Background Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture. Results We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships. Conclusions This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.
Why it matches plant phenotyping methods種子の構造・細胞領域をX線散乱／蛍光イメージングと機械学習ベースのセグメンテーションで定量解析する手法が研究の中心であり、植物器官の構造形質を抽出するため採用。
abstractWe apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.
Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。
abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying
Introduction Intercropping regimes enhance the efficiency of land use and ecological sustainability but present serious problems to automated disease analysis since the overlapping canopy and the similarity of symptoms in crop species are visually indistinguishable. Methods This work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems. The framework combines the spectral-spatial feature generators that utilize transformers including vision transformer (ViT), Swin transformer, pyramid vision transformer (PVT), and detection transformer (DETR) to identify nuanced biochemical and structural changes in crop combinations for maize-soybean and pea-cucumber. In order to reduce spectral redundancy and high dimensionality, an enhanced greedy political optimization (EGPO) algorithm is used as a wrapper-based feature selection strategy. A capsule spatial shift neural network (CSSNet) is used to predict the classification of diseases. Explainable AI methods, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) feature attribution analysis and gradient-weighted class activation mapping (Grad-CAM) visualization of disease-relevant regions, provide model transparency. The DETR + EGPO + CSSNet framework is tested on the conventional feature selection methods. Results and discussion The results or findings on publicly available hyperspectral datasets on intercropping show an average recall of 99.998% with high region consistency (Dice score: 99.997%) of activation maps and expert-marked disease regions. These findings affirm that the proposed framework is highly accurate, stable, and interpretable to identify subtle and overlapping disease in leaves in a complex system of intercropping.
Why it matches plant phenotyping methods植物葉の病害領域・病状をハイパースペクトル画像から推定する解析手法の開発と評価が中心であり、植物病害表現型の取得・抽出に直接関係する。
abstractThis work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems.
Abstract Pea ( Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near‐infrared spectroscopy data, is a cost‐effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield‐related traits in a panel of elite spring pea lines evaluated across 12 environments. Three cross‐validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic selection is as effective as genomic selection at predicting yield. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as seed yield and seed protein yield. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic‐genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near‐infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain.
Why it matches plant phenotyping methods近赤外分光データを用いるフェノミック選抜と予測モデルの性能評価が研究の中心であり、収量関連形質の推定・検証を行っているため。
abstractPhenomic selection, based on near‐infrared spectroscopy data, is a cost‐effective alternative demonstrated in various crops, but not yet undertaken in pea.
Early and accurate detection of pigeon pea leaf diseases is essential for improving crop productivity and ensuring food security, particularly under real-field agricultural conditions. This paper introduces a shallow and computationally off-the-shelf deep learning system to detect the presence of pigeon pea leaf disease with great accuracy and in real-time on resource-limited cameras. DSLR and smartphone cameras were used to make up a custom high-resolution dataset under natural field conditions, including healthy leaves and major diseases, such as Fusarium wilt, leaf spot, and powdery mildew. All the images were downsampled to 224 × 224 pixels and processed with a Gaussian smoothing filter to remove noise and a Canny edge detector to improve structural features. Disease regions were accurately isolated using a Skill Optimization Algorithm (SOA)-driven segmentation strategy that dynamically optimized threshold levels, morphological kernel sizes, and lesion area constraints to handle background clutter and illumination variations. A pretrained EfficientNet-B0 model was used to extract deep semantic features, which consisted of compact 1280-dimensional feature vectors. A novel FMDDCN approach was used to classify these features through exploiting the sensitivity to subtle disease patterns by relying on differential feature modeling and multi-layer fusion of features. The model was fitted on stochastic gradient descent with a learning rate of 1 x 10-3 and a batch size of 32, and assessed on a 60/20/20 train validation test split with 5-fold cross-validation. The results of the experiment show consistent convergence with low overfitting. The proposed framework was found to produce a classification accuracy of 94.5%, precision of 91.0%, recall of 85.5% and Matthews Correlation Coefficient of 88.5% when it was used with four optimized features. In comparison, it is demonstrated that FMDDCN performs better than traditional machine learning and deep learning models, with its F1-score of 0.965 and the overall accuracy of 0.965. The suitability of the real-time edge deployment is verified, as confirmed by the use of computational analysis to reduce inference latency and memory consumption.
Why it matches plant phenotyping methods画像から植物葉の病徴・病害状態を推定するリアルタイム手法の開発と技術評価が中心であり、植物フェノタイピング手法に該当する。
abstractThis paper introduces a shallow and computationally off-the-shelf deep learning system to detect the presence of pigeon pea leaf disease with great accuracy and in real-time on resource-limited cameras.
MilletPeaField / plotGreenhouseNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstructionVisualization / data managementGrowth / development / phenology
Plant phenotyping in precision agriculture increasingly requires high-fidelity three-dimensional reconstruction and accessible visualization methods. This study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages. We collected multi-view imagery of finger millet, proso millet, mungbean, and field pea under controlled greenhouse conditions, aligning data acquisition with standardized BBCH phenological scales. Camera pose estimation was performed using GLOMAP, followed by reconstruction via both Nerfacto and G-Splat implementations. Quantitative evaluation using PSNR, SSIM, and LPIPS metrics revealed complementary strengths of the two approaches: G-Splat achieved superior structural fidelity, while NeRF provided enhanced perceptual realism. Both reconstruction methods were successfully integrated into an immersive VR greenhouse environment deployed on Meta Quest headsets, maintaining consistently high framerates. This framework establishes a practical foundation for incorporating neural reconstruction and immersive technologies into agricultural phenotyping workflows, supporting both research applications and educational engagement.
Why it matches plant phenotyping methods植物の多視点画像からNeRFと3D Gaussian Splattingで3D形状を再構成し、画質指標で比較評価する統合フェノタイピング基盤の開発・検証が中心である。
abstractThis study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages.
Heterogeneity of cellular and subcellular elemental distribution is poorly captured by conventional techniques due to limited sensitivity, throughput, and resolution. Single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) enables quantitative single-cell element analysis but remains challenging in multicellular plants because of plant cell complexity and lack of protocols. Herein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts. An optimized fixation protocol (1% (v/v) glutaraldehyde for 15 min for protoplasts; 2.5% (v/v) for 30 min for chloroplasts) was applied to preserve structural integrity, with endogenous P and Mg identified as specific indicator elements for protoplasts and chloroplasts, respectively. To reduce interference from broken cells, a broken-signal correction method was employed during data processing. Following isolation, purification, and glutaraldehyde fixation, Pisum sativum leaf protoplasts and chloroplasts were subjected to SC-ICP-MS analysis. Quantitative analysis revealed significant elemental heterogeneity, with P as the most abundant in protoplasts (67.1-135 fg cell -1 ) and Mg as the most abundant in chloroplasts (33.6-41.5 fg cell -1 ). This technique advances single-cell element analysis in plants, enabling new insights into nutrient distribution, metal accumulation, and cellular responses to environmental stress beyond conventional techniques.
Why it matches plant phenotyping methods植物細胞・葉緑体の元素分布という生理状態を定量するSC-ICP-MS法の開発が研究の中心であり、固定条件や破損シグナル補正も技術的に検討している。
abstractHerein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts.
Common beanCucumberMaizePeaPotatoTomatoWheatMultispectral / hyperspectralLeafPhysiological trait estimation
The objective of this study was to assess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data. The dataset encompassed leaves from multiple crops, including potatoes, beans, wheat, maize, peas, tomatoes, basil, and cucumbers, collected under varying growth conditions, cultivation systems, seasonal contexts, and developmental stages. As an initial benchmark, commonly used narrow-band spectral indices and their combinations were evaluated, but they exhibited limited predictive performance for dry matter content. Consequently, several full-spectrum machine learning models were trained and compared to assess their individual predictive ability. Given their complementary strengths, these models were integrated into a stacked ensemble framework to enhance overall accuracy. The resulting ensemble, combining the outputs of multiple base learners through a meta-learner, achieved a coefficient of determination of R2=0.896 on an independent test set, outperforming all individual models. The findings highlight the potential of a multi-model stacking approach to improve the accuracy and robustness of leaf biochemical property estimation from hyperspectral data.
Why it matches plant phenotyping methodsハイパースペクトル反射データから葉乾物含量を推定する機械学習手法を開発・比較・検証しており、植物形質の取得方法が研究の中心である。
abstractassess the predictability of leaf dry matter content across a diverse range of plant species using hyperspectral reflectance data
Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.
Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。
abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 .Open asset ↗NPEC‐NL/greenhouse_m5lines:68-83Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Volatile organic compounds (VOCs) play a central role in plant communication and ecology, acting as a chemical language that mediates interactions with other organisms and responses to environmental stimuli. Analyzing changes in the plant volatilome enables the effective differentiation between biotic and abiotic stresses. Consequently, monitoring VOC emissions offers valuable insights into plant signaling pathways and health status. These insights position this approach as a promising strategy for improving crop protection. Direct infusion (DI) online analytical techniques, such as proton transfer reaction mass spectrometry (PTR-MS) and adduct ionization mechanism mass spectrometry (AIM-MS), have been developed to detect and characterize VOCs in real time. Here, we evaluated the suitability of PTR-MS and AIM-MS for monitoring VOC emissions in pea plants ( Pisum sativum L.). Comparative analysis revealed that AIM-MS, a recently developed technology, detected a higher number of distinct signals than PTR-MS. Annotation of detected and significant AIM-MS signals indicated a predominance toward those that were putative lipids-derived and amino acids-derived, whereas PTR-MS signals were primarily associated with putative phenolic compounds. These findings suggest that the newly developed AIM reactor offers a broader detection range and may enhance our ability to monitor plant VOC emissions. Consequently, AIM-MS emerges as a promising tool for the real-time assessment of pea plant health and stress responses. Further efforts are needed to improve the portability of DI-MS techniques and to integrate them with GC-MS techniques. Overall, these efforts will allow this technology to be exploited for plant protection in compromised environments.
Why it matches plant phenotyping methods植物のVOC放出を健康状態・ストレス応答の指標として測定するPTR-MSとAIM-MSを比較評価しており、植物状態の取得手法の技術的検証が中心である。
abstractHere, we evaluated the suitability of PTR-MS and AIM-MS for monitoring VOC emissions in pea plants ( Pisum sativum L.).
The legume-rhizobium symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (P nifH ) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) systems. We show that P nifH -driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and P nifH -driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify P nifH -driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobium symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. Importance The legume-rhizobium symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (P nifH ) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量法を複数宿主で評価し、深層学習画像解析パイプラインを標準法と比較検証しており、植物形質（根粒形成・占有）の取得手法が中心的です。
abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis
This research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods. The study compares three computer vision methods applied to peas: YOLO-based detection, semantic segmentation for recognizing plant elements in dry and green samples (using a proprietary digital phenotyping setup), and an original algorithm for detecting stem nodes by analyzing stem width. The detection method demonstrated low accuracy for plant parts. Semantic segmentation achieved 65 % accuracy for dry and 76 % for green plants. The node detection algorithm demonstrated 100 % accuracy. The developed software package enables objective assessment of key pea phenotypic traits. Further development of the system is aimed at integration with neural networks for determining leaf surface area and the number of productive nodes, which creates the basis for accelerated pea breeding.
Why it matches plant phenotyping methodsエンドウの表現型を抽出するコンピュータビジョン手法とソフトウェアを開発・評価しており、方法開発が研究の中心である。
abstractThis research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods.
PeaRootMorphology / geometry measurementSegmentationGrowth / time-series analysisRoot system architecture
Background With the intensification of global climate change, extreme weather events have become increasingly frequent, severely impacting the growth cycles and yield stability of crops. Against this backdrop, cultivating new crop varieties with high stress resistance has become a core task for achieving sustainable agriculture and ensuring food security. Root length, as a critical phenotypic trait that reflects a plant's ability to absorb water and nutrients, is closely related to the crop's capacity to withstand adversities, such as drought, high temperatures and salinisation. However, root length measurement technology remains a significant bottleneck in plant science research. Traditional manual methods are inefficient and prone to human-induced variability (e.g. subjective standard discrepancies, operational errors, and potential contamination or damage to seeds). Meanwhile, existing automated measurement models face challenges in large-scale practical applications due to their high deployment costs. Results This study developed a seed germination image acquisition system and constructed a pea root dataset. Based on the YOLOv8-Seg-n instance segmentation model, a lightweight automatic root measurement (ARM) model was then developed using feature distillation, structured pruning techniques, and a series of post-processing procedures for root length calculation. Experimental results demonstrated that the ARM model had only 1.81 M parameters, with 8.3 GFLOPs and a weight file size of 4.2 MB, and achieved 70.4 FPS. It realised outstanding performance with mAP@0.5 and AP root scores of 90.3% and 81.2%, respectively, showing a high consistency with manual measurement results (R² = 0.993). Compared to existing models, the ARM model significantly reduces parameter scale and computational complexity, making it more accommodating to device performance and computational requirements while also decreasing the workload associated with root sample processing. Furthermore, the application of the ARM model in a 72-hour full time-series analysis of pea root length under drought conditions validated its potential for practical use in real-world scenarios. Conclusions The ARM model offers an efficient and cost-effective technological solution for high-throughput root length measurement in peas. It achieves a favorable balance between accuracy, speed, and computational resource requirements, demonstrating broad application potential in agricultural production and breeding research. The model offers critical technical support for ensuring food security and enhancing crop stress resistance.
Why it matches plant phenotyping methodsエンドツーエンドの画像取得・セグメンテーション・根長算出モデルを開発し、手動測定との整合性および実利用を検証しており、植物表現型測定法が中心である。
abstractThis study developed a seed germination image acquisition system and constructed a pea root dataset.
Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy (R2 = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から飼料作物の成熟度という植物状態を推定し、ニューラルネットワークと可視化プラットフォームまで構築しているため、表現型取得・推定手法が中心である。
abstractachieved a high predictive accuracy (R2 = 0.985) for estimating the continuous maturity index
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Pea storage stability and germination rely on moisture content and morphology, but traditional destructive methods cause sample damage, low efficiency, and subjective errors, limiting practical use. To overcome the destructive and inefficient limitations of traditional methods for pea quality assessment, this study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning. We innovatively converted one-dimensional spectral data into two-dimensional texture images via Gramian Angular Field (GAF) encoding and input them into a residual 2D Convolutional Neural Network (2D-CNN) for moisture prediction. For dimensional analysis, a novel algorithm based on irregular polygon geometry was proposed to accurately measure pea length and width. The GAF-2D-CNN model achieved superior performance for moisture prediction (prediction set R²=0.9818, RMSEP=0.0318 %, RPD=7.4780), significantly outperforming 1D-CNN, Least Squares Support Vector Machine (LSSVM), and Partial Least Squares Regression (PLSR) models. The dimensional algorithm also demonstrated high accuracy, especially for length measurement (R²=0.9946, RPD=13.94). This framework provides a robust, accurate, and high-throughput solution for automated pea quality grading, offering significant potential for applications in precision agriculture and storage management.
Why it matches plant phenotyping methodsハイパースペクトル画像、深層学習、形状アルゴリズムを統合し、エンドウの水分含量とサイズという植物形質を非破壊・高スループットに推定する方法の開発が中心である。
abstractthis study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning.
Problem Accurate diagnosis of plant diseases is crucial for ensuring crop yield and food safety. This study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases. Methodology We propose a novel deep learning framework called TSSC. First, a three-neighbor channel attention is designed to promote the effectiveness of feature extraction. Second, a complementary squeeze and excitation mechanism is introduced to enhance the ability to extract key features. Finally, a split attention module is embedded to reduce model complexity. Results The experimental results demonstrate that the proposed model achieves an overall classification accuracy of 99.61% and outperforms other excellent deep learning models. Contribution The currently proposed system provides an effective solution for image recognition of complex plant diseases and has reference value for the development of mobile disease detection equipment.
Why it matches plant phenotyping methodsエンドツーエンドの葉画像から植物病害を識別する新規深層学習モデルを開発・評価しており、植物の病害状態を直接推定する方法が中心である。
abstractThis study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases.
