Background and aims Phytolith analysis is widely applied in palaeoecological and archaeological research, but its interpretive strength depends on the availability of robust modern reference collections. This study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms, including forbs, shrubs, trees, and C3 grasses. Methods Phytoliths were extracted from available plant parts, including leaves, stems, flowers, seeds, seed pods, cones, and roots, depending on sample availability. Morphotypes were identified following ICPN 2.0, with grass silica short cell phytoliths (GSSCPs) further classified by shape and size to examine subfamily-level patterns. Phytolith morphotype percentage data were analysed using Hellinger transformation, PerMANOVA, PCA, LDA, and hierarchical clustering to assess compositional differences among plant growth forms and grass subfamilies. Key results Phytolith production varied strongly among growth forms and plant parts. Grasses were abundant producers, whereas most forbs, shrubs, and trees were trace producers or non-producers. Leaves were the most consistent source of phytoliths, while seeds and seed pods were predominantly non-producers. Grass silica short-cell phytolith (GSSCP) morphotypes showed clear subfamily-level differentiation. Pooideae produced Rondel morphotypes. Danthonoideae produced Rondel as well as wide Bilobate types. Panicoideae and Oryzoideae exhibited a pronounced Bilobate signature, commonly associated with Polylobate and Cross forms. Non-grass taxa (woody, shrubs, and forbs) were dominated by Spheroids, Tracheary elements, Epidermal, and Polygonal sheets and other non-diagnostic forms. Phytolith assemblages differed significantly among plant families, with Poaceae uniquely producing GSSCPs, while non-grass families showed greater overlap in assemblage composition. Conclusion By expanding taxonomic and anatomical coverage, this study strengthens the capabilities of phytoliths in the reconstruction of grasslands and in general paleo vegetation in Australia, especially where other proxies such as pollen are limited.
Why it matches plant phenotyping methods植物部位の珪酸体を抽出・形態分類し、成長形態やイネ科亜科を識別する現代参照コレクションを構築しており、植物形質の取得・判別手法が研究の中心である。
abstractThis study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms
Reproduction assets foundThe authors explicitly state that the R scripts used for data analysis and figure generation are publicly available on their GitHub repository, which directly reproduces this paper's phytolith statistical analyses (PCA, LDA, PerMANOVA, clustering, plots). Supplementary data files contain the paper's measurements but noCode · publicntification of all plant specimens collected for this
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14 FUNDING
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15 Funding for this study was provided by ARC Discovery grant DP210100508 and a Ph.D.
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16 fellowship (UQGSS) to MH.
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17 DATA AVAILABILITY
18 The R scripts used for data analysis and figure generation are publicly available on GitHub
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19 repository: https://github.com/Manoshi-sporo/Australian-Phytolith-Reference-Collection.
20 CONFLICTS OF INTEREST
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21 The authors declare no competing financial or commercial interests.
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22 AUTHOR CONTRIBUTIONS
23 MH: writing original draft, conceptualization, software, investigation. AC: Supervision, Writing -
24 Review and editing, FM: Supervision, Writing-Review and editing.Open asset ↗Manoshi-sporo/Australian-Phytolith-Reference-Collectionpdf-layout-page:34 lines:1-87Code / 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 confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.
Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。
abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.
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 The annotated image dataset (14,300 images, 23 classes), trained YOLOv8-L and ResNet-50 weights, LSTM model files, DQN policy checkpoint, and all analysis scripts are archived at https://github.com/precision-agri-ai (Zenodo DOI: 10.5281/zenodo.XXXXXXX).Open asset ↗precision-agri-ailines:161-182Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Biotic stress is a major, yet under-quantified, driver of global soybean yield losses, and field-based phenotyping under pest pressure remains a critical bottleneck for crop improvement. Using multi-temporal data from soybean genotypes grown under insecticide-protected and unprotected conditions in Brazil, we present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure. We introduce a three-dimensional metric that jointly captures productivity, feature-level similarity as a proxy for tolerance, and phenological response through days to maturity. This unified formulation enables field-based quantification of pest resilience and replaces labor-intensive and often unreliable direct pest collection and counting. To operationalize this framework, we integrate vegetation indices and self-supervised visual embeddings into a common representation space linking feature stability, performance response and phenological development. This approach enables robust identification of genotypes that maintain feature integrity, minimize developmental delay and sustain yield under pest pressure, with genotypic differences peaking during the pod-fill (R3–R4) and grain-fill (R5.1–R5.5) stages. Overall, this work establishes a scalable, field-ready paradigm for quantifying crop resilience to biotic stress and provides a practical pathway to accelerate breeding for stable yields under real-world agricultural conditions.
Why it matches plant phenotyping methodsUAVによる大規模な圃場フェノタイピング基盤と、植生指数・視覚埋め込みを統合した新しい耐虫性表現型の定量手法が研究の中心である。
abstractwe present a UAV-based, large-scale and non-invasive framework for evaluating genotype performance under natural pest pressure
Reproduction assets foundThe paper explicitly states that the analysis code is publicly available in the authors' GitHub repository (jianglong26/soybean-insect-resistance), which directly reproduces this paper's phenotyping pipeline (orthomosaic processing, VI/DINOv3 feature extraction, similarity analysis, genotype ranking). The paper also声明sCode · public540 The code used for analysis is available at https://github.com/jianglong26/Open asset ↗pdf-page:16 lines:1-45Code / 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
Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research.
Why it matches plant phenotyping methodsヒマワリ頭花の発達・不稔種子数という植物形質を、画像取得とYOLOによる推定パイプラインで定量化する手法を開発・評価しており、方法が研究の中心である。
abstractwe developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads.
Reproduction assets foundThe authors state the source code is available on GitHub and the CVAT-annotated dataset is available via a public share link; the GitHub repository URL is explicitly provided and matches an allowed URL. The dataset link itself is not given, so only the code/checkpoint repository qualifies as an actionable public asset.Code · publicThe developed system is available as a Telegram bot [ 19 ] and the source code is available on GitHub [ 20 ]. The CVAT annotated dataset is available via a public share link.Open asset ↗lines:84-103Code / 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 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 confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As the demand for plant-based nutrition increases, improving the protein profile of legumes like cowpea has become a breeding priority. Cowpea, a multiuse legume and staple in many low-income regions, provides important dietary protein that can help meet the demand in our growing population. Our research used genome-wide association studies (GWAS) and phenomic tools to investigate the genetic architecture of seed protein content in cowpea and integrated 4 cohorts of undergraduate researchers through a USDA-AFRI REEU program. Using wet chemistry and near-infrared spectroscopy (NIRS), we assessed crude protein (CP) within the University of California Riverside Minicore collection, developed and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86), and performed GWAS with ∼41k single-nucleotide polymorphisms (SNPs). Significant SNPs associated with protein content were identified on chromosomes 1, 3, 7, 10, and 11, and candidate genes were linked to functions including nutrient transport, stress response, and seed storage protein regulation. These results provide a foundation for future marker validation and functional studies, and demonstrate the value of pairing trait discovery with undergraduate training.
Why it matches plant phenotyping methods種子タンパク質含量という植物形質の取得に用いるNIRS校正式を開発・検証しており、表現型測定法が研究の主要な技術的要素である。
abstractdeveloped and validated a custoMED-made NIRS calibration equation for CP (R2 = 0.86)
Reproduction assets foundThe paper's Data Availability statement deposits the phenotypic data (wet chemistry CP, NIRS-derived CP phenotypes used for calibration and GWAS) in Dryad. No author analysis code or trained NIRS model files are explicitly deposited; other URLs are generic tools or citations.Dataset · publicThe phenotypic data collected and used in this research are available in the Dryad Digital Repository under DOI: https://doi.org/10.5061/dryad.8cz8w9h72 .Open asset ↗Dryad Digital Repository · 10.5061/dryad.8cz8w9h72lines:305-345Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management ( https://maize-nclb.streamlit.app/ ), integrating advanced deep-learning for automated diagnosis and fungicide advisory. Among thirteen Machine-learning and deep-learning models evaluated for classification, the Visual Geometry Group 16-layer convolutional neural network (VGG16) outperformed all others, achieving 94.0% accuracy, with balanced precision, recall, and F1-score of 0.94, and an AUC-ROC of 0.93. Confusion matrix analysis revealed minimal misclassification, with only 12 errors out of 357 samples, confirming the model's high reliability in distinguishing healthy and infected plants, while Grad-CAM visualizations consistently highlighted biologically meaningful lesion regions, supporting the model's interpretability and alignment with plant pathological symptoms. Field validation of DSS-guided fungicide recommendations (Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC) demonstrated significant benefits, reducing disease incidence to 6.8% compared with 67.4% in controls, achieving 90% disease reduction, and enhancing grain yield by 35.4% (8.55 t/ha), with a favorable cost-benefit ratio of 1:2.49. Seasonal disease progression analysis further confirmed DSS effectiveness, with cumulative disease burden reduced by approximately 85% compared with untreated control. These results highlight the potential of integrating deep-learning with field-validated management strategies into a practical DSS, demonstrating its potential for precision disease management in maize.
Why it matches plant phenotyping methods葉の病斑を画像から分類・可視化する深層学習法を開発し、野外で検証した研究であり、植物病害状態のフェノタイピング手法が中心です。
abstractintegrating advanced deep-learning for automated diagnosis and fungicide advisory
Reproduction assets foundThe paper explicitly states that the complete implementation (model training, preprocessing, evaluation, Grad-CAM visualization) and the final trained VGG16 model are publicly available on GitHub, and the deployed Streamlit DSS is publicly accessible. The Scribd link is a cited prior-work bulletin, not a paper-specificCode · publicthe complete implementation, including model training, preprocessing, evaluation, and Grad-CAM visualization, along with deployment instructions, is publicly available at: https://github.com/anuragd02/NCLB-VGG16-Detection.Open asset ↗anuragd02/NCLB-VGG16-Detectionhtml-lines:133-143Code / dataset availability 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 · checked 5 Sept 2026
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the same sample. This study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR). Applying different pre-processing techniques to the spectra collected from intact kernels, the calibration models were developed using partial least squares regression and the reference protein content values obtained from the LECO combustion method. The best model was obtained using multiplicative scatter correction as pre-processing, resulting in a standard error of prediction of 0.83% and a relative predictive determinant of 3.40. These were indicative of the good predictive ability of the model and the instrument to be applied in quality control and sorting applications. These results highlight the potential of SKNIR to capture the inter-kernel variability in sorghum protein content and enhance screening for grain quality in breeding and grain processing.
Why it matches plant phenotyping methods単一穀粒NIRによるソルガム種子のタンパク質含量推定法を開発・評価しており、植物器官の形質取得が研究の中心である。
abstractThis study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR).
Reproduction assets foundThe paper's Data Availability Statement deposits the original single-kernel NIR spectra and reference protein data openly in Ag Data Commons, a paper-specific public dataset directly reproducing this study's measurements.Dataset · publicThe original data presented in the study is openly available in Ag Data Commons [https://doi.org/10.15482/USDA.ADC/31316725].Open asset ↗Ag Data Commons · 10.15482/USDA.ADC/31316725html-lines:226-278Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The quality of forest reproductive material is crucial for successful reforestation and afforestation. While physical seed properties like mass are known indicators of quality, the potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration. This study investigates the relationship between the seed coat color of individual Pinus sylvestris seeds, quantified in RGB (Red, Green, Blue) space using a flatbed scanner, and their subsequent germination in container nurseries. The resulting images were processed using ImageJ software to measure the mean pixel intensity (0–255) for the Red (R), Green (G), and Blue (B) channels from the segmented seed area, following the «seed–culture» passport methodology [Forestry Engineering Journal 14 | 55 (2024), 37–60]. From a population of individually tracked seeds, we compared the RGB values of germinated (N = 942) and non-germinated (N = 258) seeds after 30 days. Results from the Kolmogorov-Smirnov test showed that non-germinated seeds had significantly lower individual mass (p = 0.0045) and significantly higher pixel brightness values in the R-, G-, and B-channels (p < 0.0001) compared to germinated seeds. Normalized RGB indices also showed significant differences between groups. Our findings demonstrate that seeds with a lighter, more reflective epidermis – indicative of higher RGB brightness – are statistically associated with a lower probability of successful germination under container nursery conditions. This non-destructive, low-cost method shows significant promise for the rapid pre-sorting of Scots pine seeds. It offers a practical tool to improve the efficiency and predictability of seedling production in forest nurseries by increasing the proportion of viable seeds in sowing batches.
Why it matches plant phenotyping methods個別種子のRGB画像から種皮色を定量抽出し、発芽予測・事前選別に用いる非破壊的な表現型計測法が研究の中心である。
abstractthe potential of non-destructive, rapid color analysis for predicting germination in coniferous species requires further exploration
Reproduction assets foundThe paper openly deposits its three core phenotyping datasets in Mendeley Data: morphometric seed data (Dataset 1), the raw VIS/RGB scanner images of individual Pinus sylvestris seeds (Dataset 2), and germination outcome data (Dataset 3). All three DOIs are listed in the Data Availability statement and match allowed URDataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/8g258nbgmf.1Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:133-160Dataset · publicThe original VIS image data of Pinus sylvestris L. are openly available in Mendeley Data at DOI: https://doi.org/10.17632/dt78jhyw2j.2Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:133-160Dataset · publicThe original germination data—Dataset 3—are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:133-160Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Grain number estimation plays a crucial role in agriculture, serving as a key indicator for crop yield and quality assessment. With advances in computer vision, automatic grain detection has become a significant research area, where deep learning methods have shown remarkable promise. This study proposes a vision transformer model called Swin Transformer, which leverages hierarchical attention mechanisms across shifted windows to effectively capture both local and global features of grains in complex imagery. The model achieves the highest accuracy of 98%, outperforming baseline traditional CNN (ResNet-50) and DINO models in grain counting tasks. To support and validate model performance, explainable AI (XAI) techniques such as Grad-CAM and LIME are employed, highlighting the interpretability and focus of the model on relevant grain regions. Furthermore, a comprehensive empirical analysis is conducted using multiple statistical tests to evaluate the model's robustness and generalizability across various grain morphological parameters, establishing the Swin Transformer as a powerful and interpretable solution for intelligent grain counting in agricultural data analytics.
Why it matches plant phenotyping methods画像から穀粒数を推定する深層学習手法の開発・比較検証が研究の中心であり、植物の収量関連形質を測定するため、植物フェノタイピング手法として収録する。
abstractThis study proposes a vision transformer model called Swin Transformer
Reproduction assets foundThe paper's Data availability statement names a public Kaggle dataset of wheat grain counting images used for the study's grain counting experiments. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe dataset used and/or analyzed during the current study is publicly available at: https://kaggle.com/datasets/ociule/wheat-grain-counting-100-images.Open asset ↗kaggle · ociule/wheat-grain-counting-100-imageslines:1190-1253Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
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 python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / 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 · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published28 Mar 2026Metabolomics : Official journal of the Metabolomic SocietyCited by 0 · OpenAlex ↗
Introduction Pearl millet is a high nutritional cereal recognised for its agro-climatic resilience, making it relevant for food security under climate change scenarios. Phenotypic traits are indicative of crop performance, stability and adaptability, yet the potential of metabolomics to predict these traits has not been explored. Objectives This study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models. Methods Grains metabolic profiles were obtained using untargeted UHPLC-LTQ-Orbitrap-HRMS. Phenotypic data were sourced from standardised evaluations conducted by Embrapa across different years and field trials within the Sete Lagoas experimental station (Minas Gerais, Brazil). Generalised linear modelling with penalisation (GLM) and Random Forest was applied to explore the correlation between metabolism and 21 phenotypic traits. Results GLM successfully predicted eight qualitative and seven quantitative traits. Prediction accuracy was higher for qualitative traits, reflecting their comparatively simpler genetic architecture, whereas quantitative traits also achieved satisfactory performance (R² ≥ 0.6). Key predictors included phenolic compounds, amino acids, fatty acids, and carbohydrates. Notably, several associations corresponded to metabolites involved in nitrogen metabolism and vegetative growth, underscoring biologically meaningful links between metabolic profiles and trait variation. Conclusions This exploratory study presents the first metabolome characterisation of a pearl millet germplasm bank, coupled with predictive modelling of phenotypic traits. However, our findings are constrained by the single-environment design and the absence of population-structure assessment. To establish the stability and biological relevance of these results, future work should incorporate multi-environment trials and pathway-level analyses accounting for population structure.
Why it matches plant phenotyping methodsメタボロームを入力として機械学習で植物の表現型形質を予測し、複数形質で予測精度を評価しているため、単なる生物学的測定ではなく形質推定手法の検証が中心です。
abstractThis study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models.
Reproduction assets foundThe paper deposits its metabolomics and phenotypic metadata in a public repository (Recherche Data Gouv, DOI 10.57745/GU6WDG). No author analysis code or trained model deposit is stated; supplementary materials are not linked to a qualifying URL.Dataset · public.623/2023; 26/210.152/2023; 26/201.317/2022), National Council for Scientific and Technological Development (CNPq) (407350/2023-3; 314100/2023-7), Coordination for Improvement of Personnel with Higher Education (CAPES) (financial code 001).
Data availability
The metabolomics and metadata reported in this paper are available via https://doi.org/10.57745/GU6WDG.
Declarations
Competing interests
The authors declare no competing interests.
References
Alonso-Blanco C Méndez-Vigo B
Genetic architecture of naturally occurring quantitative traits in plants: An updated synthesis
Current Opinion in Plant Biology 2014 18 37 43
10.1016/j.pbi.2014.01.002
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Alonso-Blanco, C., & Méndez-VigoOpen asset ↗10.57745 · GU6WDGlines:121-160Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate classification of maize yield potential is essential for food security and effective agricultural planning, particularly in regions characterized by environmental variability and socio-economic constraints. This study explores the binary classification of maize kernel weight into low ( n = 160). A Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy. The model's performance was benchmarked against standard classifiers including Logistic Regression, Random Forest, XGBoost, Decision Tree, Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC). The proposed HCA-DBN achieved a peak classification accuracy of 94%, demonstrating its potential to outperform conventional baselines even under small sample conditions. Rigorous validation, including bootstrapping and stratified 10-fold cross-validation, confirmed the statistical stability of the results. While these findings serve as a proof-of-concept given the dataset constraints, this study contributes a methodological benchmark for field-based maize yield classification and provides a scalable framework for future validation on larger, multi-season datasets.
Why it matches plant phenotyping methodsトウモロコシの収量ポテンシャル（kernel weight）を分類する計算手法を提案し、複数モデルとのベンチマークおよび交差検証で技術的に評価しているため、植物形質推定法が中心である。
abstractA Hybrid Cascade - Deep Belief Network (HCA-DBN) is proposed, utilizing the feature extraction capabilities of Deep Belief Networks (DBN) coupled with Hill Climbing Algorithm (HCA) as a lightweight hyperparameter tuning strategy.
Reproduction assets foundThe paper's maize field phenotyping dataset (plant/ear traits, canopy temperature, chlorophyll from 160 tagged plants at VIT Sevur farm) is explicitly stated as publicly available via a Data in Brief DOI deposit, and the same dataset is cited in the references as a Mendeley Data deposit authored by the paper's authors.Dataset · publicPublicly available datasets were analysed in this study. This data can be found here: https://doi.org/10.1016/j.dib.2024.110367.Open asset ↗html-lines:851-875Dataset · publicRadhakrishnan S., Sandhya P., Venkatramana B., Pradeep Kumar T.
Analyzing various maize varieties grown organically: VIT Vellore’s phenotypic, yield, and canopy data. (2024) 1. Available online at: https://data.mendeley.com/datasets/6py9v57sf2/1Open asset ↗6py9v57sf2/1html-lines:900-924Code / dataset availability confirmedEurope PMC · 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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https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction/tree/main/Data
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https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction
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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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https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtraction/tree/main/Data
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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 15 Sept 2026
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Crop phenotyping of important agronomic traits in field conditions at single-plant resolution has long been a major bottleneck in both genetic analysis (e.g. large-scale association/linkage analysis) and breeding applications (e.g. genomic prediction/selection). Despite growing interest, ultra-affordable, high-throughput and accurate phenotyping tools for maize ears remain limited. Here, we developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline. The imaging platform is composed of 3D-printed parts and electronics components easily available from local retailers to perform high-quality 360° surface scanning of maize ears. Our pipeline first employs CNN-based models to identify normally-developed ears suitable for phenotyping, followed by reliable segmentation of ears and ear surface projection images by YOLOv11-based models, from which ten key traits are subsequently extracted. OpenEar demonstrates reliable agreement with manual measurements across a diverse set of ear- and kernel-related traits, including ear length ( R 2 = 0.972), ear diameter ( R 2 = 0.905), ear volume ( R 2 = 0.976), ear weight ( R 2 = 0.878), kernel number ( R 2 = 0.98), kernel row number ( R 2 = 0.888), kernel number per row ( R 2 = 0.852), kernel thickness ( R 2 = 0.705), kernel width ( R 2 = 0.515), and thousand kernel weight ( R 2 = 0.605). A user-friendly graphical interface is developed for manual inspection of ears after computer annotation. Manually annotated ear videos and images are publicly released as a resource for the crop phenomics community. Our study highlights the potential of DIY-based low-cost solutions to make phenotyping more accessible in crop genetic analysis and breeding.
Why it matches plant phenotyping methodsトウモロコシ穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との一致を検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe developed OpenEar, an open source, low-cost phenotyping system that combines a DIY maize ear imaging platform with a deep learning-based end-to-end phenotypic data extraction pipeline.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and the manual of command line interface and GUI can be found at the GitHub repository: https://github.com/Chimaco37/OpenEar.Open asset ↗Chimaco37/OpenEarhtml-lines:294-325Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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Foundation.
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Data Availability
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Code and data associated with this manuscript are available on GitHub
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(https://github.com/danforthcenter/teff-manuscript).477
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 7, 2026.
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https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度も検証しているため、植物フェノタイピング手法が中心です。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background The breeding process enables plants to inherit desirable traits, such as yield, flowering time, pest resistance, and cannabinoid and/or terpene content. As a result of these intensive genetic improvement practices, where genetically similar individuals or those from the same lineage are crossed, the expression of unfavorable recessive alleles may occur due to homozygosity. This can lead to less productive plants, increased susceptibility to diseases, and reduced quality. Despite the potential negative effects associated with inbreeding, self-pollination (a form of inbreeding) is a necessary cultivation technique used to obtain seeds that produce phenotypically female plants (feminized seeds) for commercialization and/or to fix desirable traits, albeit at the cost of reduced genetic variability. The Cannabis sativa L. seed market has grown significantly in recent decades, driven by the legalization and regulation of medicinal and recreational use. Self-pollinated feminized seeds are popular among growers and commercial seed banks because, in most cases, they guarantee that inflorescences will express the cannabinoid and terpene profile of the single parent plant. The objective of this work is to compare the morphological variation of seeds obtained from the reversal of female clones followed by self-pollination, and seeds obtained from crossing genetically distinct parental. To study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis. Results No direct relationship was observed between size and seed type, although significant differences between varieties were detected. The shape of seeds from crosses between different parents (male and female) showed lower classification accuracy compared to feminized seeds. These results support the hypothesis that inbreeding reduces the variability, as feminized seeds from self-pollination were correctly identified at a high rate using a discriminant function. Conclusions Our research demonstrates that 2D geometric morphometrics can effectively distinguish and trace feminized and self-pollinated cannabis seeds. These seeds exhibit the least morphological variation, enabling accurate identification and providing a reliable foundation for practical applications. The Random Forest classifier's high performance confirms the effectiveness of using morphological traits for seed discrimination. These results open the door for advanced machine-learning techniques aimed to improve scalability and automation.
Why it matches plant phenotyping methods2D幾何形態計測と機械学習を用いて種子の形状・サイズを抽出し、種子タイプを識別する手法が研究の中心であるため。
abstractTo study seed shape and size, we employed 2D geometric morphometrics (GM) based on landmarks and semilandmarks, coupled with a supervised machine learning approach and multivariate statistical approach for analysis.
Reproduction assets foundThe authors publicly deposited the custom Python machine-learning code and the Procrustes coordinate dataset used for the paper's seed-shape classification in a GitHub repository, explicitly stated in the Data availability section.Code · publicTo ensure full reproducibility, the Procrustes coordinates and the custom Python code used for the machine learning classification are provided in a public GitHub repository: https://github.com/Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shape.Open asset ↗Francisco-ft/Self-pollinated-cannabis-seeds-lead-to-less-variation-in-shapelines:115-151Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from imagery. Such advancements have led to phenotypic digitization and made rapid yield forecasting possible. Yield predictions are critical to assess the merit of genotypes to propel cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 m above using a DJI M300 drone at 90° nadir and 45° oblique angles. This research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN in detecting sorghum panicles, achieving a mean average precision at 50 % IoU (mAP@0.50) scores of 0.92-0.98, compared to 0.61-0.89 for Faster R-CNN. Panicle detection from field imagery showed a linear correlation of 0.86 with ground truth field panicle counts. Lab imagery analyses measured panicle area, seed counts, and seed area with correlation coefficients of 0.79, 0.94, and 0.25 with respective ground truth observations. Support Vector Regression (SVR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) were used to predict yield with correlation coefficients of 0.74, 0.71, and 0.78, respectively, and SHapley Additive exPlanation (SHAP) analysis revealed panicle seed count as the primary driver of yield prediction. We observed YOLO models are well-suited for extracting yield-predictive features from pertinent images. Such features can then be incorporated into ML regression models to predict yield per se performance with greater accuracy. The GitHub link is provided in the Data availability section.
Why it matches plant phenotyping methodsUAS・実験室画像から穂数、穂面積、種子数・面積などの植物形質を深層学習で抽出し、検出精度を検証して収量予測へ利用する方法が研究の中心である。
abstractThis research trained YOLO and Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images.
