Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.
Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。
abstractmechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield.
Common beanLeafClassificationDisease symptoms / severity
Abstract Accurate recognition of plant leaf diseases from images is essential for intelligent agriculture and precision crop protection. However, reliable disease identification remains challenging because lesion regions often exhibit subtle visual differences, complex backgrounds, and large intraclass variations, especially when available disease samples are limited. To address these challenges, this study proposes PR-CNN, a deep learning framework that integrates convolutional neural networks, pyramid split attention, and a relation network for bean leaf disease image recognition. The convolutional backbone is first used to extract visual features from support and query images. Then, the pyramid split attention module enhances multiscale spatial and channel feature representation, enabling the model to focus on discriminative lesion regions while suppressing redundant background information. Finally, the relation network learns a nonlinear similarity metric between paired samples and generates relation scores for disease category prediction. Experimental results show that PR-CNN achieves an overall classification accuracy of 99.24% on the primary bean leaf disease dataset, outperforming representative models, including ResNet50, DenseNet, Inception v4, and EfficientNet B7, in terms of recognition accuracy and adaptability. In addition, PR-CNN was evaluated on four publicly available plant disease datasets, including CGIAR, Plant Diseases, LWDCD 2020, and Plant Pathology, achieving an average accuracy of 99.84%. These results demonstrate that PR-CNN can effectively improve image based plant disease recognition and provides a robust visual classification framework for intelligent crop disease diagnosis.
Why it matches plant phenotyping methods豆葉画像から病徴・病害を認識する深層学習手法を開発し、複数データセットで性能検証しており、植物の病害状態の画像ベース計測が研究の中心である。
abstractthis study proposes PR-CNN, a deep learning framework that integrates convolutional neural networks, pyramid split attention, and a relation network for bean leaf disease image recognition.
Common beanGrapevineLeafClassificationObject detectionStress / disease detectionDisease symptoms / severity
Plant leaf disease is a grave risk to crop suitability and agricultural sustainability, and therefore, the ability to make early and accurate diagnosis is a mandatory need in the contemporary precision farming system.In recent years, deep learning has gained significant attention for image-based plant disease detection.Despite its effectiveness, model performance can be influenced by factors such as redundant feature representations and sensitivity to hyperparameter selection.To address these challenges, this study proposes a nested hybrid optimization framework that combines the Cuckoo Search Algorithm (CSA) for channel selection with the Beluga Whale Optimization Mechanism (BWOM) for tuning the hyperparameters of a Convolutional Neural Network (CNN).The proposed approach is independently evaluated on bean and grape leaf datasets under consistent experimental conditions to assess its disease classification performance.In addition to strong predictive performance, the framework incorporates explainable AI (XAI) techniques, namely Gradient-weighted Class Activation Mapping (Grad-CAM) and Gradientweighted Class Activation Mapping Plus Plus (Grad-CAM++), to enhance model interpretability.These approaches highlight the most significant visual features influencing predictions, thereby providing valuable insights for agronomists and fostering trust in AI-based systems.Experimental results show that the proposed CSA-BWOM optimized CNN achieves classification accuracies of 99.61% and 99.38% on the bean and grape datasets, respectively, outperforming baseline CNN models and exhibiting competitive performance when compared to a few existing approaches.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類するCNN最適化・説明可能AI手法が研究の中心であり、植物病害状態の表現型推定に該当する。
titleExplainable AI-based CNN Optimization Model for Plant Leaf Disease Detection
Abstract Genomic selection has accelerated genetic gain in many breeding programs worldwide but genotype-by-environment-by-management (GxExM) hampers further progress for systems where these interactions are important and not well represented in the training data. Process-based crop growth models (CGMs), which encode physiological relationships between plants and their environments, can extrapolate to novel conditions but cannot directly leverage genomic information. Coupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations, yet applications in horticultural crops remain scarce. In this study, we apply CGM-WGP to predict flowering time in broccoli ( Brassica oleracea var. italica ) and common bean ( Phaseolus vulgaris L.), two horticultural species with contrasting physiological responses to temperature and photoperiod. Genotype-specific thermal time requirements and photoperiod parameters were jointly estimated with genome-wide marker effects and predictions were compared against a Reaction Norm Genomic Best Linear Unbiased Prediction (RN-GBLUP) benchmark across four cross-validation scenarios of increasing predictive difficulty. RN-GBLUP achieved the highest accuracy under sparse-testing scenarios where training data covered all target environments, while CGM-WGP outperformed RN-GBLUP when predicting untested environments and untested genotype-environment combinations (broccoli: Pearson r = 0.66, RMSE = 9.4 days; bean: r = 0.86, RMSE = 5.2 days). These results demonstrate that CGM-WGP can be applied to horticultural crops using genome-wide markers alone, but without requiring prior identification of quantitative trait loci. CGM-WGP also provides a modular foundation that can be extended to predict the timing of other developmental transitions and output traits such as biomass and yield.