Vegetable pea (Pisum sativum L.) is a nutritionally rich food source with a balanced profile of macronutrients and micronutrients, contributing multiple health benefits and plays a crucial role in combating nutritional deficiencies. Its nutritional diversity encompassing high range of protein, starch, soluble sugars, and phenolic content, renders it an ideal candidate for nutritional profiling, which is essential for mining Nutri-dense accessions. Near-infrared reflectance spectroscopy (NIRS) is a valuable alternative to conventional methods for nutritional profiling, offering rapid, accurate, less laborious, cost-effective, and non-destructive analysis with the capability to measure multiple parameters simultaneously for large-scale germplasms. This investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid with spectral pre-processing done by standard normal variate (SNV) and detrending (DT) using 90 vegetable pea (both marketable and mature stages) dried seed flour. The best-performing models were developed for moisture content (0.938, 0.469, 3.989), protein (0.931, 0.709, 3.063), starch (0.814, 1.312, 2.317), amylose (0.847, 0.646, 2.556), TDF (0.932, 0.652, 3.473), phenol (0.925, 0.078, 3.538), TSS (0.918, 0.231, 3.494), and phytic acid (0.898, 0.095, 2.358) corresponding to coefficient of determination (RSQ), corrected standard error of prediction (SEP(C)), and ratio of performance to deviation (RPD), respectively. This study presents the first report on the development of NIRS based prediction models using MPLS method for multi-trait assessment across different developmental stages in diverse vegetable pea germplasm, exhibiting high-throughput capability of the models in an economical and precise way.
Why it matches plant phenotyping methodsNIRSとmPLSによる植物種子の栄養形質を非破壊・高スループットに推定する予測モデルを開発し、性能指標で評価しているため、植物形質取得法が中心です。
abstractThis investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid
Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.
Why it matches plant phenotyping methods気孔形質を画像取得・検出・セグメンテーションで抽出する手法と、生成AIによる合成データを用いた種間適応を中心に開発しているため、植物フェノタイピング手法に該当する。
abstractThis study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 6 Sept 2026
The legume-rhizobia symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (PnifH) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) systems. We show that PnifH-driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and PnifH-driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify PnifH-driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobia symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. IMPORTANCEThe legume-rhizobia symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (PnifH) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量手法と、深層学習による結節形質の自動画像解析パイプラインを開発・検証しており、植物フェノタイピング手法が中心です。
abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis.
Flow cytometry (FCM) and genome sequencing are complementary methods for estimating plant genome size (GS). However, discrepancies between the GS estimates derived from genome assemblies and FCM create ambiguity regarding the accuracy of these approaches. Approximately 12,000 plant GS measurements have been reported, with hardly any of them based on genome assemblies. Currently, FCM is the most frequently used method. Accurate GS estimation by FCM relies on internal standards with known GS values. However, previous GS calibrations, often based on incomplete reference genome assemblies, have led to significant discrepancies in GS estimates. Historically, the GS of a diploid plant species was estimated by doubling the size of a consensus genome assembly. However, consensus assemblies collapse homologous chromosomes into a single sequence, typically favouring the larger haplotype and potentially overestimating GS, especially in highly heterozygous species. Here, we applied haplotype-resolved genome assemblies to accurately recalibrate the reference standards. We utilized a recent gapless, telomere-to-telomere (T2T) consensus and the most complete phased genome assemblies of the Nipponbare rice as a primary standard to recalibrate five commonly used plant standards. Using the consensus genome as a reference revealed an overestimation of over 30% in widely used previous GS estimates for Pisum sativum and Nicotiana benthamiana , approximately 18% for Arabidopsis thaliana , and 5% for Sorghum bicolor and Gossypium hirsutum . The GS estimates based on phased haplotype assemblies suggested an additional 6%–7% overestimation. Haplotype-resolved genome assemblies allow the recalibration of GS estimates with the potential to yield more accurate values by capturing haplotype-specific variations previously missed in consensus assemblies.
Why it matches plant phenotyping methods植物のゲノムサイズ推定に用いるフローサイトメトリー標準の再校正が研究の中心であり、測定精度の検証・改善に該当する。
titleRe-calibration of flow cytometry standards for plant genome size estimation
Introduction The rapid growth of the global population and intensive agricultural activities has posed serious environmental challenges. In response, there is an increasing demand for sustainable agricultural solutions that ensure efficient resource utilization while maintaining ecological balance. Among these, intercropping has gained prominence as a viable method, promoting enhanced land use efficiency and fostering environment for crop development. However, disease management in intercropping systems remains complex due to the potential for cross-infection and overlapping disease symptoms among crops. Early and precise illness recognition is, therefore, critical for sustaining crop condition and efficiency. Methods This study introduces an intelligent intercropping framework for early leaf disease detection, utilizing hyperspectral imaging and hybrid deep learning models for precision agriculture. Hyperspectral imaging captures intricate biochemical and structural variations in crops like maize, soybean, pea, and cucumber-subtle markers of disease that are otherwise imperceptible. These images enable accurate identification of diseases such as rust, leaf spot, and complex co-infections. To refine disease region segmentation and improve detection accuracy, the proposed model employs the synergistic swarm optimization (SSO) algorithm. A phase attention fusion network (PANet) is utilized for deep feature extraction, minimizing false detection rates. Furthermore, a dual-stage Kepler optimization (DSKO) algorithm addresses the challenge of high-dimensional data by choosing the most applicable landscapes. The disease classification is performed using a random deep convolutional neural network (R-DCNN). Results and discussion Experimental evaluations were conducted using publicly available hyperspectral datasets for maize-soybean and pea-cucumber intercropping systems. The suggested ideal attained remarkable organization accuracies of 99.676% and 99.538% for the respective intercropping systems, demonstrating its potential as a robust, non-invasive tool for smart, sustainable agriculture.
Why it matches plant phenotyping methods植物の葉の病徴をハイパースペクトル画像から検出・分類する画像解析手法が研究の中心であり、病害状態という植物表現型を直接推定しているため含める。
abstractThis study introduces an intelligent intercropping framework for early leaf disease detection, utilizing hyperspectral imaging and hybrid deep learning models for precision agriculture.
For most digital agriculture applications, such as in-season yield predictions, information on crop phenology is a prerequisite. Phenology is largely determined by environmental factors, e.g., temperature, precipitation, and global radiation. Consequently, weather data can be used to predict phenology. Here, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions. DyMEP was trained for ten crops, among others winter wheat, spring wheat, barley, green peas, beans and oat, with a large dataset representing the Central European climate. DyMEP fills the gap between complex, highly parameterized crop growth models that are difficult to use by non-experts, and extremely simplified models such as the Growing Degree Day approach. By carefully selecting the environmental covariates to use for each phenological phase, the user can reach suitable prediction accuracy for most applications in DyMEP. If temperature, precipitation, relative humidity, and global radiation are available as covariates to select from, the package achieves absolute errors ranging from 0 to 6 days across all applied phenology phases and root mean square errors ranging from 7 to 17 days on an independent test set. Combining DyMEP-based phenology predictions with ground-based or remote sensing observations holds promise to facilitate digital agriculture applications such as large-scale yield forecasting or monitoring of fields for crop insurance.
Why it matches plant phenotyping methods作物の生育ステージ（フェノロジー）を気象データから予測するRパッケージを開発・評価しており、植物状態の計測・推定手法が研究の中心である。
abstractHere, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions.
The article is devoted to the development and testing of technology for recognizing pea sprouts and estimating its biomass based on images from UAVs using neural networks. Rocket peas were sown by the “Kuzbass” sowing complex in the Topkinsky district of the Kemerovo Region on an area of 21.55 hectares. The soil type is slightly leached chernozem. The predecessor is spring wheat. The seed depth is 6 cm, the seeding rate is 1.1 million seeds per 1 hectare. Aerial photography was performed three weeks later with a quadcopter with a 20MP camera resolution from a flight altitude of 3 m. The shooting was carried out in two stages — in the early morning in cloudy conditions to obtain images of pea shoots without shadows and in the daytime with shadows from sprouts and weeds. As a result, two sets of 120 source photos were generated to train the neural network. Based on the obtained datasets, the Ultralytics YOLOv8 neural network model was trained. Testing of the obtained models was performed in a Python program for batch image processing and counting the number of plants in each image. The accuracy of recognizing sprouts on the first dataset was 97.3%, on the second — 67.3%. This is due to the different shooting conditions. Combining the two datasets allowed for a recognition accuracy of 94.7%. This is slightly lower than the first option, but much closer to the actual conditions of aerial photography. The result of the work is a program that allows batch image processing for automatic counting of pea sprouts and calculating their area in the images.
Why it matches plant phenotyping methodsUAV画像とニューラルネットワークを用いて、エンドウ苗の認識、個体数計数、面積およびバイオマス推定技術を開発・検証しており、表現型取得手法が研究の中心である。
abstractthe development and testing of technology for recognizing pea sprouts and estimating its biomass based on images from UAVs using neural networks
Early and accurate detection of pea plant leaf diseases is critical for improving crop yield and preventing widespread damage. This paper presents a deep learning-based approach for automated classification of pea leaf diseases using the ResNetV2 convolutional neural network architecture. The study utilizes a publicly available pea plant leaf dataset comprising four classes: Downy Mildew, Powdery Mildew, Leafminer damage, and Healthy leaves. The dataset was preprocessed and augmented to enhance model generalization, and the ResNetV2 model was fine-tuned to achieve effective feature extraction and classification. Experimental results demonstrate that the proposed method achieves a validation accuracy of approximately 94%, outperforming baseline models. The model’s performance was further analyzed via precision, recall, F1-score, and confusion matrix, confirming its robustness across all disease categories. The findings indicate that ResNetV2 is a promising candidate for practical deployment in agricultural monitoring systems, enabling timely disease diagnosis and management.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類する深層学習手法が研究の中心であり、植物病害の表現型推定に該当する。
abstractThis paper presents a deep learning-based approach for automated classification of pea leaf diseases using the ResNetV2 convolutional neural network architecture.
Introduction Pea is a nutrient-dense, functionally diversified vegetable. However, its leaf diseases have a direct impact on yield and quality. Most approaches for identifying pea leaf diseases exhibit low feature extraction efficiency, significant environmental sensitivity, and limited large-scale applications, making it impossible to meet the expectations of modern agriculture for accuracy, real-time processing, and low cost. Methods Therefore, we propose a deep learning model for pea leaf disease identification based on an improved MobileNet-V3_small, deformable convolution strategy, self-attention, and additive attention mechanisms (DSA-Net). First, a deformable convolution is added to MobileNet-V3-small to increase the modeling skills for geometric changes in disease features. Second, a self-attention mechanism is integrated to improve the ability to recognize global features of complex diseases. Finally, an additive attention strategy to enhance the feature channel and spatial position response relationship in edge-blurred lesion areas. The experimental pea leaf data set consists of 7915 samples divided into five categories. It includes one healthy leaf and four diseases: brown spot, leaf miner, powdery mildew, and root rot. Results The experimental results indicate that the suggested DSA-Net has an average recognition accuracy of 99.12%. It has a parameter size of 1.48M. Discussion The proposed approach will help with future edge device deployments. The current proposed technique considerably enhances the diagnostic accuracy of pea leaf diseases and has significant promotion and application potential in agriculture.
Why it matches plant phenotyping methodsエンドツーエンドの画像ベース深層学習モデルを開発し、エンドウ葉の病徴・病害状態を直接識別する手法が研究の中心であるため、植物フェノタイピング手法として収録する。
abstractwe propose a deep learning model for pea leaf disease identification based on an improved MobileNet-V3_small, deformable convolution strategy, self-attention, and additive attention mechanisms (DSA-Net).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Pea ( Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near-infrared spectroscopy data, is a cost-effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield-related traits in a panel of elite spring pea lines evaluated across twelve environments. Three cross-validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic prediction is as effective as genomic selection at predicting yield, and is more accurate for seed protein content. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as grain yield and seed protein. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic-genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near-infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain. Key message The integration of spectral data into prediction models enhances the predictive ability for complex traits in pea.
Why it matches plant phenotyping methods近赤外スペクトルを用いたフェノミック選抜の予測性能を、複数環境・交差検証で評価しており、植物形質推定法の検証と実質的応用が研究の中心である。
abstractThis study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield-related traits in a panel of elite spring pea lines evaluated across twelve environments.
Flowering is one of the most important and sensitive processes throughout a plant's life and marks the start of the reproductive phase. Flowering traits largely define yield potential and are therefore crucial for crop breeding. To observe flowering dynamics under field conditions, visual ratings have been a standard method for decades. Today, high-throughput field phenotyping (HTFP) methods provide opportunities for objective and efficient data collection. We developed an object detection approach (based on YOLOv8) that allows to collect detailed data about flower and pod density. RGB-images from 12 pea breeding lines were automatically acquired by the field phenotyping platform (FIP) of ETH Zurich in two years. The trained model reached high accuracy for open flower detection, which allowed to monitor flowering dynamics and flower density over time. Maximal flower density (Max.Fl.Dens) was highly correlated (R 2 = 0.967) to ground truth data taken in the field. Clear differences in timing of flowering and flower density were detected between breeding lines and years. Furthermore, a high correlation was observed between the maximal flower density and yield components. This automated, data-driven method of flower and pod detection proved itself as a reliable tool. Therefore, the results are promising for the use of RGB imaging methods to objectively assess not only flowering dynamics but also flower density and fruiting efficiency. Maximal flower density allows to predict seed amount and therefore has potential as selection trait in breeding programs. Fruiting efficiency could be used to identify stress-tolerant breeding lines.
Why it matches plant phenotyping methods花と莢の密度をRGB画像から自動推定する物体検出法を開発し、精度を地上真値と比較検証しており、植物表現型取得が研究の中心です。
abstractWe developed an object detection approach (based on YOLOv8) that allows to collect detailed data about flower and pod density.
This study aims to utilise natural variation in pea seed composition from NordGen collections to identify key traits for optimized plant-based ingredients functionality while minimizing refined extraction processes. Given the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions. Protein content, thousand kernel weight, perimeter, and G-value were determined as primary criteria via PCA, capturing variations in protein composition and other key components. Protein and starch content ranged from 21.2 to 36.9 % and 21.0-48.1 %, respectively. Image analysis linked geometry to composition, aiding pea selection and application. X-ray scattering differentiates peas based on starch structure. Proteomic profiling revealed that legumin and vicilin varied most, with legumin dominant in smooth peas and vicilin in wrinkled ones, enabling control of their ratio through selection. This study highlights the potential of using natural variation of seed composition for less-refined plant-based ingredients for various applications.
Why it matches plant phenotyping methods画像由来特徴量とアルゴリズムを用いて多数のエンドウ遺伝資源から種子形質・組成を推定し、化学分析対象を選抜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の実質的応用と判断します。
abstractGiven the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable the precise and high-throughput phenotyping of plant symptoms. Among many challenges, considering local deformations of symptoms and host tissues is difficult in plant pathology. In this study, we address this question using a level-set method. We propose an innovative approach in plant pathology that allows one to reconstruct the continuous deformation of leaf and lesion contours from daily image sequences of inoculated leaves. We consider pea stipules inoculated by the fungal pathogen Peyronellaea pinodes as an example pathosystem. After extracting lesion and stipule contours from daily visible images, we use the level-set method to track their deformations within image sequences. The visual assessment of model adequacy, along with the Jaccard Index and relative error metrics, demonstrated strong overall performance. Results showed a gradual decrease in model accuracy over time for leaf contours, while lesion contours exhibited a higher relative error on the first targeted date. These findings highlight the robustness of our method while identifying specific challenges in early lesion detection. We finish by discussing the interest in this method based on partial differential equations for the study of host-pathogen interactions, especially the development of original phenotyping methods in plant pathology.
Why it matches plant phenotyping methods植物の葉・病斑輪郭を画像から抽出し、level-set法で時系列変形を追跡する病害表現型計測法の開発・検証が中心である。
abstractWe propose an innovative approach in plant pathology that allows one to reconstruct the continuous deformation of leaf and lesion contours from daily image sequences of inoculated leaves.
Abstract This study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes. The standard analytical approach for TDF analysis is based on the Association of Official Analytical Collaboration method 985.29, which requires extensive sample preparation with extended analysis times of up to 30 h. The FT‐MIR approach was developed to enhance rapid and non‐destructive analysis and minimize the traditional workload associated with phenotyping TDF in pulse crops by accomplishing the same task in a shorter time and at minimal cost. Partial least squares regression (PLSR) was applied with chemometric modeling in MIR regions (650–1480 and 2771–3700 cm −1 ), encompassing spectral bands associated with undigested polysaccharides and partially or undigested protein and fatty acid methyl ester fractions that fingerprint TDF. K‐fold cross‐validation was used for PLSR modeling to enhance computational speeds with large‐scale data processing. These PLSR models for chickpea, dry pea, and lentil have coefficients of determination ( R 2 ) as 0.91, 0.96, and 0.94 with root mean square errors of prediction in the range of 0.05–0.5 g/100 g. This technique supports rapid phenotyping of TDF from raw flour in <1 min. The FT‐MIR technique can relieve the phenotyping bottleneck in pulse breeding and pulse‐based food and feed industries, targeting the measurements of TDF and ensuring a rapid and high‐throughput pipeline for plant breeding and cultivar development.