Reproduction assets foundThe authors explicitly state that scripts, fine-tuned models, datasets, and sample images for this sorghum yield-forecasting study are publicly available on GitHub, matching the allowed URL exactly.Code · publicThe scripts, fine-tuned models, datasets, and sample images pertinent to this manuscript are available on GitHub at https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.git .Open asset ↗https://github.com/mbari78/DL_for_Sorghum_Yield_Prediction.gitlines:244-299Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The real-time, accurate detection and classification of rice seeds are crucial for improving agricultural productivity, ensuring grain quality, and promoting smart agriculture. Although significant progress has been made using deep learning, particularly convolutional neural networks (CNNs) and attention-based models, earlier methods such as threshold segmentation and single-grain classification faced challenges related to computational efficiency and latency, especially in high-density seed agglutination scenarios. This study addresses these limitations by proposing an integrated intelligent analysis model that combines object detection, real-time tracking, precise classification, and high-accuracy phenotypic measurement. Methods The proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation, which builds upon the YOLOv11 architecture. YOLOv11-LA incorporates several enhancements over YOLOv11, including separable convolutions, CBAM (Convolutional Block Attention Module) attention mechanisms, and module pruning strategies. These modifications not only improve detection accuracy but also significantly reduce the number of parameters by 63.2% and decrease computational complexity by 51.6%. For classification, the model employs a custom-designed, lightweight RiceLCNN classifier. Additionally, the DeepSORT algorithm is employed for real-time multi-object tracking, and sub-pixel edge detection along with dynamic scale calibration mechanisms are applied for precise phenotypic feature measurement. Results Compared to YOLOv11, the YOLOv11-LA model increases the mAP@0.5:0.95 score by 1.9%, showcasing its superior detection performance while maintaining lower computational overhead. The RiceLCNN classifier achieved classification accuracies of 89.78% on private datasets and 96.32% on public benchmark datasets. The system demonstrated high accuracy in measuring phenotypic features such as seed size and roundness, with measurement errors kept within 0.1 millimeters. The DeepSORT algorithm effectively managed multi-object tracking, reducing duplicate identifications and frame loss in real-time. Discussion Experimental validation confirmed that the YOLOv11-LA model outperforms the original YOLOv11 in terms of both detection speed and accuracy, while also maintaining low computational complexity. The integration of the YOLOv11-LA, RiceLCNN, and DeepSORT algorithms, combined with advanced measurement techniques, underscores the model's potential for industrial applications, particularly in enhancing smart agricultural practices.
Why it matches plant phenotyping methodsイネ種子画像からサイズや真円度を抽出するリアルタイム画像解析手法を開発し、精度・速度・測定誤差を検証しており、表現型取得が研究の中心です。
abstractThe proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository (RiceLCNN) containing the study's rice seed datasets and analysis code. The supplementary material link is generic and not confirmed to contain paper-specific assets.Dataset · publicang , Southwest Forestry University, China
Guodong Sun , Beijing Forestry University, China
Xiaofei Fan , Hebei Agricultural University, China
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/5120191452/RiceLCNN .
Author contributions
DZ: Methodology, Software, Writing – original draft. SS: Funding acquisition, Resources, Writing – review & editing. JL: Validation, Writing – review & editing. WX: Data curation, Resources, Writing – review & editing. NX: Formal Analysis, Visualization, Writing – review & editing.
Conflict of interest
ThOpen asset ↗https://github.com/5120191452/RiceLCNN · RiceLCNNlines:619-662Code / 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Plant seeds are one of the most important food sources for humans. As a result, seed morphology and the concentrations of essential and toxic elements in seeds have important implications not only for seed yield and quality, but also for human health. To identify natural variation in the accumulation of various elements in seeds and in seed morphology, high-throughput phenotyping methods are needed. Here, we employed X-ray fluorescence microscopy (μ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed a computer vision-based algorithmic workflow to automatically the extraction of elemental and morphological data from single seeds. This workflow enables rapid segmentation of individual seeds from a genome-wide association study (GWAS) panel with 1163 A. thaliana accessions, and facilitates the extraction of elemental and morphological traits at the individual seed level from the μ-XRF image. A total of 7 and 10 loci, respectively associated with the morphology and elemental concentration of A. thaliana seeds, were identified. The high-throughput and nondestructive method for automated phenotyping of plant seed libraries developed in this study provides a tool for investigating natural genetic variation controlling the seed mineral accumulation and seed morphogenesis.
Why it matches plant phenotyping methods種子の元素濃度・形態をμ-XRFとコンピュータビジョンで高速・自動取得する手法を開発しており、植物表現型取得が研究の中心である。
abstracthigh-throughput phenotyping methods are needed
Reproduction assets foundThe authors explicitly state that the u-XRF source code and algorithm (the computer vision workflow used for seed segmentation and trait extraction from μ-XRF images) are distributed under the MIT License and publicly available at their GitHub repository, making it a paper-specific, public, actionable code asset.Code · publicThe source code and algorithm of u-XRF are distributed under the MIT License, which permits academic use, distribution, and reproduction subject to the terms of the license ( https://opensource.org/license/MIT/ ), unless otherwise specified. Supporting source code, Web of Science Global Science Publications data, and additional datasets can be accessed at https://github.com/The-Wang-Lab-NAU/u-XRF/ for download and upload.Open asset ↗The-Wang-Lab-NAU/u-XRFlines:291-309Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
High-throughput phenotyping has become essential for plant breeding programs, replacing traditional methods that rely on subjective scales influenced by human judgment. Machine learning (ML) computer vision systems have successfully used convolutional neural networks (CNNs) for image segmentation, providing greater flexibility than thresholding methods that may require carefully staged images. This study compares two quantitative image analysis methods, rule-based thresholding using the magick package in R and an instance-segmentation pipeline based on the widely used Mask-R-CNN architecture, and then compares the output of each to two different sets of human evaluations. Walnuts were collected over three years from over 3000 individual trees maintained by the UC Davis walnut breeding program. The resulting 90,961 kernels were placed into 100-cell trays and imaged using a 20-megapixel Basler camera with a Sony IMX183 sensor. Quantitative data from both image analysis methods were highly correlated for both lightness (L*; r2 = 0.997) and size (r2 = 0.984). The thresholding method required many manual adjustments to account for minor discrepancies in staging, while the CNN method was robust after a rapid initial training on only 13 images. The two human scoring methods were not highly correlated with the image analysis methods or with each other. Pixel classification provides data similar to human color assessments but offers greater consistency across different years. The thresholding approach offers flexibility and has been applied to other color-based phenotyping tasks, while the CNN approach can be adapted to images that are not perfectly staged and be retrained to quantify more subtle kernel characteristics such as spotting and shrivel.
Why it matches plant phenotyping methodsクルミ核の色・サイズ形質を抽出する2つの画像解析手法を開発・比較・人手評価と検証しており、フェノタイピング手法が研究の中心である。
abstractThis study compares two quantitative image analysis methods, rule-based thresholding using the magick package in R and an instance-segmentation pipeline based on the widely used Mask-R-CNN architecture, and then compares the output of each to two different sets of human evaluations.
Reproduction assets foundThe MDPI supplementary materials for this paper contain the paper-specific phenotype dataset (Table S1: all CNN and thresholding color/size output for 92,839 kernels), the authors' R thresholding script, and its coordinates file. The CNN script is stated to be on GitHub (www.github.com/DigitalAgSL/walnutpheno), but no Dataset · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14213335/s1 , Figure S1: Linear regression between median L* from thresholding and human color score; Table S1: 92,839 × 72 table containing all the color data from both CNN and thresholding methods; Figure S2: Pixel distributions of WIP human scored nuts for all four classifications; Figure S3: Correlation matrices between both methods anOpen asset ↗lines:117-276Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction The germination rate of rice seed is a critical indicator in agricultural research and production, directly influencing crop yield and quality. Traditional assessment methods based on manual visual inspection are often time-consuming, labor-intensive, and prone to subjectivity. Existing automated approaches, while helpful, typically suffer from limitations such as rigid germination standards, strict imaging requirements, and difficulties in handling the small size, dense arrangement, and variable radicle lengths of rice seeds. Methods To address these challenges, we present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis. SeedRuler integrates three core components: SeedRuler-IP, a traditional image processing-based module; SeedRuler-YOLO, a deep learning model built on YOLOv5 for high-precision object detection; and SeedRuler-SAM, which leverages the Segment Anything Model (SAM) for fine-grained seed segmentation. A dataset of 1,200 rice seed images was collected and manually annotated to train and evaluate the system. An interactive module enables users to flexibly define germination standards based on specific experimental needs. Results SeedRuler-YOLO achieved a mean average precision (mAP) of 0.955 and a mean absolute error (MAE) of 0.110, demonstrating strong detection accuracy. Both SeedRuler-IP and SeedRuler-SAM support interactive germination standard customization, enhancing adaptability across diverse use cases. In addition, SeedRuler incorporates an automated seed size measurement function developed in our prior work, enabling efficient extraction of seed length and width from each image. The entire analysis pipeline is optimized for speed, delivering germination results in under 30 seconds per image. Conclusions SeedRuler overcomes key limitations of existing methods by combining classical image processing with advanced deep learning models, offering accurate, scalable, and user-friendly germination analysis. Its flexible standard-setting and automated measurement features further enhance usability for both researchers and agricultural practitioners. SeedRuler represents a significant advancement in rice seed phenotyping, supporting more informed decision-making in seed selection, breeding, and crop management.
Why it matches plant phenotyping methodsイネ種子の発芽状態と種子サイズを画像から抽出するウェブ型フェノタイピング手法を開発し、データセットで性能評価しているため、方法が研究の中心である。
abstractwe present SeedRuler, a versatile, web-based application designed to improve the accuracy, efficiency, and usability of rice seed germination analysis.
Reproduction assets foundThe paper's rice seed germination image dataset (1,200 annotated images) is publicly deposited on Kaggle, and the SeedRuler platform (web tool plus offline software package with user manual) is freely available at the authors' lab site.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://www.kaggle.com/jinfengzhao/riceseedgermination .Open asset ↗Kaggle · jinfengzhao/riceseedgerminationlines:744-763Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction: spp.) is a perennial legume traditionally cultivated as a forage crop and is now emerging as a promising candidate for development as a perennial grain legume. Despite its potential, no research has addressed the breeding of sainfoin varieties with superior grain processing properties. Methods: We conducted a multifactorial experiment to evaluate the depodding and dehulling efficiency of five commercially available sainfoin varieties. Seeds were processed using two different methods (belt thresher and impact dehuller) across five sample sizes. A pre-trained Faster R-CNN (Region-based Convolutional Neural Network) object detection model was fine-tuned to identify intact pods, whole seeds, and split seeds from images of the processed mixtures. These predictions were used to calculate processing efficiency (PE) for each variety. A comprehensive power analysis was performed to determine the minimum sample size of sainfoin pods required to detect differences in PE with high statistical power. Results: We observed strong varietal differences in PE, as well as clear effects of the processing method. Belt threshing produced mixtures with more intact pods, while the impact dehuller generated a higher proportion of split seeds. Increasing sample size led to more intact pods across all varieties and methods, and notably decreased seed proportion in belt-threshed samples. Statistical modeling combined with object detection outputs revealed that a minimum of 2 g of pods is required to reliably detect an absolute proportional difference of 0.25 in PE between two breeding lines with 80% power. Discussion: Our findings demonstrate that sainfoin varieties differ significantly in processing efficiency and that processing outcomes depend strongly on both method and sample size. Integrating deep learning-based phenotyping with robust statistical design enables efficient evaluation of processing traits and provides actionable guidelines for breeding programs. While deep learning models offer powerful, cost-effective tools for plant phenotyping, their outputs must be paired with rigorous statistical design to yield reliable and actionable insights for crop improvement.
Why it matches plant phenotyping methods画像からポッド・種子を検出し、処理効率という植物由来形質を算出する深層学習ベースの表現型解析が研究の中心であるため。
titleDeep learning driven, image-based phenotyping of seed processing efficiency in sainfoin
Reproduction assets foundThe paper's data availability statement explicitly deposits the seed image dataset and Faster R-CNN model weights in two public Zenodo repositories and all Python/R analysis code in a public GitHub repository, all with direct URLs.Dataset · publicThe image dataset and FasterRCNN model weights presented in the study are deposited in publicly available Zenodo repositories under accession numbers https://doi.org/10.5281/zenodo.8346923Open asset ↗Zenodo · 10.5281/zenodo.8346923lines:501-517Code · publicAll Python and R code used in this study are deposited in a public GitHub repository at https://github.com/BoMeyering/sainfoin_seed_RCNNOpen asset ↗GitHub · BoMeyering/sainfoin_seed_RCNNlines:501-517Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Why it matches plant phenotyping methodsトウモロコシ穂上のカーネル表現型を画像認識でマッピングし、空間的位置と遺伝子型伝達比を解析するプラットフォームおよび統計パイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape).
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds. The edge computing-based computer vision approach, termed Edge CV, was developed using the Jetson Nano, embedded cameras, and deployed with the proposed Edge CV model to enable intelligent evaluation with constrained computing resources and GPU power. The Edge CV model: First, leveraging semantic segmentation in computer vision tasks to differentiate between A. flavus -infected and uninfected; Second, utilizing post-processing techniques to accurately separate connected peanut seeds while merging segments belonging to the same ones; Third, analyzing and quantifying infection indices, as well as results presentation. Finally, deep transfer learning was employed to validate the model's transferability for other crop seeds. As a result, Edge CV inference showed agreement with manual measurements (R 2 = 0.901, RMSE = 0.07) and superior consistency, with only a 0.01 % fluctuation compared to 4.2 % for human assessments. Moreover, Edge CV demonstrated its transferability to other crop seeds, such as maize (R 2 = 0.968, RMSE = 0.13) and rice (R 2 = 0.949, RMSE = 0.26). These results underscore the potential of Edge CV as a transferable solution for assessing toxic fungal infections. The approach developed also offers valuable insights for enhancing proximal machine vision, improving the distinction of adjacent seeds, and enabling more accurate calculation of the infection index.
Why it matches plant phenotyping methods種子の真菌感染状態を画像から定量化するエッジコンピューティング画像手法を開発し、手動測定との一致および他作物種への移 transfer 性を検証しており、植物状態の取得・抽出が研究の中心である。
abstractThis study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of Aspergillus flavus infection in crop seeds.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' data and code for the Edge CV peanut A. flavus infection assessment pipeline.Code · publicThe data and code will be made available on this URL: https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessment.Open asset ↗https://github.com/lililibin2022/Edge-CV-for-peanut-AF-infection-assessmenthtml-lines:349-374Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeMilletRiceSeed / grainMorphology / geometry measurementSegmentationRoot system architecture
Radicle length is a critical indicator of seed vigor, germination capacity, and seedling growth potential. However, existing measurement methods face challenges in automation, efficiency, and generalizability, often requiring manual intervention or re-annotation for different seed types. To address these limitations, this paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops. The method leverages the Segment Anything Model (SAM) as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids, which are then fed into SAM for precise segmentation of the seed coat and radicle. The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length. Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944, demonstrating excellent segmentation performance. Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error (MAE) was 0.273 mm and the coefficient of determination (R 2 ) was 0.982, confirming the method's high precision for rice. On the Otherseed dataset, the predicted radicle lengths for maize ( Zea mays ), pearl millet ( Pennisetum glaucum ), and rye ( Secale cereale ) are consistent with the observed radicle length distributions, demonstrating strong cross-species performance. These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice, with validated applicability to other crop species.
Why it matches plant phenotyping methodsイネを中心に複数作物の幼根長を画像から自動抽出・推定する手法を開発し、セグメンテーション性能と測定精度を検証しているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops.
Reproduction assets foundThe authors explicitly state that the LenRuler code and software are publicly available on GitHub, providing the paper's radicle-length phenotyping analysis pipeline (SAM-based segmentation, YOLO detection, Gaussian classification).Code · publicThe code and software are available on GitHub at https://github.com/cccccabbage/LenRuler .Open asset ↗cccccabbage/LenRulerlines:351-417Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Abstract To watch the growth of 1200 P. sylvestris cv. Negorelskaya trees from seeds to young or even old stage is a big grant project. We want to make a «seed–culture» passport. Each individual seed (N = 1200) was weighed, and image acquisition via a flatbed scanner in the VIS wavelength region and seeded into an individual 120 cm 3 cell of a 40-cell container. On day 30, container-grown germination was evaluated according to the following dichotomous criterion: 1 – germinated (n 1 = 942), 0 – did not germinate (n 0 = 258), and 0-group and 1-group datasets were formed. The RGB space color of the individual seed epidermis between the 0- and the 1-group were compared via the Kolmogorov‒Smirnov criterion D. The lower individual weight of the seed in the 0-group compared with the 1-group was not accidental (p = 0.0045). Additionally, in the 0 group, the median values of R, G, and B brightness of pixels from individual seeds are not accidental (p = 0.0000381) compared with those of the 1 group. Therefore, in this experiment, seeds that reflected most of the light from the epidermis showed a lower germination when placed in the container.
Why it matches plant phenotyping methods個体種子を対象にスキャナ画像からRGB形質を抽出し、発芽との関連を評価する画像ベースの表現型取得が研究の中心である。
abstractimage acquisition via a flatbed scanner in the VIS wavelength region
Reproduction assets foundThe paper's data availability statement openly deposits all three paper-specific phenotyping assets in Mendeley Data: Dataset 1 (individual seed morphometric/weight data, N=1200), Dataset 2 (original VIS flatbed-scanner seed images, N=1200), and Dataset 3 (individual container germination data, N=1200). These directly供Dataset · publicThe original morphometric data—Dataset 1—of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/8g258nbgmf.1.Open asset ↗Mendeley Data · 10.17632/8g258nbgmf.1lines:152-174Dataset · publicThe original VIS image data of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/dt78jhyw2j.2.Open asset ↗Mendeley Data · 10.17632/dt78jhyw2j.2lines:152-174Dataset · publicThe original germination data—Dataset 3—of Pinus sylvestris L. cv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1.Open asset ↗Mendeley Data · 10.17632/hrs3fgc8tt.1lines:152-174Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Correlative imaging is a powerful tool for revealing information on cell-type structures and their biochemistry, with the potential to inform healthier food choices and improved dietary recommendations. Determination of plant structures and their structural biochemistry advances our understanding of specific structures designed to store different biomolecules within cells and tissues. Compared to the classical biochemical separation techniques, the key advantage of sequential correlative imaging techniques is in relating spatial plant (micro)structures to their biochemistry in a nondestructive manner. Sequential imaging reported here comprises six methodologies on a single sample, a cross-section of a Tartary buckwheat (Fagopyrum tataricum) grain, namely, bright-field and autofluorescence microscopy, fluorescence microspectroscopy, MeV-secondary ion mass spectrometry, micro-particle-induced X-ray emission, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry. Results confirm that the stepwise addition of the desired information across several classes of biomolecules and several spatial scales informs the quality and safety of plant-based produce across scales. Therefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity. The advantages and disadvantages of the selected methodologies were critically evaluated.
Why it matches plant phenotyping methods植物粒の構造とその化学的特徴を複数の相関イメージング法で取得する再利用可能なワークフローを提案し、各手法の長短も評価しているため、表現型取得法が中心である。
abstractTherefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity.
Reproduction assets foundThe paper explicitly points to a public Zenodo deposit containing the correlative imaging data (SEM, micro-PIXE, MeV-SIMS maps) used in its analyses, with instructions for reproducing image fusion in Wolfram Mathematica or ImageJ.Dataset · publicsed to reveal the allocation of K
to cotyledons (Supplementary Fused Image 1). Similarly, on
the same SEM image, MeV-SIMS distribution maps under the
selected peak were overlaid (Supplementary Fused Image 2).
Custom combinations can be done in the Wolfram
Mathematica program or in ImageJ (Merge Channels) using
data available at https://doi.org/10.5281/zenodo.14628251, fol
lowing the instructions in the Materials and Methods.
Conclusions
The low emission properties of fluorescence biomolecules,
when excited with 405 nm light, inherently limit the informa
tion acquired using fluorescence imaging. At this excitation
wavelength, catechin may be the primary fluorophore in
Tartary buckwheat cotOpen asset ↗zenodo · 10.5281/zenodo.14628251pdf-raw-page:13 lines:1-89Code / dataset availability confirmedCrossref · checked 15 Sept 2026
This study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras. In contrast to labor-intensive and spatially limited traditional methods that rely on manual sampling and extrapolation, our proposed methodology uses UAVs to rapidly and comprehensively survey entire paddy fields at optimized altitudes (4, 6, 8, and 10 m). Aerial imagery was autonomously acquired 17 days post-sowing, following a pre-defined flight path. The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. Second, a tailored three-layer convolutional neural network (CNN) accurately classifies vegetative situations. To maximize the training efficiency and performance, we implemented both a pretrained model and a deep learning model, conducting a rigorous comparative analysis against the state-of-the-art YOLOv10. Notably, under favorable imaging conditions, our findings indicate that a 6-m flight altitude yields optimal results, achieving a high degree of accuracy with rice plant density estimates that closely align with those obtained through traditional ground-based methods. This investigation unequivocally highlights the significant advantages of UAV-based monitoring as an economically viable, spatially comprehensive, and demonstrably accurate tool for precise rice field management, ultimately contributing to enhanced crop yields, improved food security, and the promotion of sustainable agricultural practices.
Why it matches plant phenotyping methodsUAV RGB画像と適応的セグメンテーション、CNNを用いてイネ個体密度を推定する手法を開発・比較・検証しており、植物形質の取得が研究の中心です。
abstractThis study presents a novel and efficient approach to accurately assess post-sowing rice plant density by leveraging unmanned aerial vehicles (UAVs) equipped with high-resolution RGB cameras.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's datasets (UAV RGB imagery/labels used for rice plant density estimation). No separate author analysis code repository is stated.Dataset · publicvaluate the accuracy of the proposed labels, subsequently enhancing the training model's speed, convergence, accuracy, and efficiency.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://zenodo.org/records/10960906 .
Author contributions
TH: Writing – original draft. TN: Data curation, Resources, Validation, Writing – original draft. QN: Data curation, Writing – review & editing. HN: Funding acquisition, Investigation, Methodology, Writing – review & editing. PP: Methodology, Software, Supervision, Writing – review & editing.
Funding
TheOpen asset ↗zenodo · 10960906lines:500-523Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Seed / grainGrowth / time-series analysisGrowth / development / phenology
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian inference, which utilizes previous measurements, historical data, and an expected model. Five Bayesian methods are assessed in this study: Important sampling (IS), Markov chain Monte-Carlo (MCMC), Gaussian process (GP), Extended Kalman filtering (EKF) and Sampling Importance Resampling particle filtering (SIR-PF). We test these five Bayesian sampling methods for the monitoring of germination rate in terms of compression, distortion and computation cost. The best trade-off is found by the MCMC method, which offers a compression rate of 0.2 with very little distortion. GP offers the most unbiased parameter estimation and the capability to adapt to various germination speeds. It also has reasonable computational times.
Why it matches plant phenotyping methods発芽率の時系列フェノタイピングに対するベイズ適応サンプリング法を開発・比較し、圧縮率、歪み、計算コストで評価しており、表現型取得・監視手法が研究の中心である。
abstractWe propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage.
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository with the code implementing the five Bayesian adaptive sampling methods, and a public germination kinetics dataset (red clover accessions) deposited at doi.org/10.57745/JECJUI, which is the raw phenotype data analyzed in the study.Code · publicwe provide the codes and data to perform the computation and discuss the convergence of the process: The code is available at the following address: https://github.com/Fatryuk/BayesianAdaptivSampling.gitOpen asset ↗https://github.com/Fatryuk/BayesianAdaptivSampling.gitlines:29-41Dataset · publicData used in the article are table in.csv format containing raw germination along time available at the following repository https://doi.org/10.57745/JECJUIOpen asset ↗https://doi.org/10.57745/JECJUI · 10.57745/JECJUIlines:297-334Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSeed / grainObject detectionGrowth / development / phenology
The germination potential of corn seeds, a key index for assessing their quality and directly associated with the ultimate corn yield, is currently defined in a way that cannot effectively portray the seed germination rate, and the prevalent measurement methods are traditional, consuming substantial process resources. To tackle these issues, this paper employs a public corn seed germination dataset, adds noise to it to simulate real - world production conditions, and ultimately acquires a dataset comprising 8148 images. It then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination. Specifically, the ECA lightweight attention mechanism is introduced to decrease small - target feature loss, assist in accurate target recognition, and remove redundant features; simultaneously, the P2BiFPN multiscale feature fusion technique is utilized to boost the detection ability for small targets; furthermore, the ScConv convolution is adopted to enhance the feature - extraction capacity and improve detection accuracy. Combined with the improved model, this paper also proposed a mathematical modeling algorithmnew method for measuring seed germination potential and observing seed germination rate. The results indicate that the proposed model attains a mean average precision at 50% Intersection over Union (mAP50) value of 98.9%, a mean average precision in the range of 50% - 95% Intersection over Union (mAP50 - 95) value of 95.8%, an accuracy of 96.7%, and a recall of 96.3%. In comparison with the original model, the mAP50 has increased by 0.9% and the mAP50 - 95 value has witnessed a 3.7% increment. The experiments have demonstrated that the research method for germination potential put forward in this paper can effectively depict the rate variation of seeds during the germination process, thus offering a novel perspective for future research on seed germination potential.
Why it matches plant phenotyping methodsトウモロコシ種子の発芽状態・発芽率を画像から検出・定量する改良YOLOv8モデルと数理測定法を開発し、データセット上で性能検証しているため、植物フェノタイピング手法が中心である。
abstractIt then proposes an enhanced YOLOv8 target detection model, EBS - YOLOv8, for detecting corn seed germination.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset used in this experiment can be accessed at ‘ http://dx.doi.org/10.17332/4wkt6thgp6.2 ’.Open asset ↗10.17332/4wkt6thgp6.2lines:333-347Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology and heritable morphometric traits using AI, which can be applied to fruits and other plant organs. This workflow has been implemented in almond (Prunus dulcis ), a species where efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals have been phenotyped, making this the largest morphological study conducted in almond. As result, new heritable morphometric traits of interest have been identified. These findings pave the way for more efficient breeding strategies, ultimately facilitating the development of improved cultivars with desirable traits.
