in the Dryad Digital Repository under DOI 10.5061/dryad.7f138. This covers the paper-specific phenotype data (NDVI, canopy temperature, grain yield BLUPs) and scripts. Note: no Dryad/DODataset · publicAll data sets and scripts are available from the Dryad Digital Repository: 10.5061/dryad.7f138 .Dryad Digital Repository · 10.5061/dryad.7f138lines:223-263Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wheat (Triticum aestivum L.) is an important food crop, and biotic and abiotic stresses significantly impact grain yield. Wheat leaf and stem surface waxes are associated with traits of biological importance, including stress resistance. Past studies have characterized the composition of wheat cuticular waxes, however protocols can be relatively low-throughput and narrow in the range of metabolites detected. Here, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems. Further, waxes from four wheat cultivars were assayed to evaluate the potential for GC-MS metabolomics to describe wax composition attributed to differences in wheat genotype. A total of 263 putative compounds were detected and included 58 wax compounds that can be classified (e.g., alkanes and fatty acids). Many of the detected wax metabolites have known associations to important biological functions. Principal component analysis and ANOVA were used to evaluate metabolite distribution, which was attributed to both tissue type (leaf, stem) and cultivar differences. Leaves contained more primary alcohols than stems such as 6-methylheptacosan-1-ol and octacosan-1-ol. The metabolite data were validated using scanning electron microscopy of epicuticular wax crystals which detected wax tubules and platelets. Conan was the only cultivar to display alcohol-associated platelet-shaped crystals on its abaxial leaf surface. Taken together, application of GC-MS metabolomics enabled the characterization of cuticular wax content in wheat tissues and provided relative quantitative comparisons among sample types, thus contributing to the understanding of wax composition associated with important phenotypic traits in a major crop.
Why it matches plant phenotyping methodsGC-MSメタボロミクスを用いた植物表面ワックス組成の包括的な取得・比較を主題とし、SEMによる検証も行っているため、化学的な植物形質の測定法として中心的です。
abstractHere, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems.
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 are available online at http://www.mdpi.com/1422-0067/19/2/249/s1 . Figure S1. Wax density.docx provides a semi-quantitative analysis of wheat epicuticular wax density using image processing tools on SEM micrographs; Table S1. Wax metabolite annotations.txt provides detailed information on detected metabolites.Open asset ↗lines:522-564Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Conclusions Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアの開発・評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe authors explicitly state that the Leaf-GP software package, source code, executables, the 4.3 GB raw image datasets, processed images, and phenotypic CSV trait measurements for the Arabidopsis and wheat case studies are freely available at the public GitHub releases URL.Dataset · publicAll the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely available from our online repository https://github.com/Crop-Phenomics-Group/Leaf-GP/releasesOpen asset ↗Crop-Phenomics-Group/Leaf-GPlines:367-436Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Conclusions Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.
Why it matches plant phenotyping methodsX線マイクロCT画像からコムギの穂・粒形態形質を自動抽出・測定する画像解析パイプラインの開発が研究の中心であり、実データへの適用も行っている。
abstractwe describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT).
Reproduction assets foundThe paper's μCT wheat grain phenotyping pipeline is publicly available: author analysis code (microCT_grain_analyser, ISQ-Reader on GitHub) and the reconstructed 3D volumes/segmented images and trait datasets in the Aberystwyth University research data catalogue.Code · publicAll the source code as well as user instructions are available from https://github.com/NPPC-UK/microCT_grain_analyser .Open asset ↗NPPC-UK/microCT_grain_analyserlines:49-62Dataset · publicAll reconstructed 3D volumes and segmented images can be accessed at https://www.aber.ac.uk/en/research/data-catalogue/a11df174-d73d-4443-a7fd-ab5b7039df79/ [ 30 ].Open asset ↗lines:49-62Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. Results We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Conclusions Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.
Why it matches plant phenotyping methods小麦の穂数・穂面積を画像から抽出するニューラルネットワーク手法を開発・評価しており、植物表現型取得が研究の中心です。
abstractWe have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection.
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 Matlab programs and sample data are available from https://sourceforge.net/projects/spike-detection .Open asset ↗spike-detectionlines:189-225Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.
Why it matches plant phenotyping methods作物の形質取得を目的とした自動・スケーラブルな圃場フェノタイピング基盤、制御システム、ハイスループット形質解析パイプラインを開発・適用しており、方法論が研究の中心である。
abstractHere, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments.