Why it matches plant phenotyping methodsFT-MIR分光法とPLSRモデルを開発・検証し、パルス作物のTDFという植物形質を高速・非破壊測定する手法が研究の中心である。
abstractThis study uses Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy as a high‐throughput phenotyping tool to quantify total dietary fiber (TDF) in chickpea ( Cicer arietinum L.), dry pea ( Pisum sativum L.), and lentil ( Lens culinaris Medik.) for pulse crop breeding purposes.
Volatile organic compounds (VOCs) produced by plants during plant-pathogen interactions can be highly informative for early disease detection. The real-time capability of field asymmetric ion mobility spectrometry (FAIMS) offers a valuable opportunity to monitor plant VOCs nondestructively and dynamically. This study evaluated the FAIMS system reliability in measuring VOC profiles for an early diagnosis of Aphanomyces root rot (ARR) in pea ( Pisum sativum L.). This evaluation utilized pea lines with a major quantitative trait locus (QTL Ae-Ps7.6 ) and lines without QTL, identified to provide partial resistance against ARR. For the first time, a VOC biomarker associated with ARR was detected as early as 2 days after inoculation (DAI). Furthermore, at 7 DAI, one of the biomarkers showed significant differences between lines with and without QTL Ae-Ps7.6 in the noninoculated samples. These findings demonstrate the potential applicability of the FAIMS system as a valuable tool for detecting volatile biomarkers for early plant disease detection.
Why it matches plant phenotyping methodsFAIMSによる植物VOCの非破壊・動的測定を用いた病害状態の早期検出法を評価しており、センサー法の信頼性評価と植物病害表現型の抽出が中心である。
abstractThis study evaluated the FAIMS system reliability in measuring VOC profiles for an early diagnosis of Aphanomyces root rot (ARR) in pea
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure.
Why it matches plant phenotyping methodsRGB画像からNDVI、PRI、Fv/Fmなどの植物形質・生理状態を推定する手法の関係解析と回帰モデル構築が研究の主目的であり、方法開発として中心的です。
abstractMeasurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors.
Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R2 = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R2 = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R2 = 0.86), AWP with red-edge reflectance (R2 = 0.71), turnips with NIR, GNDVI, and CHM (R2 = 0.95), and mixed species with NIR and blue band reflectance (R2 = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.
Why it matches plant phenotyping methodsUAV搭載マルチスペクトル画像と植生指数・キャノピー高モデルを用いて、被覆作物のバイオマスという植物形質を推定し、モデル性能を比較・検証しているため、フェノタイピング手法が中心である。
abstractThis study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Plant height is an important trait for evaluating plant lodging, drought, and stress. Standard measurement techniques are expensive, laborious, and error-prone. Although UAS-based sensors and digital aerial photogrammetry have been tested on plants with an erect growth habit, further study is needed in the application of these technologies to prostrate crops such as dry peas. This study has compared the performance of LiDAR, RGB, and multispectral sensors across different flight configurations (altitudes, speeds), and image overlaps over dry pea plots to identify the optimal setup for accurate plant height estimation. Data were assessed to determine the effect of sensor fusion on plant height accuracy using LiDAR’s digital terrain model (DTM) as the base layer, and digital surface models (DSMs) generated from RGB and multispectral sensors. All sensors, particularly RGB, tended to underestimate plant height at higher flight altitudes. However, RMSE and MAE values showed no significant difference, indicating that higher flight altitudes can reduce data collection time and cost without sacrificing accuracy. Multispectral and LiDAR sensors were more sensitive to changes in flight speed than RGB sensors; However, RMSE and MAE values did not vary significantly across the tested speeds. Increased image overlap resulted in improved accuracy across all sensors. The Wilcoxon–Mann–Whitney test showed no significant difference between sensor fusion and individual sensors. Although LiDAR provided the highest accuracy of dry peas height estimation, it was not consistent across all canopy structures. Therefore, future research should focus on the integrating machine learning models with LiDAR to improve plant height estimation in dry peas.
Why it matches plant phenotyping methods乾燥エンドウの草丈推定を対象に、LiDAR・RGB・マルチスペクトルセンサーの融合、飛行条件、画像重複率を比較・検証しており、植物表現型取得手法が研究の中心である。
abstractThis study has compared the performance of LiDAR, RGB, and multispectral sensors across different flight configurations (altitudes, speeds), and image overlaps over dry pea plots to identify the optimal setup for accurate plant height estimation.
Abiotic stress severely hinders plant growth and development, resulting in a considerable reduction in crop yields. Salicylic acid (SA) serves as a central signal mediating abiotic stress responses in plants. Real-time fluorescence tracking using specific probes can enhance our understanding of the SA-triggered modulation underlying these events. However, in complicated living plant microenvironments, selective recognition and bioimaging of SA is a great challenge for scientists due to the severe background interference and SA analogues. Herein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings. Experimental findings reveal that phoxrodam demonstrates exceptional selectivity (fluorescence intensity: I Phoxrodam+SA /I Phoxrodam+SA analogues > 4.29-fold), high sensitivity (limit of detection = 6.42 nM, fluorescence quantum yield: Φ Phoxrodam+SA = 0.36) and good anti-interference properties. Furthermore, we confirmed that phoxrodam accurately detects SA in the roots of salt-stressed wheat seedlings, the low-temperature resistance of Nicotiana benthamiana and the heavy metal resistance of pea seeds, using in vivo confocal imaging. This study provides a feasible strategy for efficiently tracking plant signalling molecules and promotes the in-depth research of SA-mediated physiological mechanisms, laying a key foundation for the future development of new immune activation inducers.
Why it matches plant phenotyping methods植物体内のサリチル酸を選択的に可視化・定量する蛍光プローブ技術の開発が中心であり、植物の生理状態を取得する実質的なフェノタイピング手法に該当する。
abstractHerein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings.
The swift detection of allergenic protein and other nutritional indicators in pea protein is crucial for food and breeding efforts, facilitating the targeted selection of specific pea varieties and the advancement and processing of healthful foods. Using near-infrared (NIR) spectroscopy, spectral data for different pea varieties in the range of 908-1676 nm were collected, which were subsequently integrated with chemical values obtained by conventional methods. Multivariate statistical analysis was employed to optimize, develop, and validate the model for the spectral data. The correlation coefficients of the calibration set based on partial least squares regression (PLSR) models ranged from 0.74 to 0.99, while those of the validation set ranged from 0.20 to 0.99. This study offers a precise and straightforward approach for evaluating the levels of several nutritional indicators, including allergenic proteins in peas, and for classifying different types.
Why it matches plant phenotyping methodsNIRスペクトルからエンドウ種子・品種の栄養指標やアレルゲンタンパク質を推定するモデルを開発・検証しており、形質取得法が中心である。食品分析にも関係するが、品種評価と育種選抜に結び付く植物種子形質の測定である。
abstractUsing near-infrared (NIR) spectroscopy, spectral data for different pea varieties in the range of 908-1676 nm were collected, which were subsequently integrated with chemical values obtained by conventional methods. Multivariate statistical analysis was employed to optimize, develop, and validate the model for the spectral data.
Plant leaf and root reflectance resulting from plant–pathogen interaction can be informative about disease status, making them useful for early disease detection. In controlled conditions, this research utilized a hyperspectral imaging (HSI) system to evaluate the early response of pea plants (Pisum sativum L.) inoculated with Aphanomyces euteiches Drechs, the causal agent of Aphanomyces root rot (ARR), using hyperspectral imaging. Two ARR partially resistant lines (NIL5-7.6b and NIL8-7.6b) with the quantitative trait locus (QTL) Ae - Ps7.6 and corresponding controls (NIL5-0b and NIL8-0b, without QTL) were grown in hydroponic conditions and organized in a split-plot design using two treatments, non-inoculated and inoculated (1 × 10⁵ zoospores ml⁻¹) with six replications. The HSI data were collected from the youngest leaflets 3 days after inoculation (DAI). At 8 DAI, HSI data from roots were collected. The HSI hypercubes of leaflets and roots were processed to remove the background and extract the mean value of each sample across wavelengths. Leaflet hyperspectral signatures were used to calculate normalized difference spectral indices. Then, a recursive feature elimination with cross-validation and a random forest classifier was used to select important features and test them with inferential analysis. For root data, a similar approach was used, however, the selected important features were used in random forest and gradient boosting classifiers. The leaflet results showed the red-edge wavelength of 745 nm was an essential feature for treatment separability at 3 DAI. Meanwhile, root analysis displayed a high classification accuracy of 83% and 92% with random forest and gradient boosting, respectively. This research offers valuable insights into the potential of HSI for ARR detection, particularly in the early pre-symptomatic stages of the plant disease.
Why it matches plant phenotyping methodsHSIによる植物の病害状態・早期症状の取得と、特徴選択および分類性能評価が研究の中心であり、植物病害フェノタイピング手法の応用・検証に該当する。
abstractthis research utilized a hyperspectral imaging (HSI) system to evaluate the early response of pea plants
Forage quality evaluation is essential in breeding and selecting forage crops to improve livestock health and reduce greenhouse gas emissions. A non-destructive and high-throughput process to assess forage quality can advance cultivar development research and commercial feed analysis. The overall objective of this study was to estimate 12 biomass quality traits of field peas in the 2019 and 2020 field seasons using in-field hyperspectral spectroscopy (350-2500 nm) at the leaf level. Six machine-learning models were utilized to develop the relationships between each quality trait and four feature extraction datasets (first derivative reflectance data, vegetation indices, normalized difference spectral indices, and ratio spectral indices) of leaf reflectance spectra from the hyperspectral data. The high and consistent performance of all quality traits was produced from only ridge regression, elastic net regression, and random forest regression models in first derivative reflectance and vegetation indices datasets. In addition, higher prediction performance (0.81 <R² < 0. 93; 0.05 < root mean square error (%) < 1.80; 0.03 < mean absolute error (%) < 1.32) was found in the random forest model using normalized difference spectral indices and ratio spectral indices datasets after utilizing a feature selection technique (leave one feature out). The results suggest that the proposed evaluation approach of spectroscopy may be applied to predict field pea quality traits in a timely fashion to assist breeders and farmers in their decision-making.
Why it matches plant phenotyping methods葉レベルのハイパースペクトル分光と機械学習により、エンドウの12 biomass quality traitsを非破壊・高スループットに推定する評価手法を開発・検証しており、植物形質取得が研究の中心である。
abstractA non-destructive and high-throughput process to assess forage quality can advance cultivar development research and commercial feed analysis.
This study aims to utilise natural variation in pea seed composition from NordGen collections to identify key traits for optimized plant-based ingredients functionality while minimizing refined extraction processes. Given the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions. Protein content, thousand kernel weight, perimeter, and G-value were determined as primary criteria via PCA, capturing variations in protein composition and other key components. Protein and starch content ranged from 21.2 to 36.9 % and 21.0–48.1 %, respectively. Image analysis linked geometry to composition, aiding pea selection and application. X-ray scattering differentiates peas based on starch structure. Proteomic profiling revealed that legumin and vicilin varied most, with legumin dominant in smooth peas and vicilin in wrinkled ones, enabling control of their ratio through selection. This study highlights the potential of using natural variation of seed composition for less-refined plant-based ingredients for various applications.
Why it matches plant phenotyping methods画像由来特徴量とアルゴリズムを用いて、化学分析対象のエンドウ種子アクセッションを事前選抜し、画像形状と組成の関係を評価するワークフローが研究の主要手法です。
abstractan algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
The changing climate could expand northwards in Europe the autumn sowing of cool-season grain legumes to take advantage of milder winters and to escape the increasing risk of terminal drought. Greater frost tolerance is a key breeding target because sudden frosts following mild-temperature periods may produce high winter mortality of insufficiently acclimated plants. The increasing year-to-year climate variation hinders the field-based selection for frost tolerance. This study focused on pea and white lupin with the objectives of (i) optimizing an easy-to-build, high-throughput phenotyping platform for frost tolerance assessment with respect to optimal freezing temperatures, and (ii) verifying the consistency of genotype plant mortality responses across platform and field conditions. The platform was a 13.6 m2 freezing chamber with programmable temperature in the range of −15°C to 25°C. The study included 11 genotypes per species with substantial variation for field-based winter plant survival. Plant seedlings were evaluated under four freezing temperature treatments, i.e., −7°C, −9°C, −11°C, and −13°C, after a 15-day acclimation period at 4°C. Genotype plant mortality and lethal temperature corresponding to 50% mortality (LT50) were assessed at the end of a regrowth period, whereas biomass injury was observed through a 10-level visual score based on the amount of necrosis and mortality after recovery and regrowth. On average, pea displayed higher frost tolerance than white lupin (mean LT50 of −12.8 versus −11.0°C). The genotype LT50 values ranged from −11.6°C to −14.5°C for pea and from −10.0°C to −12.0°C for lupin. The freezing temperature that maximized the genotype mortality variation was −13°C for pea and −11°C for lupin. The genotype mortality at these temperatures exhibited high correlations with LT50 values (0.91 for pea and 0.94 for lupin) and the biomass injury score (0.98 for pea and 0.97 for lupin). The frost tolerance responses in the platform showed a good consistency with the field-based winter survival of the genotypes. Our study indicates the reliability of genotype frost tolerance assessment under artificial conditions for two cool-season grain legumes, offering a platform that could be valuable for crop improvement as well as for genomics and ecophysiological research.
Why it matches plant phenotyping methods凍結チャンバーを用いた植物の耐霜性評価プラットフォームを最適化し、圃場条件と検証しており、表現型取得法が研究の中心である。
abstractoptimizing an easy-to-build, high-throughput phenotyping platform for frost tolerance assessment
Pea ( Pisum sativum ) is an important agricultural legume crop, but powdery mildew disease caused by the biotrophic fungus Erysiphe pisi regularly limits its annual yield. Assays to evaluate the efficacy of potential antifungal compounds or resistance genes for disease control require a simple fungal inoculation method that provides control over the initial inoculum concentration and enables uniform inoculum distribution within a leaf and across replicates as well as a method for the quantitative assessment of disease severity. Here, we present an easy spray inoculation method for the uniform distribution of a defined concentration of E. pisi conidia on the leaves of pea plants and a semi-automated image analysis-based quantification of disease symptoms. The uniformity in conidial distribution was validated using a novel grading system termed the uniformity index. In addition, RT-qPCR was used to validate the reproducibility of the spray inoculation method and image analysis-based disease quantification. These procedures permit the accurate quantification of powdery mildew disease severity at macroscopic and molecular levels.•Uniform and reproducible inoculum distribution on leaves using a simple and inexpensive spray device•Rapid and reproducible quantification of powdery mildew disease symptoms using open-source software without the requirement of computational expertise.
Why it matches plant phenotyping methodsエンドウ葉のうどんこ病症状を画像解析で定量する手法を開発し、接種均一性と再現性を検証しており、植物病害表現型の取得が中心である。
abstracta semi-automated image analysis-based quantification of disease symptoms
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 13 Sept 2026
Abstract Background Flowering is one of the most important and sensitive process throughout a plants life as it marks the start of the reproductive phase. Therefore, phenotyping the continuous development of flowering is crucial for crop breeding. For phenotyping, visual ratings have been a standard method for decades, to observe flowering dynamics by determining timepoints, such as start, end or duration. However, high throughput field phenotyping (HTFP) methods have emerged, providing an objective and efficient approach. We developed an approach that allows to collect detailed data not only about pea flowering dynamics, but additionally flower intensity (flowers per area). For this purpose, an object detection model, based on YOLOv8 was trained on RGB-images. The images were automatically acquired by the field phenotyping platform (FIP) of ETH Z¨urich for 12 pea breeding lines over two years. Results The trained model reached high accuracy for open flower detection, which allowed to monitor flower dynamics and intensity over time. Flower intensity throughout the development of the plants was highly correlated (R2= 0.967) to ground truth data taken in the field. Clear differences in timing, intensity of flowering and fruiting efficiency were detected between breeding lines and years. Furthermore, high correlation between maximal flower numbers and yield components such as seed amount were observed. Conclusion This automated, data-driven method of flower detection proved itself as a reliable tool. This is promising for the use of RGB imaging methods to objectively assess not only timing but also flower intensity. Flower intensity allows to predict seed amount and has therefore potential as selection trait in breeding program. In addition, fruiting efficiency could be included in breeding programs.