Why it matches plant phenotyping methodsAIを用いたRGB画像から果実の形態・形状形質を抽出するオープンPythonワークフローを開発・適用しており、植物表現型取得法が研究の中心である。
abstractwe have developed an open Python workflow for analyzing morphology and heritable morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe paper's authors explicitly state their phenotyping workflow is open source and provide a public GitHub repository URL containing the Jupyter-notebook-based analysis pipeline (segmentation, morphometric analysis) used in this almond phenotyping study.Code · publicbe found in the workflow’s GitHub repository:
385
https://github.com/jorgemasgomez/almondcv2.386
Clearly, recent advancements in AI segmentation models, such as YOLO (Redmon et al.,
387
2016) and SAM (Kirillov et al., 2023), enable breeding programs to develop fine-tuned
388
models for specific applications, even without large datasets. Additionally, progress in
389
labeling tools like CVAT (Sekachev et al., 2020), which iOpen asset ↗https://github.com/jorgemasgomez/almondcv2.386pdf-raw-page:16 lines:1-90Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Plants adapt seed traits in response to different environmental triggers, supporting the survival of the next generation. To elucidate the mechanistic understanding of such adaptations it is important to characterize the distributions of seed traits by phenotyping seeds on an individual scale and to correlate these traits with corresponding plant properties. Here we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants. It includes previously published measurement platforms ( pheno Seeder, Growscreen), which were improved for very small seeds. We demonstrate the performance of the pipeline by comparing seeds from two consecutive generations of elevated temperature during flowering with control seeds. Relative standard deviation of repeated seed mass measurements was reduced to 0.2%. We identified an increase in seed mass, volume, length, width, height, and germination time as well as a darkening of the seeds under the treatment. A correlation analysis revealed relationships between seed and plant traits, e.g., a highly significant negative correlation between seed brightness and germination time, and a positive correlation between seed mass and early growth rate, but no correlation between time of emergence and morphometric seed traits (e.g., mass, volume). Thus, the seed-to-plant tracking provides the basis for investigating the mechanism of seed and plant trait variation and transgenerational inheritance.
Why it matches plant phenotyping methods種子から植物までを追跡し、種子形質の高精度自動計測、発芽検出、初期成長定量を行うパイプラインを開発・改良しており、表現型取得法が研究の中心である。
abstractHere we introduce a seed-to-plant-tracking pipeline which enables automated handling and high precision phenotyping of Arabidopsis seeds as well as germination detection and early growth quantification of emerging plants.
Reproduction assets foundThe paper deposits its seed and plant phenotyping datasets in Jülich DATA (DOI 10.26165/JUELICH-DATA/KZDQYD), explicitly stated in the data availability statement. Supplementary tables also contain the paper's measurement data. No author analysis code repository is stated.Dataset · publicThe author(s) declare that no financial support was received for the research and/or publication of this article.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.26165/JUELICH-DATA/KZDQYD .
Author contributions
DK: Formal Analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing. AF: Investigation, Methodology, Resources, Software, Writing – review & editing. VS: Formal Analysis, Investigation, Methodology, Resources, Software, Writing – review & editing. JK: MethodOpen asset ↗JUELICH-DATA · 10.26165/JUELICH-DATA/KZDQYDlines:333-387Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1539424/full#supplementary-material
Supplementary Table 1
Data of seed mass vs volume and projected seed area, respectively, shown in
Figure 6
.
Supplementary Table 2
Data of repeatability measurements analysed in
Table 1
and
2
.
Supplementary Table 3
Data of leaf area time series used for estimation of plant growOpen asset ↗lines:333-387Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
As one of the important indicators of soybean seed quality identification, the appearance of soybeans has always been of great concern to people, and in traditional detection, it is mainly through the naked eye to check whether there are defects on its surface. The field of machine learning, particularly deep learning technology, has undergone rapid advancements and development, making it possible to detect the defects of soybean seeds using deep learning technology. This method can effectively replace the traditional detection methods in the past and reduce the human resources consumption in this work, leading to decreased expenses associated with agricultural activities. In this paper, we propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc. and greatly reduces the noise in them, to separate the soybean seeds from the original images. Based on the Yolov9 network, the soybean seed features are extracted, and the defects of soybean seeds are detected. Based on the experiments' findings, the recall rate can reach 98.6%, and the mAP0.5 can reach 99.2%. This shows that the model can provide a solid theoretical foundation and technical support for agricultural breeding screening and agricultural development.
Why it matches plant phenotyping methods大豆種子表面の欠陥という植物器官の状態を、画像処理と改良YOLOv9で抽出・検出する手法開発が研究の中心であり、性能評価も行っている。
abstractwe propose a Yolov9-c-ghost-Forward model improved by introducing GhostConv, a lightweight convolutional module in GhostNet, which enhances the recognition of soybean seed images through grayscale conversion, filtering processing, image segmentation, morphological operations, etc.
Reproduction assets foundThe paper's soybean seed defect detection study uses a public Kaggle dataset of 4,388 soybean seed images (intact, broken, skin-damaged, spotted), explicitly stated in the Data Availability statement. No author analysis code or trained model checkpoints are deposited; makesense.ai is a generic annotation tool, not a deDataset · publicuthors reviewed and approved the final manuscript.
Funding
This work was supported in part by the Special Support Plan for High level Talents in Zhejiang Province (2021R52019), and the Education Department of Hainan Province (Hnky2024-18).
Data availability
The data used in this article can be downloaded from the following link https://www.kaggle.com/datasets/warcoder/soyabean-seeds .
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.
Contributor Information
Xia Yu, Email: 100170@hainnu.edu.cn.
Qi Dai, Email: daiqi@zstu.edu.cn.
ReferenOpen asset ↗Kaggle · warcoder/soyabean-seedslines:388-411Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Corn seed breeding is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for corn kernel recognition and classification has great potential in terms of constructing environmentally friendly agriculture, and saving manpower. Existing segmentation methods that utilize U-shaped architectures typically operate by processing images in discrete pixel-based segments. This approach often overlooks the finer pixel-level structural details within these segments, leading to models that struggle to preserve the continuity of target edges effectively. In this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI, which aims to integrate MSADI module into the encoder and decoder of the VMUnet architecture. Our VMUnet-MSADI model benefits from self-attention computation in VMUnet and multi-scale coding to effectively model non-local dependencies and context relationships at the scale layer, thus improving the segmentation quality of different images. Unlike previous Unet-based improvement schemes, the proposed VMUnet-MSADI adopts a multiscale convolutional attention module coding mechanism at the depth level and an efficient multiscale deep convolutional decoder at the spatial level to extract coarse-grained features and fine-grained features at different semantic scales and effectively avoid the loss of information at the target boundary to improve the quality and accuracy of target segmentation. We introduce a Visual State Space (VSS) block to capture a wide range of contextual information and a Detail Infusion Block (DIB) to enhance the fusion of low-level and high-level features, which further fills in the remote contextual information during the up-sampling process. Comprehensive experiments were conducted on open-source datasets and the results demonstrate that the VMUnet-MSADI model excels in the task of corn kernel segmentation. The model achieved a segmentation accuracy of 95.96%, surpassing the leading method by 0.9%. Compared to other segmentation models, our method exhibits superior performance in both accuracy and loss metrics. Extensive comparative experiments conducted on various benchmark datasets further substantiate that our approach outperforms the state-of-the-art models. Code, pre-trained models and data processing protocols are available at https://github.com/corbining/VMUnet-MSADI .
Why it matches plant phenotyping methodsトウモロコシ種子画像から不健全カーネルを抽出する新規セグメンテーション手法の開発・ベンチマークが中心であり、画像ベースの植物器官状態の表現型取得に該当する。
abstractIn this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI
Reproduction assets foundThe paper reports corn kernel segmentation experiments and explicitly states that code, pre-trained models, and data processing protocols are publicly available at the authors' GitHub repository, which is a paper-specific actionable asset. The corn kernel dataset itself is described as open-source from GaoZhe Tech but,Code · publicCode, pre-trained models and data processing protocols are
available at https://github.com/corbining/VMUnet-MSADI.Open asset ↗corbining/VMUnet-MSADIpdf-page:1 lines:1-57Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research. However, the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models. In this work, we present a comprehensive multicultivar and multiview rice plant image dataset (CVRP) created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields. The dataset includes images capturing rice plants in their natural environment, as well as indoor images focusing specifically on panicles, allowing for a detailed investigation of cultivar-specific differences. A semiautomatic annotation process using deep learning models was designed for annotations, followed by rigorous manual curation. We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art (SOTA) semantic segmentation models. We also conducted 3D plant reconstruction with organ segmentation via images and annotations. The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable resources for challenging tasks such as automatic rice cultivar identification, panicle and grain counting, and 3D plant reconstruction. The database and the model for image annotation are available at https://bic.njau.edu.cn/CVRP.html.
Why it matches plant phenotyping methodsイネ画像データセットとアノテーションモデルを開発・評価し、セグメンテーション、器官再構成、穂・粒数計測などの再利用可能な表現型解析を中心に扱っているため。
abstractwe present a comprehensive multicultivar and multiview rice plant image dataset (CVRP)
Reproduction assets foundThe paper's own CVRP rice image dataset (images + annotations), accompanying code, and trained Mask2Former annotation model are explicitly stated as publicly available on Hugging Face and the authors' NJAU site.Dataset · publicThe CVRP dataset is publicly available on Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP for academic use under the specified license.Open asset ↗CVRPDataset/CVRPhtml-lines:236-252Code · publicThe accompanying code and trained models are available at https://huggingface.co/CVRPDataset/Model.Open asset ↗CVRPDataset/Modelhtml-lines:236-252Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Published26 Feb 2025TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 8 · OpenAlex ↗
Key message Phenomic selection using intact seeds is a promising tool to improve gain and complement genomic selection in corn breeding. Models that combine genomic and phenomic data maximize the predictive ability. Phenomic selection (PS) is a cost-effective method proposed for predicting complex traits and enhancing genetic gain in breeding programs. The statistical procedures are similar to those utilized in genomic selection (GS) models, but molecular markers data are replaced with phenomic data, such as near-infrared spectroscopy (NIRS). However, the use of NIRS applied to PS typically utilized destructive sampling or collected data after the establishment of selection experiments in the field. Here, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits. Three models were employed on a diversity panel: genomic and phenomic best linear unbiased prediction models, which used relationship matrices based on SNP and NIRS data, respectively, and a combined model. The genomic relationship matrices were evaluated with varying numbers of SNPs. Additionally, the PS model trained on the diversity panel was used to select doubled haploid (DH) lines for germination before planting, with predictions validated using observed data. The findings indicate that PS generated good predictive ability (e.g., 0.46 for plant height) and distinguished between high and low germination rates in untested DH lines. Although GS generally outperformed PS, the model combining both information yielded the highest predictive ability, with higher accuracies than GS when low marker densities were used. This study highlights NIRS's potential to achieve genetic gain where GS may not be feasible and to maintain/improve accuracy with SNP-based information while reducing genotyping costs.
Why it matches plant phenotyping methods単一種子NIRSを用いた非破壊フェノタイピングと予測モデルを開発・適用し、圃場形質および発芽を観測値で検証しているため、植物表現型取得・推定法が研究の中心です。
abstractHere, we explored the application of PS using nondestructive, single-kernel NIRS in a sweet corn breeding program, focusing on predicting future, unobserved field-based traits of economic importance, including ear and vegetative traits.
Reproduction assets foundThe paper explicitly states that all code and data used in the analyses are publicly available in the authors' GitHub repository (Resende-Lab/Graciano_skNIR_Phenomic_Seleciton), and additionally points to a second public repository (Resende-Lab/PLS_skNIR_Audrey) containing the kernel composition trait dataset derived/详Code · publicAll the codes and the data used in the analyses are available at https://github.com/Resende-Lab/Graciano_skNIR_Phenomic_Seleciton .Open asset ↗Resende-Lab/Graciano_skNIR_Phenomic_Selecitonlines:120-132Dataset · publicFor further information, the dataset is available at: https://github.com/Resende-Lab/PLS_skNIR_Audrey .Open asset ↗Resende-Lab/PLS_skNIR_Audreylines:78-85Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.
Why it matches plant phenotyping methods葉の浸透ポテンシャル測定におけるサンプル数の妥当性と、収量との関係に対する影響を再サンプリングで評価しており、測定プロトコルの技術的検証が中心である。
titleDoes sample size of leaf osmotic potential affect its relationship with cotton yield?
Reproduction assets foundThe paper's field-measured leaf osmotic potential and seed cotton yield dataset, plus the authors' resampling/regression computer code, are explicitly deposited publicly on Zenodo (record 14635663), as stated in the Data availability section.Dataset · publicect 9574-
2, is appreciated. We thank Jose Teran and Joe Gonzalez,
Farm Manager and Farm Foreman, respectively, at Uvalde
Research Center, and collaborating farmer Rick Kruger for
time/efforts invested in crop management.
Data availability
The data and computer code for reproduc-
ing the results of this paper are available from
https://zenodo.org/records/14635663.Bibliography
1. Megan K. Bartlett, Ya Zhang, Christine Scoffoni, Shanwen Sun, Rico Ardy, Kunfang Cao,
and Lawren Sack. Rapid determination of comparative drought tolerance traits: using an
osmometer to predict turgor loss point. Methods in Ecology and Evolution, 3:880–888, 2012.
2. Y. N. S. Cheung, M. T. Tyree, and J. Dainty. WOpen asset ↗Zenodo · 14635663pdf-raw-page:3 lines:1-85Code / 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 · checked 6 Sept 2026
Dissecting the drought resistance (DR) mechanism and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical drought-avoidant (DA) variety IRAT109 and drought-tolerant (DT) variety Hanhui15 as the parents to develop a stable recombinant inbred line (RIL) population (F 8 , 1,262 lines). The de novo assembled genomes of both parents were released. Through re-sequencing of the RIL population, a set of 1,189,216 reliable SNPs were obtained and used for constructing a dense genetic map. Using both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period and identified 32,586 drought-responsive quantitative trait loci (QTLs) including 2,097 unique QTLs. The QTLs related to panicle i-traits occurred on the middle of chromosome 8 over 600 times, while the QTLs related to leaf i-traits on the 5’ end of chromosome 3 over 800 times, indicating potential effect of these QTLs on plant phenotypes. We chose three candidate genes ( OsMADS50, OsGhd8, OsSAUR11 ) related to leaf, panicle, and root traits respectively and verified their functions in resisting drought. Gene OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and root downward growth. Furthermore, a total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were screened from 597 lines in RIL population under drought conditions in field experiments, and composite QTL region was highly overlapped (76.9%) with known DR gene region. Based on three candidate DR genes, we proposed the haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multi-modal and time-series phenomic analyses, deciphers the genetic mechanism of DA and DT rice varieties, and offers a molecular navigation map for breeding DR variety.
Why it matches plant phenotyping methods地下・地上フェノミックプラットフォームとマルチモーダルカメラで全生育期間の画像形質を大量取得しており、フェノタイピング手法の適用と技術的ワークフローが研究の中核です。
abstractUsing both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period
Reproduction assets foundThe paper's phenome data (aboveground and belowground rice images/i-traits) and the authors' data-handling code and deep-learning model are explicitly deposited at public URLs listed in the Data Availability Statement. Genome data (riceome.hzau.edu.cn) is molecular omics and excluded.Code · publicAll the phenome data and core data-handling code have been deposited online.Open asset ↗lines:140-175Code / dataset availability confirmedbioRxiv · Europe PMC · checked 7 Sept 2026
Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival. TubeTracker integrates manual and automatic image processing routines and the graphical user interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/624782v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1fc2a63org.highwire.dtl.DTLVardef@42f3a2org.highwire.dtl.DTLVardef@18911d6org.highwire.dtl.DTLVardef@1f236f0_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Graphical user interface of TubeTracker showing all supported functionalities. C_FIG
Why it matches plant phenotyping methods植物の花粉管画像から発芽時間、伸長速度、生存性などの表現型を抽出するソフトウェア手法を開発し、複数トマト品種で精度検証しているため。
abstractHere we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and pollen tube survival.
Reproduction assets foundThe paper's authors publicly released TubeTracker, the Python software used to perform all automated pollen germination, elongation, and survival phenotyping measurements in this study, on GitHub with explicit availability language and a video sample for training.Code · publicWe further encourage users to independently
improve upon our tool and have provided the complete python code at
https://github.com/souonkap/TubeTracker, along with installation instructions and a video
sample for training purposes.Open asset ↗souonkap/TubeTrackerpdf-page:22 lines:1-44Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole's biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus' entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis . Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.
Why it matches plant phenotyping methods種子形態計測とクラスタリング・機械学習による形態群分類を中心に扱い、分類アルゴリズムの検証も行っているため、植物形質の計測・抽出手法として収録対象。
abstractEmploying cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability.
Reproduction assets foundThe authors openly deposited the study's seed morphometric phenotype data (2773 seeds, 557 individuals, 84 populations) in Zenodo, as stated in the Data Availability Statement. The MDPI supplementary materials contain population tables and model statistics but the Zenodo deposit is the paper-specific public dataset.Dataset · publicThe data presented in this study are contained within the article and Supplementary Materials and openly available in Zenodo at https://doi.org/10.5281/zenodo.13972509 .Open asset ↗Zenodo · 10.5281/zenodo.13972509lines:397-411Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The increase in the global population is leading to a doubling of the demand for protein. Soybean ( Glycine max ), a key contributor to global plant-based protein supplies, requires ongoing yield enhancements to keep pace with increasing demand. Precise, on-plant seed counting and localization may catalyze breeding selection of shoot architectures and seed localization patterns related to superior performance in high planting density and contribute to increased yield. Traditional manual counting and localization methods are labor-intensive and prone to error, necessitating more efficient approaches for yield prediction and seed distribution analysis. To solve this, we propose MSANet: a novel deep learning framework tailored for counting and localization of soybean seeds on mature field-grown soy plants. A multi-scale attention map mechanism was applied to maximize model performance in seed counting and localization in soybean breeding fields. We compared our model with a previous state-of-the-art model using the benchmark dataset and an enlarged dataset, including various soybean genotypes. Our model outperforms previous state-of-the-art methods on all datasets across various soybean genotypes on both counting and localization tasks. Furthermore, our model also performed well on in-canopy 360° video, dramatically increasing data collection efficiency. We also propose a technique that enables previously inaccessible insights into the phenotypic and genetic diversity of single plant vertical seed distribution, which may accelerate the breeding process. To accelerate further research in this domain, we have made our dataset and software publicly available: https://github.com/UTokyo-FieldPhenomics-Lab/MSANet.
Why it matches plant phenotyping methods大豆種子の計数・位置推定と垂直分布という植物形質を対象に、深層学習手法を開発・比較検証し、データセットとソフトウェアも公開しているため、フェノタイピング手法が中心である。
abstractwe propose MSANet: a novel deep learning framework tailored for counting and localization of soybean seeds on mature field-grown soy plants.
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 have made our dataset and software publicly available: https://github.com/UTokyo-FieldPhenomics-Lab/MSANet .Open asset ↗UTokyo-FieldPhenomics-Lab/MSANetlines:1-25Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Corn seed breeding is a global issue, and has attracted great attention in recent years. Deploying autonomous robots for corn kernel recognition and classification has great potential in terms of constructing environment friendly agriculture, and saving manpower. Existing segmentation methods that utilize U-shaped architectures typically operate by processing images in discrete pixel-based segments. This approach often overlooks the finer pixel-level structural details within these segments, leading to models that struggle to preserve the continuity of target edges effectively. In this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI, which aims to integrate MSADI module into the encoder and decoder of the VMUnet architecture. Our VMUnet-MSADI model benefits from self-attention computation in VMUnet and multiscale coding to efficiently model non-local dependencies and multiscale contexts to improve the segmentation quality of different images. Unlike previous Unet-based improvement schemes, the proposed VMUnet-MSADI adopts a multiscale convolutional attention module coding mechanism at the depth level and an efficient multiscale deep convolutional decoder at the spatial level to extract coarse-grained features and fine-grained features at different semantic scales and effectively avoid the loss of information at the target boundary to improve the quality and accuracy of target segmentation. In addition, we introduce a Visual State Space (VSS) block to capture a wide range of contextual information and a Detail Infusion Block (DIB) to enhance the fusion of low-level and high-level features, which further fills in the remote contextual information during the up-sampling process. Comprehensive experiments were conducted on open-source datasets and the results demonstrate that the VMUnet-MSADI model excels in the task of corn kernel segmentation. The model achieved a segmentation accuracy of 95.96%, surpassing the leading method by 0.9%. Compared to other segmentation models, our method exhibits superior performance in both accuracy and loss metrics. Extensive comparative experiments conducted on various benchmark datasets further substantiate that our approach outperforms the state-of-the-art models. Code, pre-trained models and data processing protocols are available at https://github.com/corbining/VMUnet-MSADI
Why it matches plant phenotyping methodsトウモロコシ種子の健全性状態を画像から分割・認識する新規深層学習手法を中心に開発し、公開データセットで比較検証しているため、植物フェノタイピング手法として収録する。
abstractIn this paper, we propose a new framework for corn seed image segmentation, called VMUnet-MSADI
Reproduction assets foundThe paper states that code, pre-trained models, data processing protocols, and supporting data are openly available at the authors' GitHub repository, which is an allowed URL. The repository is paper-specific (named after the model) and covers the corn kernel segmentation analysis.Code · publicCode, pre-trained models and data processing protocols are available at
https://github.com/corbining/VMUnet-MSADIOpen asset ↗VMUnet-MSADIpdf-page:2 lines:1-48Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Identification of high carotenoid germplasm is crucial to assist breeders in provitamin-A biofortification of sorghum (Sorghum bicolor [L.] Moench). High-performance liquid chromatography is the gold standard for carotenoid quantification, however, it is not feasible for large scale phenotyping due to its high cost and low throughput. In this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding. We hypothesized that visual, color-based selection can be an effective strategy to identify high-carotenoid accessions. Yellow grain had significantly higher carotenoid content than red, brown, and white grain. The degree of yellowness could distinguish the presence or absence of carotenoids, but could not distinguish carotenoid concentrations within yellow-only accessions. The degree of luminosity of the grain, however, was able to better predict carotenoid concentrations within yellow-only accessions. Genome-wide association studies identified significant marker-trait associations for qualitative and quantitative grain color traits and carotenoid concentrations near carotenoid pathway genes-ZEP, PDS, CYP97A, NCED, CCD, and LycE-three of which were common between grain color and carotenoid traits. These findings suggest that using grain color as a method for screening germplasm may be an effective high-throughput selection tool for prebreeding and early-stage breeding in carotenoid biofortification.
Why it matches plant phenotyping methods穀粒色を用いたカロテノイド含量推定・高スループット選抜法の実現可能性を検証しており、植物形質取得法が研究の中心である。
abstractIn this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding.
Reproduction assets foundThe paper's grain-color/carotenoid phenotyping data are in public supplementary files (Supplementary Data S1–S3: GRIN color traits, visual scores, colorimeter measurements), and the authors' analysis code is publicly deposited on GitHub with an explicit availability statement.Code · publicAll other data files are available in the supplemental files and code is available at: https://github.com/rmcdower/sorghumbiofortification/tree/a8457f87068867eb687235c255a6222863102e1aOpen asset ↗rmcdower/sorghumbiofortification · a8457f87068867eb687235c255a6222863102e1alines:134-147Dataset · publicThree grains each per accession were scored independently by two individuals and classified as white, yellow, red, or brown (Supplementary Data S2).Open asset ↗lines:71-78Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods高解像度画像時系列と複数の植物形質を含む大規模な公開圃場フェノタイピングデータセットであり、再利用可能なフェノタイピング基盤・ベンチマークとして中心的です。
abstractThis data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Reproduction assets foundThis data note directly publishes its own phenotyping measurements and image time series: the FIP 1.0 dataset (images, aligned image sequences, eight wheat traits, environmental and marker data) is publicly available on the ETH Research Collection and Hugging Face, and the authors' analysis/processing code is publicly,Dataset · publicn
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and Hugging Face Data set): https://doi.org/10.5061/dryad.n02v6wwzc
• Private Agroscope marker data repository: Confidential (Con-
tact: Boulos Chalhoub, boulos.chalhoub@agroscope.admin.ch).
This repository contains marker data (Illumina InfiniumOpen asset ↗mikeboss/FIP1pdf-raw-page:7 lines:1-110Dataset · publicn with FAIR principles [26]:
• Findable: This publication and the Hugging Face data set card
(https://doi.org/10.57967/hf/3191) provide detailed meta-
data and a comprehensive description of the data set’s contents,
making it discoverable to researchers.
• Accessible: The data is hosted on the Research Collection of ETH
Zurich (https://doi.org/20.500.11850/697773), a reliable and
openly accessible data storage.
• Interoperable: The use of the open-source Hugging Face
datasets [27] package makes it easy to use and export to differ-
ent formats. The data is fully MIAPPE v1.1 [28] conform. Given
the shared genotypes the data set can be used to enhance the
data by Gogna et al. [13] by 6 envOpen asset ↗20.500.11850/697773pdf-raw-page:2 lines:84-130Code · publiclly aggregating the derived data into the final
data set using the fip1-dataset repository.
In addition, the data set can be recreated using the fip1-dataset
repository from the derived data that is freely available in the ETH
research collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL Open asset ↗fip-1.0-data-set-traitspdf-raw-page:7 lines:1-110Code · publicresearch collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data AvailabiOpen asset ↗fip1-alignmentpdf-raw-page:7 lines:1-110Code · publicramming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and HOpen asset ↗fip1-datasetpdf-raw-page:7 lines:1-110Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.
Why it matches plant phenotyping methods麦芽大麦粒の欠陥・品種という植物器官の状態・属性を、両面画像、画像処理、深層学習、画像取得装置で分類する方法が研究の中心であり、比較検証も行っている。
abstractThis study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system.
Reproduction assets foundThe paper's dual-sided malting barley kernel image dataset (MaBaKI) is publicly deposited in a repository with an explicit DOI, matching an allowed URL. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Malting Barley Kernel Images (MaBaKI) database repository. The MaBaKI dataset is available at https://doi.org/10.34658/RDB.MMLNNX.Open asset ↗MaBaKI · 10.34658/RDB.MMLNNXlines:170-191Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Phenotyping of plant traits presents a significant bottleneck in Quantitative Trait Loci (QTL) mapping and genome-wide association studies (GWAS). Computerized phenotyping using digital images promises rapid, robust, and reproducible measurements of dimension, shape, and color traits of plant organs, including grain, leaf, and floral traits. RESULTS: We introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods. This command-line enabled tool, which is adept at managing varying light conditions, background disturbances, and overlapping objects, uses digital images to measure plant organ characteristics accurately and efficiently. GRABSEED has advanced features including label recognition and color correction in a batch setting. CONCLUSION: GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Why it matches plant phenotyping methods植物器官画像から形状・寸法・色などの形質を抽出するソフトウェア手法の開発が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe introduce GRABSEEDS, which is specifically tailored to extract a comprehensive set of features from plant images based on state-of-the-art computer vision and deep learning methods.