Why it matches plant phenotyping methods開花期という植物形質を対象に、CGM-WGPによる予測手法を適用し、RN-GBLUPとの交差検証で精度比較・評価しており、形質推定ワークフローが研究の中心である。
abstractCoupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations
ABSTRACT The common bean is vital for food security, but its productivity is often limited by competition with weeds, requiring the use of herbicides. The response of genotypes to herbicides such as fomesafen and imazamox is variable, and the traditional evaluation of phytotoxicity through visual methods is subjective. Therefore, the present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes, aiming to reduce the subjectivity of traditional visual assessments; (ii) characterize common bean genotypes under the effects of different herbicides and their doses in progenies and parental lines, based on morphophysiological traits and indices derived from visible RGB (red, green, and blue) digital images. The experiment was conducted in a completely randomized design under a 3 × 3 × 6 factorial scheme (herbicide × dose × genotype) with three replications, evaluating fomesafen and imazamox at doses of 0%, 100%, and 200% of the recommended rates. Data were collected on visual phytotoxicity, plant height, stem diameter, number of leaves, and image indices (Green Index, Excess Green Index, Excess Red Index, and Color Index of Vegetation Extraction). Results indicated that the triple interaction was significant, revealing the complexity of plant responses to herbicides. Canonical discriminant analysis explained 78.66% of the total variation, with the first canonical discriminant function (29.42%) contrasting structural development and vitality with stress, the second canonical discriminant function (27.59%) reflecting overall plant vigor, and the third canonical discriminant function (21.65%) capturing stress and phytotoxicity negatively affecting growth. The analysis demonstrated that image‐based indices combined with multivariate techniques are effective for quantifying phytotoxicity and distinguishing genotypes (tolerant and sensitive to herbicide effects), overcoming the limitations of visual evaluations, and should be used as a complementary tool to traditional techniques. Therefore, the parental genotype IPR Campos Gerais and the progeny F1A were tolerant to herbicides at different doses, while the parental genotype BAF36 and the progeny F2B were sensitive. Hence, the proposed methodology is effective for identifying herbicide‐tolerant and sensitive genotypes.
Why it matches plant phenotyping methodsRGB画像解析と多変量解析による除草剤誘発 phytotoxicity の表現型評価法の提案が研究の中心であり、従来の主観的評価を改善する方法開発に該当する。
abstractthe present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Introduction Accurate disease diagnosis is crucial for enhancing agricultural productivity and reducing postharvest losses, directly impacting food quality and safety. Traditional detection methods often rely on extensive feature modeling and perform poorly in complex field environments. Methods This study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism. The classical ZFNet is first optimized to improve feature extraction efficiency. A combined channel and spatial attention mechanism is then incorporated to refine feature representation for disease identification in key crops. Finally, a residual module is added to boost accuracy. Results Evaluated on a dataset of 11,903 bean leaf images covering healthy leaves and four disease types, including leaf mould, rust, mosaic, and white spot, the model achieves an average recognition accuracy of 99.02%, outperforming MobileMamba, Vision Transformer, and Chest- OMD. Discussion This approach offers a scalable solution for automated disease monitoring, supporting postharvest quality preservation and sustainable crop production.