Why it matches plant phenotyping methodsピー花の開花時期と花数密度をRGB画像および物体検出で推定する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractWe developed an approach that allows to collect detailed data not only about pea flowering dynamics, but additionally flower intensity (flowers per area).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable precise and high-throughput phenotyping. In this study, we propose an innovative approach in plant pathology by combining image processing techniques with the level set method. This integrated approach leverages the strengths of both methodologies to provide accurate, robust, and detailed analysis of leaf and lesion evolution. By employing this combination, we achieve precise delineation of lesion boundaries and track their progression over time, offering clear visual feedback. This enhances the ability of the method to monitor plant health status comprehensively. The results, which track the growth of Peyronellaea pinodes on the stipules of two pea cultivars and the associated leaf deformation, provide an accurate visual representation of disease progression. This model represents a significant advancement in plant disease phenotyping, offering precise and detailed insights that can enhance our understanding of host-pathogen interactions.
Why it matches plant phenotyping methods画像処理とレベルセット法を組み合わせ、葉・病斑の境界、進展、葉の変形を定量的に追跡する植物病害フェノタイピング手法が研究の中心である。
abstractIn this study, we propose an innovative approach in plant pathology by combining image processing techniques with the level set method.
Field peas are grown by smallholder farmers in Ethiopia for food, fodder, income, and soil fertility. However, leaf diseases such as ascochyta blight, powdery mildew, and leaf spots affect the quantity and quality of this crop as well as crop growth. Experts use visual observation to detect field pea disease. However, this approach is expensive, labor-intensive, and imprecise. Therefore, in this study, we presented a transfer learning approach for the automatic diagnosis of field pea leaf diseases. We classified three field pea leaf diseases: Ascochyta blight, leaf spot, and powdery mildew. A softmax classifier was used to classify the diseases. A total of 1600 images of both healthy and diseased leaves were used to train, validate, and test the pretrained models. According to the experimental results, DenseNet121 achieved 99.73% training accuracy, 99.16% validation accuracy, and 98.33% testing accuracy after 100 epochs. we expect that this research work will offer various benefits for farmers and farm experts. It reduced the cost and time needed for the detection and classification of field pea leaf disease. Thus, a fast, automated, less costly, and accurate detection method is necessary to overcome the detection problem.
Why it matches plant phenotyping methods圃場エンドウ葉の病害状態を画像から自動分類する深層学習手法が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractTherefore, in this study, we presented a transfer learning approach for the automatic diagnosis of field pea leaf diseases.
In the field of photosynthesis, only a limited number of approaches of super-resolution fluorescence microscopy can be used, as the functional architecture of the thylakoid membrane in chloroplasts is probed through the natural fluorescence of chlorophyll molecules. In this work, we have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy, to obtain 2D images and 3D-reconstucted volumes of isolated chloroplasts, obtained from pea (Pisum sativum), spinach (Spinacia oleracea) and Arabidopsis thaliana. In agreement with previous studies, SPiRI images exhibit larger thylakoid grana diameters when extracted from plants under low-light regimes. The three-dimensional thylakoid architecture, revealing the complete network of the thylakoid membrane in intact, non-chemically-fixed chloroplasts can be visualized from the volume reconstructions obtained at high resolution. From such reconstructions, the stromal connections between each granum can be determined and the fluorescence intensity in the stromal lamellae compared to those of neighboring grana.
Why it matches plant phenotyping methods植物葉緑体のチラコイド膜構造を高解像度・3D画像から抽出するカスタム蛍光顕微鏡法を開発・適用しており、植物形態・細胞内構造の表現型取得が中心である。
abstractwe have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract The standard method of estimating in vitro protein digestibility, the protein digestibility corrected amino acid score (PDCAAS) assay, does not support the expected workflow of a pulse breeding program. This is mainly due to its low‐throughput design and long processing time (∼16–24 h) per sample. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses. The mid‐infrared region representing the amide I band (1756.81–1586.27 cm −1 ) was utilized to perform chemometric modeling with partial least squares regression (PLSR) to estimate in vitro protein digestibility in dry pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), and chickpea ( Cicer arietinum L.) flours. The root mean square error of predictions of the developed PLSR models for dry pea, lentil, and chickpea were 0.00039, 0.00024, and 0.00017, respectively. Phenotyping with the FT‐MIR approach is more rapid than with the PDCAAS assay and estimates protein digestibility from the flour of a single seed in a shorter time (∼1–2 min). The FT‐MIR approach is robust as the spectroscopic data are consistent and chemically fingerprint this nutritional trait. Accordingly, FT‐MIR can resolve the phenotypic bottleneck of pulse breeding related to in vitro protein digestibility measurements by enabling a high‐throughput phenotyping workflow.
Why it matches plant phenotyping methodsFT-MIR分光とPLSRモデルを用いて、豆類のタンパク質消化性という植物形質を高速推定する方法を開発・評価しており、表現型取得手法が研究の中心である。
abstractFourier‐transform mid‐infrared (FT‐MIR) spectroscopy has been developed as a high‐throughput phenotyping tool to estimate protein digestibility in pulses.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract The root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively. Electrical capacitance (C R ), dissipation factor (D R ) and electrical conductance (G R ) were detected during the 24 to 168 h after Cd treatment (0, 20, 50 mg Cd 2+ kg –1 substrate) in potted maize, cucumber and pea. Stress was also evaluated by measuring leaf chlorophyll content, F v /F m and stomatal conductance (g s ) in situ , and shoot and root mass and total root length after harvest. C R showed a clear diurnal pattern, reflecting the water uptake rate, and decreased significantly in response to excessive Cd due to impeded root growth, the reduced tissue permittivity caused by accelerated lignification, and root ageing. Cd exposure markedly increased D R , indicating greater conductive energy loss due to oxidative membrane damage and enhanced electrolyte leakage. G R , which was coupled with root hydraulic conductance and varied diurnally, was increased transiently by Cd toxicity due to enhanced membrane permeability, but declined thereafter owing to stress-induced leaf senescence and transpiration loss. The time series of impedance components indicated the comparatively high Cd tolerance of the applied maize and the sensitivity of pea cultivar, which was confirmed by visible shoot symptoms, repeated physiological investigations and biomass measurements. The results demonstrated the potential of single-frequency dielectric measurements to follow certain aspects of the stress response of different species on a fine timescale without plant injury. The approach can be combined with widely used plant physiological methods and could contribute to breeding crop genotypes with improved stress tolerance.
Why it matches plant phenotyping methods植物のCd毒性・ストレス状態を、無傷根系の誘電応答で非破壊かつ高時間分解能に測定する手法が研究の中心であり、他の生理測定や生体重による確認も行っている。
abstractThe root dielectric response was measured on a minute scale to assess its efficiency for monitoring short-term cadmium (Cd) toxicity non-destructively.
PeaGrowth chamberRootMorphology / geometry measurementRoot system architecture
Abstract Root system architecture (RSA) plays a central role in water and nutrient acquisition in plants. Plasticity and genetic variation in RSA can be used as an adaptive strategy to optimize plant performance under variable environments. We quantified phenotypic variation for seedling RSA among 44 diverse pea ( Pisum sativum L.) genotypes, including breeding lines and germplasm accessions, grown under controlled conditions for 14 days using two‐dimensional hydroponic root imaging. Root image analysis revealed significant genotypic variability among the lines for all root traits, namely root length (RL), root diameter (RD), root volume, root surface area, number of tips, network width (NW), network depth (ND), and network convex area. Significant positive correlations were observed among the evaluated root traits, ranging from 0.5 to 0.9. Pea lines were ranked based on estimated means for root traits, with lines E20, F1, and F8 showing high rankings, while E4 and F5 received low rankings for most traits. To associate root traits with nitrogen (N) fixation and field agronomic performance, we performed redundancy analysis (RDA). The quantified root traits accounted for significant variation in the agronomic traits ( R 2 = ∼30%, p
Why it matches plant phenotyping methods二次元根画像解析を用いた幼植物の根系形態形質の定量が研究の中心であり、遺伝子型間比較と農業形質との関連解析に用いられているため、画像ベースの表現型解析の実質的応用と判断します。
abstractRoot image analysis revealed significant genotypic variability among the lines for all root traits
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 methods育種試験の農業形質とUAVマルチスペクトル画像を含む再利用可能な植物フェノタイピングデータセットであり、画像から植生指数などの形質を抽出できる点が中心です。
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Seed traits, such as seed color and seed size, directly impact seed quality, affecting the marketability and value of dry peas [1]. Assessing seed quality is integral to a plant breeding programs to ensure optimal seed standards. This research introduced a phenotyping tool to assess seed quality traits specifically tailored for pulse crops, which integrates image processing with cutting-edge deep learning models. The proposed method is designed for automation, seamlessly processing a sequence of images while minimizing human intervention. The pipeline standardized red-green-blue (RGB) images captured from a color light box and used deep learning models to segment and detect seed features. Our method extracted up to 86 distinct seed characteristics, ranging from basic size metrics to intricate texture details and color nuances. Compared to traditional methods, our pipeline demonstrated a 95 percent similarity in seed quality assessment and increased time efficiency (from 2 weeks to 30 minutes for processing time). Specifically, we observed an improvement in the accuracy of seed trait identification by simply using an RGB value instead of a categorical, non-standard description, which allowed for an increase in the range of detectable seed quality characteristics. By integrating conventional image processing techniques with foundational deep learning models, this approach emerges as a pivotal instrument in pulse breeding programs, guaranteeing the maintenance of superior seed quality standards.
Why it matches plant phenotyping methods画像処理と深層学習を統合した種子形質抽出ツールの開発が中心であり、種子のサイズ・色・質感などの植物表現型を自動測定している。
abstractThis research introduced a phenotyping tool to assess seed quality traits specifically tailored for pulse crops, which integrates image processing with cutting-edge deep learning models.
Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red-green-blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R2] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R2 = 0.46, NRMSE = 20.08%) and MS (faba bean: R2 = 0.50, NRMSE = 15.16%; pea: R2 = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R2 = 0.37, NRMSE = 16.47%; pea: R2 = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R2 = 0.64, NRMSE = 13.76%; pea: R2 = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データと機械学習により、作物の収穫指数を推定する方法を開発・評価しており、表現型取得が研究の中心です。
abstractwe explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red-green-blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background The measurement of root dielectric response is a useful non-destructive method to evaluate root growth and function. Previous studies tracked root development throughout the plant growing cycle by single-time electrical measurements taken repeatedly. However, it is known that root conductivity and uptake activity can change rapidly, coupled with the day/night cycles of photosynthetic and transpiration rate. Therefore, the low-frequency dielectric monitoring of intact root–substrate systems at minute-scale temporal resolution was tested using a customized impedance measurement system in a laboratory environment. Electrical capacitance (C R ) and conductance (G R ) and the dissipation factor (D R ) were detected for 144 h in potted maize, cucumber and pea grown under various light/dark and temperature conditions, or subjected to progressive leaf excision or decapitation. Photosynthetic parameters and stomatal conductance were also measured to evaluate the stress response. Results The C R and G R data series showed significant 24-h seasonality associated with the light/dark and temperature cycles applied. This was attributed to the diurnal patterns in whole-plant transpiration (detected via stomatal conductance), which is strongly linked to the root water uptake rate. C R and G R decreased during the 6-day dark treatment, and dropped proportionally with increasing defoliation levels, likely due to the loss of canopy transpiration caused by dark-induced senescence or removal of leaves. D R showed a decreasing trend for plants exposed to 6-day darkness, whereas it was increased markedly by decapitation, indicating altered root membrane structure and permeability, and a modified ratio of apoplastic to cell-to-cell water and current pathways. Conclusions Dynamic, in situ impedance measurement of the intact root system was an efficient way of following integrated root water uptake, including diurnal cycles, and stress-induced changes. It was also demonstrated that the dielectric response mainly originated from root tissue polarization and current conduction, and was influenced by the actual physiological activity of the root system. Dielectric measurement on fine timescale, as a diagnostic tool for monitoring root physiological status and environmental response, deserves future attention.
Why it matches plant phenotyping methods根系の誘電特性を高時間分解能で測定するシステムを開発・検証し、根の吸水や生理状態を非破壊的に推定する手法が研究の中心である。
abstractThe measurement of root dielectric response is a useful non-destructive method to evaluate root growth and function.
When plants are infected with pathogens, disease response can result in changes in the profiles of volatile organic compounds (VOC). These changes in volatile organic compounds (VOC) profiles can be utilized for disease detection and quantification. In this study, field asymmetric ion mobility spectrometry (FAIMS) was used to evaluate the VOC profile variability in a pea near isogenic line (Pisum sativum L.) inoculated with zoospores of Aphanomyces euteiches Drechs, which causes Aphanomyces root rot disease. Pots were filled with silica sand and six plants per pot were grown under controlled conditions in a randomized complete block design with four replications. Four treatments, namely non-inoculated, 1 × 10⁵, 1 × 10⁶, and 2.79 × 10⁶ zoospores ml⁻¹ were applied to plants at 5 and 7 days after emergence. FAIMS was used to collect volatile profiles at 2, 4, 7 and 9 days after inoculation. Specific regions of interest – extracted from the ion current intensity from the FAIMS spectra – were analyzed using ANOVA. Similarly, multiple regions of interest were evaluated using principal component analysis and k-means clustering. Ion current profiles and curvature profiles were incorporated into the analysis using k-means clustering. Other ground reference data such as root rot index and physiological parameters were also recorded. The results showed a biomarker in a specific region of interest demonstrating ample ability to quantify and differentiate treatment effects during non-destructive sampling at 14 DAE (7 DAI). Data from this region could be used for early and non-destructive quantification and differentiation of treatment effects based on zoospore inoculation levels. The k-means clustering of ion current and curvature profiles showed patterns based on the treatments. These findings demonstrated that FAIMS could be used as a tool to assess plant-pathogen interactions using volatile biomarkers to evaluate disease responses and severity under controlled conditions.
Why it matches plant phenotyping methodsFAIMSによる揮発性成分プロファイルを用いて、エンドウの病害応答・重症度を非破壊かつ早期に定量・識別する手法を評価しており、植物表現型取得法が中心である。
abstractFAIMS was used to collect volatile profiles at 2, 4, 7 and 9 days after inoculation.
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:1-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Considering the rising concern over climate change and the need for local food security, productive blue-green roofs (PBGR) can be an effective solution to mitigate many relevant environmental issues. However, their cost of operation is high because they are intensive, and an economical operation and maintenance approach will render them as more viable alternative. Low-cost sensors with the Internet of Things can provide reliable solutions to the real-time management and distributed monitoring of such roofs through monitoring the plant as well soil conditions. This research assesses the extent to which a low-cost image sensor can be deployed to perform continuous, automated monitoring of a urban rooftop farm as a PBGR and evaluates the thermal performance of the roof for additional crops. An RGB-depth image sensor was used in this study to monitor crop growth. Images collected from weekly scans were processed by segmentation to estimate the plant heights of three crops species. The devised technique performed well for leafy and tall stem plants like okra, and the correlation between the estimated and observed growth characteristics was acceptable. For smaller plants, bright light and shadow considerably influenced the image quality, decreasing the precision. Six other crop species were monitored using a wireless sensor network to investigate how different crop varieties respond in terms of thermal performance. Celery, snow peas, and potato were measured with maximum daily cooling records, while beet and zucchini showed sound cooling effects in terms of mean daily cooling.
Why it matches plant phenotyping methodsRGB-D画像センサーとセグメンテーションにより作物の草丈を自動推定し、観測値と相関検証しており、植物表現型取得手法が中心的である。
abstractThis research assesses the extent to which a low-cost image sensor can be deployed to perform continuous, automated monitoring of a urban rooftop farm as a PBGR
Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.
Why it matches plant phenotyping methods画像ベースの病斑追跡と時空間モデルを組み合わせ、病斑拡散パラメータを推定する手法が研究の中心であるため。
abstractHere, we address this question by combining image-based phenotyping with mathematical modelling.