Reproduction assets foundThe authors publicly distribute the CropQuant source code, SD card image, and high-definition field phenotyping movies via a Google Drive folder, with explicit availability statements. The picamera documentation link is a generic third-party library and is excluded.Code · publica link to the Creative Commons license, and indicate
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if changes were made. Unless otherwise stated The Creative Commons Public Domain
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Dedication waiver applies to the data and results made available in this paper.
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Source code
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Source code is freely available for academic usage, which can be downloaded at
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https://drive.google.com/drive/folders/0B17ZL8AzLo8wNFJUVS1lOFkzb3M?usp=s
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haring (an online Github repository is being prepared and will be updated in bioRxiv
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as soon as possible)
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CC-BY-NC-ND 4.0 International license
available under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in Open asset ↗pdf-raw-page:13 lines:1-75Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 15 Sept 2026
Background Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Results Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively. Conclusions Leaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases .
Why it matches plant phenotyping methods植物の画像から複数の成長形質を自動抽出するソフトウェアと解析ワークフローが研究の中心であり、異なる画像装置での評価も行っているため。
abstractwe present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application
Reproduction assets foundThe paper's authors publicly released the Leaf-GP phenotyping software (source code and executables) together with the 4.3 GB raw image datasets and experimental results used in the Arabidopsis and wheat growth studies, via their GitHub releases repository.Code · publich Park, Norwich UK
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John Innes Centre, Norwich Research Park, Norwich UK
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University of East Anglia, Norwich Research Park, Norwich UK
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All the 4.3 GB image datasets as well as The Leaf-GP software package and source code are freely
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The software is distributed under the terms of the Creative Commons Attribution 4.0 International
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License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
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reproduction in any medium, provided you give appropriate credit to the originOpen asset ↗https://github.com/Crop-Phenomics-Group/Leaf-GP/releasespdf-raw-page:14 lines:1-97Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 10 Sept 2026
Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds - primarily recently off patent lines - were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.
Why it matches plant phenotyping methods高速画像・ハイパースペクトル等を用いた植物表現型データセットの構築と、画像測定値を手動測定と比較する技術的検証が中心であるため、収載する。
abstractA total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released.
Reproduction assets foundThe paper releases ~485 GB of maize phenotyping image data (RGB, hyperspectral, fluorescence, thermal) publicly at plantvision.unl.edu/dataset, and the authors' validation/analysis source code is posted on GitHub (https://github.com/shanwai1234/Maize Phenotype Map). Both are paper-specific, public, and actionable.Dataset · publicA subset of the RGB images within
this dataset were previously analyzed in18 , and were made available for download from http://plantvision.unl.edu/dataset under
the terms of the Toronto Agreement.Open asset ↗pdf-page:6 lines:1-51Code · publicSource codes for all validation analysis are posted online (https://github.com/shanwai1234/Maize Phenotype Map).Open asset ↗shanwai1234/Maizepdf-page:6 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. Results In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. Conclusions We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.
Why it matches plant phenotyping methodsハイパースペクトル画像から小麦の穀粒収量を推定するベイズ機能回帰モデルを開発・比較しており、植物形質の取得・推定手法が研究の中心である。
abstractwe propose Bayesian functional regression models that take into account all available bands
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 phenotypic, genotypic, HTP data and other materials used in this study can be downloaded from the link: https://1drv.ms/u/s!Api6vPbBKxJYmw2rH35iq-t4gqRm .Open asset ↗lines:3867-3999Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract. Ground reference data are a prerequisite for the calibration, update and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data was gathered with an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT5, Landsat-7 and -8) with a cloud coverage ≤ 25 % is available for 10 and 16 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. In the article, the experimental design of the field campaigns, and methods employed in the determination of all parameters are described in detail. Insights into the database are provided and potential fields of application are discussed. We hope these data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (doi:10.1594/PANGAEA.874251).
Why it matches plant phenotyping methods作物の生育・生物物理／生化学的パラメータと反射スペクトル等を反復取得する地上観測データベースを構築し、衛星リモートセンシング検索モデルの校正・検証に用いる方法とデータが中心であるため、植物フェノタイピングのデータセット／測定基盤として含める。
abstractHere, we describe the acquisition of a comprehensive ground reference database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS).