Why it matches plant phenotyping methods豆葉の病害状態を画像から推定する深層学習モデルを開発し、複数モデルとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。
abstractThis study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publictomato leaf disease data from the open-source dataset New Plant Disease Dataset ( https://www.kaggle.com/vipoooool/new-plant-diseases-dataset ) were also utilized. Both datasets include healthy samples and four disease categories: rust disease, mosaic disease, leaf mold disease, and white spot disease. ( https://pan.baidu.com/s/197Lyn2TGdIjLCE2gylsiHA?pwd=krpw )Open asset ↗pan.baidu.comlines:405-484Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Virus-associated diseases are among the biological stresses that affect common bean yield, such as Carlavirus vignae ( Cowpea mild mottle virus , CPMMV), transmitted by the whitefly Bemisia tabaci . CPMMV is present in different continents, and recent outbreaks have concerned Brazilian farmers and researchers. Integrated Pest Management routines for field monitoring of viral spread are laborious and may be limited to visible symptoms. We hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance. To test this hypothesis, we used a hyperspectral sensor mounted on a drone to capture images of CPMMV-inoculated and non-inoculated field plots in 2022 and 2023, across a tolerant common bean cultivar (BRS FC420 RMD) and a susceptible one (BRS FC401 RMD). Results showed that CPMMV was detected in common bean plants by hyperspectral imaging at early infection stages (~ 6 DAI) before symptom onset and at an advanced infection stage (~ 22 DAI). Reflectance within the visible light spectrum was affected by soil cover on all flights, and in most of these, also in the near-infrared region. The main differences between CPMMV-inoculated and control plants were consistent across two years of experiments, regardless of the common bean phenological stage and genotype. Fit statistics using the sum of squared errors, R 2 and AIC indicated that reflectance from 401 to 425 nm, especially near 415 nm, differed significantly between infected and healthy plants. Such changes are associated with chlorophyll degradation and disruption of the photosynthetic apparatus, and are detectable even before symptom onset. Progress in the disease severity index also differentiated the tolerant cultivar from the susceptible one. CPMMV infection significantly reduced common bean yield by ~ 21% compared with healthy plants. CPMMV detection by hyperspectral imaging enables early scouting to optimize disease management.
Why it matches plant phenotyping methodsハイパースペクトル画像を用いて、症状発現前の感染植物の反射特性と病害状態を検出し、複数年・品種で技術性能を検証しているため、植物フェノタイピング手法が中心である。
abstractWe hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance.
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published1 Jun 2026International Journal of Electrical and Computer Engineering (IJECE)Cited by 0 · OpenAlex ↗
Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT–PCA–SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定するViT–PCA–SVM解析パイプラインが研究の中心であり、植物表現型（病斑・病害状態）の画像ベース推定手法に該当する。
titleTransformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine
Reproduction assets foundThe paper's only qualifying asset is the public Bean Leaf Lesions dataset (leaf images used as phenotyping input for disease classification), explicitly declared in the DATA AVAILABILITY section with a Kaggle URL. No author analysis code, trained models, or checkpoints are released.Dataset · publicI R D O E Vi Su P Fu
Vijayalakshmi S. Abbigeri ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Geetha D. Devanagavi ✓ ✓
CONFLICT OF INTEREST STATEMENT
All authors declare that they have no conflicts of interest.
DATA AVAILABILITY
The data that support the findings of this study are openly available in Kaggle, "Bean leaf lesions
dataset," [Online] at https://www.kaggle.com/datasets/advayprasad/bean-leaf-lesions-dataset.
REFERENCES
[1] Food and Agriculture Organization (FAO), “Climate change fans spread of pests and threatens plants and crops, new FAO study,”
Food and Agriculture Organization (FAO), 2021. https://www.fao.org/newsroom/detail/Climate-change-fans-spread-of-pests-
and-threatens-plants-and-crops-new-FAOOpen asset ↗Kagglepdf-layout-page:7 lines:1-70Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
The authors declare that they have no conflicts of interest to
this work.
Data Availability Statement
The data that support the findings of this study are openly
available in the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 May 2026Computers and Electronics in Agriculture.