Reproduction assets foundThe article cites two public Recherche Data Gouv deposits containing this paper's own phenotyping assets: the image sequences of growing lesions on pea stipules used for monitoring, and the segmentation outputs used for image-based phenotyping. Both are explicitly referenced with DOIs in the reference list.Dataset · publicImage sequences of growing lesions—Ascochyta blight of pea. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/MQXKCP .Open asset ↗Recherche Data Gouv · 10.57745/MQXKCPlines:296-384Dataset · publicSegmentation of ascochyta blight symptoms on pea stipules. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/5B1XGU .Open asset ↗Recherche Data Gouv · 10.57745/5B1XGUlines:296-384Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
After crosses, the identification of true hybrids is not only the most important step in the initiation of a breeding program but also plays a crucial role in the improvement of hybrid varieties. However, current morphological or molecular-based hybrid identification methods are time-consuming and costly approaches that require knowledge and skill, as well as specific lab equipment. In the current study, xenia , direct or immediate effect of pollen on seeds was used to identify true hybrids in the genus Pisum L. for the first time without growing F 1 plants. The current study was therefore aimed to (i) elucidate the xenia effect on seeds in intra- and interspecific crosses between P. sativum L. subsp. sativum var. sativum or var. arvense L. Poir. and its wild relatives, including P. sativum subsp. elatius (M. Bieb.) Aschers & Graebn. and P. fulvum Sibth. & Sm., and (ii) illuminate the beneficialness of the xenia effect in a practical improvement of the genus Pisum L. The pea cultivars, including P. sativum subsp. sativum var. sativum and P. sativum subsp. sativum var. arvense, were therefore crossed with P. sativum subsp. elatius and P. fulvum , and the occurrence of the xenia effect was studied on the seeds of fertilized female plants immediately after the crosses. It was concluded that using the xenia effect for the early detection of true hybrid immediately after crossing was not only the fastest, most reliable, and least expensive option as early selection criteria, but that xenia also provided information about dominant seed and pod traits after double fertilization.
Why it matches plant phenotyping methods交雑直後の種子・莢の観察可能な形質（キセニア効果）を用いて真正雑種を早期識別する方法を開発・実証しており、表現型取得が研究の中心です。
abstractxenia , direct or immediate effect of pollen on seeds was used to identify true hybrids in the genus Pisum L. for the first time without growing F 1 plants.
Abstract Background Modern field pea breeding faces a significant challenge in selecting lines with strong stems that resist lodging. Traditional methods of assessing stem strength involve destructive mechanical tests on mature stems after natural senescence, such as measuring stem flexion, stem buckling or the thickness of dry stems when compressed, but these measurements may not correspond to the strength of stems in the living plant. Optical coherence tomography (OCT) can be used as a noncontact and nondestructive method to measure stem wall thickness in living plants by acquiring two- or three-dimensional images of living plant tissue. Results In this proof-of-principle study, we demonstrated in vivo characterisation of stem wall thickness using OCT, with the measurement corrected for the refractive index of the stem tissue. This in vivo characterisation was achieved through real-time imaging of stems, with an acquisition rate of 13 milliseconds per two-dimensional, cross-sectional OCT image. We also acquired OCT images of excised stems and compared the accuracy of in vivo OCT measurements of stem wall thickness with ex vivo results for 10 plants each of two field pea cultivars, Dunwa and Kaspa. In vivo OCT measurements of stem wall thickness have an average percent error of − 3.1% when compared with ex vivo measurements. Additionally, we performed in vivo measurements of both stem wall thickness and stem width at various internode positions on the two cultivars. The results revealed that Dunwa had a uniform stem wall thickness across different internode positions, while Kaspa had a significantly negative slope of − 0.0198 mm/node. Both cultivars exhibited an increase in stem width along the internode positions; however, Dunwa had a rate of increase of 0.1844 mm/node, which is three times higher than that of Kaspa. Conclusions Our study has demonstrated the efficacy of OCT for accurate measurement of the stem wall thickness of live field pea. Moreover, OCT shows that the trends of stem wall thickness and stem width along the internode positions are different for the two cultivars, Dunwa and Kaspa, potentially hinting at differences in their stem strength. This rapid, in vivo imaging method provides a useful tool for characterising physical traits critical in breeding cultivars that are resistant to lodging.
Why it matches plant phenotyping methodsOCTを用いた生体植物の茎壁厚測定法を開発・実証し、ex vivo測定との比較で精度検証しているため、植物フェノタイピング手法が中心である。
abstractOptical coherence tomography (OCT) can be used as a noncontact and nondestructive method to measure stem wall thickness in living plants by acquiring two- or three-dimensional images of living plant tissue.
Introduction Drought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor. Methods We constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance. Results The experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties. Discussion Our model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.
Why it matches plant phenotyping methodsYOLOv8を改良した画像ベースのエンドツーエンド手法を開発し、エンドウ種子の発芽勢を検出・定量する方法が研究の中心である。データセット構築、性能評価、遺伝子型のスクリーニングまで含む。
abstractWe propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.
Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.
Why it matches plant phenotyping methodsエンドツーエンドのRGB画像解析パイプラインを開発・最適化・検証し、エンドウ葉のさび病症状と病勢進展を定量化する方法が研究の中心である。
abstractThis study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.
Reproduction assets foundThe authors deposited the R analysis script and the 600 pea leaflet images used in this rust phenotyping study in a public Zenodo repository, explicitly cited in the Data Availability statement and reference list.Dataset · publicThe datasets generated during and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7991462 [ 93 ].Open asset ↗Zenodo · 10.5281/zenodo.7991462lines:147-229Code / 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:1-65Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10-20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection.
Why it matches plant phenotyping methods植物の形態をフォトグラメトリで3次元モデル化し、ドリフト散布液の植物捕捉効率を推定・検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractWe also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants.
Abstract Aphanomyces root rot (ARR) is a devastating disease in field pea (Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time‐demanding activity that is relatively low‐throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse‐based high‐throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants of advanced breeding lines developed by the North Dakota State University Pulse Breeding Program. Precision estimated through broad‐sense heritability (H2) was consistently higher for RGB‐derived indices (H2, Exg = 0.86) than the conventional visual scores (H2, disease severity index = 0.59). Prediction of disease severity using a random forest modeling of RGB‐derived indices achieved 0.69 accuracy on the test sets, with inaccurate classification partly attributed to the presence of tolerant lines (displaying root rot but no foliar symptoms) and within‐line genetic heterogeneity. We genetically dissected variation for ARR resistance from the population using RGB‐derived indices and visual scores through genome‐wide association mapping and identified a total of 260 associated single nucleotide polymorphism (SNP). The number of associated SNP for RGB‐derived indices was consistently higher than the number of associated SNP identified using visual scores, with the most significant SNP explaining about 5%–9% of variance per index. We identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR and a few novel QTLs with small‐effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long‐term and durable resistance to ARR.
Why it matches plant phenotyping methods温室内を移動するRGB画像ベースの高スループット表現型解析プラットフォームを開発し、根腐病症状の取得・定量化と従来法との検証を行っているため、方法が研究の中心である。
abstractwe developed a greenhouse‐based high‐throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea.
Ascochyta blight (AB) is a destructive disease of the field pea ( Pisum sativum L.) caused by necrotrophic fungal pathogens known as the AB-disease complex. To identify resistant individuals to assist AB resistance breeding, low-cost, high throughput, and reliable protocols for AB screening are needed. We tested and optimized three protocols to determine the optimum type of pathogen inoculum, the optimal development stage for host inoculation, and the timing of inoculation for detached-leaf assays. We found that different plant development stages do not affect AB infection type on peas, but the timing of inoculation affects the infection type of detached leaves due to wound-induced host defense response. After screening nine pea cultivars, we discovered that cultivar Fallon was immune to A. pisi but not to A. pinodes or the mixture of the two species. Our findings suggest that AB screening can be done with any of the three protocols. A whole-plant inoculation assay is necessary for identifying resistance to stem/node infection. Pathogen inoculation must be completed within 1.5 h post-detachment to avoid false positives of resistance for detach-leaf assays. It is essential to use a purified single-species inoculum for resistant resource screenings to identify the host resistance to each single species.
Why it matches plant phenotyping methodsエンドウの病害抵抗性という植物状態を評価するため、ABスクリーニング法を最適化・比較検証しており、表現型取得プロトコルが研究の中心です。
abstractlow-cost, high throughput, and reliable protocols for AB screening are needed
Introduction Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model. Methods The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points. Results and discussion The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.
Why it matches plant phenotyping methods圃場画像の融合・特徴抽出とランダムフォレストを用いて、エンドウの種子収量をプロット単位で推定する手法を比較・評価しており、フェノタイピング手法が中心です。
abstractThis study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials
Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled remains unclear. Here, we use supervised machine learning algorithms to generate models that learn to discriminate between the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features that contribute to the classification tasks. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.
Why it matches plant phenotyping methods植物の回旋運動（circumnutation）の運動特徴を抽出し、支持体の有無を教師あり機械学習で分類する手法が研究の中心であるため、植物状態・運動表現型の計測／推定として収載する。
abstractHere, we use supervised machine learning algorithms to generate models that learn to discriminate between the presence/absence of a support in the environment.
ArabidopsisPeaLaboratory / benchtopRootMorphology / geometry measurementStress / disease detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
The study of root growth and plasticity in situ is rendered difficult by the opacity and mechanical barrier of soil substrates. Therefore, for the analysis of developmental processes and abiotic stress and development relationships, it is essential to set up cultivation systems that overcome these hindrances in a non-invasive and non-destructive manner. For this purpose, we have developed a useful and powerful rhizobox culture system, where the roots are separated from the soil substrate by a porous membrane with a mesh of such width that allows the exchange of water and solutes without allowing the roots to penetrate the soil. This system provides direct, easy, and quick access to the roots and allows to follow root growth and development, root system architecture, and root system plasticity at different stages of plant development and under various environmental conditions. Moreover, these rhizoboxes provide clean and intact roots that can be easily harvested to perform further physiological, biochemical, and molecular analyses at different stages of development and in response to various environmental constraints. This rhizobox method was validated by assessing root response plasticity of drought-stressed Arabidopsis and pea plants grown in soil displaying water content alterations. This rhizobox system is suitable for many types of abiotic stress-development studies, including the comparison of different stress intensities or of various mutants and genotypes.
Why it matches plant phenotyping methods根系の成長・形態・可塑性を非破壊的に追跡するためのrhizobox培養システムを開発し、干ばつ条件のArabidopsisとエンドウで検証しており、根系表現型の取得法が中心です。
abstractwe have developed a useful and powerful rhizobox culture system
This study integrated field‐level sensor data into the FAO‐56 Penman-Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman-Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R² = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman-Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field.
Why it matches plant phenotyping methods圃場レベルの作物センシング（分光反射、群落温度、草丈）を用いて作物蒸発散量を推定し、土壌水分収支法と比較検証している。単なる生物学的実験のルーチン測定ではなく、作物の水利用状態を取得・推定するセンシング手法が中心である。
abstractDaily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height).
Breeding for increased protein concentration is a priority in field peas. Having a quick, accurate, and non-destructive protein quantification method is critical for screening breeding materials, which the near-infrared spectroscopy (NIRS) system can provide. Partial least square regression (PLSR) models to predict protein concentration were developed and compared for DA7250 and FT9700 NIRS systems. The reference protein data were accurate and exhibited a wider range of variation (15.3−29.8%). Spectral pre-treatments had no clear advantage over analyses based on raw spectral data. Due to the large number of samples used in this study, prediction accuracies remained similar across calibration sizes. The final PLSR models for the DA7250 and FT9700 systems required 10 and 13 latent variables, respectively, and performed well and were comparable (R2 = 0.72, RMSE = 1.22, and bias = 0.003 for DA7250; R2 = 0.79, RMSE = 1.23, and bias = 0.055 for FT9700). Considering three groupings for protein concentration (Low: 25%), none of the entries changed from low to high or vice versa between the observed and predicted values for the DA7250 system. Only a single entry moved from a low category in the observed data to a high category in the predicted data for the FT9700 system in the calibration set. Although the FT9700 system outperformed the DA7250 system by a small margin, both systems had the potential to predict protein concentration in pea seeds for breeding purposes. Wavelengths between 950 nm and 1650 nm accounted for most of the variation in pea protein concentration.
Why it matches plant phenotyping methodsエンドウ種子のタンパク質濃度をNIRSとPLSRで非破壊推定する手法を開発・比較し、精度を評価しており、表現型取得法が中心である。
abstractHaving a quick, accurate, and non-destructive protein quantification method is critical for screening breeding materials, which the near-infrared spectroscopy (NIRS) system can provide.
Nitrogenase, an enzyme present in a select group of prokaryotes reduces inert N 2 into NH 3 that can be utilized through biological pathways. This process, termed biological nitrogen fixation, plays a crucial role in the biogeochemical N cycle. The ability of nitrogenase to reduce acetylene to ethylene has been exploited to develop a reliable and accessible biochemical assay to measure this enzyme's activity. Biological nitrogen fixation by rhizobia bacteria that occupy root nodules of legume crops is a major source of sustainable nitrogen nutrition in agriculture. Environmental stresses exacerbated by climate change necessitate the need to evaluate nitrogen fixation in root nodules under various stress conditions. Here, we provide a detailed step-by-step protocol for nitrogenase activity measurements using acetylene reduction assay in field pea plants under saline stress. The protocol can be easily adapted for use with other biological systems.
Why it matches plant phenotyping methods根粒における窒素固定活性という植物の生理状態を測定するアッセイの詳細プロトコルであり、測定手法自体が中心であるため。
abstractHere, we provide a detailed step-by-step protocol for nitrogenase activity measurements using acetylene reduction assay in field pea plants under saline stress.
PeaMRI / PETRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
In natural ecosystems and low-input agriculture systems often the main source of nitrogen is biological nitrogen fixation by symbiotic coexistence with root colonizing microorganisms such as in root nodules in legumes. In return for this nutrient supply, plants allocate significant amount of photosynthetically fixed carbon (C) belowground, fueling activity and growth of the nodules. However, there is still a lack in understanding how plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. Traditional approaches based on destructive sampling make investigations of localized carbon allocation dynamics difficult. Non-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants. MRI allows for repetitive measurements and quantification of root system architecture traits nodule structures while growing. PET was employed to follow the spatial distribution of leaf-supplied 11 C tracer to nodules and roots. Using Pisum sativum as model for legumes and applying nitrate as an additional N source we investigated short term C allocation dynamics in the root system. We found that the fraction of 11 C tracer arriving in the most active nodules decreased by almost 40% and remained stable between 16h and 42h after the N application. Our results highlight that the combination of MRI-PET enables deeper insights into short term C dynamics of roots and interactions with colonizing microbes. We expect that this modality has high potential for revealing mechanisms that relate to dynamic fitness traits supporting breedingprograms for future crops.
Why it matches plant phenotyping methodsMRI-PETによる非破壊3D画像化と定量化が研究の主要手法であり、根系構造・根粒構造および炭素分配という植物形質・状態を測定しているため。
abstractNon-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants.
Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.
Why it matches plant phenotyping methods植物病害の症状を画像から検出する深層学習手法を開発し、データセット、モデル比較、外部検証、交差検証まで行っており、植物状態の取得・推定が中心である。
abstractThis paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification.
Reproduction assets foundThe paper's NZDLPlantDisease-v2 plant disease image dataset is explicitly stated as publicly available on the authors' GitHub repository.Dataset · publicThe dataset presented in this study is made publicly available in a GitHub repository: https://github.com/kmarif/NZDLPlantDisease-v2 .Open asset ↗kmarif/NZDLPlantDisease-v2 · NZDLPlantDisease-v2lines:981-1043Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Multi-trait genomic selection (MT-GS) has the potential to improve predictive ability by maximizing the use of information across related genotypes and genetically correlated traits. In this study, we extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure. Using 300 advanced breeding lines from North Dakota State University (NDSU) pulse breeding program and ∼200 USDA accessions that were evaluated for 10 nutritional traits, our results show that the proposed sparse phenotyping aided MT-GS can further improve predictive ability by >12% across traits compared with univariate (UNI) genomic selection. The proposed strategy departed from the previous reports that weak genetic correlation is a limitation to the advantage of MT-GS over UNI genomic selection, which was evident in the partially balanced phenotyping-enabled MT-GS. Our results point to heritability and genetic correlation between traits as possible metrics to optimize and further improve the estimation of model parameters, and ultimately, prediction performance. Overall, our study offers a new approach to optimize the prediction performance using the MT-GS and further highlight strategy to maximize the efficiency of GS in a plant breeding program. The sparse-testing-aided MT-GS proposed in this study can be further extended to multi-environment, multi-trait GS to improve prediction performance and further reduce the cost of phenotyping and time-consuming data collection process.
Why it matches plant phenotyping methods疎な表現型測定から欠測植物形質を予測し、表現型取得コストを削減する計算的方法が研究の中心であるため、植物育種への応用であっても表現型推定手法として含める。
abstractwe extended the use of sparse phenotyping method into the MT-GS framework by split testing of entries to maximize borrowing of information across genotypes and predict missing phenotypes for targeted traits without additional phenotyping expenditure.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.