Reproduction assets foundThe paper's authors publicly release the GRABSEEDS software (the computational phenotyping tool used for all measurements in this paper) along with the example images and datasets generated, at the GitHub wiki URL stated in the abstract, availability section, and data availability statement.Code · publicures including label recognition and color correction in a batch setting.
Conclusion
GRABSEEDS streamlines the plant phenotyping process and is effective in a variety of seed, floral and leaf trait studies for association with agronomic traits and stress conditions. Source code and documentations for GRABSEEDS are available at: https://github.com/tanghaibao/jcvi/wiki/GRABSEEDS .
Keywords: Image analysis, Phenotype, Seed traits, High throughput, QTL mapping
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2024 May 21; Accepted 2024 Sep 6; Collection date 2024.
IntroductionOpen asset ↗github.com/tanghaibao/jcvi · GRABSEEDSlines:1-28Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops. Specifically for sorghum ( Sorghum bicolor L.), rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle. In this context, the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time. Therefore, this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions. A preharvest benchmark dataset was collected at field scale (2023 season, Kansas, USA), with 648 images of sorghum panicles retrieved via smartphone device, and grain number counted. Each sorghum panicle image was manually labeled, and the images were augmented. Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation, with an average precision of 75% and 89%, respectively. For the grain number, 3 models were trained: MCNN (multiscale convolutional neural network), TCNN-Seed (two-column CNN-Seed), and Sorghum-Net (developed in this study). The Sorghum-Net model showed a mean absolute percentage error of 17%, surpassing the other models. Lastly, a simple equation was presented to relate the count from the model (using images from only one side of the panicle) to the field-derived observed number of grains per sorghum panicle. The resulting framework obtained an estimation of grain number with a 17% error. The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.
Why it matches plant phenotyping methodsスマートフォン画像からソルガム穂の検出・分割と粒数推定を開発・検証しており、植物形質取得手法が研究の中心である。
abstractthis study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.
Reproduction assets foundThe paper's authors explicitly state that the code used to train, test, and analyze the data is publicly available on GitHub. The phenotype image datasets are only available upon request.Code · publicThe code used to train, test, and analyze the data is available at https://github.com/GustavoSantiago113/Sorghum_Grain_Counter .Open asset ↗GustavoSantiago113/Sorghum_Grain_Counterlines:169-296Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models with complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.
Why it matches plant phenotyping methods大麦穀粒の画像取得装置、画像処理、深層学習によって品種と欠陥を分類する手法を開発・比較しており、植物器官の形態・状態の取得が研究の中心である。
abstractThis study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system.
Reproduction assets foundThe paper's barley kernel image datasets (MaBaKI) are explicitly stated as publicly available in a repository with a DOI matching an allowed URL. No analysis code or trained models are reported as available.Dataset · publicThe datasets generated and/or analysed during the current study are available in the Malting Barley Kernel Images (MaBaKI) database repository. The MaBaKI dataset is available at https://doi.org/10.34658/RDB.MMLNNX.Open asset ↗10.34658/RDB.MMLNNXlines:230-256Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes - colored metabolites in sorghum seeds - and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.
Why it matches plant phenotyping methods画像解析モデルを用いたソルガム種子色の定量化を中心に、手作業評価との再現性比較と遺伝解析による妥当性評価を行っているため、植物表現型計測手法として採用。
abstractWe assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images.
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 · public12 using codes available in https://github.com/NikeeShrestha/SorghumSeedSegmentation.Open asset ↗NikeeShrestha/SorghumSeedSegmentationpdf-page:6 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から穀粒のメラニン着色状態を抽出する解析パイプラインとソフトウェアを開発・適用しており、表現型取得・解析手法が中心である。
abstractThis paper presents a hyperspectral image processing pipeline
Reproduction assets foundThe paper's authors publicly release the hyperspectral image processing pipeline (Python source code) used for the barley grain melanin analysis, and the supplementary material lists the 313 barley accessions with their pigmentation (melanin-containing vs. non-containing) phenotype labels used in the study.Code · publicThe source code is available at: https://github.com/igor2704/Hyperspectral_images.Open asset ↗https://github.com/igor2704/Hyperspectral_imageslines:1-42Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.
Why it matches plant phenotyping methodsドローン画像由来の時系列植生指数をFPCAで要約し、穀粒収量・草丈予測へ統合するフェノタイピング手法と予測モデルを複数年・環境で評価しており、表現型取得・抽出と技術的評価が中心である。
titleField-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Reproduction assets foundThe authors deposited a public figshare archive ('Data.zip') containing the paper's phenomic FPCA result files, plant height and grain yield BLUEs, genomic numerical files, and the R prediction/FPCA code needed to reproduce the analysis.Dataset · publicData are available at figshare: https://doi.org/10.25387/g3.24657666 .Open asset ↗figshare · 10.25387/g3.24657666lines:273-291Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.
Why it matches plant phenotyping methods大豆の莢・種子数を高スループットに推定する画像解析モデルを開発し、既存モデルと比較検証しているため、植物表現型取得手法が研究の中心です。
abstractthe present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping
Reproduction assets foundThe paper's DEKR-SPrior analysis code is publicly available on GitHub with an explicit availability statement and URL. The homemade soybean pod/seed image datasets are not publicly deposited and require contacting the corresponding author.Code · publicThe source code is publicly available. It can be accessed at the following GitHub repository: https://github.com/Cyncihe/DEKR-SPrior.gitOpen asset ↗https://github.com/Cyncihe/DEKR-SPrior.gitlines:300-403Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improving model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-of-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, with the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, with the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets related to this study are available at Github.
Why it matches plant phenotyping methods植物画像向け超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子の計数性能を評価しており、表現型取得・抽出法が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper's authors publicly released their analysis code (PlantSR super-resolution model and experiments) on GitHub, and also deposited the PlantSR dataset and HR soybean images on figshare; however, only the GitHub URL is among the allowed URLs, so only the code asset is reported.Code · publicThe source code is available at https://github.com/SkyCol/PlantSR (accessed on 28 November 2023).Open asset ↗SkyCol/PlantSRlines:94-293Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.
Why it matches plant phenotyping methods植物画像の超解像モデルとデータセットを開発・ベンチマークし、リンゴおよびダイズ種子のカウント性能を改善する手法を検証しており、植物器官数の画像ベース推定が中心である。
abstractwe built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset.
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the PlantSR plant image dataset (figshare), and the high-resolution soybean seed images used for transfer learning (figshare). The P2PNet-Soy repository is cited prior work, not a paper-specific asset.Code · publicThe source codes and associated
datasets are available at https://github.com/SkyCol/PlantSR.Open asset ↗SkyCol/PlantSRpdf-page:2 lines:1-63Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently proven beneficial when integrated into across-environment sparse genomic prediction models. One phenomic data modality is whole grain near-infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of hybrid maize grain yield (GY) and 500-kernel weight (KW) across 2 years (2011-2012) and two management conditions (water-stressed and well-watered) were conducted using combinations of reflectance data obtained from high-throughput, F 2 whole-kernel scans and genomic data obtained from genotyping-by-sequencing within four different cross-validation (CV) schemes (CV2, CV1, CV0, and CV00). When predicting the performance of untested genotypes in characterized (CV1) environments, genomic data were better than phenomic data for GY (0.689 ± 0.024-genomic vs. 0.612 ± 0.045-phenomic), but phenomic data were better than genomic data for KW (0.535 ± 0.034-genomic vs. 0.617 ± 0.145-phenomic). Multi-kernel models (combinations of phenomic and genomic relationship matrices) did not surpass single-kernel models for GY prediction in CV1 or CV00 (prediction of untested genotypes in uncharacterized environments); however, these models did outperform the single-kernel models for prediction of KW in these same CVs. Lasso regression applied to the NIRS data set selected a subset of 216 NIRS bands that achieved comparable prediction abilities to the full phenomic data set of 3112 bands predicting GY and KW under CV1 and CV00.
Why it matches plant phenotyping methodsトウモロコシ粒の高スループットNIRS測定と回帰モデルを用いて収量・千粒重を予測し、ゲノム予測との比較検証を行っており、表現型取得・推定法が研究の中心である。
titleNear-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the annotated R analysis code and all files needed to reproduce the NIRS phenomic and genomic prediction results. The NIRS/phenotype data themselves are from prior works (Lane et al. 2020, Farfan et al. 2015) and no separate public,Code · publicAn annotated script of the R code used in this research can be accessed via GitHub (DeSalvio, 2023) [https://github.com/ajdesalvio/Maize‐NIRS‐GBS.git]. All files needed to reproduce the results are provided in the GitHub repository for users to access.Open asset ↗ajdesalvio/Maize‐NIRS‐GBShtml-lines:240-391Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
Why it matches plant phenotyping methods画像処理・形態計測・色彩計測と機械学習を用いて遺伝資源の表現型記述子を開発・比較し、遺伝資源管理ワークフローへ統合することが中心であるため。
abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms.
Reproduction assets foundThe paper's Data Availability statement explicitly points to a public GitHub repository containing the phenomics and traditional descriptor data underlying the study, which directly reproduces the paper's plant-phenotyping measurements. Figures and tables in the article are not treated as separate assets.Dataset · publicData Availability: The data underlying the results presented in the study are available from https://github.com/agrocompuepidemlab/Digital-descriptors-genebank The data of the phenomics and traditional descriptors of the evaluated accessions are associated to this one.Open asset ↗agrocompuepidemlab/Digital-descriptors-genebanklines:143-155Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract. Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales. The six plant functional trait maps for China with 1 km spatial resolution are now available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).
Why it matches plant phenotyping methods植物機能形質を機械学習で推定・検証し、空間形質マップとデータセットを作成することが研究の中心であり、単なる生態学的な形質測定ではない。
abstractused to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees).
Reproduction assets foundThe paper's in situ plant functional trait dataset (34,589 measurements) and the six 1-km trait maps are publicly deposited on figshare by the authors, as stated in the Data availability section.Dataset · publicThe original plant functional trait data collected in this study that were used for machine learning models (named by the data file used for machine learning models.csv) and the final maps of plant functional traits in GeoTIFF format (named by the plant functional trait category) are available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).Open asset ↗figshare · 10.6084/m9.figshare.22351498lines:350-355Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The Burkholderia glumae bacterium causes bacterial grain rot in rice, posing significant threats to the crop's yield, particularly thriving during the rice flowering and grain filling stages. This disease is especially evident in rice grains before harvest, presenting challenges in the detection and classification of rice panicles. Firstly, diseased grains may mix with healthy ones, complicating their separation. Secondly, the size of grains on a panicle varies from small to large, which can be problematic when detected using object detection methods. Thirdly, disease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield. Finally, the challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods and support smart agriculture.
Why it matches plant phenotyping methodsイネ穂・粒の病徴を画像で検出・分類するデータセットであり、植物の病害状態を推定するフェノタイピング用途が中心です。
titleGrain rot dataset caused by Burkholderia Glumae Bacteria.
Reproduction assets foundThis Data in Brief article describes its own publicly deposited rice grain rot image dataset (1528 annotated images, YOLO format) on Zenodo, with explicit direct URL and DOI, qualifying as a paper-specific public phenotyping image dataset.Dataset · publicrice fields in the Mekong Delta region using a mobile phone.
Data source location
Provinces in the Mekong Delta
Latitude: 10.063363, Longitude: 105.594339
Data accessibility
Repository name: Bacterial Grain Rot Dataset Caused by Burkholderia Glumae Bacteria
Data identification number: 10.5281/zenodo.10805462
Direct URL to data: https://zenodo.org/records/10805462
Guidance on retrieving this dataset: Individuals may obtain the dataset by downloading it from the provided link and then unzipping the files for use.
Related research article
Quach, Luyl-Da, et al. “Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small ObjectsOpen asset ↗zenodo · 10.5281/zenodo.10805462lines:1-57Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction Plants undergo various natural changes that dramatically modify their genomes. One is polyploidization and the second is hybridization. Both are regarded as key factors in plant evolution and result in phenotypic differences in different plant organs. In Silene , we can find both examples in nature, and this genus has a seed shape diversity that has long been recognized as a valuable source of information for infrageneric classification. Methods Morphometric analysis is a statistical study of shape and size and their covariations with other variables. Traditionally, seed shape description was limited to an approximate comparison with geometric figures (rounded, globular, reniform, or heart-shaped). Seed shape quantification has been based on direct measurements, such as area, perimeter, length, and width, narrowing statistical analysis. We used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica . Results We generated synthetic tetraploids of Silene latifolia and performed controlled crosses between diploid S. latifolia and Silene dioica to analyze seed morphology. After imaging capture and post-processing, statistical analysis revealed differences in seed size, but not in shape, between S. latifolia diploids and tetraploids, as well as some differences in shape among the parentals and hybrids. A detailed inspection using fluorescence microscopy allowed for the identification of shape differences in the cells of the seed coat. In the case of hybrids, differences were found in circularity and solidity. Overal seed shape is maternally regulated for both species, whereas cell shape cannot be associated with any of the sexes. Discussion Our results provide additional tools useful for the combination of morphology with genetics, ecology or taxonomy. Seed shape is a robust indicator that can be used as a complementary tool for the genetic and phylogenetic analyses of Silene hybrid populations.
Why it matches plant phenotyping methods種子画像を処理し、幾何学的形態計測と楕円フーリエ解析で種子形状・サイズを定量化する手法が研究の中心であり、植物器官の形態表現型を抽出している。
abstractWe used seed images and processed them to obtain silhouettes. We performed geometric morphometric analyses, such as similarity to geometric models and elliptic Fourier analysis, to study the hybrid offspring of S. latifolia and S. dioica .
Reproduction assets foundThe authors state that the raw seed images used for the morphometric phenotyping analyses are publicly deposited in Zenodo (DOI 10.5281/zenodo.8366177). This is a paper-specific, publicly accessible dataset of the seed/cell images underlying this study's measurements. No author analysis code with an explicit public URLDataset · publicRaw images used in this work are available in Zenodo DOI 10.5281/zenodo.8366177 .Open asset ↗Zenodo · 10.5281/zenodo.8366177lines:436-491Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Why it matches plant phenotyping methodsトウモロコシ種子の発芽・活力をRGB画像で推定するデータセットであり、植物表現型の画像取得・解析基盤が中心と判断できる。
titleAn RGB image dataset for seed germination prediction and vigor detection - maize.
Reproduction assets foundThe authors publicly deposited the paper's maize seed germination RGB image dataset (19,800 annotated images, PASCAL VOC XML labels) on Kaggle and IEEE DataPort, with explicit URLs in the text. No analysis code was deposited.Dataset · public. germinating:7042; 3. germinated:1936; 4. primary root:5087; 5. secondary root:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection Open asset ↗Kaggle · Seed Vigor Detection RGB Imagelines:59-108Dataset · publict:17343. For easier download, we uploaded the 120-folder dataset separately, which was generated each hour. It could be accessed on the Kaggle public dataset titled Seed Vigor Detection RGB Image. The dataset is available at the following two address: https://www.kaggle.com/datasets/chengchengchen/seed-vigor-detection-rgb-image
http://ieee-dataport.org/documents/rgb-image-dataset-seed-germination-prediction-and-seed-vigor
3.5.
Seed viability object detection experiments
In order to verify the validity of the dataset, we perform experiments on the seeds vitality object detection using the two-stage object detection model Faster RCNN ( Girshick, 2015 ), the one-stage model SSD ( Liu et al., 20Open asset ↗rgb-image-dataset-seed-germination-prediction-and-seed-vigorlines:59-108Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Background The lint percentage of seed cotton is one the most important parameters in evaluation the seed cotton quality, which affects the price of the seed cotton during the purchase and sale. The traditional method of measuring lint percentage is labor-intensive and time-consuming, and thus there is a need for an efficient and accurate method. In recent years, classification-based machine learning and computer vision have shown promise in solving various classification tasks. Results In this study, we propose a new approach for detecting lint percentage using MobileNetV2 and transfer learning. The model is deployed on the Lint Percentage detection instrument, which can rapidly and accurately determine the lint percentage of seed cotton. We evaluated the performance of the proposed approach using a dataset of 66924 seed cotton images from different regions of China. The results from the experiments showed that the model achieved an average accuracy of 98.43% in classification with an average precision of 94.97%, an average recall of 95.26%, and an average F1-score of 95.20%. Furthermore, the proposed classification model also achieved an average ac-curacy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-samples t test, t = 0.019, p = 0.860). Conclusions This study demonstrates the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton. The proposed approach is a promising alternative to the traditional method, offering a rapid and accurate solution for the industry.
Why it matches plant phenotyping methods種子綿のリント率という植物器官・収量関連形質を、画像と深層学習で非破壊推定する手法を開発し、専門家およびデータセットで性能評価しており、表現型取得法が研究の中心である。
abstractwe propose a new approach for detecting lint percentage using MobileNetV2 and transfer learning.
Reproduction assets foundThe authors explicitly state their LPOSC dataset of 66,924 seed cotton images in six categories is available online via a Baidu Netdisk link, which matches an allowed URL. This is a paper-specific public phenotype image dataset. No code or model checkpoint availability is stated; the declarations say data available on,Dataset · publicThe proposed dataset for LPOSC, which consists of 66924 seed cotton images and six distinct categories, was collected in a real-life scenario. This dataset is unique in its scarcity of available data sets for the study of lint percentage, making it a valuable resource for the development of algorithms for the calculation of lint percentage and a potential stimulus for further research in this area. The dataset is available online at the following link:
https://pan.baidu.com/s/12pnAShYJbaFxMItiF6KdQw?pwd=juq9Open asset ↗pan.baidu.comlines:548-820Code / 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 · checked 14 Sept 2026
Laboratory / benchtopSeed / grainVisualization / data managementGrowth / development / phenology
Seed germination is a crucial phase of plant responses in early life to current and future environmental conditions. However, germination data are still scarce or disaggregated for many plant lineages and regions, including global biodiversity hotspots such as the Mediterranean Basin. We present MedGermDB, the first germination database for characteristic species of Mediterranean habitats, as defined by the EUNIS classification. We also present a systematic approach to build germination databases using automatic and semi‐automatic data extraction from the literature. MedGermDB contains germination data for 4680 laboratory tests performed with 236 angiosperm species from 43 families, extracted from 125 literature sources (2837 sources screened). Each test is associated to a seed lot (i.e., a seed collection of a plant species obtained from a specific location at a specific time) and its metadata, recording geographical information and experimental conditions (storage, dormancy‐breaking treatments, incubation temperature, and photoperiod). MedGermDB is available as a csv file, and through a web app: https://dianamariacruztejada.shinyapps.io/medgermdb/. MedGermDB can be used to explore eco‐evolutionary questions and provides a backbone data set for informing effective seed‐based conservation and ecological restoration activities targeting EUNIS habitats. Our methodological approach to data extraction can be extended to other study systems, contributing to global efforts to mobilize germination data.
Why it matches plant phenotyping methods植物の発芽状態に関する大規模データセットを構築し、文献からの自動・半自動データ抽出手法とWebアプリを提示しており、単なる生物学的実験のルーチン測定ではない。
abstractWe present MedGermDB, the first germination database for characteristic species of Mediterranean habitats
Reproduction assets foundThe paper's MedGermDB germination database (supplementary CSVs) and the code/workflow to join database files are publicly available in the authors' GitHub repository, with a Zenodo version of record and a Shiny app for visualization.Code · publicty and Research (MUR) as part of the PON 2014–
2020 “Research and Innovation” resources—Green/Innovation
Action—DM MUR 1061/2022, Number: DOT13GFICX-
2.
CONFLICT OF INTEREST STATEMENT
None.
DATA AVAILABILITY STATEMENT
All data are available as supplementary materials. The data and codes
to join the database files are stored at https://github.com/DianaCruzT
ejada/
MedGe
rmDB and visualized with the shiny app at https://diana
mariacruztejada.shinyapps.io/medgermdb/. A version of record of the
repository can be found at https://
doi.
org/
10.
5281/
zenodo.
10915154.
All people interested in contributing to the growth of this germination
database are encouraged to contact the correspOpen asset ↗MedGermDBpdf-raw-page:6 lines:1-151Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と抽出可能なデジタル形質を含む、育種利用可能な植物表現型データセットとして構築・公開されているため。
abstractThis dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.
Reproduction assets foundThis Data in Brief article describes its own pulse crop phenotyping dataset (agronomic trait tables and 275 UAV multispectral orthomosaic images), publicly deposited on Zenodo with an explicit DOI listed in the Specification Table under Data accessibility. This is a paper-specific, public, directly actionable dataset.Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.8280431 .Open asset ↗Zenodo · 10.5281/zenodo.8280431lines:1-49Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopSeed / grainTissueVisualization / data management
Abstract Motivation The propensity of plant tissues to burn (i.e. their flammability) is a key trait to understand fire regimes in many ecosystems across the globe. Measuring plant flammability under laboratory conditions allows us to improve both our understanding of plant evolutionary processes and modelling tools for simulating fire hazard and behaviour. Plant flammability has been studied from different but complementary disciplines (e.g. physics, chemistry, ecology, evolution, forestry). However, information is scattered and standardized terminology is lacking, which slows down the progress of research on plant flammability. Here we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology; and (c) find geographical, ecological, and taxonomic gaps in our knowledge on plant flammability. We hope this database will stimulate transdisciplinary research and provide useful information to better cope with an increasingly flammable planet. Main Types of Variables Contained The FLAMITS database contains 19,972 records of 40 flammability variables (classified according to the measured component of flammability). For each record, relevant details of the flammability experiment are given, such as the burning device, the ignition source, and the burnt plant part. In addition, FLAMITS compiles taxonomic and functional data of the studied species and information on the study site (i.e. locality, geographic coordinates, biome, biogeographic realm, and fire activity). Spatial Location and Grain We compiled data from 295 studies in 39 countries and distributed across 12 biomes worldwide. Time Period and Grain The last 62.5 years (1961 to 15th May 2023). Major Taxa and Level of Measurement 1790 plant taxa from 186 families, 883 genera, and 1784 species. Software Format Five text files (.csv), relationally linked.
Why it matches plant phenotyping methods植物の可燃性という観察可能な形質を対象に、測定法の多様性を整理したグローバルデータベースを構築しており、形質取得・方法標準化が中心です。
abstractHere we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology
Reproduction assets foundThe paper's core asset is the FLAMITS database itself: five text files (Data, Taxa, Synonymy, Site, Source) containing 19,972 flammability trait records for 1790 taxa. The Data Availability Statement explicitly deposits these files openly in DRYAD (DOI 10.5061/dryad.h18931zr3). The exact Dryad URL is not among the whitDataset · publicDATA AVAILABILITY STATEMENT
The five text files composing the database are openly available in
DRYAD at https://
doi.
org/
10.
5061/
dryad.
h1893
1zr3.Open asset ↗DRYADpdf-raw-page:11 lines:1-102Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m2.ha-1 and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.
Why it matches plant phenotyping methods航空レーザースキャンと現地データを統合して樹木の直径・樹高・種を大規模に推定する再利用可能なワークフローを提示し、交差検証で評価しているため、植物形質取得法が中心である。
abstractTree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach.
Reproduction assets foundThe paper's generated tree-level dataset (42 million trees with dbh, height, species for three European landscapes) is publicly deposited on Zenodo, and the ALS point cloud input for the Bauges northern part is publicly available on Recherche Data Gouv. Other underlying data (local inventories, southern Savoie ALS, MilDataset · publicALS data in the northern part (Haute-Savoie) are available to download from the Recherche Data Gouv dataverse at
https://doi.org/10.57745/ZUT1MJ , under the Etalab open license 2.Open asset ↗Recherche Data Gouv · 10.57745/ZUT1MJlines:912-955Code / 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 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 confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
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 authors' repository, which is an allowed URL.Code · publicAll 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_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measurements of rice physical traits, such as length, width, and percentage of filled/unfilled grains, are essential steps of rice breeding. A new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques. Backlight photography was used to capture a grayscale image of a group of rice grains, which was then analyzed using a clustering algorithm to differentiate between filled and unfilled grains based on their grayscale values. The impact of backlight intensity on the accuracy of the method was also investigated. The results show that the proposed method has excellent accuracy and high efficiency. The mean absolute percentage error of the method was 0.24% and 1.36% in calculating the total number of grain particles and distinguishing the number of filled grains, respectively. The grain size was also measured with a little margin of error. The mean absolute percentage error of grain length measurement was 1.11%, while the measurement error of grain width was 4.03%. The method was found to be highly accurate, non-destructive, and cost-effective when compared to conventional methods, making it a promising approach for characterizing physical traits for crop breeding.
Why it matches plant phenotyping methodsイネ籾の長さ・幅・充実度を画像処理で測定する方法を開発し、精度とバックライト条件の影響を検証しており、植物表現型取得が中心です。
abstractA new approach for measuring the physical traits of rice grains for breeding purposes was presented in this study, utilizing image processing techniques.
Reproduction assets foundThe paper's phenotype reference measurements (grain counts, filled/unfilled counts, and grain sizes for the validation experiments) are reported in Appendices A–C, which are included in the article's supplementary material, publicly available at the Frontiers supplementary-material URL. No author analysis code or imageSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1213486/full#supplementary-material
Click here for additional data file.
References
Al-Tam F., Adam H., Anjos A. D., Lorieux M., Larmande P., Ghesquière A., et al. (2013). P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol.