Reproduction assets foundThis is a data descriptor paper whose entire contribution is a public ground-reference plant-phenotyping database (biophysical/biochemical plant parameters, phenology, hyperspectral reflectance, FVC/PSM imagery-derived traits) hosted on PANGAEA. The article text explicitly provides public PANGAEA DOIs for the main 2013Dataset · publicThe database is freely available at PANGAEA
(https://doi.org/10.1594/PANGAEA.874251).Open asset ↗PANGAEA · 10.1594/PANGAEA.874251pdf-page:1 lines:1-54Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
BarleyChickpeaWheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.
Why it matches plant phenotyping methods植物根系の3D画像から根長・根角度を抽出する画像解析ツールの開発と精度評価が研究の中心であるため。
abstractThe objective of this study was to develop a flexible and free image processing and analysis solution
Reproduction assets foundThe paper's μCT root image data and analysis files (including the Root1 macro workflow) are stated to be publicly deposited in a Harvard Dataverse dataset with an explicit DOI, directly supporting this paper's root phenotyping measurements and analysis.Dataset · publicAll files are available from the database https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DXG4AH .Open asset ↗doi:10.7910/DVN/DXG4AHlines:45-53Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 10 Sept 2026
Background Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. Results We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. Conclusions The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.
Why it matches plant phenotyping methods圃場フェノタイピング用に、照明条件の分類、画像セグメンテーション、品質管理を統合した画像解析パイプラインを開発し、コムギ群落被覆率の時系列を定量化しているため、フェノタイピング手法が中心である。
abstractWe proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series.
Reproduction assets foundThe paper's segmentation reference/original wheat images are publicly deposited on figshare, and the image analysis pipeline code is publicly available on GitHub, both with explicit availability statements and URLs.Dataset · publicAll of the segmentation reference images and the corresponding original images used in this study are publicly available in the ‘figshare’ repository, https://dx.doi.org/10.6084/m9.figshare.4176573 (see [ 30 ]), which can be used for evaluation of image segmentation methods.Open asset ↗figshare · 10.6084/m9.figshare.4176573lines:117-181Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. Results This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. Conclusions We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
Why it matches plant phenotyping methodsキャノピーのハイパースペクトル反射を用いて穀粒収量を推定するモデルを提案し、複数の手法および植生指数と予測精度を比較しており、表現型取得・推定手法が中心である。
titlePredicting grain yield using canopy hyperspectral reflectance in wheat breeding 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。Dataset · publicThe data and materials used in this study can be downloaded from the link: http://hdl.handle.net/11529/10693 . The links contains file corresponding to the phenotypic and bands data for each environments, Drought.Phe_and_Bands.RData, EarlyHeat.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData, Irrigated.Phe_and_Bands.RData.Open asset ↗hdl.handle.net · 11529/10693lines:4711-4843Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.
Why it matches plant phenotyping methods植物の老化を画像から自動推定する補正・復元・解析手法を開発し、手動評価と比較検証しており、表現型取得が研究の中心です。
abstractIn this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping 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 · publicInstructions for users, software and sample image data will be available online at: https://sourceforge.net/projects/plant-senescence-analysis/ .Open asset ↗plant-senescence-analysislines:246-291Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Within the HarvestPlus program there are many collaborators currently using X-Ray Fluorescence (XRF) spectroscopy to measure Fe and Zn in their target crops. In India, five HarvestPlus wheat collaborators have laboratories that conduct this analysis and their throughput has increased significantly. The benefits of using XRF are its ease of use, minimal sample preparation and high throughput analysis. The lack of commercially available calibration standards has led to a need for alternative calibration arrangements for many of the instruments. Consequently, the majority of instruments have either been installed with an electronic transfer of an original grain calibration set developed by a preferred lab, or a locally supplied calibration. Unfortunately, neither of these methods has been entirely successful. The electronic transfer is unable to account for small variations between the instruments, whereas the use of a locally provided calibration set is heavily reliant on the accuracy of the reference analysis method, which is particularly difficult to achieve when analyzing low levels of micronutrient. Consequently, we have developed a calibration method that uses non-matrix matched glass disks. Here we present the validation of this method and show this calibration approach can improve the reproducibility and accuracy of whole grain wheat analysis on 5 different XRF instruments across the HarvestPlus breeding program.
Why it matches plant phenotyping methods小麦粒のFe・Zn濃度という植物形質を測定するXRF校正法を開発し、5台の装置で再現性と精度を検証しており、測定法が中心的です。
abstractConsequently, we have developed a calibration method that uses non-matrix matched glass disks.
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 material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00784
Click here for additional data file.Open asset ↗lines:369-504