Why it matches plant phenotyping methods植物根画像から病害重症度を推定するCNNと、クラス不均衡対策を体系的に評価しており、画像ベース表現型解析手法の技術評価が中心です。
abstractPhenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable.
Summary The study the effect of contrast on seeds, growth and the associated anatomy and physiology, with upgraded imaging systems. The use of phase information to explore new information at various stage of the growth. This work benefits, the use of Synchrotron-based DEI and DEI-CT systems to enhance the contrast in plant root architecture and contrast mechanisms, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. These non-destructive, imaging systems available at the X-15A beamline, at NSLS, BNL, USA, are utilized. Noticed detailed anatomical and physiological observations, contrast mechanisms, with these upgraded systems, compared to other conventional techniques, equipped with tube source of X-rays. Examined the potential of these systems to quantify the plant roots in situ. The acquired images provided good contrast, anatomical structures and physiology of the plant root micro-architecture. We observed some of the complex plant traits, such as growth, development, root architecture and the associated physiology. The interior structure, root architecture, root morphology, growth of laterals and subsequent laterals can be visualized directly by synchrotron-based imaging techniques. Root architecture of the plant grown from seeds provides new information about the structure and enhancement of some desired property, for example, interior micro-structure of the root laterals and the subsequent laterals and the clear visibility of the leaves in detail. This way, it will be possible to differentiate the weakly and strongly attenuation of the signal traversing within the sample, clearly reflects the acceptable visibility in root laterals, subsequent laterals and the associated opaque matrix with enhanced contrast. The sample has a thin layer of hard structure outside and protein inside. Extinction properties of these samples will be characterized by Sy-DEI and Sy-DEI-CT. This way, we may be able to differentiate softly and weakly attenuation within the sample, to know more about the contrast mechanisms. The visibility, contrast and porosity, with finer details, can be noticed, with Sy-DEI-CT systems as distinguished from Sy-DEI. However, limited field of view, may limit the problems associated with Sy-DEI-CT.
Why it matches plant phenotyping methodsシンクロトロンX線DEI/DEI-CTによる植物根の解剖構造・根系形態・成長・生理の非破壊画像化と定量化可能性が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThe use of phase information to explore new information at various stage of the growth.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Aphanomyces root rot (ARR) is a devastating disease in field pea (Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time-demanding activity that is relatively low-throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection, which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse-based high throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants. Precision estimated through broad-sense heritability ( H 2 ) was consistently higher for the HTP-indices ( H 2 Exg =0.86) than the traditional visual scores ( H 2 DSI=0.59), potentially increasing the power of genetic mapping. We genetically dissected variation for ARR resistance using the HTP-indices, and identified a total of 260 associated single nucleotide polymorphism (SNP) through genome-wide association (GWA) mapping. The number of associated SNP for HTP-indices was consistently higher with some SNP overlapped to the associated SNP identified using the visual scores. We identified numerous small-effect QTLs, with the most significant SNP explaining about 5 to 9% of the phenotypic variance per index, and identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR, including Psat5g280480, Psat5g282800, Psat5g282880, and Psat2g167800. We also identified a few novel QTLs with small-effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long-term, durable resistance to ARR.
Why it matches plant phenotyping methods温室内を走行する撮像型HTPプラットフォームを開発し、エンドウの根腐病症状を高スループットに取得・定量化して従来法と比較検証しているため、表現型取得法が研究の中心である。
abstractIn this study, we developed a greenhouse-based high throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea.
Monitoring electrical signals in plants allows the examination of their acute and chronic physiological changes and responses to stimuli. Understanding how plant roots/rhizoids respond to chemical cues in their environment will provide insight into how these structures acquire resources. Chronic exposure to L-glutamate alters root growth and is known to alter Ca 2+ flux inside roots. The ionic flux can be detected by electrical changes. A rapid and relatively easy approach is presented to screen the electrical sensitivity of roots/rhizoids to compounds such as amino acids and known agonists/antagonists to receptors and ion channels. The approach uses a background-flow system of basal salt or water; then, the administered compounds are added to the roots/rhizoids while monitoring their electrical responses. As a proof of concept, the response to flow-through of glutamate (1 mM) was targeted at the root/rhizoids of three plants ( Arabidopsis thaliana , Pisum sativum and Marchantia inflexa ). Both Arabidopsis thaliana and Pisum sativum produced rapid depolarization upon exposure to glutamate, while M. inflexa did not show an electrical response. In some experiments, simultaneous recordings with impedance measures for acute changes and glass electrodes for chronic electrical potential changes were used. The effect of potassium chloride (300 mM) as a depolarizing stimulus produced responses in both P. sativum and M. inflexa. The protocol presented can be used to screen various compounds in a relatively rapid manner for responsiveness by the roots/rhizoids of plants.
Why it matches plant phenotyping methods植物の根・仮根の化合物応答を電気信号として取得するフロースルー測定プロトコルを提示し、複数植物で実証しているため、表現型取得法が中心である。
abstractA rapid and relatively easy approach is presented to screen the electrical sensitivity of roots/rhizoids to compounds such as amino acids and known agonists/antagonists to receptors and ion channels.
Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques—vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds—were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, α = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (r = 0.78–0.81, p
Why it matches plant phenotyping methodsUAV画像・点群・3D再構成を用いて、エンドウの草高・体積から地上部バイオマスを推定し、地上基準値と比較検証する手法研究であり、表現型取得・抽出が中心である。
abstractThis study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials
Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack of understanding of how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localised carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ). Nitrate was used for triggering a reduction of biological nitrogen fixation (BNF), which was expected to rapidly affect carbon allocation dynamics in the root-nodule system. The nitrate treatment led to a decrease in 11 C tracer allocation to nodules by 40% to 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11 C tracers in a PET approach to quantify C-allocation to individual nodules following a BNF reduction. Our study demonstrates the strength of using 11 C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development.
Why it matches plant phenotyping methodsマメ科植物の根粒への炭素配分動態をPETで非侵襲・三次元定量する実験ワークフローを確立しており、植物生理状態の取得法が研究の中心である。
abstractWe established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ).
The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.
Why it matches plant phenotyping methods航空ハイパースペクトル画像から収量・バイオマスを推定するAutoML解析ワークフローを構築・評価しており、植物形質推定手法が中心である。
abstractThis research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass
In radioecological studies, there is a significant need for understanding the plant uptake of radionuclides on a cellular level. The present work applies mass spectrometry to image the radionuclide distribution within the cellular structures of plants at varying concentrations. In a first step, plants of Daucus carota and Pisum sativum labelled with iodine and rhenium were examined, at concentrations in the range of 10 mM. Cross sections of several plant parts were imaged by secondary ion mass spectrometry (SIMS) after cryogenation in order to preserve cell structure. In a second step, the distribution of 99 Tc in the two plant species was determined. For radiological reasons, a concentration three orders of magnitude lower was used, rendering measurements with SIMS impossible. Therefore, resonant laser secondary neutral mass spectrometry (rL-SNMS) was used for the first time to image 99 Tc with suppression of molecular isobaric interferences. The measurement of only about 10 10 atoms of 99 Tc atoms is demonstrated and the distribution of 99 Tc within a single epidermal cell is imaged.
Why it matches plant phenotyping methods植物細胞内の放射性核種分布・取り込み状態を画像化するSIMS/rL-SNMS手法が研究の中心であり、99Tcの高感度イメージングを技術的に実証している。
abstractThe present work applies mass spectrometry to image the radionuclide distribution within the cellular structures of plants at varying concentrations.
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.
Why it matches plant phenotyping methods画像ベースの病斑計測と時空間数理モデルを統合し、病斑拡大の定量的パラメータを推定する手法が研究の中心であるため。
abstractHere, we address this question by combining image-based phenotyping with mathematical modelling.
Abstract Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is a high‐throughput, cost‐effective method to quantify nutritional traits, such as total protein and sulfur‐containing amino acid (SAA) concentrations, in plant matter. This study used the spectroscopic technique FT‐MIR coupled with attenuated total internal reflectance sampling interface to develop multivariate models for total protein concentration in chickpea (Cicer arietinum L.), dry pea (Pisum sativum L.), and lentil (Lens culinaris Medik.), in addition to SAA concentration in lentil. Total nitrogen data from combustion analysis and SAA data from high‐performance liquid chromatography analysis following acid hydrolysis were used for model calibration and validation. Models for the total protein concentration of chickpea (calibration root mean square error [RMSE] = 0.093, R2 = 0.948, prediction RMSE = 0.10), dry pea (calibration RMSE = 0.096, R2 = 0.845, prediction RMSE = 0.093), and lentil (calibration RMSE = 0.13, R2 = 0.845, prediction RMSE = 0.11) utilized infrared regions associated with protein structures, namely amide bands A, I, and II. In sulfur‐related models for lentil total SAA (calibration RMSE = 0.014, R2 = 0.827, prediction RMSE = 0.022) and methionine (calibration RMSE = 0.0075, R2 = 0.815, prediction RMSE = 0.014) models utilized the C‐S and S‐CH3 stretching and bending bands. Study findings support the conclusion that FT‐MIR spectroscopy is a promising high‐throughput and cost‐effective phenotyping technique that will allow quantifying protein traits quickly and easily in pulse crops.
Why it matches plant phenotyping methodsFT-MIR分光法を用いてマメ科作物のタンパク質・アミノ酸形質を定量するモデルを開発・校正・検証しており、表現型取得法が研究の中心である。
titleFourier‐transform infrared spectroscopy (FTIR) as a high‐throughput phenotyping tool for quantifying protein quality in pulse crops
Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植生指数抽出を、太陽天頂角の影響と地上基準形質との関係を用いて評価・検証しており、フェノタイピング手法が中心である。
abstractNormalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications
Lentil and field pea are each commonly marketed as split and dehulled product. For plant-breeding programmes, the genetic improvement in split-yield is a targeted trait. However, the standard laboratory method for assessment of split-yield requires milled grain to be manually sorted into split and dehulled fractions. This process is time-consuming and impacts the number of germplasm lines that can be evaluated.A machine vision approach, based on artificial neural networks, was proposed to classify split and dehulled fractions from multispectral images of grains. Three neural networks were trained on different inputs derived from the images. The networks were: (1) a convolutional network trained on the full images, (2) a convolutional network trained on distributions of image-features, and (3) a fully connected network trained on mean and standard deviation values of image-features. The accuracy and training times were compared to determine the trade-offs between training networks with smaller inputs for computational efficiency and full-image inputs for accuracy.The networks with reduced input-data dimensionality completed network training and predictions in half the time of the image-based network. The convolutional network based on the distributions of image-features achieved a validation accuracy of 88.1%. On average, this was 1.6% greater than the image-based convolutional network and 4.6% greater than the fully connected network based on simple (mean and standard deviation) features. Feature-distributions extracted from the multispectral images captured the diversity of image data required to differentiate milling categories, leading to gains in computational efficiency over the image-based network without loss of network generality.
Why it matches plant phenotyping methods穀粒の分別・split-yieldという育種対象形質を、マルチスペクトル画像とニューラルネットワークで自動分類する手法を開発・比較しており、表現型取得・抽出が中心です。
abstractA machine vision approach, based on artificial neural networks, was proposed to classify split and dehulled fractions from multispectral images of grains.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.
Why it matches plant phenotyping methods種子の品種分類を目的としたハイパースペクトル画像と深層転移学習の手法開発・評価が研究の中心であり、育種・遺伝資源スクリーニングに再利用可能な表現型取得ワークフローを扱うため。
abstractThis study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning.
Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack in understanding how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localized carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ). Nitrate was used for triggering the shutdown of biological nitrogen fixation (BNF) expected to rapidly affect carbon allocation dynamics in the root-nodule system. This nitrate treatment lead to a reduction of 11 C tracer allocation to nodules by 40% – 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11 C tracer in a PET approach to quantify C-allocation to individual nodules following a BNF shutdown. This demonstrates the strength of using 11 C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development. One sentence summary Positron Emission Tomography for quantification of carbon allocation dynamics in individual nodules within a 3D root system revealed strong effect of nitrate on carbon allocation.
Why it matches plant phenotyping methodsPETを用いた非侵襲的な根粒別炭素配分の3D定量ワークフローと測定パイプラインの確立が中心であり、植物の生理状態を抽出する方法研究に該当する。
abstractWe established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ).
A drought, which can be often accompanied by increased temperature, is a key adverse factor for agricultural plants. Remote sensing of early plant changes under water shortage is a prospective way to improve plant cultivation; in particular, the sensing can be based on measurement of difference reflectance indices (RIs). We complexly analyzed the efficiency of RIs based on 400–700 nm wavelengths for revealing the influences of water shortage and short-term heating on plant seedlings. We measured spectra of reflected light in leaves of pea, wheat, and pumpkin under control and stress conditions. All possible RIs in the 400–700 nm range were calculated, significances of differences between experimental and control indices were estimated, and heatmaps of the significances were constructed. It was shown that the water shortage (pea seedlings) changed absolute values of large quantity of calculated RIs. Absolute values of some RIs were significantly changed for 1–5 or 2–5 days of the water shortage; they were strongly correlated to the potential quantum yield of photosystem II and relative water content in leaves. In contrast, the short-term heating (pea, wheat, and pumpkin seedlings) mainly influenced light-induced changes in RIs. Our results show new RIs, which are potentially sensitive to the action of stressors.
Why it matches plant phenotyping methods植物葉の反射スペクトルからストレス状態を推定する反射指数を体系的に評価し、新規指数を提案しており、表現型取得手法が中心である。
abstractRemote sensing of early plant changes under water shortage is a prospective way to improve plant cultivation; in particular, the sensing can be based on measurement of difference reflectance indices (RIs).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations ( P r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering ( r up to 0.76 and 0.85, respectively), and days to physiological maturity ( r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と半自動画像処理により、作物形質を抽出・推定し、複数環境で検証するフェノタイピング手法の実質的応用である。
abstractAn unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Plant breeders are interested in plant height data, which is an important agronomic data associated with lodging and mechanical harvest. Manual measurement of plant height with limited samples per plot and data acquisition frequency remains the standard method in breeding programs. To overcome such limitations, this study focuses on plant height estimation in canola (Brassica napus L./winter canola, and B. napus L. and B. rapa L./spring canola), pea (Pisum sativum L.), chickpea (Cicer arietinum L.), and camelina (Camelina sativa L.) breeding trials using sensors. Plant height data were collected using a light detection and ranging (LiDAR) sensor system mounted on a tractor (for pea and chickpea) and an unmanned aerial system (UAS) integrated with a Red–Green–Blue (RGB) camera (for four crops). The LiDAR data and UAS‐based images were processed to extract six plant height features. Significant (P < .0001) correlations between LiDAR estimated and manually measured plant height data were observed with correlation coefficient (r) of .74 and .91 in chickpea and pea, respectively. Image‐based plant height estimations were also correlated (P < .0001) with manually measurement in the four crops (r = .57 – .98). This study demonstrated that the plant height of four cool‐season crops can be estimated using either proximal or remote sensing techniques even if the canopy architectures of these crops pose challenges. Such high throughput phenotyping technologies can be applied in plant breeding and crop production to monitor plant height and associated traits such as lodging in an efficient and timely manner.
Why it matches plant phenotyping methodsLiDARおよびUAS画像を用いて作物の草丈を推定し、手測定と相関検証した高スループット表現型計測が研究の中心である。
abstractthis study focuses on plant height estimation in canola (Brassica napus L./winter canola, and B. napus L. and B. rapa L./spring canola), pea (Pisum sativum L.), chickpea (Cicer arietinum L.), and camelina (Camelina sativa L.) breeding trials using sensors.
Abstract A method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue. CDTA chelates divalent cations that cross‐link the pectic polysaccharides of the middle lamella, allowing gentle separation of the cells without harsh physical treatments. These individual cells are ideal for observing starch granules in situ by microscopy without the requirement for fixation, embedding, sectioning, or prior starch extraction. Starch can easily be observed either unstained, or by polarizing optics, or after staining with iodide (I 2 /KI). Staining with I 2 /KI in combination with polarizing optics gives information on polarizing colors that indicate compositional differences within granules. Examples of the starch complement in developing, mature, and cooked rr wrinkled pea cells, and in banana and potato tissue are shown. The CDTA‐separation method is ideal for the survey of starch mutants and other cell components as it preserves cytoplasmic organization and prevents microbial degradation during storage.
Why it matches plant phenotyping methods植物組織から細胞を分離し、顕微鏡でデンプン顆粒を観察する方法自体が中心であり、デンプン変異体の調査など植物形質評価への再利用性が示されている。
abstractA method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue.