13 (1), 1–14. doi: 10.1186/1471-2229-13-122Open asset ↗lines:182-208Code / dataset availability confirmedCrossref · checked 14 Sept 2026
As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing “seed loss and waste” due to slight shortfalls in eligibility rates. In this study, we applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars. Candidate discriminants for quality evaluation were derived by linear sparse modeling using the seed reflectance spectra as explanatory variables. To systematically proceed with model selection, we defined the sorting condition where the recovery rate of seeds matches the initial eligibility rate ( iP ) as “standard condition”. How much the eligibility rate after sorting ( P ) increases from iP under this condition offers a reasonable criterion for ranking candidate models. Moreover, the model performance under conditions with adjusted discrimination strength was verified using a metric “relative precision” ( rP ) defined as ( P–iP )/(1 –iP ). Because rP , compared to precision (= P ), is less dependent on iP in relation to recall ( R ), i.e., recovery rate of eligible seeds, the rP-R curve and area under the curve also offer useful criteria for spotting better discriminant models. We confirmed that the batches of seeds given higher discriminant scores by the models selected with reference to these criteria were more enriched with eligible seeds. The method presented can be readily implemented in developing a sorting device that enables “last-percent improvement” in eligibility rates of crop seeds.
Why it matches plant phenotyping methods近赤外イメージング分光と機械学習を用いて種子の発芽能力などの品質形質を評価・選別する手法が研究の中心であり、モデル選択基準と性能評価も提示している。
abstractwe applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars.
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 for directly visualizing discriminant scores of seeds within hyperspectral images is provided as S2 FileOpen asset ↗lines:157-171Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R 2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
Why it matches plant phenotyping methodsイネ穂上粒の形態形質を画像・深層学習で抽出する手法を開発し、手動測定との比較で検証しており、表現型取得が研究の中心である。
abstractThis study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository (BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection) hosting the study's datasets, which per the statement contain the paper's rice panicle images and phenotyping resources. No separate trained-model checkpoint or analysis URLDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/BME-PhenoTeam/Method-for-on-panicle-rice-grain-detection .Open asset ↗BME-PhenoTeam/Method-for-on-panicle-rice-grain-detectionlines:521-553Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R 2= 0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs
Why it matches plant phenotyping methodsRGB画像から種子形態形質を抽出し、機械学習で種子重量を予測する画像ベース表現型解析が研究の中心である。
abstractThe present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean.
Reproduction assets foundThe paper's image-based seed architectural trait (i-trait) measurements and hundred-seed weight data for 164 soybean accessions are stated to be included in the article's Supplementary Material (e.g., Supplementary Table 1 of genotypes and trait data), publicly available at the Frontiers supplementary-material URL. No专Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1206357/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Abdelhakim L. O. A., Rosenqvist E., Wollenweber B., Spyroglou I., Ottosen C. O., Panzarová K. (2021). Investigating combined drought-and heat stress effects in wheat under controlled conditions Open asset ↗lines:434-465Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
Click here for additional data file.
References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Soy is the main product of Brazilian agriculture and the fourth most cultivated bean globally. Since soy cultivation tends to increase and due to this large market, the guarantee of product quality is an indispensable factor for enterprises to stay competitive. Industries perform vigor tests to acquire information and evaluate the quality of soy planting. The tetrazolium test, for example, provides information about moisture damage, bedbugs, or mechanical damage. However, the verification of the damage reason and its severity are done by an analyst, one by one. Since this is massive and exhausting work, it is susceptible to mistakes. Proposals involving different supervised learning approaches, including active learning strategies, have already been used, and have brought significant results. Therefore, this paper analyzes the performance of non-supervised techniques for classifying soybeans. An extensive experimental evaluation was performed, considering (9) different clustering algorithms (partitional, hierarchical, and density-based) applied to 5 image datasets of soybean seeds submitted to the tetrazolium test, including different damages and/or their levels. To describe those images, we considered 18 extractors of traditional features. We also considered four metrics (accuracy, FOWLKES, DAVIES, and CALINSKI) and two-dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbor embedding) for validation. Results show that this paper presents essential contributions since it makes it possible to identify descriptors and clustering algorithms that shall be used as preprocessing in other learning processes, accelerating and improving the classification process of key agricultural problems.
Why it matches plant phenotyping methods大豆種子画像から損傷とその程度を分類するため、複数のクラスタリング手法・特徴量・評価指標を比較検証しており、種子状態の表現・抽出手法が中心である。
titleAssessment of clustering techniques to support the analyses of soybean seed vigor.
Reproduction assets foundThe paper's Data Availability statement points to the authors' public GitHub repository containing the soybean seed image datasets and feature files used in the clustering experiments. JFeatureLib is a generic third-party library, not a paper-specific asset.Dataset · publiclf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
Data Availability
All files are available from https://github.com/BioinfoCP/visual-features-soybean-vigor .
1 Introduction
Soy is the fourth most cultivated bean globally and the main product in Brazilian agriculture. In 2021/22, Brazil estimates a production record of 142,009 million tons of soybeans. This Open asset ↗BioinfoCP/visual-features-soybean-vigorlines:48-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69 , ZCN8, sh1, wx1, du1 ) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.
Why it matches plant phenotyping methodsNIRSを用いた植物試料の表現型推定と、ゲノム予測との比較検証が研究の中心であり、複数のトウモロコシ農業形質・種子組成形質を対象としているため。
abstractOne phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition.
Reproduction assets foundThe paper's Data Availability section and Methods explicitly state that the annotated R analysis script, plus the data files (CSVs.zip, SNP60000.hmp.zip) needed to reproduce the prediction results, are publicly available in the authors' GitHub repository ajdesalvio/Maize-NIRS-GBS.Code · public52
1038
Data Availability
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An annotated script of the R code used in this research can be accessed via GitHub
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(https://github.com/ajdesalvio/Maize-NIRS-GBS.git). Supplementary Data 1
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(Supplementary_Data_1.xlsx) contains prediction results, GWAS results, and variable importance
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scores for NIRS bands. Files necessary to run the R script and reproduce the prediction results are
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available in the CSVs.zip folder and the SNP60000.hmp.zip folder. Supplementary figures are
1044Open asset ↗ajdesalvio/Maize-NIRS-GBSpdf-raw-page:52 lines:1-26Code / dataset availability 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 confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
help improve model parameterization.
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MODEL AND DATA AVAILABILITY
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Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
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The source code used for this study, along with the operational commands and user guide, is
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28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This paper presents a dataset with 5513 images of individual soybean seeds, which encompass five categories : (Ⅰ) Intact, (Ⅱ) Immature, (Ⅲ) Skin-damaged, (Ⅳ) Spotted, and (Ⅴ) Broken . Furthermore, there are over 1000 images of soybean seeds in each category. Those images of individual soybeans were classified into five categories based on the Standard of Soybean Classification ( GB1352-2009 ) [1]. The soybean images with the seeds in physical touch were captured by an industrial camera. Subsequently, individual soybean images (227×227 pixels) were divided from the soybean images (3072×2048 pixels) using an image-processing algorithm with a segmentation accuracy of over 98%. The dataset can serve to study the classification or quality assessment of soybean seeds.
Why it matches plant phenotyping methods大豆種子の画像データセットを構築し、画像分割アルゴリズムと品質カテゴリ分類を提示しており、植物器官の状態・品質を画像から抽出する方法が中心です。
abstractThis paper presents a dataset with 5513 images of individual soybean seeds
Reproduction assets foundThe paper is a data descriptor for a public soybean seed image dataset (5513 images, five quality classes) deposited in Mendeley Data, with explicit direct URL and DOI, directly reproducing the paper's phenotyping measurements.Dataset · public7 × 227 pixels) from the soybean images (3072×2048 pixels). Finally, the individual soybean images were saved in JPG format.
Data source location
Nanjing Agricultural University, Nanjing, China
Data accessibility
Repository name: Soybean Seeds
Data identification number:
https://doi.org/10.17632/v6vzvfszj6.6
Direct URL to data:
https://data.mendeley.com/datasets/v6vzvfszj6
Instructions for accessing these data: Download the data from Soybean Seeds repository in ZIP formats.
Value of the Data
•
The soybean image dataset can meet the practical requirement of assessing soybean quality. Because those individual soybean images in our dataset were classified based on the Standard of Soybean ClassiOpen asset ↗10.17632/v6vzvfszj6.6lines:1-64Code / 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 14 Sept 2026
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
Why it matches plant phenotyping methodsハイパースペクトル画像からイネ種子の温度処理状態を分類する深層学習手法を開発・検証し、校正・前処理・分類用GUIも提供しており、種子表現型の取得・抽出方法が中心である。
abstractA framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for the DL framework for hyperspectral seed image calibration, preprocessing, segmentation, and classification are available at: https://gitfront.io/r/vido6/vC64GLsxCDZx/classificationRice/ , accessed on 23 March 2023.Open asset ↗classificationRicelines:95-200Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Phenotypic plasticity is defined as the phenotypic variation of a trait when an organism is exposed to different environments, and it is closely related to genotype. Exploring the genetic basis behind the phenotypic plasticity of ear traits in maize is critical to achieve climate-stable yields, particularly given the unpredictable effects of climate change. Performing genetic field studies in maize requires development of a fast, reliable, and automated system for phenotyping large numbers of samples. Results Here, we develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field. Using this platform, we analyze 15 common ear phenotypes and their phenotypic plasticity variation in 3819 transgenic maize inbred lines targeting 717 genes, along with the wild type lines of the same genetic background, in multiple field environments in two consecutive years. Kernel number is chosen as the primary target phenotype because it is a key trait for improving the grain yield and ensuring yield stability. We analyze the phenotypic plasticity of the transgenic lines in different environments and identify 34 candidate genes that may regulate the phenotypic plasticity of kernel number. Conclusions Our results suggest that as an integrated and efficient phenotyping platform for measuring maize ear traits, MAIZTRO can help to explore new traits that are important for improving and stabilizing the yield. This study indicates that genes and alleles related with ear trait plasticity can be identified using transgenic maize inbred populations.
Why it matches plant phenotyping methodsMAIZTROという自動化・高スループットのトウモロコシ穂形質フェノタイピング基盤の開発と適用が研究の中心であり、複数の穂形質を測定する方法論的貢献が明示されている。
abstractwe develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field.
Reproduction assets foundThe paper deposits its maize ear phenotyping data and analysis code publicly: ear image data on Zenodo (7796696), R scripts on Zenodo (7792895), and scripts/data on GitHub (liumiguo/paper_ear_pp_code) under GPL-3.0.Code · publicThe scripts and data used in this study are available under a GPL-3.0 license in Github: https://github.com/liumiguo/paper_ear_pp_code.git [ 50 ]Open asset ↗GitHub · liumiguo/paper_ear_pp_codelines:145-221Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Methods Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels. Results and discussion We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.
Why it matches plant phenotyping methodsトウモロコシ穀粒画像から胚の向き分類と分割を行う自己教師あり画像解析手法を開発・評価しており、植物器官の表現型抽出が研究の中心である。
abstractwe implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels.
Reproduction assets foundThe paper's maize kernel image datasets (classification and segmentation with masks) are deposited on Zenodo and the authors' analysis code is on GitHub, both explicitly linked in the data availability statement.Dataset · publicat self-supervised learning provides a meaningful path forward in advancing agricultural efficiency with computer vision and machine learning.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/7577017 , https://github.com/ddavidd23/ssl_corn.git .
Author contributions
UF, TJ, TL, and BG conceived the project. RW, UF, and TJ conducted physical experiments, data collection and data curation. RW and UF annotated the ground truth images. DD, KN, and TJ developed the machine learning framework. DD performed computational experimenOpen asset ↗zenodo · record/7577017lines:221-235Code / dataset availability confirmedEurope PMC · checked 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 7 Sept 2026
The rapidly changing climate affects an extensive spectrum of human-centered environments. The food industry is one of the affected industries due to rapid climate change. Rice is a staple food and an important cultural key point for Japanese people. As Japan is a country in which natural disasters continuously occur, using aged seeds for cultivation has become a regular practice. It is a well-known truth that seed quality and age highly impact germination rate and successful cultivation. However, a considerable research gap exists in the identification of seeds according to age. Hence, this study aims to implement a machine-learning model to identify Japanese rice seeds according to their age. Since agewise datasets are unavailable in the literature, this research implements a novel rice seed dataset with six rice varieties and three age variations. The rice seed dataset was created using a combination of RGB images. Image features were extracted using six feature descriptors. The proposed algorithm used in this study is called Cascaded-ANFIS. A novel structure for this algorithm is proposed in this work, combining several gradient-boosting algorithms such as XGBoost, CatBoost, and LightGBM. The classification was conducted in two steps. First, the seed variety was identified. Then, the age was predicted. As a result, seven classification models were implemented. The performance of the proposed algorithm was evaluated against 13 state-of-the-art algorithms. Overall, the proposed algorithm has a higher accuracy, precision, recall, and F1-score than the others. For the classification of variety, the proposed algorithm scored 0.7697, 0.7949, 0.7707, and 0.7862, respectively. The results of this study confirm that the proposed algorithm can be employed in the successful age classification of seeds.
Why it matches plant phenotyping methodsRGB画像からコメ種子の品種・年齢を抽出する機械学習手法を開発し、新規データセットを構築して複数手法と性能比較しているため、種子状態の画像ベース表現型解析が中心である。
abstractthis study aims to implement a machine-learning model to identify Japanese rice seeds according to their age
Reproduction assets foundThe paper's authors constructed a novel rice seed image dataset (six varieties, three harvest ages) and explicitly state it is publicly available on Kaggle under the author's account, matching an allowed URL. No code or model release is stated.Dataset · publicgle data repository accessed on 15 January 2023 (https://www.kaggle.com/datasets/namalrathnayake1Open asset ↗Kagglepdf-page:16 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R 2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
Why it matches plant phenotyping methods落花生種子の活力という植物形質をハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、分類・回帰性能も評価している。
abstractIn this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' original contributions (hyperspectral seed vigor data and analysis). The repository URL in the text (https://github.com/cjkka/cjkka/tree/main) is under the allowed base URL https://github.com/cjkka/. No separate code orDataset · publicavailable. This data can be found here: https://github.com/cjkka/ absence of any commercial or financial relationships that could beOpen asset ↗cjkkapdf-page:12 lines:1-54Code / 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 15 Sept 2026
RiceSeed / grainCountingSegmentationGrowth / development / phenology
The germination rate of rice grain is recognized as one of the most significant indicators of seed quality assessment. Currently, grain germination rate is generally determined manually by experienced researchers, which is time-consuming and labor-intensive. In this paper, a new method is proposed for counting the number of grains and germinated grains. In the coarse segmentation process, the k-means clustering algorithm is applied to obtain rough grain-connected regions. We further refine the segmentation results obtained by the k-means algorithm using a one-dimensional Gaussian filter and a fifth-degree polynomial. Next, the optimal single grain area is determined based on the area distribution curve. Accordingly, the number of grains contained in the connected region is equal to the area of the connected region divided by the optimal single grain area. Finally, a novel algorithm is proposed for counting germinated grains. This algorithm is based on the idea that the length of the intersection between the germ and the grain is less than the circumference of the germ. The experimental results show that the mean absolute error of the proposed method for germination rate is 2.7%. And the performance of the proposed method is robust to changes in grain number, grain varieties, scale, illumination, and rotation.
Why it matches plant phenotyping methodsイネ種子の発芽率という植物形質を画像処理で自動抽出・定量する手法を開発し、誤差と頑健性を評価しており、表現型取得法が中心である。
abstractIn this paper, a new method is proposed for counting the number of grains and germinated grains.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' analysis code and the 90 rice grain images used for germination-rate phenotyping in a public GitHub repository, matching an allowed URL. The web application URL is a live service, not a deposited asset, and is excluded.Code · publicges of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Acknowledgments
We are deeply grateful to the editor and reviewers for their assistance with reviews and guidance of the paper.
Data Availability
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .
Funding Statement
This study was supported by the National Natural Science Foundation of China (Grants No. 61373004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Wu W, Zhou L, Chen J, Qiu Z, He Y. GainTKW: a measurement sysOpen asset ↗DoctorXiong123456/CodeOfPaperlines:233-285Dataset · publicmages of II you 534 and the germination detection results.
(DOCX)
Click here for additional data file.
S3 Table
30 images of Luyou 911 and the germination detection results.
(DOCX)
Click here for additional data file.
Data Availability Statement
The code and 90 images used in this study are available from our GitHub repository: https://github.com/DoctorXiong123456/CodeOfPaper .Open asset ↗DoctorXiong123456/CodeOfPaperlines:286-308Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
SoybeanField / plotRootSeed / grainCountingGrowth / development / phenologyRoot system architecture
The symbiotic relationship between soybean [ Glycine max L. (Merr.)] roots and bacteria ( Bradyrhizobium japonicum ) lead to the development of nodules, important legume root structures where atmospheric nitrogen (N 2 ) is fixed into bio-available ammonia (NH 3 ) for plant growth and development. With the recent development of the Soybean Nodule Acquisition Pipeline (SNAP), nodules can more easily be quantified and evaluated for genetic diversity and growth patterns across unique soybean root system architectures. We explored six diverse soybean genotypes across three field year combinations in three early vegetative stages of development and report the unique relationships between soybean nodules in the taproot and non-taproot growth zones of diverse root system architectures of these genotypes. We found unique growth patterns in the nodules of taproots showing genotypic differences in how nodules grew in count, size, and total nodule area per genotype compared to non-taproot nodules. We propose that nodulation should be defined as a function of both nodule count and individual nodule area resulting in a total nodule area per root or growth regions of the root. We also report on the relationships between the nodules and total nitrogen in the seed at maturity, finding a strong correlation between the taproot nodules and final seed nitrogen at maturity. The applications of these findings could lead to an enhanced understanding of the plant- Bradyrhizobium relationship and exploring these relationships could lead to leveraging greater nitrogen use efficiency and nodulation carbon to nitrogen production efficiency across the soybean germplasm.
Why it matches plant phenotyping methodsSNAPという機械学習ベースの表現型取得・解析パイプラインを用いて、根粒数・サイズ・面積を定量化することが研究の中心的手法として明示されている。
titleUsing machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean
Reproduction assets foundThe paper states that all data and code will be shared through the authors' Singh group GitHub. This is the only paper-specific availability statement; it points to a lab-level GitHub organization rather than a specific repository, and uses future tense ('will be shared'), so the exact deposit for this paper's nod phenCode · publicAll data and code will be shared through the Singh group GitHub https://github.com/SoylabSingh .Open asset ↗SoylabSinghlines:1171-1263Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Introduction Evaluations of interspecific hybrids are limited, as classical genebank accession descriptors are semi-subjective, have qualitative traits and show complications when evaluating intermediate accessions. However, descriptors can be quantified using recognized phenomic traits. This digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid. In this study, a line of P. vulgaris , P. acutifolius and P. parvifolius accessions and their crosses were sown in the mesh house according to CIAT seed regeneration procedures. Methodology Three accessions and one derived breeding line originating from their interspecific crosses were characterized and classified by selected phenomic descriptors using multivariate and machine learning techniques. The phenomic proportions of the interspecific hybrid (line INB 47) with respect to its three parent accessions were determined using a random forest and a respective confusion matrix. Results The seed and pod morphometric traits, physiological behavior and yield performance were evaluated. In the classification of the accession, the phenomic descriptors with highest prediction force were Fm', Fo', Fs', LTD, Chl, seed area, seed height, seed Major, seed MinFeret, seed Minor, pod AR, pod Feret, pod round, pod solidity, pod area, pod major, pod seed weight and pod weight. Physiological traits measured in the interspecific hybrid present 2.2% similarity with the P. acutifolius and 1% with the P. parvifolius accessions. In addition, in seed morphometric characteristics, the hybrid showed 4.5% similarity with the P. acutifolius accession. Conclusions Here we were able to determine the phenomic proportions of individual parents in their interspecific hybrid accession. After some careful generalization the methodology can be used to: i) verify trait-of-interest transfer from P. acutifolius and P. parvifolius accessions into their hybrids; ii) confirm selected traits as "phenomic markers" which would allow conserving desired physiological traits of exotic parental accessions, without losing key seed characteristics from elite common bean accessions; and iii) propose a quantitative tool that helps genebank curators and breeders to make better-informed decisions based on quantitative analysis.
Why it matches plant phenotyping methodsインタースペシフィック雑種の形質を定量化・分類し、ランダムフォレストと混同行列で親由来のフェノミック形質割合を検証する方法論が研究の中心である。
abstractThis digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid.
Reproduction assets foundThe paper's MultispeQ physiological phenotyping measurements (1,022 observations) are publicly available on the PhotosynQ platform as the authors' own project 'domestication-syndrome' (ID 5685). No author analysis code or trained model deposit is stated; the data availability statement only promises raw data on requestDataset · publicThe classical protocol was used: Leaf Photosynthesis MultispeQ V1.0 (the raw data are available at: https://photosynq.org/projects/domestication-syndrome ; ID 5685).Open asset ↗PhotosynQ · ID 5685lines:319-327Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The lentil ( Lens culinaris Medik.) is one of the major pulse crops cultivated worldwide. However, in the last decades, lentil cultivation has decreased in many areas surrounding Mediterranean countries due to low yields, new lifestyles, and changed eating habits. Thus, many landraces and local varieties have disappeared, while local farmers are the only custodians of the treasure of lentil genetic resources. Recently, the lentil has been rediscovered to meet the needs of more sustainable agriculture and food systems. Here, we proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology. The results indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment. Lentil size measurements combined with seed coat descriptors and the color attributes of the grains allowed us to develop an algorithm that was able to identify 64 red lentil genotypes collected at ICARDA with an accuracy approaching 98% for seed size grading and close to 93% for the classification of seed coat morphology.
Why it matches plant phenotyping methods画像解析を用いてレンズマメ種子のサイズ、形状、種皮形態、色を非破壊測定・分類する手法を開発し、精度も評価しているため、植物表現型取得が中心です。
abstractwe proposed an image analysis approach that, besides being a rapid and non-destructive method, can characterize seed size grading and seed coat morphology.
Reproduction assets foundThe paper's lentil seed image dataset (64 images of ICARDA genotypes used for the computer vision phenotyping pipeline) is explicitly stated to be publicly available on the authors' GitHub repository. The Data Availability Statement only offers other data upon request, but the image dataset itself has a public URL.Dataset · publicpaigns, and we found that its repositioning contained a non-negligible error for the purposes of the evaluation process here described. This means that the images in some cases showed a different scaling factor that was handled by the algorithms. In Figure 1 , the acquisition setup is shown. The dataset is publicly available at https://github.com/beppe2hd/unconstrainedLentils (accessed on 10 November 2022).
2.1. Plant Materials
In the present study, we analyzed the grains of 64 lentil genotypes received by ICARDA in Lebanon, including 48 varieties released in 19 different countries between 1984 and 2018, 9 germplasm accessions, and 7 elite breeding lines developed at ICARDA in Lebanon ( TablOpen asset ↗https://github.com/beppe2hd/unconstrainedLentilslines:31-42Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.
Why it matches plant phenotyping methods植物形質の大規模再利用可能データセットを構築し、確率的品質管理と外部情報による検証を実施しており、形質データ基盤が研究の中心である。
abstractHere we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits.
Reproduction assets foundThe paper's core asset is the 'Global Spectrum of Plant Form and Function Dataset' (species mean values for six plant traits plus categorical traits and references), explicitly deposited publicly under a CC-BY license in the TRY File Archive with DOI 10.17871/TRY.81. This is a paper-specific, publicly actionable trait/Dataset · publicThe dataset is available under a CC-BY license at the TRY File Archive (https://www.try-db.org/TryWeb/Data.php):
Díaz, S. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. TRY File
Archive https://doi.org/10.17871/TRY.81 (2022)244Open asset ↗TRY File Archive · 10.17871/TRY.81pdf-page:5 lines:1-62Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Why it matches plant phenotyping methods自動化された高スループット画像表現型プラットフォームを開発し、77種類の画像形質を定量化・検証し、機械学習による草丈予測も評価しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages.
Reproduction assets foundThe paper explicitly states that all images and phenotypic data are available on figshare and that the HTP/RGB image and GWAS analysis pipeline code is available on the authors' GitHub repository (maizeHTP). Both are paper-specific, public, and actionable.Dataset · publicAll the images and phenotypic data are available at https://figshare.com/account/home#/projects/141743 .Open asset ↗figshare · projects/141743lines:168-200Code · publicThe code for HTP from LemnaTec and the code for the RGB image and GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizeHTP .Open asset ↗GitHub · GUOWEIJUN/maizeHTPlines:168-200Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without having a ground-truth point cloud. Finally, a counting method is presented where the density of seed centers in the 3D model allows 2D counts from multiple views to be effectively combined into a whole-panicle count. We demonstrate that using this method to estimate seed count and weight for sorghum outperforms count extrapolation from 2D images, an approach used in most state of the art methods for seeds and grains of comparable size.
Why it matches plant phenotyping methodsソルガム穂の3D再構成と種子計数という植物形質取得手法を開発し、再構成品質評価指標と種子数・重量推定を検証しており、フェノタイピング手法が中心である。
abstractwe present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments
Reproduction assets foundThe paper's authors publicly release their sorghum panicle stereo-image dataset (camera poses, human-labeled seed segmentations, panicle weights, seed counts) via the CMU AIIRA resources page, which is an allowed URL.Dataset · publicection, some unremoved husks were counted as seeds by the counting machine despite manual efforts to separate seeds from husks. We expect the effect on the ground truth to be small. The stereo images, camera poses, human-labeled seed segmentations, panicle weights, and human-counted seed counts can be found in our dataset 3 3
3
https://labs.ri.cmu.edu/aiira/resources/ .
Figure 8: (a) 100 sorghum panicles from 10 different sorghum species. (b) Our data collection system, a stereo camera attached to the UR5 robot arm. (c) Seeds were manually stripped and (d) counted using a seed counting machine.
IV-B 3D Reconstruction Quality
We assess the effectiveness of our approach with ablation tests usiOpen asset ↗lines:141-165Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Why it matches plant phenotyping methodsピーナッツ種子の品種分類とカビ状態検出を目的に、ハイパースペクトル画像と前処理・機械学習モデルを中心的に開発・比較しており、植物の状態を推定するフェノタイピング手法に該当する。
abstracthyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds.
Reproduction assets foundThe paper's data availability statement explicitly deposits the original study contributions (peanut seed hyperspectral data) in a public GitHub repository, which is listed among the allowed URLs.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://github.com/wuqingsongwj/Peanut-seed .Open asset ↗wuqingsongwj/Peanut-seedlines:589-618Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Researchers interested in the seed size of rapeseed need to quantify the cell size and number of cells in the seed coat, embryo and silique wall. Scanning electron microscope-based methods have been demonstrated to be feasible but laborious and costly. After image preparation, the cell parameters are generally evaluated manually, which is time consuming and a major bottleneck for large-scale analysis. Recently, two machine learning-based algorithms, Trainable Weka Segmentation (TWS) and Cellpose, were released to overcome this long-standing problem. Moreover, the MorphoLibJ and LabelsToROIs plugins in Fiji provide user-friendly tools to deal with cell segmentation files. We attempted to verify the practicability and efficiency of these advanced tools for various types of cells in rapeseed. Results We simplified the current image preparation procedure by skipping the fixation step and demonstrated the feasibility of the simplified procedure. We developed three methods to automatically process multicellular images of various tissues in rapeseed. The TWS-Fiji (TF) method combines cell detection with TWS and cell measurement with Fiji, enabling the accurate quantification of seed coat cells. The Cellpose-Fiji (CF) method, based on cell segmentation with Cellpose and quantification with Fiji, achieves good performance but exhibits systematic error. By removing border labels with MorphoLibJ and detecting regions of interest (ROIs) with LabelsToROIs, the Cellpose-MorphoLibJ-LabelsToROIs (CML) method achieves human-level performance on bright-field images of seed coat cells. Intriguingly, the CML method needs very little manual calibration, a property that makes it suitable for massive-scale image processing. Through a large-scale quantitative evaluation of seed coat cells, we demonstrated the robustness and high efficiency of the CML method at both the single-cell level and the sample level. Furthermore, we extended the application of the CML method to developing seed coat, embryo and silique wall cells and acquired highly precise and reliable results, indicating the versatility of this method for use in multiple scenarios. Conclusions The CML method is highly accurate and free of the need for manual correction. Hence, it can be applied for the low-cost, high-throughput quantification of diverse cell types in rapeseed with high efficiency. We envision that this method will facilitate the functional genomics and microphenomics studies of rapeseed and other crops.
Why it matches plant phenotyping methodsアブラナの種皮・胚・長角果壁の細胞形質を画像から自動定量する手法を開発・検証しており、フェノタイピング手法が研究の中心です。
abstractWe developed three methods to automatically process multicellular images of various tissues in rapeseed.
Corn is an important human food crop and animal feed source. Purity of corn seed is critical to yield and marketing. Screening of corn ears is an important but time-consuming and labor-intensive task in seed production. In recent years, deep learning has made great achievements in tasks such as image classification, object detection, face recognition, etc. In this paper , a method combining convolutional neural network VGG16 and machine vision to quickly classify different varieties of corn using corn ear images is proposed. By collecting RGB images of corn ears with intact phenotypic traits of 5 varieties, a data set containing 1000 images was constructed, and divided into training set, validation set and test set according to the ratio of 7:2:1. By improving the fully connected layer structure of the VGG16 network, optimizing the training parameters, and using transfer learning and data enhancement techniques, the optimal performance model was obtained after training all layers of the VGG16, and the accuracy rate reached 98.00% on the test set. Under the same experimental conditions, comparing the three methods of training from scratch, pre-feature extraction and only training the fully connected layer, the accuracy rates obtained were 94.00%, 96.88%, and 94.00%, respectively. The improved model achieved the highest classification accuracy rate and stable performance. In the experiments, the effects of network parameters on the model classification results were also discussed. The experiment showed that the phenotypic characteristics of the corn ears could better realize the classification and identification of different varieties of corn, which provides a reference for the intelligent sorting of corn seed production.
Why it matches plant phenotyping methodsトウモロコシ穂の画像から品種を分類・識別する機械視覚とCNN手法の開発が中心で、植物器官の表現型特徴を用いた再利用可能な解析ワークフローである。
abstracta method combining convolutional neural network VGG16 and machine vision to quickly classify different varieties of corn using corn ear images is proposed.
Reproduction assets foundThe preprint's Data Availability Statement deposits the paper's own corn ear image dataset (1000 RGB images of 5 varieties used for VGG16 classification) on Mendeley Data with a public DOI, making it a paper-specific, publicly actionable phenotyping image dataset.Dataset · publicThe datasets generated and/or analysed during the current study are available in the [Xu, jinpu (2022), “five_corn_ears”, Mendeley Data, V2,] repository, http://dx.doi.org/10.17632/hb3hbsz6t9.1Open asset ↗Mendeley Data · 10.17632/hb3hbsz6t9.1lines:382-409Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method, in which only about 100 to 150 images are required for each model reconstruction. The 3D models reflect the realistic morphology and genuine color of the carpological materials. The 3D models are scaled to represent the true size of the materials even as small as 3 mm in diameter. The interfaces are interactive, in which the 3D models can be rotated in 360° to observe the structures and be zoomed to inspect the macroscopic details. This new platform is beneficial for developing a virtual herbarium of carpological collection which is thus the most important to botanical authentication and education.
Why it matches plant phenotyping methods植物の果実・種子等の形態を高解像度3D再構成するフォトグラメトリ手法とインタラクティブ基盤が中心であり、観察可能な植物形態を取得する方法論研究である。
abstractThis study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method
Reproduction assets foundThe authors publicly host the 3D models of carpological materials reconstructed in this study (100 models from the paper, over 250 released) in the 'Virtual Carpological Herbarium of Fruits and Seeds' online database. This is a paper-specific public asset directly reproducing the paper's phenotyping outputs. Software (Dataset · publicAll the 3D models were uploaded to an open online database ( https://syhuherbarium.sls.cuhk.edu.hk/collections/3d-specimen/ ; Username: syhuherbarium; Password: @CUHK). Currently, over 250 3D models were uploaded to the online database and more will be released in the future.Open asset ↗lines:94-123Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
Why it matches plant phenotyping methodsトウモロコシ雌穂の画像取得・深層学習による形質抽出システムを開発し、手動測定との比較で各形質の信頼性を検証しており、植物フェノタイピング手法が研究の中心です。
abstractWe developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears.
Reproduction assets foundThe authors' full analysis pipeline (MATLAB GUI plus Python deep-learning code for grain segmentation and phenotypic trait extraction) is explicitly stated to be fully available on a public GitHub repository. The phenotype datasets themselves are only available on request, so they do not qualify as public assets.Code · publicThe code for the analysis using a Graphical User Interface in MATLAB is fully available on a public repository ( https://github.com/Phymea-Systems/Earbox ). It is used in combination with a Python code applying the trained neural network to extract the DL2 images, also available in the same public repository.Open asset ↗Phymea-Systems/Earboxlines:104-113Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.
Why it matches plant phenotyping methodsイネの収量関連形質を高スループットに取得する統合計測・画像解析システムを開発し、検出精度と測定効率を検証しているため、植物フェノタイピング手法が中心である。
abstracta novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy.
Reproduction assets foundThe article explicitly states that all threshed panicle training and testing data (1,072 threshed panicle images with PASCAL VOC annotations, augmented to 3,432 training and 856 testing images) are publicly available via a Baidu Netdisk link with an extraction code for non-commercial research. This is a paper-specific,Dataset · publicAll the training and testing data are available at https://pan.baidu.com/s/1-XawHGseIc5bboVOP48Fkw?pwd=153w with the extraction code ‘153w’ for non-commercial research purposes.Open asset ↗pan.baidu.com · 1-XawHGseIc5bboVOP48Fkw?pwd=153wlines:330-340Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background and aims Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research. Methods Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe. Key results In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures. Conclusions Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.
Why it matches plant phenotyping methods熱勾配プレート実験の種子発芽表現型を解析・予測するRスクリプトとワークフローが中心で、観測値との検証も行っているため。
abstractwe have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature.
Reproduction assets foundThe paper's TGP germination data and R analysis workflow are openly deposited on Zenodo (record 5457222), with explicit availability statements in the Methods and Data Availability sections.Dataset · publicThe data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.5457222 .Open asset ↗Zenodo · 10.5281/zenodo.5457222lines:223-251Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm 3 , and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized.
Why it matches plant phenotyping methodsトウモロコシ穀粒の組成・物理形質を高スループットに取得・推定するハイパースペクトル画像プラットフォームと解析手法の開発が研究の中心である。
abstractwe developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels
Reproduction assets foundThe paper's authors explicitly state that all analysis code for the hyperspectral phenotyping pipeline (PLSR trait prediction, PLS-DA kernel-side classification, image analysis) is publicly available in their GitHub repository.Code · publicgenerate a confusion
matrix, along with specificity and sensitivity rates (Supplemental
Table 1).
2.7. Complete pipeline
The processes, measurements, and analyses described in Sections
2.3-2.6 were combined to produce a pipeline shown in Fig. 1B-E. All of
the code created to execute the analyses is available in this repository,
https://github.com/jivarelao/Hyperspectral_Scanner.3. Results and discussion
3.1. Variability of maize kernel traits in ground-truth sets
Directly measured traits ranged widely across the kernel samples
(Table 1). Kernel volume displayed the largest range (5.6-fold). Kernel
weight was second at 5-fold, followed by vitreousness (2.7-fold), protein
(2.4-fold) and densitOpen asset ↗https://github.com/jivarelao/Hyperspectral_Scanner.3pdf-raw-page:5 lines:1-77Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.
Why it matches plant phenotyping methodsメタボロームスペクトルから大麦の麦芽品質形質を予測し、複数モデルと交差検証で予測精度を比較・検証しており、育種利用可能な形質推定法が中心です。
abstractWe investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes.
Reproduction assets foundThe article states that all data used (malting quality trait records and metabolomic features for 2667 plots of 564 spring barley lines) are deposited in a public Mendeley Data repository with a direct link, making the paper's phenotyping measurements publicly available.Dataset · publicAll the data used are available in a public accessible repository with the direct link as https://data.mendeley.com/datasets/s3s4ft92wj/1 .Open asset ↗s3s4ft92wjlines:160-241Code / 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 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 15 Sept 2026
Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
Why it matches plant phenotyping methodsマルチスペクトル画像と多変量解析により、アルファルファ種子の活力・生存性・発芽率を非破壊推定する手法が研究の中心であるため。
abstractAs a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation.
Reproduction assets foundThe authors provide a public Google Drive supplement containing the paper's own multispectral imaging data: mean reflectance at 19 wavelengths for all seeds (Table S1), morphological feature data for all seeds (Table S2), and multispectral images of the alfalfa seed samples (Figures S1–S6). These directly reproduce theDataset · publicThe following are available online at https://drive.google.com/file/d/13CXchEm81qnbIZCXLqdvupDPib7BS8FM/view?usp=sharing , Table S1: Mean reflectance of 19 wavelengths in all seeds, Table S2: Data of morphological feature in all seeds. Figure S1: Multispectral image of seeds harvested in 2004. Figure S2: Multispectral image of seeds harvested in 2008. Figure S3: Multispectral image of seeds harvested in 2019. Figure S4: Multispectral image of seeOpen asset ↗lines:79-239Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana . An image segmentation‐based method using the software I mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation‐based method was error‐prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts ( r 2 = 0.9996) and highly accurate fruit counts ( r 2 = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.
Why it matches plant phenotyping methodsFaster R-CNNによる種子・果実数という植物形質の画像ベース測定法を開発・比較検証し、解析パイプラインとモデルを提示しているため、フェノタイピング手法が中心的です。
abstractAn image segmentation‐based method using the software I mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all analysis scripts and the final seed and fruit counting models (trained Faster R-CNN phenotyping models) in a public GitHub repository under the authors' ShiuLab account, making it a paper-specific, publicly actionable asset.Code · publicAll the scripts used in this study and the final seed and fruit counting models are available on GitHub at: https://github.com/ShiuLab/Manuscript_Code/tree/master/2022_Arabidopsis_seed_and_fruit_count .Open asset ↗ShiuLab/Manuscript_Code · 2022_Arabidopsis_seed_and_fruit_countlines:244-645Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Why it matches plant phenotyping methods穀粒という植物器官のタンパク質含量をNIRセンサーと機械学習で推定する校正モデルを開発・評価しており、形質取得・抽出法が研究の中心である。
abstractWe explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra.
Reproduction assets foundThe authors explicitly state that the custom CNN analysis code for this paper's NIR protein-prediction models is publicly available on GitHub at the authors' repository URL, which matches an allowed URL. Supplementary tables are only referenced via a placeholder (www.mdpi.com/xxx/s1) and are not actionable; the Video SCode · publicced by the quality
and size of the datasets used for training the model. To minimize the “over-fitting” error,
the large dataset was used and split carefully to include the different multi-cereal species
in both the calibration and validation dataset (as described in section 2.5.1). The code is
available on the GitHub platform (https://github.com/adamavip/nirs-protein-prediction)
and its particular parts can be now utilized to enhance and develop other pipelines and
products.
For our dataset, the algorithms built using ML-based methods (particularly, the
stacked ensemble model via Hone Create and custom-designed CNN; section 3.4)
achieved the better comparative metrics for both spectra typesOpen asset ↗adamavip/nirs-protein-predictionpdf-raw-page:14 lines:1-54Code / dataset availability 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. RESULTS: We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. CONCLUSIONS: We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.
Why it matches plant phenotyping methodsCNNとGrad-CAMにより米粒画像から白未熟粒（chalkiness）を検出・定量する高スループット表現型計測手法の開発であり、方法が研究の中心です。
abstractlack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness
Reproduction assets foundThe authors explicitly state that the datasets (rice grain images) generated and analyzed in this study, as well as the trained models, are publicly available on GitHub at the authors' repository. This is a paper-specific, public, actionable asset directly reproducing the paper's phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during the current study are available on GitHub, https://github.com/cwang16/Phenotyping-of-Chalkiness-in-Rice .Open asset ↗cwang16/Phenotyping-of-Chalkiness-in-Ricelines:212-226Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background Most plants encounter water stress at one or more different stages of their life cycle. The maintenance of genetic stability is the integral component of desiccation tolerance that defines the storage ability and long-term survival of seeds. Embryonic axes of desiccation-sensitive recalcitrant seeds of Acer pseudoplatnus L. were used to investigate the genotoxic effect of desiccation. Alkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks and 8-oxo-7,8-dihydroguanine (8-oxoG) formation during progressive steps of desiccation and rehydration. Results The loss of DNA integrity and impairment of damage repair were significant predictors of the viability of embryonic axes. In contrast to the comet assay, automated electrophoresis failed to detect changes in DNA integrity resulting from desiccation. Notably, no significant correlation was observed between hydroxyl radical ( ٠ OH) production and 8-oxoG formation, although the former is regarded to play a major role in guanine oxidation. Conclusions The high-throughput comet assay represents a sensitive tool for monitoring discrete changes in DNA integrity and assessing the viability status in plant germplasm processed for long-term storage.
Why it matches plant phenotyping methods植物胚軸のDNA完全性と生存性を評価する高スループットコメットアッセイを最適化・検証しており、表現型状態の取得法が研究の中心である。
abstractAlkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks
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 · publicAdditional file 3: Fig. S3. (download PDF )
The representative comet measurements and images captured by Comet Assay IV analysis software.Open asset ↗lines:516-614Code / 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 · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.
Why it matches plant phenotyping methods画像セグメンテーションと深層学習によりイネ籾の充実・不稔を識別し、種子登熟率という植物形質を自動・高スループット推定する手法が研究の中心である。
abstractThis method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR.
Reproduction assets foundThe paper's rice panicle image dataset used for seed setting rate phenotyping is publicly deposited on Kaggle per the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited; supplementary material contains only figures/tables, not datasets or code.Dataset · publicice researchers to obtain RSSR information more efficiently and accurately, which will be a reliable method for further estimating rice yield.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/soberguo/riceseedsettingrate .
Author Contributions
YG: formal analysis, investigation, methodology, visualization, and writing—original draft. SL: supervision and validation. YL, ZH, and ZZ: project administration and resources. DX: writing—review and editing and funding acquisition. QC: writing—review and editing, funding acquisition, and resoOpen asset ↗Kaggle · soberguo/riceseedsettingratelines:555-571Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.
Why it matches plant phenotyping methods種子のハイパースペクトル画像取得、セグメンテーション、スペクトル解析を一体化したプラットフォームとソフトウェアを開発しており、植物形質取得法が中心である。
abstractwe have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds.
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 and data for testing is accessible in Github: https://github.com/tgaochn/HyperSeed (accessed on 3 December 2021).Open asset ↗tgaochn/HyperSeedlines:168-188Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
O_LIProper collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties, some of which, we believe, can be avoided. C_LIO_LIHere, we present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data. Besides providing a convenient command-line interface to existing software libraries, it comprises several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines. C_LIO_LIWe demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata, and extraction of leaf silhouettes from herbarium specimens. C_LIO_LIGinJinn2 will enable users with a primary background in biology to apply deep learning-based methods for object detection and segmentation in order to automate feature extraction from image data. C_LI
Why it matches plant phenotyping methods植物画像から種子、気孔、葉形状などを抽出する深層学習ツールを開発・提示しており、植物表現型取得のためのソフトウェアが中心である。
abstractwe present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data.
Reproduction assets foundThe paper's GinJinn2 source code and manual are explicitly stated to be freely available on the authors' GitHub repository. The annotated Seeds, Yellow-sticky-traps, Leucanthemum, and stomata annotation datasets are only promised via GfBio 'will be supplied as soon as available', so they are not yet actionable public; Code · public, and wrote the manuscript. Both authors approved the final version of the
379 manuscript. We further note that UL and TO contributed equally to this work. The
380 order of their names in the author list was decided by coin toss.
381
382 Data availability
383 GinJinn2’s source code and manual are freely available at GitHub
384 (https://github.com/AGOberprieler/GinJinn2). The annotated Seeds, Yellow-sticky-
385 traps and Leucanthemum datasets are hosted by the German Federation for
386 Biological Data (GfBio; Link A, Link B, Link C; will be supplied as soon as available).
387 The images used for the Stomata analysis are hosted by the Cuticle Database
388 (Barclay et al., 2012), a Python scripOpen asset ↗https://github.com/AGOberprieler/GinJinn2pdf-raw-page:15 lines:1-35Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting. However the huge computational complexity and massive storage requirements make it a great challenge to deploy them in real-time applications, especially on devices with limited resources. In this study, a rapid and highly efficient lightweight CNN based on visual attention, namely SeedSortNet, is proposed for seed sorting. First, a dual-branch lightweight feature extraction module Shield-block is elaborately designed by performing identity mapping, spatial transformation at higher dimensions and different receptive field modeling, and thus it can alleviate information loss and effectively characterize the multi-scale feature while utilizing fewer parameters and lower computational complexity. In the down-sampling layer, the traditional MaxPool is replaced as MaxBlurPool to improve the shift-invariant of the network. Also, an extremely lightweight sub-feature space attention module (SFSAM) is presented to selectively emphasize fine-grained features and suppress the interference of complex backgrounds. Experimental results show that SeedSortNet achieves the accuracy rates of 97.33% and 99.56% on the maize seed dataset and sunflower seed dataset, respectively, and outperforms the mainstream lightweight networks (MobileNetv2, ShuffleNetv2, etc.) at similar computational costs, with only 0.400M parameters (vs. 4.06M, 5.40M).
Why it matches plant phenotyping methods種子画像から種子の外観・純度に関わる状態を分類する軽量CNNを開発しており、画像取得・特徴抽出手法が研究の中心である。
abstractSeed sorting based on machine vision provides an effective solution to this problem.
Reproduction assets foundThe paper's Data Availability statement provides public access to both datasets and the authors' analysis code: the haploid/diploid maize seed dataset (from Altuntaş et al. 2019) hosted at rovile.org, and the SeedSortNet code plus the authors' sunflower seed dataset on GitHub.Dataset · publicThe maize seed dataset comes from Altuntaş et al. (2019): https://doi.org/10.1016/j.compag.2019.104874 and is available at: http://www.rovile.org/datasets/haploid-and-diploid-maize-seeds-dataset/ .Open asset ↗lines:571-586Code · publicThe seedsortnet code and sunflower seed dataset are available at GitHub: https://github.com/Huanyu2019/Seedsortnet .Open asset ↗Huanyu2019/Seedsortnetlines:571-586Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Summary Revealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana . An image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts. Although straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r 2 =0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r 2 =0.9996) and highly accurate fruit counts (r 2 =0.980). By examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes. Our study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions.
Why it matches plant phenotyping methods画像分割とFaster R-CNNを用いて種子数・果実数という植物形質を高スループット測定し、精度比較・検証を行うことが中心であるため。
titleHigh throughput measurement of Arabidopsis thaliana fitness traits using transfer learning
Reproduction assets foundThe paper's Data availability statement explicitly deposits all analysis scripts and the final seed and fruit counting models (trained Faster R-CNN phenotyping models) on the authors' public GitHub repository, which is listed in allowed_urls.Code · public, PD, SH,
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NLP, EV, EW, JKC, PJK, and MDL performed data collection and analysis. PW, FM,
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MDL, and SHS wrote the manuscript. All authors read and approved the final manuscript.
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Data availability
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All the scripts used in this study and the final seed and fruit counting models are available
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on Github at:
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https://github.com/ShiuLab/Manuscript_Code/tree/master/2021_Arabidopsis_seed_and_f
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ruit_count
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References
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Abadi M, Barham P, Chen JM, Chen ZF, Davis A, Dean J, Devin M, Ghemawat S,
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Irving G, Isard M et al. 2016. TensorFlow: A system for large-scale machine
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learning. 12th USENIX Symposium on Operating Systems Design and
726
Implementation. USENIXOpen asset ↗ShiuLab/Manuscript_Code · 2021_Arabidopsis_seed_and_fpdf-raw-page:33 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A collection of flax accessions from Russian Federal Research Center for Bast Fiber Crops was characterised to evaluate its phenotypic diversity. 406 samples representing different morphotypes were selected for thorough quantitative assessment of various agronomic traits. We measured height, length of technical part of the stem, technical part weight, inflorescence length, number of bolls and seeds per plant, 1000 seed weight, the diameter of the stem, the number of internodes and finally, distance between internodes. The fiber quality was estimated by calculating stem slenderness, stem taperingness and elementary fiber length. The dataset was produced in a framework of a project focused on characterization of diversity of flax genotypes and phenotypes, as well as on identification of genomic regions associated with various traits, it is hosted on Figshare.
Why it matches plant phenotyping methods植物遺伝資源の多形質表現型を体系的に収集したデータセットであり、表現型データセットとして中心的な対象である。
titleA comprehensive dataset of flax ( Linum uitatissimum L.) phenotypes.
Reproduction assets foundThe paper is a Data in Brief article describing a flax phenotype dataset (406 accessions, agronomic and fiber quality traits) hosted publicly on Figshare. The Figshare link is explicitly given as the direct URL to the data and matches an allowed URL, making it a paper-specific, publicly accessible phenotype dataset.Dataset · publicear of phenotyping.
Data source location
Institution: Federal Research Center for Bast Fiber Crops
City/Town/Region: Torzhok/Tver Region
Country:Russia
Latitude and longitude for collected samples/data: 57°02′N, 34°58′E; Altitude: 165 m
Data accessibility
Repository name: Figshare
Data identification number:
Direct URL to data: https://figshare.com/s/86a68ecfacf6872ef239
Value of the Data
•
The data on flax phenotypic diversity provides insight into flax domestication history and facilitates flax breeding efforts.
•
Flax raw material has multiple uses in various sectors of the economy including textile, medical, food and chemical industries as a source of fiber, linseed and oil. This data haOpen asset ↗Figsharelines:47-144Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Why it matches plant phenotyping methodsUAS画像から植生指数と草冠高を抽出し、機械学習で収量・開花期を推定する高スループット表現型解析が研究の中心です。
abstractUnoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicand PLSR regression. All R codes are available in Github repository (https://github.com/Open asset ↗pdf-page:8 lines:1-175Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman’s rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key Message Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.
Why it matches plant phenotyping methodsNIRスペクトルとSVMを用いて、トウモロコシ種子の加工中水分含量を高スループットかつ定量的に推定する方法の開発・検証が研究の中心であり、育種規模への適用も示している。
abstractThis study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels.
Reproduction assets foundThe paper's Code Availability section states all analysis code is publicly available on GitHub at the HirschLabUMN ML_Moisture_Prediction repository, which is an allowed URL. This is the authors' code for the NIR/machine-learning moisture prediction analysis. No separate public phenotype dataset deposit is explicitly aCode · publicCode Availability
All code is publicly available on GitHub at https://github.com/HirschLabUMN/ML_Moisture_Prediction.Open asset ↗HirschLabUMN/ML_Moisture_Predictionpdf-page:15 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Digital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments. A fast and non-destructive approach can allow rapid decision making to breed and select relevant rice varieties. Hence, this study compares the predictive potential of near-infrared (NIR) spectroscopy for three physical forms of rice i.e., rice kernel (with glume), whole grain brown rice and powdered rice. The aim is to identify the best physical form to be adapted in future use for high-throughput protein content prediction in rice samples. The models were optimized by selecting key wavelengths most correlated to the protein content in rice. For variable selection, a total of 8 recently developed chemometric variable selection techniques were used. As a baseline comparison to variable selection techniques, partial-least square (PLS) regression analysis was used. The results showed that for all forms of rice samples, variable selection improved the predictive performance compared to the PLS regression modelling. The best accuracies were obtained for the brown rice samples with a prediction error of 0.349%. Further, this was achieved with only 12 wavelengths compared to the 304 wavelengths available in the original data set. Based on the results, this study indicates that there is no need to grind the rice samples into powder for using NIR spectroscopy. Hence, NIR spectroscopy can directly be used on brown rice samples and can support the rapid assessment of protein content in rice to support digital phenotyping.
Why it matches plant phenotyping methodsNIR分光と波長選択・回帰モデルを用いて、玄米のタンパク質含量を非破壊かつ高スループットに推定する方法を比較・最適化しており、植物表現型取得が研究の中心である。
abstractDigital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments.