The measurement of chlorophyll fluorescence in remote way represents a tool that is becoming increasingly important in relation to the diagnosis of plant health and carbon budget on the planet. However, the detection of this emission is severely affected by distortions, due to processes of light re-absorption both in the leaf and in the canopy. Even though some advances have been made to correct the signal in the far-red, the whole spectral range needs to be addressed, in order to accurately assess plant physiological state. In 2018, we introduced a model to obtain fluorescence spectra at leaf level, from what was observed at canopy level. In this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment. In addition, multiple scattering between the soil and the canopy, and the fraction of land covered by vegetation have also been taken into consideration. We validate this model upon experimental measures, in three types of crops of agronomic interest (Pea, Rye grass and Maize) with different architecture. Our model accurately predicts both the shape of fluorescence spectra at leaf level from that measured at canopy level and the fluorescence ratio. Furthermore, not only do we eliminate artifacts affecting the spectral shape, but we are also able to calculate the quantum yield of fluorescence corrected for re-absorption, from the experimental quantum yield at canopy level. This represents an advance in the study of these systems because it offers the opportunity to make corrections for both the fluorescence ratio and the intensity of the observed fluorescence.
Why it matches plant phenotyping methodsキャノピー蛍光から葉レベルの蛍光スペクトルと量子収率を推定・補正する物理モデルを改良し、複数作物で実測値により検証しており、植物生理状態の取得手法が研究の中心である。
abstractIn this present work, we publish a revision of that physical model, with a more rigorous and exact mathematical treatment.
Abstract Aims Specific root respiration (RR S ) is a key root trait, determining i.e. nutrient foraging and uptake efficiencies. However, a considerable uncertainty exists regarding the effects of storage time and conditions on RR S measurements. Methods Fine root CO 2 efflux rates of three plant types (tree seedling Carpinus betulus , legume Pisum sativum , grass Lolium perenne ) were measured as depending on storage time (30–1440 min post-rinsing) and conditions (i.e. attached to plant, warm and cold water storage, and storage under dry conditions). Results Short-term storage conditions (30 min) had a significant effect on measured RR S rates, in specific, RR S rates of all three species were significantly lower under dry storage. Irrespective of plant species or temperature, storage of excised roots in water did not affect RR S for 300 min,. RR S measurements remained stable for 1 day if roots were stored cold. Conclusions Our results have important implications on measurement routines of RR S —a generally understudied root trait. Henceforth it seems reasonable to collect roots in the field and transport them, hydrated but even uncooled, to the laboratory for subsequent measurements for at least 300 min post-rinsing.
Why it matches plant phenotyping methods根呼吸という植物生理形質の測定について、保存時間・条件が測定値に及ぼす影響を検証し、測定ルーチンへの具体的示唆を示しているため、測定法の技術的検証が中心である。
abstractHowever, a considerable uncertainty exists regarding the effects of storage time and conditions on RR S measurements.
Background and aims Plant and bacteria are able to synthesise proline, which acts as a compound to counteract the negative effects of osmotic stresses. Most methodologies rely on the extraction of compounds using destructive methods. This work describes a new proline biosensor that allows the monitoring of proline levels in a non-invasive manner in root exudates and nodules of legume plants. Methods The proline biosensor was constructed by cloning the promoter region of pRL120553, a gene with high levels of induction in the presence of proline, in front of the lux cassette in Rhizobium leguminosarum bv. viciae . Results Free-living assays show that the proline biosensor is sensitive and specific for proline. Proline was detected in both root exudates and nodules of pea plants. The luminescence detected in bacteroids did not show variations during osmotic stress treatments, but significantly increased during recovery. Conclusions This biosensor is a useful tool for the in vivo monitoring of proline levels in root exudates and bacteroids of symbiotic root nodules, and it contributes to our understanding of the metabolic exchange occurring in nodules under abiotic stress conditions.
Why it matches plant phenotyping methods植物の根圏滲出液と根粒中のプロリン状態を非破壊・生体内で測定する新規バイオセンサーを開発し、感度・特異性を検証しているため、植物生理表現型の取得法が研究の中心である。
abstractThis work describes a new proline biosensor that allows the monitoring of proline levels in a non-invasive manner in root exudates and nodules of legume plants.
Why it matches plant phenotyping methods単粒NIRSによる pea 種子の重量・タンパク質・油含量推定を開発・外部検証し、圃場試験にも適用しており、表現型取得法が研究の中心である。
abstractIn this study we evaluated the accuracy of single‐seed near‐infrared spectroscopy (NIRS) for measuring pea‐seed weight, protein, and oil content.
Legumes tend to be nodulated by competitive rhizobia that do not maximize nitrogen (N 2 ) fixation, resulting in suboptimal yields. Rhizobial nodulation competitiveness and effectiveness at N 2 fixation are independent traits, making their measurement extremely time-consuming with low experimental throughput. To transform the experimental assessment of rhizobial competitiveness and effectiveness, we have used synthetic biology to develop reporter plasmids that allow simultaneous high-throughput measurement of N 2 fixation in individual nodules using green fluorescent protein (GFP) and barcode strain identification (Plasmid ID) through next generation sequencing (NGS). In a proof-of-concept experiment using this technology in an agricultural soil, we simultaneously monitored 84 different Rhizobium leguminosarum strains, identifying a supercompetitive and highly effective rhizobial symbiont for peas. We also observed a remarkable frequency of nodule coinfection by rhizobia, with mixed occupancy identified in ∼20% of nodules, containing up to six different strains. Critically, this process can be adapted to multiple Rhizobium -legume symbioses, soil types, and environmental conditions to permit easy identification of optimal rhizobial inoculants for field testing to maximize agricultural yield.
Why it matches plant phenotyping methods個々の根粒における窒素固定をGFPで、菌株同定をバーコードNGSで同時・高スループット測定する技術を開発しており、根粒の生理状態の取得が研究の中心である。
abstractwe have used synthetic biology to develop reporter plasmids that allow simultaneous high-throughput measurement of N 2 fixation in individual nodules using green fluorescent protein (GFP) and barcode strain identification (Plasmid ID) through next generation sequencing (NGS).
Why it matches plant phenotyping methods単一種子NIRSによる種子重量・タンパク質・油分の推定精度を外部検証しており、植物形質取得法の技術評価が研究の中心です。
abstractIn this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea-seed weight, protein, and oil content.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.
Why it matches plant phenotyping methods画像センサー、解像度、画像処理を比較・評価し、開花強度を定量化するフェノタイピング手法の検証が中心であるため。
abstractwe evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity
Background: Pea ( Pisum sativum ) is a prevalent cool season crop that produces seeds valued for high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single-seed NIRS and wet chemistry methods. To demonstrate field relevance, the single-seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on protein content of harvested dry peas in a field trial. Results: External validation of Partial Least Squares (PLS) regression models showed high prediction accuracy for protein and weight (R 2 = 0.94 for both) and less accuracy for oil (R 2 = 0.75). Single seed weight was not significantly correlated with protein or oil content in contrast to previous reports. In the field study, the single-seed NIRS predicted protein values were within 1% of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. Conclusion: The high accuracy of protein and weight estimation show that single-seed NIRS could be used in the dual selection of high protein, high weight peas early in the breeding cycle allowing for faster genetic advancement toward improved pea nutritional quality.
Why it matches plant phenotyping methods単粒NIRSによる種子重量・タンパク質・油含量推定を開発・外部検証し、圃場試験で独立基準法とも比較しており、植物形質取得法が中心である。
abstractIn this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea seed weight, protein, and oil content.
CoffeePeaSpinachLeafClassificationPhysiological trait estimationWater status / transpiration
Abstract Background The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.
Why it matches plant phenotyping methods植物葉の含水量という生理形質を、テラヘルツ計測と機械学習で非侵襲推定する手法の開発・評価が中心である。
abstractThis paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves
Seed samples from 117 genetically diverse pea breeding lines were used to determine the robustness of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid nutritional profiling of seeds. The FT-MIR results were compared to wet chemistry methods for assessing the concentrations of total protein, starch, fiber, phytic acid, and carotenoids in pea seed samples. Of the five partial least square regression models (PLSR) developed, protein, fiber and phytic acid concentrations predicted by the models exhibited correlation coefficients greater than 0.83 when compared with data obtained using the wet chemistry methods for both the calibration and validation sets. The starch PLSR model had a correlation greater than 0.75, and carotenoids had correlation of 0.71 for the validation sets. The methods implemented in this research show the novelty and usefulness of FT-MIR as a simple, fast, and cost-effective technique to determine multiple seed constituents simultaneously.
Why it matches plant phenotyping methodsFT-MIRによるエンドウ種子成分の迅速推定法を開発し、湿式化学法との比較で校正・検証しており、種子形質の取得法が研究の中心である。
abstractused to determine the robustness of Fourier transform mid-infrared spectroscopy (FT-MIR) for the rapid nutritional profiling of seeds.
This research was conducted to evaluate the utility and reliability of X‐ray fluorescence (XRF) spectroscopy to analyze macro‐ (K and Ca) and micronutrients (Mn, Fe, Cu, Zn, and Se) in pea (Pisum sativum L.) seeds. The pea seed samples were ground into flour and pelleted to collect the XRF spectra. Seventy‐three pea seed samples were selected to cover the expected concentration ranges for each element to develop calibration curves by correlating the XRF results with atomic absorption spectroscopy (AAS). The XRF results were validated by a systematic comparison of data obtained from AAS on a set of 80 additional and independent pea seed samples. Element concentrations were also predicted using the fundamental parameter approach collectively for 153 samples. For all the calibration curves, the R² value was >0.8, except for K (0.54). For Mn, Fe, Cu, Zn, and Se, the XRF predictions were similar to AAS measurements at a 95% confidence level. Similar results were obtained with the fundamental parameter approach except for Fe for which significant bias of ∼6 mg kg⁻¹ was calculated. Except for K, R value for all the validation curves was >0.85. Thus, the results obtained using XRF and the fundamental parameter approach were statistically not different from the AAS method. This study demonstrated that the XRF technique is a fast and reliable, nondestructive, and noninvasive analytical tool for mineral analysis, particularly for transition metals, does not produce waste, and requires no chemical reagents.
Why it matches plant phenotyping methodsエンドウ種子の元素濃度という植物器官形質を対象に、XRF分析の校正と独立試料による妥当性検証を中心課題としているため、植物フェノタイピング手法として収録する。
abstractThis research was conducted to evaluate the utility and reliability of X‐ray fluorescence (XRF) spectroscopy to analyze macro‐ (K and Ca) and micronutrients (Mn, Fe, Cu, Zn, and Se) in pea (Pisum sativum L.) seeds.
Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.
Why it matches plant phenotyping methods航空マルチスペクトル画像から植生指数を抽出し、遺伝子型別の収量ポテンシャルを推定する手法を開発・評価しており、表現型取得と推定が研究の中心です。
abstractaerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes
Post-harvest change in the colour of green field pea (Pisum sativum L.) is undesirable as this impacts the visual quality and market value of the seed. To date, there is no standard, objective method to determine bleaching. Therefore, the aim of this study was to develop an objective method for scoring bleaching based on colour reflectance spectra, measured both by spectrophotometer and multispectral Image Analysis (IA). Green field pea seeds were sorted into samples of uniform colour and these were used to train the model. Spectra calculated from multispectral images (with colour bands at 405,470,530,590,660 and 850nm) were matched to the spectrophotometer output through multiple linear regression. All spectra were transformed to emphasize the wavelength regions most impacted during bleaching, following which two critical reflectance values were scaled to a single bleaching score. The bleaching assessment method was tested in a time-course experiment comprising seeds from five green-pea genotypes stored for six months. Each sample was divided into two so that half of the seeds were stored in the dark and the remainder were exposed to controlled light to exaggerate bleaching. Throughout this period, the samples were imaged at six-weekly intervals. Assessment of bleaching by the IA method agreed well with spectrophotometer measurements, achieving a Lin's concordance statistic of 0.99 and 0.96 for the calibration and time-course samples respectively. The IA method proved more versatile because assessments could be made on individual seeds enabling the computation of bleaching uniformity within each sample. This method captured differences between genotypes in the extent, rate and uniformity of bleaching. All genotypes exhibited susceptibility to bleaching when stored under the controlled light conditions. Excell was observed to be the most susceptible genotype with the greatest bleaching-rate and OZB1308 displayed the most colour-stability.
Why it matches plant phenotyping methods緑色エンドウ種子の退色という植物器官の状態を、分光測定とマルチスペクトル画像から定量する客観的方法を開発し、校正・時系列検証しているため、方法が中心的である。
abstractTherefore, the aim of this study was to develop an objective method for scoring bleaching based on colour reflectance spectra, measured both by spectrophotometer and multispectral Image Analysis (IA).
LEDFLEX is a micro-lidar dedicated to the measurement of vegetation fluorescence. The light source consists of 4 blue Light-Emitting Diodes (LED) to illuminate part of the canopy in order to average the spatial variability of small crops. The fluorescence emitted in response to a 5-μs width pulse is separated from the ambient light through a synchronized detection. Both the reflectance and the fluorescence of the target are acquired simultaneously in exactly the same field of view, as well as the photosynthetic active radiation and air temperature. The footprint is about 1 m 2 at a distance of 8 m. By increasing the number of LEDs longer ranges can be reached. The micro-lidar has been successfully applied under full sunlight conditions to establish the signature of water stress on pea (Pisum Sativum) canopy. Under well-watered conditions the diurnal cycle presents an M shape with a minimum (Fmin) at noon which is Fmin > Fo. After several days withholding watering, Fs decreases and Fmin < Fo. The same patterns were observed on mint (Menta Spicata) and sweet potatoes (Ipomoea batatas) canopies. Active fluorescence measurements with LEDFLEX produced robust fluorescence yield data as a result of the constancy of the excitation intensity and its geometry fixity. Passive methods based on Sun-Induced chlorophyll Fluorescence (SIF) that uses high-resolution spectrometers generate only flux data and are dependent on both the 3D structure of vegetation and variable irradiance conditions along the day. Parallel measurements with LEDFLEX should greatly improve the interpretation of SIF changes.
Why it matches plant phenotyping methods植物キャノピーの蛍光・反射を測定するマイクロライダーを開発し、ストレス検出への適用と蛍光データの頑健性を示しており、植物表現型取得法が研究の中心である。
abstractLEDFLEX is a micro-lidar dedicated to the measurement of vegetation fluorescence.
Plant phenotyping to date typically comprises morphological and physiological profiling in a high-throughput manner. A powerful method that allows for subcellular characterization of organelle stoichiometric/functional characteristics is still missing. Organelle abundance and crosstalk in cell dynamics and signaling plays an important role for understanding crop growth and stress adaptations. However, microscopy can not be considered a high-throughput technology. The aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner. A combination of subcellular non-aqueous fractionation and liquid chromatography mass spectrometry was applied to assign membrane-marker proteins to cell compartmental abundances and functions of Pisum sativum leaves. Based on specific subcellular affiliation, proteotypic marker peptides of the chloroplast, mitochondria and vacuole membranes were selected and synthesized as heavy isotope labelled standards. The rapid and unbiased Mass Western approach for accurate stoichiometry and targeted absolute protein quantification allowed for a proportional organelle abundances measure linked to their functional properties. A 3D Confocal Laser Scanning Microscopy approach was developed to evaluate the Mass Western. Two P. sativum cultivars of varying morphology and physiology were compared. The Mass Western assay enabled a cultivar specific discrimination of the chloroplast to mitochondria to vacuole relations.
Why it matches plant phenotyping methods植物の細胞内オルガネラ量と機能的特性を高スループットに推定するフェノタイピング手法を開発し、3D共焦点顕微鏡で評価・検証しているため、方法が中心的である。
abstractThe aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner.
Reproduction assets foundThe paper's plant-phenotyping measurements and analysis outputs are available as public supplementary material hosted on the Frontiers article page: Tables S1–S4 (Mass Western target peptide lists, confocal organelle volume/area abundance values, NAF LFQ peak intensities, and proteotypic peptide subcellular localizatonSupplement · publicand Thomas Joch for plant cultivation at the department-associated greenhouse facility.
Footnotes
Funding. This study was funded by the Austrian Science Fund (FWF) [ P24870 -B22] and [W 1257-820], and supported by the COST action FA1306.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2019.00638/full#supplementary-material
Figure S1
Morphological phenotypes of the Pisum sativum cultivars Protecta (left) and Messire (right). Length of internodes and leaf weight n = 5 biol. replicates, error bars = standard error, p < 0.05 (Kruskal Wallis). ** p < 0.01, *** p < 0.005.