Reproduction assets foundThe paper's NIR spectra and reference protein content dataset (201 rice samples in three physical forms) is explicitly stated to be publicly accessible on Mendeley Data. No author analysis code or trained models are reported as available.Dataset · publicThe original data set used in this study is/are accessible at: https://
data.mendeley.com/datasets/zvgy65m2rc/1.data.mendeley.com · zvgy65m2rc/1pdf-raw-page:3 lines:1-78Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches in ampelography. The objective of this study was the classification of representative cultivars of Vitis vinifera conserved in the Spanish collection of IMIDRA according to seed shape. Thirty eight cultivars representing the diversity of this collection were analyzed. A consensus seed silhouette was defined for each cultivar representing the geometric figure that better adjusted to their seed shape. All the cultivars tested were classified in ten morphological groups, each corresponding to a new model. The models are geometric figures defined by equations and similarity to each model is evaluated by quantification of percent of the area shared by the two figures, the seed and the model (J index). The comparison of seed images with geometric models is a rapid and convenient method to classify cultivars. A large proportion of the collection may be classified according to the new models described and the method permits to find new models according to seed shape in other cultivars.
Why it matches plant phenotyping methods種子画像から形状を抽出し、幾何モデルと面積共有率でブドウ品種を分類する画像解析手法が研究の中心であり、再利用可能な植物形態フェノタイピング法に該当する。
abstractNew methods of image analysis may help to optimize morphological approaches in ampelography.
Reproduction assets foundThe paper deposits its seed-image datasets and analysis materials in public Zenodo records: composed images of 30 seeds per accession (record 4433813), a video protocol for obtaining average silhouettes (record 4478344), a video of the J index calculation process (record 4478315), and the Mathematica code for the ten新的Dataset · publicified in ten groups defined by their
similarity to each of the respective models.
2.4.1. Obtention of an average silhouette for each cultivar
The average silhouette is a representative image of seed shape for each cultivar. It
was obtained in Corel Photo Paint, by the following protocol (a detailed video is available
at Zenodo: https://zenodo.org/record/4478344#.YBPOguhKiM8):
The layers containing the seeds are superimposed and the opacity is given a value of
3 in all layers. All the layers are combined, and the brightness is adjusted to a minimum
value. From this image we are interested in the inner region representing the area where
most of the seeds coincide, which is the darkest area. ToOpen asset ↗Zenodo · 4478344pdf-raw-page:3 lines:1-37Code · publicbelow, the model in white.
Right: Reed zones show the areas quantified in each of the figures. ImageJ gives the total area for
the seed with the model in black, while shared area is obtained with the white model.
3. Results
3.1. New models
The Mathematica code for the ten new models described in this work is stored in
Zenodo: (https://zenodo.org/record/4478500#.YBPetOhKiM8). The following nine models
were obtained from modifications in Model 7 [24] (between parenthesis the cultivars to
which the model applies):
Model Listán Prieto (Listán Prieto and Tortozona Tinta):
(
2
17
(√3300 − 90𝑥2 −
400
24 + 5𝑥2
) + 𝑦) (
25
187
(−√3300 − 90𝑥2 −
1200
60 + 𝑥6
) + 𝑦) = 0;
Model Sylvestris (wild varietiesOpen asset ↗Zenodo · 4478500pdf-raw-page:4 lines:1-48Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The seeds of Arabidopsis thaliana become encapsulated by a layer of mucilage when imbibed. This polysaccharide-rich hydrogel is constituted of two layers, an outer layer that can be easily extracted with water and an inner layer that must be examined in situ in order to study its properties and structure in a non-destructive manner or disintegrated through hydrolysis or physical means in order to analyze its constituents. Mucilage production is an adaptive trait and we have exploited 19 natural accessions previously found to have atypical and varied outer mucilage characteristics. A detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates. This data will be a rich resource for genetic, biochemical, structural and functional analyses investigating mucilage constituent polysaccharides or their role as adaptive traits.
Why it matches plant phenotyping methodsアラビドプシス種子の粘液形質を対象に、33形質・4反復の再利用可能なデータセットを生成した研究であり、植物形質データセットの構築が中心です。
abstractA detailed study using biochemical, histological and Time-Domain NMR analyses has been used to generate three related datasets covering 33 traits measured in four biological replicates.
Reproduction assets foundThe paper deposits its plant-phenotyping measurements in three Data INRAE datasets. Two of them (dataset 1: 33 mucilage/seed traits; dataset 3: individual microscopy measurements of mucilage and seed width) have DOIs matching allowed_urls entries and are directly citable public assets. Dataset 2's DOI (10.15454/EYABB2)Dataset · publicCambert, M. et al. Seed mucilage traits for Arabidopsis thaliana natural accessions with atypical outer mucilage - dataset 1. Portail
Data INRAE https://doi.org/10.15454/1MZ1ZC (2021).Open asset ↗10.15454/1MZ1ZCpdf-page:9 lines:1-70Dataset · publicBerger, A., Sallé, C. & North, H. M. Measurements of inner mucilage and seed width for Arabidopsis natural accessions - dataset 3.
Portail Data INRAE https://doi.org/10.15454/LBUN4X (2021).Open asset ↗Portail Data INRAE · 10.15454/LBUN4Xpdf-page:9 lines:1-70Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Brassica oleracea is an important crop species that at early growth stages may exhibit failure of the apical growing point, an abnormality called “blindness”. The occurrence of blindness is promoted by exposure to low temperatures during imbibition and germination, but the causes of sensitivity to such conditions are unknown. We combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness. For image analysis, we used the VideometerLab instrument, which can scan 19 wavelengths from ultraviolet to infrared and utilize that information in any combination to potentially identify unique criteria related to seed quality. The iXeed CF Analyzer was utilized to obtain chlorophyll fluorescence values for individual seeds. Chlorophyll contents of many seeds can be used as an indicator of seed maturity, a major contributor to seed quality. Finally, oxygen consumption measurements of individual seeds as obtained with the Q2 instrument are highly correlated with their performance under a wide variety of conditions. Six Brassica seed lots differed in their susceptibility to induction of blindness or loss of viability due to 48 h hydrated incubation at 1.5 ∘C. Analysis of physical and respiratory parameters identified some measurements that were highly correlated with the occurrence of blindness. Higher chlorophyll content, as detected by the CF-Mobile and certain wavelengths in the Videometer, was associated with greater occurrence of blindness or death following the induction treatment, suggesting that more immature seeds may be susceptible to blindness. Further research is required, but methods to detect and sort such seeds based on physical characteristics appear to be feasible.
Why it matches plant phenotyping methods種子の画像・蛍光・呼吸測定を組み合わせ、物理特性から発芽品質やblindness感受性を評価・選別する方法が研究の中心であり、単なる生物学的結果測定ではない。
abstractWe combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness.
Reproduction assets foundThe paper's individual-seed phenotyping measurements (chlorophyll fluorescence, multispectral imaging, Q2 respiration, plant blindness scores) are consolidated in Supplemental Table S1 (Seed parameters database) and related supplements, publicly hosted on the MDPI article site. No author analysis code was deposited; CRDataset · publicSupplementary Materials: The following are available at https://www.mdpi.com/2077-0472/11/3
/220/s1, Table S1: Seed parameters database, Table S2: Q2 parameters, Table S3: MFA EigenvaluesOpen asset ↗pdf-page:20 lines:1-58Code / 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 confirmedEurope PMC · checked 9 Sept 2026
Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length ( GS3 ) and width ( qSW5/GW5 ) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.
Why it matches plant phenotyping methods種子画像から形状・色などの表現型を抽出するオープンソースGUIを開発し、複数作物およびイネ231アクセッションで有用性を検証しており、表現型取得手法が中心である。
abstractwe have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color
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 11
Seed color intensities for three channels in RGB color space for the RDP1.Open asset ↗lines:438-529Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
ArabidopsisLaboratory / benchtopSeed / grainObject detectionGrowth / development / phenology
A high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This technology was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics.
Why it matches plant phenotyping methods種子発芽を自動画像解析で測定する高スループット表現型解析法を開発し、再現性・精度・既存法との一致を検証しており、測定手法自体が中心である。
abstractA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed
Reproduction assets foundThe paper's seed-by-seed germination time measurements (the core phenotyping data) are published as online supplementary XLSX files (Supplementary Data S3, S4, S5), and a ZIP supplement (Supplementary Information 2) corresponds to the image time series (Supplementary Data S2). These are hosted with the open-access (CC Dataset · publicAll the germination time seed by seed in the analyses are provided in the Supplementary Data S3 online for Col-0 accession in water condition, in Supplementary Data S4 online for comparison with standard assays and in Supplementary Data S5 online for the analyse of Col-0 and L er ABA dose response.Open asset ↗lines:101-105Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley collection of the Federal ex situ Genebank for Agricultural and Horticultural Crops with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.
Why it matches plant phenotyping methodsゲノムワイド予測によって未表現型化アクセスionsの農業形質を推定し、交差検証で予測性能を評価しており、植物表現型推定手法が研究の中心である。
abstractThe prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios.
Reproduction assets foundThe paper reuses previously published, publicly deposited phenotypic (FT, PH, TGW) and genomic (306,049 SNPs for 20,454 barley accessions) datasets, explicitly linked in its data availability statement via two DOIs. No author analysis code or trained model checkpoints are disclosed; supplementary material is not shown.Dataset · publicThe datasets used for this study were published and are available at https://doi.org/10.1038/sdata.2018.278 and https://doi.org/10.1038/s41588-018-0266-x .Open asset ↗10.1038/sdata.2018.278lines:750-813Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Why it matches plant phenotyping methods種子の発芽状態を画像と機械学習で自動抽出し、手動計数と性能比較しているため、植物表現型取得法が中心です。
abstractWe here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.
Reproduction assets foundThe paper's authors publicly released the labeled germination image dataset (~24,000 annotated images of 2449 seeds) on Mendeley Data and their machine learning analysis code on GitHub, both explicitly stated in the Availability of data and materials section.Dataset · publicThe generated and labeled training data is freely available on Mendeley Data: http://dx.doi.org/10.17632/4wkt6thgp6.2 .Open asset ↗Mendeley Data · 10.17632/4wkt6thgp6.2lines:164-248Code · publicThe code for our proposed machine learning–based model can be found on GitHub: https://github.com/grimmlab/GerminationPrediction .Open asset ↗GitHub · grimmlab/GerminationPredictionlines:164-248Code / dataset availability 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 15 Sept 2026
Abstract Grain size and weight are important yield components in rice ( Oryza sativa L.). There is still uncertainty about the genetic control of these traits under drought stress, the most pressing emerging issue in many rice cultivation areas. To address this lack of knowledge, we investigated the genetic architecture of seed size, shape, and weight using the rice Global Multi-parent Advanced Generation Intercross (MAGIC) population, grown under well-watered and vegetative drought conditions. We measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV). Besides being affordable, rapid, and accurate, our method captured the phenotypic divergence between drought and well-watered samples, expressed as 12 different traits that include traditional size metrics and new grain shape measures. Overall, under water deficit, the MAGIC lines produced smaller and shorter seeds. We identified ten MAGIC lines with traits that make them good candidates for the release of rice cultivars with high yield potential under vegetative drought stress. We ran a marker-trait association analysis for the measured seed-related traits. Most of the identified marker-trait associations showed strong genotype-by-environment interactions (GxE), with most allele effects being conditionally neutral. These results suggest dynamic genetic control of seed size, shape, and weight under vegetative drought stress in rice, highlighting the importance of understanding the contribution of GxE interactions on trait variation to develop resilient and high-yielding rice varieties. Our study confirms that combining low-cost and high-throughput phenotyping strategies with a diverse genetic material suited for multi-environmental trial provides solutions for adapting rice cultivation to current and future environmental adversities.
Why it matches plant phenotyping methodsイネ種子の形状・サイズ・重量を、デスクトップスキャナーとPlantCVによる新規かつ高スループットな表現型取得法で測定しており、方法開発と実質的な適用が研究の中心です。
abstractWe measured variation in seed size and shape with a new high-throughput phenotyping method based on a desktop scanner and the open-source package Plant Computer Vision (PlantCV).
Reproduction assets foundThe paper deposits two paper-specific public assets: the authors' PlantCV image-analysis code (Zenodo 4156942) and the raw rice seed scan images used for phenotyping (Zenodo 4158169). Other URLs (PlantCV docs, 3K rice genome registry, R project) are generic resources or cited prior work, not paper-specific assets.Code · publicr standards
179 (white and grey cards) for image exposure normalization, and a ruler as a size standard.
180
181 We processed the RGB (Red Green Blue) images generated with the scanner using a personal
182 laptop with Intel® Core™ i7 8650u CPU @1.90Ghz and 16 GB RAM. The PlantCV code used for
183 this manuscript is available at https://doi.org/10.5281/zenodo.4156942, and more details on
184 the PlantCV functions used in our pipeline can be found in the online user manual of PlantCV
185 (https://plantcv.readthedocs.io/en/latest/). Briefly, for each RGB image, the pipeline first
186 standardizes image exposure using the white standard color. Then, it separates the seeds from
187 the backgrouOpen asset ↗zenodo · 10.5281/zenodo.4156942pdf-raw-page:9 lines:1-32Dataset · publicas described at
196 https://plantcv.readthedocs.io/en/stable/pipeline_parallel/. All trait estimates per seed and per
197 sample are saved in JSON text files, which are then merged and converted to a final CSV table
198 file using the accessory tool “plantcv-utils.py” implemented in PlantCV. All seed images are
199 available at https://doi.org/10.5281/zenodo.4158169.200 We also measured the grain weight of 50 seeds per sample using an analytical scale
201 (Adventurer® Analytical, Ohaus, USA). We then converted the weight of grains to 1000-seed
202 weight for easy comparisons with previous studies.
203 Statistical analyses of phenotypic data
204 We performed all statistical analyses of theOpen asset ↗zenodo · 10.5281/zenodo.4158169pdf-raw-page:10 lines:1-31Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
Abstract Walnut pellicle color is a key quality attribute that drives consumer preference and walnut sales. For the first time a high-throughput, computer vision-based phenotyping platform using a custom algorithm to quantitatively score each walnut pellicle in L* a* b* color space was deployed at large-scale. This was compared to traditional qualitative scoring by eye and was used to dissect the genetics of pellicle pigmentation. Progeny from both a bi-parental population of 168 trees (‘Chandler’ × ‘Idaho’) and a genome-wide association (GWAS) with 528 trees of the UC Davis Walnut Improvement Program were analyzed. Color phenotypes were found to have overlapping regions in the ‘Chandler’ genetic map on Chr01 suggesting complex genetic control. In the GWAS population, multiple, small effect QTL across Chr01, Chr07, Chr08, Chr09, Chr10, Chr12 and Chr13 were discovered. Marker trait associations were co-localized with QTL mapping on Chr01, Chr10, Chr14, and Chr16. Putative candidate genes controlling walnut pellicle pigmentation were postulated.
Why it matches plant phenotyping methodsクルミ果皮色を対象に、コンピュータビジョンと独自アルゴリズムで色形質を定量化する高スループット表現型解析プラットフォームを開発・適用しており、手法が研究の中心である。
abstractFor the first time a high-throughput, computer vision-based phenotyping platform using a custom algorithm to quantitatively score each walnut pellicle in L* a* b* color space was deployed at large-scale.
Reproduction assets foundThe paper's supplemental files on figshare contain the paper-specific phenotyping assets: raw color score data for both populations, CVS calibration and CIE analysis FIJI macros (analysis code), genetic maps with phenotypic data, breeding value phenotypes, and GWAS principal components. The figshare DOI is explicitly aDataset · publicSupplemental data file S8 contains raw color score data for both the GWAS population and ‘Chandler’ x ‘Idaho’ population. Supplemental material available at figshare: https://doi.org/10.25387/g3.12276539 .Open asset ↗figshare · 10.25387/g3.12276539lines:66-83Code / 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 confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Summary Soil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition ‘ionome’, of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool-IonExplorer to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory QTL, GWA studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.
Why it matches plant phenotyping methods大規模植物イオノーム取得のための高スループット実験・正規化パイプラインとデジタルツールを開発し、再利用可能なデータ資源として提示しているため、測定法が中心的です。
abstractWe present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection.
Reproduction assets foundThe paper's species-wide leaf and seed ionomic dataset for 1,135 A. thaliana accessions is made publicly accessible through the authors' interactive web tool Ion Explorer, which allows filtering, analysis, and download of the complete dataset as .csv files. This is a paper-specific, public, actionable asset. No author-Dataset · publicwe present an interactive web-based tool made available online: Ion Explorer
https://ffionexplorer.nottingham.ac.uk/ionmap/. Ion Explorer allows interactive visualisation,
analysis and comparison of the two large datasets.Open asset ↗Ion Explorerpdf-page:11 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
OatRootSeed / grainMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Abstract Seed vigor is crucial for crop early establishment in the field and is particularly important for forage crop production. Oat (Avena sativa L.) is a nutritious food crop and also a valuable forage crop. However, little is known about the genetics of seed vigor in oats. To investigate seed vigor-related traits and their genetic architecture in oats, we developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE). Root number, root surface area, and shoot length were measured in two replicates. Variables such as growth rate were derived. Using a genome-wide association (GWA) approach, we identified 34 and 16 unique loci associated with root traits and shoot traits, respectively, which corresponded to 41 and 16 unique SNPs at a false discovery rate < 0.1. Nine root-associated loci were organized into four sets of homeologous regions, while nine shoot-associated loci were organized into three sets of homeologous regions. The context sequences of five trait-associated markers matched to the sequences of rice, Brachypodium and maize (E-value < 10−10), including three markers matched to known gene models with potential involvement in seed vigor. These were a glucuronosyltransferase, a mitochondrial carrier protein domain containing protein, and an iron-sulfur cluster protein. This study presents the first GWA study on oat seed vigor and data of this study can provide guidelines and foundation for further investigations.
Why it matches plant phenotyping methods画像ベースの表現型取得パイプラインを開発し、根・シュート形質を抽出して大規模適用しており、フェノタイピング手法が中心的です。
abstractwe developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE).
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 · publicPhenotypic data collected in the study have been uploaded to T3/Oat: https://triticeaetoolbox.org/oat/ .Open asset ↗T3/Oatlines:89-100Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R 2 values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.
Why it matches plant phenotyping methodsX線CTを用いてイネ穀粒の22種類の3D形質を抽出する画像解析パイプラインを開発し、手動測定との一致性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す。
abstractIn this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits.
Reproduction assets foundThe paper's MATLAB 3D image analysis pipeline for extracting rice grain traits from X-ray CT is explicitly stated to be publicly available, with an authors' GitHub repository URL matching an allowed URL. The phenotypic data (Supplementary File 1) is only available as a PMC supplement with no allowed URL, so it is not aCode · publicthe source codes of all the scripts are available online in Supplementary File 3 or at the following link: http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action , or https://github.com/cancanzc/ricePanicle_grainTraits_ProcessingOpen asset ↗cancanzc/ricePanicle_grainTraits_Processinglines:109-117Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Laboratory / benchtopSeed / grainCountingGrowth / development / phenology
Background Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite's life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite ( Striga seed germination. Results Here, we introduce DiSCount ( Di gital S triga Count er): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. Conclusions DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956.
Why it matches plant phenotyping methodsStriga種子の発芽状態を画像から自動定量するコンピュータビジョン手法の開発・検証が研究の中心であり、植物状態の表現型取得に該当する。
abstractwe introduce DiSCount ( Di gital S triga Count er): a computer vision tool for automated quantification of total and germinated Striga seed numbers
Reproduction assets foundThe paper's DiSCount software (code, manual, trained YOLOv3 model) is hosted on GitLab and archived on Zenodo; the manually counted test image dataset and the installation-test input dataset are also publicly available on Zenodo. The pytorch-yolo-v3 repository is a third-party code base, not a paper-specific asset.Code · publicThe complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI https://doi.org/10.5281/zenodo.3627138 .Open asset ↗lodewijk-track32/discount_paperlines:1-79Code · publicThe DiSCount software is available at https://doi.org/10.5281/zenodo.3627138 along with detailed training settings and a complete installation and user manual.Open asset ↗10.5281/zenodo.3627138lines:84-90Dataset · publicThe dataset analysed to assess the performance of the software is publically available at https://doi.org/10.5281/zenodo.3403956 .Open asset ↗10.5281/zenodo.3403956lines:122-173Dataset · publicThe input dataset used to test the installation of the software is publically available at https://doi.org/10.5281/zenodo.3404131 .Open asset ↗10.5281/zenodo.3404131lines:122-173Code / 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 confirmedCrossref · Europe PMC · checked 9 Sept 2026
Summary Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. Here, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor , which vary extensively in numbers, lengths, and angles of primary branches, as well as the three‐dimensional shape, size, and distribution of the seed. We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.
Why it matches plant phenotyping methodsX線CTと詳細な形態計測を組み合わせ、ソルガム穂の3次元形態を取得・解析する画像ベース表現型手法が研究の中心であるため。
abstractHere, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture.
Reproduction assets foundThe paper explicitly states that the full 3D X-ray imaging dataset of sorghum panicles is publicly downloadable from the Topp lab resources page, and that all image processing, feature extraction, and statistical analysis code is available in a public GitHub repository (Topp-Roots-Lab/3D-Sorghum-Inflorescence). Both URDataset · publicThe full 3D imaging dataset for this work can be downloaded from: https://www.danforthcenter.org/scientists‐research/principal‐investigators/chris‐topp/resourcesOpen asset ↗lines:51-62Code · publicAll code used for image processing, digital feature extraction, and statistical analysis from this study can be found at the following GitHub repository: https://github.com/Topp‐Roots‐Lab/3D‐Sorghum‐InflorescenceOpen asset ↗Topp‐Roots‐Lab/3D‐Sorghum‐Inflorescencelines:51-62Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を画像から自動取得・定量化する撮像システムと解析ソフトウェアを開発しており、植物フェノタイピング手法が研究の中心である。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper explicitly states that the phenoSEED analysis script is publicly available on GitLab and that the BELT-captured seed image datasets are available on the first author's Figshare page. Both are paper-specific, public, and actionable.Code · publicAt the time of publication, a version of the processing script is available at https://gitlab.com/usask-speclab/phenoseed .Open asset ↗usask-speclab/phenoseedlines:145-153Dataset · publicThe image datasets captured by BELT analysed for the sample study are available from https://figshare.com/authors/Keith_Halcro/8363580 . The phenoSEED script is available from https://gitlab.com/usask-speclab/phenoseed .Open asset ↗lines:173-192Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as Arabidopsis thaliana, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods. Results We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual seeds and sort them according to size, allowing an average of 12,000 seeds/hour to be processed. To achieve this high throughput, post harvested seeds are first separated from remaining plant material (dust and chaff) using a rapid sedimentation-based method. Then, classification algorithms are used to refine the separation process in silico. Accurate identification of all seeds in the samples was achieved, with relative errors below 2%. Conclusion The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.
Why it matches plant phenotyping methods種子数と種子サイズという植物形質を高スループットに取得するフローサイトメトリー法を開発・検証し、解析用Rパッケージも提供しているため、植物フェノタイピング手法が研究の中心である。
abstractWe report on the development of a high throughput method for counting seeds and measuring individual seed sizes.
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 · publicTo facilitate the use of this method, all necessary computations have been implemented into an R package ( SeedSorter ) that is freely available online at https://github.com/aleMorales/SeedSorter .Open asset ↗aleMorales/SeedSorterlines:76-82Code · publicThe R scripts and data required to reproduce these results can be obtained at https://github.com/aleMorales/SeedSorterPaper .Open asset ↗aleMorales/SeedSorterPaperlines:120-135Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental conditions. More generally, these traits are also measured in the field of ecology as simple traits that can be correlated to other adaptative traits more difficult to measure on adult plants, as for example traits of the rooting system. Methods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth. The present dataset comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first sugar beet crop ontology. This dataset corresponds to the first automated phenotyping of a population of 198 genotypes and 4 commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q. The equipment and methods presented here are available on a phenotyping platform opened to collaborative research and adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping values can also be linked to genomic information to study the genetic determinism of the trait values.
Why it matches plant phenotyping methods種子から幼苗までの形質を対象とした自動・高スループット表現型解析手法、データセット、公開プラットフォームが研究の中心であるため。
abstractMethods were developed for deep high throughput phenotyping of hundreds of genotypes from dry seed to the end of heterotrophic growth.
Reproduction assets foundThe paper is a data descriptor whose sugar beet seed/seedling phenotyping dataset (28 traits for 202 genotypes) is publicly deposited in the URGI Plant and Fungi Dataverse with DOI 10.15454/AKNF4Q. No author analysis code is publicly released (scripts in Avizo/TCL/MATLAB and Fiji are described but no deposit URL is给定).Dataset · publiculgaris L.) grown area and an exotic accession of Beta vulgaris maritima from Denmark. Institution: Florimond Desprez; City/Town/Region: Cappelle-en Pévèle; Country: France. Latitude and longitude for collected samples 50.5167; 3.1667
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Repository name: URGI Plant and Fungi Dataverse
Data identification number: https://doi.org/10.15454/AKNF4Q
Direct URL to data: https://doi.org/10.15454/AKNF4Q
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Seed and seedling traits are increasingly measured in the field of ecology as simple traits that can be used to describe species diversity. A deeper phenotyping of genetic diversity in crops is also necessary to better understand their tolOpen asset ↗URGI Plant and Fungi Dataverse · 10.15454/AKNF4Qlines:129-170Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.
Why it matches plant phenotyping methods大豆種子の形態・色を対象に、画像取得・処理・解析による高スループット表現型計測法を開発・検証しており、方法自体が研究の中心です。
abstractwe propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis.
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/1424-8220/20/1/248/s1 , Table S1: Measurement feature of ImageJ used in this study, Table S2: Morphological data of soybean seeds in the 400 lines.Open asset ↗lines:45-107Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the P anicle I maging Plat form (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Why it matches plant phenotyping methodsイネ穂の非破壊3D画像再構成とデジタル形質抽出を行うPI-Platを開発・比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractThese images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity.
Reproduction assets foundThe paper publicly shares (1) a partial raw image dataset on a UNL Box repository and (2) the authors' PI-Plat Panicle-3D-Reconstruction workflow scripts at wrchr.org. Full raw images and the manual phenotyping dataset are only available on request, so those portions would be request_only, but the two public assets areDataset · publicRaw image data is large and hence only part of them is shared for user testing on a UNL Box repository ( https://unl.box.com/s/g0bof1mpfp33hn66b2qabrk9kiwmhbzv ).Open asset ↗unl.box.comlines:117-127Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Peanut allergy is one of the serious health concern and affects more than 1% of the world's population mainly in Americas, Australia, and Europe. Peanut allergy is sometimes life-threatening and adversely affect the life quality of allergic individuals and their families. Consumption of hypoallergen peanuts is the best solution, however, not much effort has been made in this direction for identifying or developing hypoallergen peanut varieties. A highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens. Results revealed a wide phenotypic variation for all the five allergens studied i.e. , Ara h 1 (4-36,833 µg/g), Ara h 2 (41-77,041 µg/g), Ara h 3 (22-106,765 µg/g), Ara h 6 (829-103,892 µg/g), and Ara h 8 (0.01-70.12 µg/g). The hypoallergen peanut genotypes with low levels of allergen proteins for Ara h 1 (4 µg/g), Ara h 2 (41 µg/g), Ara h 3 (22 µg/g), Ara h 6 (829 µg/g), and Ara h 8 (0.01 µg/g) have paved the way for their use in breeding and genomics studies. In addition, these hypoallergen peanut genotypes are available for use in cultivation and industry, thus opened up new vistas for fighting against peanut allergy problem across the world.
Why it matches plant phenotyping methods多様性パネルを対象に、モノクローナル抗体ELISAで落花生種子の主要アレルゲン量を大規模に定量し、低アレルゲン遺伝子型を同定する測定ワークフローが研究の中心である。
abstractA highly diverse peanut germplasm panel was phenotyped using a recently developed monoclonal antibody-based ELISA protocol to quantify five major allergens.
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:1096-1135Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.
Why it matches plant phenotyping methodsソルガム種子形状という植物形態形質の定量化手法を比較・標準化し、GP精度とGWAS結果で評価しており、形質取得・抽出法が研究の中心である。
abstractAmong geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations.
Reproduction assets foundThe paper's seed contour shape data (the phenotyping measurements used for GP/GWAS) are publicly deposited in the authors' GitHub repository, explicitly stated in the Data Availability statement. Supporting tables (S1–S3) also contain accession lists and GWAS results but the GitHub repository is the primary paper-qualiDataset · publicData Availability: All seed counter shape data are available from the https://github.com/risasakamoto/Comparison-of-shape-quantification-methods .Open asset ↗risasakamoto/Comparison-of-shape-quantification-methodslines:177-188Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background Seed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information. Results A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil ( Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions Increased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.
Why it matches plant phenotyping methods種子の色・サイズ・形状・種皮模様を自動取得・抽出する撮像システムと解析ソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis.
Reproduction assets foundThe paper's phenoSEED image-analysis script (used for seed shape, size, colour, and clustering phenotyping) is explicitly stated to be publicly available on the authors' GitLab repository. The BELT image datasets themselves are only available on request, so they do not qualify as public assets.Code · publicde and hard-
ware plans available for academic and non-commercial
use. It is hoped that this will support the collection
of more easily cross-comparable data and encourage
other research groups to contribute to further de-
velopment of the project. At the time of publica-
tion, a version of the processing script is available at
https://gitlab.com/usask-speclab/phenoseed. Further
information on a comprehensive hardware and soft-
ware bundle will be made available as it is packaged
for distribution.
Methods
BELT System Design and Description
BELT (Figure 1) was designed around a 150 mm wide
conveyor with at white, low-gloss belt (Mini-Mover
Conveyors, Volcano CA) mounted on an audio-visual
carOpen asset ↗usask-speclab/phenoseedpdf-raw-page:8 lines:1-99Code / 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 15 Sept 2026
To explore the relationship between the attributes of the rice panicle and its weight parameters, 6 different rice cultivars from Sihong City, Jiangsu Province, China were selected for sampling in 2017. Then, their weight parameters were measured. The images of rice panicles were scanned to obtain grain area. The significant correlation between the grain area and the panicle weight was found on the base of the analysis for the data obtained [1]. Now the weight and area data were present here for exploring the rapid yield estimation models and crop phenotype research.
Why it matches plant phenotyping methodsイネ穂の画像スキャンから粒面積を取得し、収量推定や作物表現型研究に再利用するデータを提示しており、画像ベース形質データセットとして方法論的価値がある。
abstractThe images of rice panicles were scanned to obtain grain area.
Reproduction assets foundThis Data in Brief article presents the paper's own rice panicle phenotyping measurements (grain area, panicle/grain weight parameters for 6 cultivars, 1200 panicles) and states that the raw data files (.xlsx) were uploaded as supplementary material, publicly accessible online at the article DOI. No separate author-depDataset · publica-
tional Natural Science Fund, China (31701321).
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relation-
ships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.104667.
Reference
[1] S. Zhao, H. Zheng, M. Chi, X. Chai, Y. Liu, Rapid yield prediction in paddy fields based on 2D image modelling of rice panicles,
Comput. Electron. Agric. 162 (2019) 759e766, https://doi.org/10.1016/j.compag.2019.05.020.Open asset ↗pdf-layout-page:6 lines:1-23Code / 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
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 10 Sept 2026
Published12 Mar 2019Metabolomics : Official journal of the Metabolomic SocietyCited by 21 · OpenAlex ↗
Introduction Oats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels. Objectives Oats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated. Methods Four winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha -1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach. Results Application of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed. Conclusions Although the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism.
Why it matches plant phenotyping methodsUHPLC-MSによる植物代謝表現型の迅速取得法を開発・反復性評価し、大規模穀類サンプルへの適用可能性と限界も示しているため、代謝測定が単なる生物学的実験の補助ではなく方法論の中心である。
abstractThe UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples.
Reproduction assets foundThe paper's UHPLC-MS metabolite profiling data (oat nitrogen supplementation study) is publicly deposited in MetaboLights (MTBLS804), and the authors' ASCA/PLS-S analysis scripts are publicly available on GitHub.Code · publicASCA and PLS-S with RFE were performed within MATLAB 2016a using in-house scripts which are made available freely online at https://github.com/Biospec/cluster-toolbox-v2.0 .Open asset ↗GitHub · Biospec/cluster-toolbox-v2.0lines:106-112Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Agribusiness has a great relevance in the world׳s economy. It generates a considerable impact in the gross national product of several nations. Hence, it is the major driver of many national economies. Nowadays, from each new planting to harvesting process it is mandatory and crucial to apply some kind of technology to optimize a given singular process, or even the entire cropping chain. For instance, digital image analysis joined with machine learning methods can be applied to obtain and guarantee a higher quality of the harvest, leading to not only a greater profit for producers, but also better products with lower cost to the final consumers. Thus, to provide this possibility this work describes a visual feature dataset from soybean seed images obtained from the tetrazolium test. This is a test capable to define how healthy a given seed is (e.g. how much the plant will produce, or if it is resistant to inclement weather, among others). To answer these questions we proposed this dataset which is the cornerstone to provide an effective classification of the soybean seed vigor (i.e. an extremely tiresome human visual inspection process). Besides, as one of the most prominent international commodity, the soybean production must follow rigid quality control process to be part of world trade. Hence, small mistakes in the seed vigor definition of a given seed lot can lead to huge losses.
Why it matches plant phenotyping methodsテトラゾリウム試験を受けたダイズ種子画像の視覚特徴データセットを構築し、種子活力の分類を支援することが中心であり、植物状態の画像ベース表現型データセットに該当する。
abstractthis work describes a visual feature dataset from soybean seed images obtained from the tetrazolium test.
Reproduction assets foundThe paper describes a public visual-feature dataset from soybean seed tetrazolium test images, explicitly deposited on GitHub by the authors.Dataset · publicratory.
Data source location
The seeds were scanned and annotated in the seed analysis laboratory in Tamarana, Paraná, Brazil. The preprocessing and feature extraction phases occurred at the Federal University of Technology - Paraná, in Cornélio Procópio, Paraná, Brazil.
Data accessibility
Data is publicly available on github ( https://github.com/BioinfoCP/visual-features-soybean-vigor ).
Related Research Article
Pereira et al. [1] . An image analysis framework for effective classification of seed damages. Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC), ACM, 2016, pp. 61–66.
Value of the data
•
The first open-access visual feature dataset that describes characteristiOpen asset ↗BioinfoCP/visual-features-soybean-vigorlines:1-53Code / 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 confirmedEurope PMC · checked 10 Sept 2026
The scarce knowledge on phenotypic characterization restricts the usage of genetic diversity of plant genetic resources in research and breeding. We describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions hosted at the Federal ex situ Genebank for Agricultural and Horticultural Plant Species. The dataset gathers records for three traits with agronomic relevance: flowering time, plant height and thousand grain weight. This information was collected for seven decades for winter and spring barley during the seed regeneration routine. The curated data represent a source for research on genetics and genomics of adaptive and yield related traits in cereals due to the importance of barley as model organism. This data could be used to predict the performance of non-phenotyped individuals in other collections through genomic prediction. Moreover, the dataset empowers the utilization of phenotypic diversity of genetic resources for crop improvement.
Why it matches plant phenotyping methods大規模な植物表現型データセットの構築・整理と再利用を主題としており、形質測定自体は再生時の routine だが、データセット提供が中心的な貢献である。
abstractWe describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions
Reproduction assets foundThe paper is a data descriptor publishing its own barley phenotypic dataset (FT, PH, TGW; original, outlier-corrected, and BLUEs) in ISA-Tab format at the IPK PGP repository, together with the authors' R/ASReml-R scripts for outlier detection and BLUE estimation, deposited under DOI 10.5447/IPK/2018/10.Code · publicScripts used for outlier detection and estimating BLUEs are included together with the
dataset in the public repository described below (Data Citation 1).Open asset ↗pdf-page:4 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
The mucilage is a model to study the polysaccharide biosynthesis since it is produced in large amounts and composed of complex polymers. In addition, it is of great economic interest for its technical and nutritional value. A fast method for phenotyping the released mucilage and the seed morphometric parameters will be useful for fundamental, food, pharmaceutical and breeding researches. Current strategies to phenotype soluble mucilage are restricted to visual evaluations or are highly time-consuming. Here, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters. Within this context, we combined a biochemical assay and an open-source computer-aided image analysis tool, MuSeeQ. The biochemical assay consists in sowing seeds on an agarose medium containing the dye toluidine blue O, which specifically stains the mucilage once it is released on the gel. The second part of MuSeeQ is a macro developed in ImageJ allowing to quickly extract and analyse 11 morphometric data of seeds and their respective released mucilages. As an example, MuSeeQ was applied on a flax recombinant inbred lines population (previously screened for fatty acids content.) and revealed significant correlations between the soluble mucilage shape and the concentration of some fatty acids, e.g. C16:0 and C18:2. Other fatty acids were also found to correlate with the seed shape parameters, e.g. C18:0 and C18:2. MuSeeQ was then showed to be used for the analysis of other myxospermous species, including Arabidopsis thaliana and Camelina sativa. MuSeeQ is a low-cost and user-friendly method which may be used by breeders and researchers for phenotyping simultaneously seeds of specific cultivars, natural variants or mutants and their respective soluble mucilage area released on a gel. The script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .
Why it matches plant phenotyping methods種子形態と放出粘液を画像から同時測定する高スループット手法およびImageJツールを開発・適用しており、植物表現型取得が研究の中心である。
abstractHere, we developed a high-throughput phenotyping method for the simultaneous measurement of the soluble mucilage content released on a gel and the seed morphometric parameters.
Reproduction assets foundThe paper's MuSeeQ ImageJ macro (the authors' phenotyping analysis code) is explicitly stated to be freely available, with video tutorials, at the authors' dedicated public website http://MuSeeQ.free.fr, which appears in the allowed URLs.Code · publicThe script of MuSeeQ and video tutorials are freely available at http://MuSeeQ.free.fr .Open asset ↗MuSeeQ.free.frlines:1-73Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( www.try-db.org ) to be included in the next version release.
Why it matches plant phenotyping methods植物の形態・機能形質を大規模に収録した再利用可能なデータベースであり、植物フェノタイピング用データセットとして中心的な貢献がある。
abstractThe Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome.
Reproduction assets foundThe paper's own Tundra Trait Team (TTT) trait database (raw and cleaned csv data) plus the authors' R data-cleaning scripts are publicly released in the authors' GitHub repository, with additional deposition in TRY and the Polar Data Catalogue.Dataset · publicthis cleaning protocol is primarily useful for species with large num‐
bers of observations of a given trait, and that much of the variation
within a species may be due to environmental or other differences
among sites (not error).
2.3 | Data availability and access
The TTT database will be maintained at the GitHub repository
(https://github.com/TundraTraitTeam/TraitHub). Trait data collec‐
tion is ongoing; thus, we will periodically release updated versions
of the database. A new version number will be assigned every time
there is a database update, and old database versions will be ar‐
chived for reference. A static version of the cleaned database (v. 1.0)
will also be available at the PolOpen asset ↗TundraTraitTeam/TraitHubpdf-raw-page:7 lines:1-59Code · publicSoftware format: csv file and GitHub repository with data cleaning scripts in R; con‐
tribution to TRY plant trait database (www.try-db.org) to be included in the next ver‐
sion release.Open asset ↗pdf-raw-page:4 lines:1-79Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Background The formation of crossovers during meiosis is pivotal for the redistribution of traits among the progeny of sexually reproducing organisms. In plants the molecular mechanisms underlying the formation of crossovers have been well established, but relatively little is known about the factors that determine the exact location and the frequency of crossover events in the genome. In the model plant species Arabidopsis , research on these factors has been greatly facilitated by reporter lines containing linked fluorescence marker genes under control of promoters active in seeds or pollen, allowing for the visualization of crossover events by fluorescence microscopy. However, the usefulness of these reporter lines to screen for novel modulators of crossover frequency in a high throughput manner relies on the availability of programs that can accurately count fluorescent seeds. Such a program was previously not available in scientific literature. Results Here we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik. Using the previously published reporter line Col3-4/20 as an example, we explain the use of MeioSeed and the steps taken to optimize the thresholding settings of the program to fit the published model for recombination frequency and transgene segregation. The use of MeioSeed is illustrated by investigating salt stress as a novel abiotic trigger for changes in crossover frequency in Col3-4/20 (♂) × Ler-0 (♀) F 1 hybrids. Salt stress was found to trigger increases in crossover frequency between the marker genes of up to 70% compared to the control treatment without salt stress. Genotyping of control and salt treated populations revealed that the changes in crossover frequency were not limited to the region between the marker genes, but that fluctuations in crossover frequency are likely to occur genome-wide after treatment with high salt concentrations. Conclusions MeioSeed allows for the high throughput recognition and counting of fluorescent Arabidopsis seeds and can facilitate the screening for novel abiotic and biotic modulators of crossover frequency using reporter lines in Arabidopsis .
Why it matches plant phenotyping methods蛍光種子を機械学習・画像解析で高スループットに認識・計数し、交差頻度という植物の遺伝的状態を推定するソフトウェア開発が中心であるため。
abstractHere we present MeioSeed, a novel CellProfiler-based program that accurately counts GFP and RFP fluorescent Arabidopsis seeds with adjustable detection thresholds for fluorescence intensity, making use of a robust seed classifier which was trained by machine learning in Ilastik.
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 MeioSeed package is available at http://cellprofiler.org/examples/published_pipelines .Open asset ↗lines:44-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We explore a recent, innovative variation of closed-system respirometry for terrestrial organisms, whereby oxygen partial pressure ( P O 2 ) is repeatedly measured fluorometrically in a constant-volume chamber over multiple time points. We outline a protocol that aligns this technology with the broader literature on aerial respirometry, including the calculations required to accurately convert O 2 depletion to metabolic rate (MR). We identify a series of assumptions, and sources of error associated with this technique, including thresholds where O 2 depletion becomes limiting, that impart errors to the calculation and interpretation of MR. Using these adjusted calculations, we found that the resting MR of five species of angiosperm seeds ranged from 0.011 to 0.640 ml g -1 h -1 , consistent with published seed MR values. This innovative methodology greatly expands the lower size limit of terrestrial organisms that can be measured, and offers the potential for measuring MR changes over time as a result of physiological processes of the organism.
Why it matches plant phenotyping methods蛍光式閉鎖系呼吸測定法のプロトコル、計算補正、誤差要因を開発・検証し、植物種子の代謝率という生理形質を測定しているため、植物フェノタイピング手法が中心です。
abstractWe explore a recent, innovative variation of closed-system respirometry for terrestrial organisms, whereby oxygen partial pressure ( P O 2 ) is repeatedly measured fluorometrically in a constant-volume chamber over multiple time points.
Reproduction assets foundThe paper's authors developed an annotated R script (Script 1) that automates their metabolic-rate calculations from Q2 fluorometric respirometry data, and it is explicitly stated to be available in the journal's supplementary material, which is publicly accessible at the supplemental URL. The seed respirometry raw/repCode · publicThe R script is available in the
supplementary material, and is heavily annotated to provide
guidance to its use (Script 1).Open asset ↗pdf-raw-page:2 lines:1-110Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
PREMISE OF THE STUDY: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. CONCLUSIONS: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.
Why it matches plant phenotyping methods低コストの画像取得プラットフォームを開発・記述し、植物形質の定量化に適用した方法論研究である。
abstractTo make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.
Reproduction assets foundThe paper provides public author analysis scripts (PlantCV-based phenotyping pipelines for Arabidopsis, quinoa seeds, and quinoa plants) hosted on the authors' GitHub repository, explicitly linked in the text and appendices.Code · publicsimilar vantage point (a 4 × 5
grid of pots) in each field of view, such that very similar computa-
tional pipelines can be used to process images from all 12 cameras.
An example image has been processed with PlantCV (Fahlgren
et al., 2015) in Fig. 2, and a script showing and describing each step
in the analysis is provided at https://github.com/danforthcenter/apps-phenotyping. Further image-
processing tutorials and tips can
be found at http://plantcv.readthedocs.io/en/latest/.Raspberry Pi camera stand
An adjustable camera stand is a versatile piece of laboratory equip-
ment for consistent imaging. Appendix 3 is a protocol for pairing a
low-cost home-built camera stand with a Raspberry POpen asset ↗danforthcenter/apps-phenotypingpdf-raw-page:3 lines:1-86Code / 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 · checked 10 Sept 2026
Seed / grainClassificationGrowth / development / phenology
Background Miscanthus is a leading second generation bio-energy crop. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small, germination is often poor and carried out without sterilisation; therefore, automated methods applied to germination detection must be able to cope with, for example, thresholding of small objects, low germination frequency and the presence or absence of mould. Results Machine learning using k -NN improved the scoring of different phenotypes encountered in Miscanthus seed. The k -NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k -NN result was 0.69-0.7, as measured using the area under a ROC curve. When the k -NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved. The method compared favourably to an established technique. Conclusions With non-ideal seed images that included mould and broken seed the k -NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k -NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score.
Why it matches plant phenotyping methods画像から種子発芽形質を自動抽出するk-NN手法を開発・検証しており、発芽判定が研究の中心です。
abstractMachine learning using k -NN improved the scoring of different phenotypes encountered in Miscanthus seed.
Reproduction assets foundThe paper's Miscanthus sinensis seed germination image dataset (the ~5000 seeds / 16,896 seed images used for k-NN phenotyping) is publicly deposited on OSF, with explicit availability language and URL in the article.Dataset · publicAvailability of data and materials.
The dataset analysed in this study is available at https://osf.io/aud9n/ [ 31 ].Open asset ↗osf.io · aud9nlines:144-226Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
Why it matches plant phenotyping methods高スループットのマイクロCT-RGB画像システムを開発し、イネの形態形質を多数・時系列で抽出することが研究の中心であるため。GWASはその応用にあたる。
abstractwe developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points.
Reproduction assets foundThe paper's own micro-CT-RGB phenotyping outputs (CT images, side-view RGB images, and extracted phenotypic traits for 234 rice accessions at 9 time points) are explicitly stated to be publicly viewable and downloadable from the authors' Huazhong Agricultural University plant phenotyping database. RiceVarMap is an exteDataset · public276
277
Phenotyping database extracted by HCR at 9 time points
278
During the tillering stage, 234 rice plants were automatically measured by HCR at 9
279
different development time points (once every 3 d, starting from 41 ~ 67 d after
280
sowing). All the phenotypic data and images can be viewed and downloaded via the
281
link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action and then following
282
these steps: (1) select ‘rice’; (2) select ‘2015-tiller’ in the year section; (3) select one
283
of the accession IDs in the ID section and then press ‘search images’; (4) 9 CT images
284
and 9 side-view color images can be viewed and downloaded; (5) a similar process
285
can be used to viOpen asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:10 lines:1-76Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We demonstrate that optical coherence tomography (OCT) is a plausible optical tool for in vivo detection of plant seeds and its morphological changes during growth. To investigate the direct impact of salt stress on seed germination, the experiment was conducted using Capsicum annuum seeds that were treated with different molar concentrations of NaCl. To determine the optimal concentration for the seed growth, the seeds were monitored for nine consecutive days. In vivo two-dimensional OCT images of the treated seeds were obtained and compared with the images of seeds that were grown using sterile distilled water. The obtained results confirm the feasibility of using OCT for the proposed application. Normalized depth profile analysis was utilized to support the conclusions.
Why it matches plant phenotyping methodsOCTを用いた種子の形態変化の非破壊・生体内モニタリング手法を開発・実証しており、植物表現型の取得方法が中心である。
abstractWe demonstrate that optical coherence tomography (OCT) is a plausible optical tool for in vivo detection of plant seeds and its morphological changes during growth.
Reproduction assets foundThe paper's supplementary material (Table S1) contains the paper-specific phenotyping measurements: seed weight and embryo thickness statistics for all NaCl-treated and control seed groups across the 9-day monitoring period, publicly available at the MDPI supplementary URL. No analysis code or image datasets are statedSupplement · publicugh Advanced Production Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (No. 314031-3). Additionally, this study was also supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).
Supplementary Materials
The following are available online at http://www.mdpi.com/1424-8220/17/12/2887/s1 . Table S1, The average weight gain observed and the averaged embryo thickness values for each group, along with its standard deviation value and the maximum and minimum values of seeds in each group that was observed during the entire monitoring process.
Click here for additional data file.
Author Contributions
The experimeOpen asset ↗lines:63-81Code / 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 confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 10 Sept 2026
O_LIPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.\nC_LIO_LIMethods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.\nC_LIO_LIConclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.\nC_LI
Why it matches plant phenotyping methods低コストの画像取得プラットフォームを開発・記述し、植物形質の定量化への適用性を示すことが中心である。
abstractTo make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.
Reproduction assets foundThe paper's PlantCV image-analysis scripts for its Raspberry Pi phenotyping examples are publicly available in the authors' danforthcenter/apps-phenotyping repository. The outreach and gphoto URLs are not paper-specific analysis assets.Code · publicAn example image has been p rocessed with PlantCV (Fahlgren et al., 2015), and
the analysis script is available at https://github.com/danforthcenter/apps-phenotypingOpen asset ↗danforthcenter/apps-phenotypingpdf-page:8 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Detailed and realistic tree form generators have numerous applications in ecology and forestry. Here, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules. The algorithm is designed to produce tree forms, i.e. morphological clones, similar as a whole (coarse-grain scale), but varying in minute details of organization (fine-grain scale). We present a general procedure for obtaining these morphological clones. Although we opted for certain choices in our algorithm, its various parts may vary depending on the application. Namely, we have shown that specific multi-purpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we have developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies in question by means of empirical distributions describing geometrical and topological features of a tree. Our algorithm can be used in variety of applications and contexts for exploration of the morphological potential of the growth models, arising in all sectors of plant science research. Summary Statement We present an algorithmic framework, based on the Bayesian inference, for generating morphological tree clones using a combination of stochastic growth models and experimentally derived tree structures.
Why it matches plant phenotyping methodsレーザースキャンによる樹木形態の再構成と形態類似度の計算、形態クローン生成アルゴリズムが研究の中心であり、植物形態を抽出・生成する方法論研究である。
abstractHere, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules.
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 Bayes-Forest toolbox is freely available at http://math.tut.fi/inversegroup/app/bayesforest/v1/.Open asset ↗Bayes-Forest toolboxpdf-page:15 lines:1-44Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and non-destructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of Sorghum bicolor, an important grain, forage, and bioenergy crop, at multiple developmental timepoints from a greenhouse-grown recombinant inbred line population. A semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci (QTL) for standard measures of shoot architecture such as shoot height, leaf angle and leaf length, and for novel composite traits such as shoot compactness. The phenotypic variability associated with some of the QTL displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動抽出を行う半自動パイプラインを開発し、ソルガムのシュート構造形質を取得・評価しており、表現型取得法が研究の中心です。
abstractA semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe paper explicitly deposits its authors' image acquisition/processing and QTL mapping code on GitHub, and its per-plant depth images, RGB images, and segmented meshes on the Dryad repository. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for
QTL mapping, the genotype and phenotype data, and the full multiple-QTL models for each phenotype
by timepoint combination can be found on GitHub at
https://github.com/MulletLab/SorghumReconstructionAndPhenotyping.Open asset ↗MulletLab/SorghumReconstructionAndPhenotypingpdf-page:8 lines:1-43Code / 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:369-504Code / dataset availability confirmedOpenAlex · Europe PMC · checked 11 Sept 2026
BACKGROUND: In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding-after data editing-57000 polymorphic and informative SNPS, among which 54% were in genic regions. RESULTS: In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10-7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10-7) and for plant height on chromosome 6 (FDR = 0.011). CONCLUSIONS: We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームの開発・適用が明示され、植物形態・根系・穀粒形質の測定とGWASを結び付けているため、表現型取得基盤が研究の主要部分と判断する。
abstractWe demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice.
Reproduction assets foundThe authors state that all relevant data (phenotypic and genotypic data underlying the GWAS) are publicly available in a Zenodo repository, which qualifies as a paper-specific public data asset.Dataset · publicData Availability All relevant data are publicly available in a Zenodo repository at the following URL: https://zenodo.org/record/50803#.VytVnrp97CI .Open asset ↗Zenodo · record/50803lines:48-55