Click here for additional data filOpen asset ↗lines:102-128Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height ( r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.
Why it matches plant phenotyping methodsUASのRGB・マルチスペクトル画像からバイオマス、草丈、被覆率などの植物形質を抽出・推定し、地上真値との相関およびLASSOモデルで技術評価しており、フェノタイピング手法が研究の中心である。
abstractSensor-based phenotyping of such traits can greatly enhance crop breeding efficiency.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 10 Sept 2026
PeaTomatoWheatX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Roots naturally exert axial and radial pressures during growth, which alter the structural arrangement of soil at the root–soil interface. However, empirical models suggest soil densification, which can have negative impacts on water and nutrient uptake, occurs at the immediate root surface with decreasing distance from the root. Here, we spatially map structural gradients in the soil surrounding roots using non‐invasive imaging, to ascertain the role of root growth in early stage formation of soil structure. X‐ray computed tomography provided a means not only to visualize a root system in situ and in 3‐D but also to assess the precise root‐induced alterations to soil structure close to, and at selected distances away from the root–soil interface. We spatially quantified the changes in soil structure generated by three common but contrasting plant species (pea, tomato, and wheat) under different soil texture and compaction treatments. Across the three plant types, significant increases in porosity at the immediate root surface were found in both clay loam and loamy sand soils and not soil densification, the currently assumed norm. Densification of the soil was recorded, at some distance away from the root, dependent on soil texture and plant type. There was a significant soil texture × bulk density × plant species interaction for the root convex hull, a measure of the extent to which root systems explore the soil, which suggested pea and wheat grew better in the clay soil when at a high bulk density, compared with tomato, which preferred lower bulk density soils. These results, only revealed by high resolution non‐destructive imagery, show that although the root penetration mechanisms can lead to soil densification (which could have a negative impact on growth), the immediate root–soil interface is actually a zone of high porosity, which is very important for several key rhizosphere processes occurring at this scale including water and nutrient uptake and gaseous diffusion.
Why it matches plant phenotyping methodsX線CTによる非侵襲・高解像度イメージングを中心に、根系形態と根周辺土壌構造を3Dで可視化・定量しており、植物の表現型取得が主要な方法的要素である。
abstractHere, we spatially map structural gradients in the soil surrounding roots using non‐invasive imaging
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 10 Sept 2026
Early vigour of seedlings is a beneficial trait of field pea (Pisum sativum L.) that contributes to weed control, water use efficiency and is likely to contribute to yield under certain environments. Although breeding is considered the most effective approach to improve early vigour of field pea, the absence of a robust and high-throughput phenotyping tool to dissect this complex trait is currently a major obstacle of genetic improvement programs to address this issue. To develop this tool, separate trials on 44 genetically diverse field pea genotypes were conducted in the automated plant phenotyping platform of Plant Phenomics Victoria, Horsham and in the field, respectively. High correlation between estimated plant parameters derived from the automated phenotyping platform and important early vigour traits such as shoot biomass, leaf area and plant height indicated that the derived plant parameters can be used to predict vigour traits in field pea seedlings. Plant growth analysis demonstrated that the "broken-stick" model fitted well with the growth pattern of all field pea genotypes and can be used to determine the linear growth phase. Further analysis suggested that the estimated plant parameters collected at the linear growth phase can effectively differentiate early vigour across field pea genotypes. High correlation between normalised difference vegetation indices captured from the field trial and estimated shoot biomass and top-view area confirmed the consistent performance of early vigour field pea genotypes under controlled and field environments. Overall, our results demonstrated that this robust screening tool is highly applicable and will enable breeding programs to rapidly identify early vigour traits and utilise germplasm to contribute to the genetic improvement of field peas.
Why it matches plant phenotyping methodsフィールドエンドウの初期生育形質を推定する自動画像フェノタイピングツールを開発し、圃場測定との相関で性能を検証しているため、方法が研究の中心である。
abstractthe absence of a robust and high-throughput phenotyping tool to dissect this complex trait is currently a major obstacle
Main conclusion ATR-FTIR spectroscopy in combination with uni- and multivariate analysis was used to quantify the spectral-chemical composition of the leaf cuticle of pea, investigating the effects of variety and heat stress. Field pea (Pisum sativum L.) is sensitive to heat stress and our goal was to improve canopy cooling and flower retention by investigating the protective role of lipid-related compounds in leaf cuticle, and to use results in the future to identify heat resistant genotypes. The objective was to use Attenuated Total Reflection (ATR)-Fourier Transform Infrared (FTIR) spectroscopy, a non-invasive technique, to investigate and quantify changes in adaxial cuticles of fresh leaves of pea varieties that were subjected to heat stress. Eleven varieties were grown under control (24/18 °C day/night) and heat stress conditions (35/18 °C day/night, for 5 days at the early flowering stage). These 11 had significant spectral differences in the integrated area of the main lipid region, CH 2 region, CH 3 peak, asymmetric and symmetric CH 2 peaks, ester carbonyl peak, and the peak area ratio of CH 2 to CH 3 and ester carbonyl to CH 2 asymmetric peak, indicating that cuticles had spectral-chemical diversity of waxes, cutin, and polysaccharides. Results indicated considerable diversity in spectral-chemical makeup of leaf cuticles within commercially available field pea varieties and they responded differently to high growth temperature, revealing their diverse potential to resist heat stress. The ATR-FTIR spectral technique can, therefore, be further used as a medium-throughput approach for rapid screening of superior cultivars for heat tolerance.
Why it matches plant phenotyping methodsATR-FTIRを用いて生葉の葉クチクラの化学スペクトル特性を定量し、品種間差と熱ストレス応答を評価する手法を中心的に適用しており、耐熱性スクリーニングへの再利用可能性も示しているため。
abstractThe objective was to use Attenuated Total Reflection (ATR)-Fourier Transform Infrared (FTIR) spectroscopy, a non-invasive technique, to investigate and quantify changes in adaxial cuticles of fresh leaves of pea varieties that were subjected to heat stress.
Plant phenotyping links genomics with plant ecophysiology and agronomy.It is usually performed by non-destructive, automated and image-based technology and generates information for efficient and searchable digital characterization of crop that can be performed during routine, periodical regeneration of accessions in germplasm collections.In the present work, ninety-two accessions of Pisum from different species and subspecies were studied during 2015 and 2016.Size and colour traits were measured using digital images from a Samsung CLX 3300 scanner and analysed with appropriate software; also seed weight, plant height and days to flowering were measured.Highly significant differences between accessions and species and subspecies for all these traits were found.When distances among species and subspecies are calculated, P. sativum subsp.sativum showed the greatest distance with P. fulvum (8.02) followed by P. abyssinicum (7.13); while the smallest distance was found between P. fulvum and P. sativum subsp.transcaucasicum (3.16).A Neighbour-joining tree with a cofenetic r of 0.985 was obtained.Seed and pod characteristics as colour parameters and size, obtained by digital phenotyping, have proved to be suitable markers for genetic diversity evaluation and they are useful in evolutionary analysis, allowing the discrimination of the main wild and cultivated species in the genus Pisum.
Why it matches plant phenotyping methodsデジタル画像とソフトウェアで種子・莢のサイズおよび色を抽出し、遺伝的多様性評価に利用するフェノタイピング手法が研究の主要部分である。
abstractSize and colour traits were measured using digital images from a Samsung CLX 3300 scanner and analysed with appropriate software
Background Boron (B) tolerance has been identified as a key target for field pea improvement. Screening for B tolerance in the field is problematic due to variability in space and time, and robust B molecular markers are currently unavailable in field pea. There has been recent progress in developing protocols that can accelerate the life cycle of plants to enable rapid generation turnover in single seed descent breeding programs. A robust B screening protocol that can be fully integrated within an accelerated single seed descent system could lead to rapid identification and introgression of B tolerance into field pea genotypes. Integration with an accelerated single seed descent system requires: (1) screening under artificially lit, temperature-controlled conditions; (2) capacity to use immature precociously germinated seed (PGS); (3) recovery of lines without significant time penalty; and (4) good correlation with results from established screening protocols. Results We present herein a B toxicity screening system for field pea based on hydroponic growth of PGS in a light and temperature controlled environment that allows recovery of seedlings for rapid seed production. Screening results were compared to traditional methods for B tolerance screening in B-laced soil and with published field tolerance ratings. B tolerance was scored 17 days after sowing using leaf symptoms as a metric. Plants were then transferred to soil with maximum of six days delay in flowering compared to a typical accelerated single seed descent system generation. The use of PGS had minimal impact on B tolerance rankings compared to plants grown from mature seed. The leaf tolerance rankings from hydroponic-grown plants correlated well with those from soil-grown plants, and consistently identified the most tolerant genotypes. Conclusions Our 17 day screening protocol represents a major time-saving over previously published B screening protocols for field pea, thereby extending the application of the protocol to traditional single seed descent systems or RIL screening. We anticipate that small modifications to the proposed technique will make it applicable to screen for other individual abiotic stresses, or allow studies of the interactions between B tolerance and stresses such as salinity.
Why it matches plant phenotyping methodsエンドウのホウ素耐性を葉症状で評価する高速・高スループット水耕スクリーニング法を開発し、土壌法および圃場評価との相関で検証しているため、表現型取得法が研究の中心である。
abstractWe present herein a B toxicity screening system for field pea based on hydroponic growth of PGS in a light and temperature controlled environment that allows recovery of seedlings for rapid seed production.
Abstract The use of in vivo X-ray microcomputed tomography ( μ CT) to study plant root systems has become routine, but is often hampered by poor contrast between roots, soil, soil water, and soil organic matter. In clinical radiology, imaging of poorly contrasting regions is frequently aided by the use of radio-opaque contrast media. In this study, we present evidence for the utility of iodinated contrast media (ICM) in the study of plant root systems using μ CT. Different dilutions of an ionic and nonionic ICM (Gastrografin 370 and Niopam 300) were perfused into the aerial vasculature of juvenile pea plants via a leaf flap ( Pisum sativum ). The root systems were imaged via μ CT, and a variety of image-processing approaches used to quantify and compare the magnitude of the contrast enhancement between different regions. Though the treatment did not appear to significantly aid extraction of full root system architectures from the surrounding soil, it did allow the xylem and phloem units of seminal roots and the vascular morphology within rhizobial nodules to be clearly visualized. The nonionic, low-osmolality contrast agent Niopam appeared to be well tolerated by the plant, whereas Gastrografin showed evidence of toxicity. In summary, the use of iodine-based contrast media allows usually poorly contrasting root structures to be visualized nondestructively using X-ray μ CT. In particular, the vascular structures of roots and rhizobial nodules can be clearly visualized in situ .
Why it matches plant phenotyping methods植物根系のX線マイクロCTにおける造影法を開発・評価し、根および根粒の維管束形態を非破壊的に可視化する手法として中心的に扱っているため。
abstractwe present evidence for the utility of iodinated contrast media (ICM) in the study of plant root systems using μ CT.
MaizePeaWheatSeed / grainPhysiological trait estimationWater status / transpiration
Abstract The objective of the paper was to investigate the moisture change ability of bean, broad bean, pea, lupine, radish, beetroot, winter wheat, maize, cucumber, pumpkin and sunflower seeds. Seeds absorbed water in the system for measurement of the water absorption kinetics with the capillary and weighting method. The research results were presented in the form of plots of the relation of kinetics of the moisture change of seeds in the time for the first and second stage of the process which precedes germination. Moreover, the water content in seeds for the time of the end of the second stage of water absorption was determined and the maximum water absorption speed coefficient and the time of its obtaining was calculated. The water content for the end of the 2nd stage of its collection was the highest for seeds which include a hard caryopsis and for broad bean plant seeds. The maximum water absorption speed coefficient was the highest for seeds with low initial moisture - radish, beetroot, cucumber and pumpkin. The measurement of the water absorption speed with the use of the capillary and weighting method enabled investigation of seeds with a varied size, construction of a seed coat and chemical composition for a long period of time to the moment seedlings were obtained.
Why it matches plant phenotyping methods種子の吸水・含水変化を毛管法と重量法で測定し、吸水速度係数などの種子形質を定量化する測定法が研究の中心であるため。
abstractSeeds absorbed water in the system for measurement of the water absorption kinetics with the capillary and weighting method.
PeaLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.
Why it matches plant phenotyping methods幼植物の根表現型から成熟根系形質を推定するRSAモデルを開発・評価し、根表現型ワークフローへの組込みを検証しており、方法が研究の中心です。
abstractWe suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
PeaGreenhouseRootClassificationRoot system architecture
Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding - especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) - Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0-5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars.
Why it matches plant phenotyping methods根系形態形質データから重要形質を選択し、品種分類する機械学習ワークフローが中心であり、植物フェノタイピング手法への実質的な応用に該当する。
abstractnovel approaches are need to apply them on root phenotyping data of mature plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 11 Sept 2026
GrapevinePeaRapeseed / canolaWheatRootWhole plant / canopy / plot / fieldMorphology / geometry measurementVisualization / data managementGrowth / development / phenologyRoot system architecture
BACKGROUND: In order to maintain high yields while saving water and preserving non-renewable resources and thus limiting the use of chemical fertilizer, it is crucial to select plants with more efficient root systems. This could be achieved through an optimization of both root architecture and root uptake ability and/or through the improvement of positive plant interactions with microorganisms in the rhizosphere. The development of devices suitable for high-throughput phenotyping of root structures remains a major bottleneck. RESULTS: Rhizotrons suitable for plant growth in controlled conditions and non-invasive image acquisition of plant shoot and root systems (RhizoTubes) are described. These RhizoTubes allow growing one to six plants simultaneously, having a maximum height of 1.1 m, up to 8 weeks, depending on plant species. Both shoot and root compartment can be imaged automatically and non-destructively throughout the experiment thanks to an imaging cabin (RhizoCab). RhizoCab contains robots and imaging equipment for obtaining high-resolution pictures of plant roots. Using this versatile experimental setup, we illustrate how some morphometric root traits can be determined for various species including model (Medicago truncatula), crops (Pisum sativum, Brassica napus, Vitis vinifera, Triticum aestivum) and weed (Vulpia myuros) species grown under non-limiting conditions or submitted to various abiotic and biotic constraints. The measurement of the root phenotypic traits using this system was compared to that obtained using "classic" growth conditions in pots. CONCLUSIONS: This integrated system, to include 1200 Rhizotubes, will allow high-throughput phenotyping of plant shoots and roots under various abiotic and biotic environmental conditions. Our system allows an easy visualization or extraction of roots and measurement of root traits for high-throughput or kinetic analyses. The utility of this system for studying root system architecture will greatly facilitate the identification of genetic and environmental determinants of key root traits involved in crop responses to stresses, including interactions with soil microorganisms.
Why it matches plant phenotyping methodsRhizoTubesとRhizoCabによる根・シュートの非破壊画像取得および根形態形質抽出システムの開発、比較検証、ハイスループット表現型解析への応用が中心である。
abstractRhizotrons suitable for plant growth in controlled conditions and non-invasive image acquisition of plant shoot and root systems (RhizoTubes) are described.
Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.
Why it matches plant phenotyping methodsデジタル画像とレーザーから種子の色・形状・サイズ形質を抽出し、欠陥および市場等級を分類する高スループット計測・判別手法の開発と検証が中心である。
abstractwe developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images.
Accurate quantification of belowground plant biomass (BGP) is crucial to account for the carbon (C) and nitrogen (N) stored by plants. As soil sieving to recover roots leaves a large proportion of root borne compounds defined as rhizodeposits (fine roots, root fragments, exudates) unaccounted for, isotope-labelling approaches have frequently been used. The aim of the present study was to compare two approaches that estimate BGP-N from isotope labelling experiments and assess their potential error. Pisum sativum was grown in a pot experiment and repeatedly pulse labelled with a 13C glucose and 15N urea solution using a cotton wick method. Additionally, data from a previous study using the same labelling approach were used for comparative BGP-N calculations. In both experiments, the amount of BGP-N calculated with a mass balance approach was significantly lower compared with the classical calculation, indicating substantial overestimation of N rhizodeposition in previous studies. Multiple pulse labelling of plants with 15N can result in homogeneous label distribution, which allows both calculation approaches to be used. However, when label distribution is heterogeneous, the classical approach overestimates N rhizodeposition and BGP-N.
Why it matches plant phenotyping methods植物の地下部バイオマス定量法を比較し、推定誤差を評価・検証しているため、測定法の妥当性評価が中心です。
abstractThe aim of the present study was to compare two approaches that estimate BGP-N from isotope labelling experiments and assess their potential error.