Common beanAerial / UAVField / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldClassificationGrowth / development / phenology
Accurate and scalable prediction of physiological maturity (PM) in leguminous crops remains a key challenge due to indeterminate growth and canopy heterogeneity. Thus, causing difficulties for optimizing breeding decisions and field management. This study develops a novel UAV-based multispectral-index fusion framework for high-throughput maturity classification in dry bean. This is conducted by combining parametric and non-parametric machine learning (ML) classification models to capture non-linear maturity signatures. Multispectral imagery was collected over three growing seasons across multiple genotypes. Six spectral bands and five maturity-relevant vegetation indices capturing chlorophyll degradation, senescence, and canopy greenness were evaluated through 63 feature-set combinations and 10 parametric and non-parametric ML classifiers. Model performance was assessed using stratified five-fold cross-validation, composite z-score aggregation, and Friedman-Nemenyi statistical ranking to jointly evaluate accuracy and consistency. Among all the model-feature combinations, the Support Vector Classifier (SVC) paired with Band_All6 + MCARI feature-set achieved the highest test accuracy (>70%), with class-wise recall of 63.2%, 71.4% and 79.4% for early, medium and late maturity, respectively. Hybrid feature-sets integrating chlorophyll-sensitive indices with spectral bands consistently outperformed index-only or band-only sets, confirming the synergistic effect of bands and engineered index coupling. This research establishes a statistically validated pipeline linking UAV multispectral data, vegetation spectral-index and machine learning classification to quantify PM variability in dry bean with potential for validation in other legume crops. The proposed framework offers a generalizable approach for non-destructive maturity prediction, enabling breeders to accelerate genotype selection and harvest scheduling under field-scale conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習による乾燥豆の生理的成熟度推定パイプラインを開発・統計検証しており、植物状態の取得・抽出手法が研究の中心である。
abstractThis study develops a novel UAV-based multispectral-index fusion framework for high-throughput maturity classification in dry bean.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Context Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification. Techniques Three deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes-angular leaf spot, bean rust, and healthy leaves-was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance. Outcomes ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis. In conclusion The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.
Why it matches plant phenotyping methods豆葉の病徴を画像から認識する深層学習手法を比較・ベンチマークしており、植物病害状態の取得手法が研究の中心である。
abstractThree deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification.
Reproduction assets foundThe paper's data availability statement points to the Augmented iBean dataset on IEEE DataPort, the public bean leaf image dataset used for all phenotyping/classification experiments in this study. No author analysis code or trained model checkpoints are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/bean-leaf-disease-augmented-ibean-dataset.Open asset ↗ieee-dataport · bean-leaf-disease-augmented-ibean-datasethtml-lines:446-496Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Common beanMultimodalClassificationStress / disease detectionDisease symptoms / severity
Abstract Recent multimodal agricultural research emphasizes large-scale vision-language systems, while lightweight reproducible approaches remain underexplored for constrained deployments. We present AgroMM-GSF++, a compact image-text fusion framework for plant disease recognition. The model combines a small visual backbone and text-prototype branch with confidence-adaptive gating. To strengthen empirical evidence, we run repeated multi-seed stratified cross-validation (three seeds, repeated folds) and include stronger pretrained transfer baselines (EfficientNet-B0 and ViT-B/16 fine-tunes). We further evaluate on an external PlantVillage subset to test cross-dataset robustness under the same protocol family. On beans, transfer baselines lead absolute macro-F1 (EfficientNet-B0-FT: 0.6355±0.0340), while AgroMM-GSF++ reaches 0.5891±0.0552 with much lower latency than transfer models (9.80 vs 205.66 ms/image for EfficientNet-B0-FT). On the external subset, AgroMM-GSF++ improves over fixed lightweight fusion (0.8719 vs 0.8608 macro-F1) while remaining below transfer-heavy baselines. We provide an end-to-end reproducible workspace including scripts, datasets, figures, and camera-ready tables.
Why it matches plant phenotyping methods植物病害状態を画像から認識する軽量マルチモーダル手法を開発し、交差検証と外部ベンチマークで評価しており、植物フェノタイピング手法が研究の中心です。
abstractWe present AgroMM-GSF++, a compact image-text fusion framework for plant disease recognition.
Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.
Reproduction assets foundThe preprint's DATA AVAILABILITY section states that all processed data required to reproduce the results are publicly available in a Google Drive repository, which qualifies as a paper-specific public phenotype dataset asset. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicCommon Bean Breeding Program for
facilitating field trials. We also thank the Phenomics team for their valuable assistance with UAV-based
data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY
The datasets generated and/or analyzed during the current study are publicly available at:
https://drive.google.com/drive/folders/1fN3Q9n3bK_YoXFK8VFKZ3uEb13y9iRWj?usp=sharing. This
repository includes all processed data required to reproduce the results presented in this study.
SUPPLEMENTAL MATERIAL
Supp. Figure 1. Drone-based field view of the bean trial site at CIAT Palmira Research Station: A) RGB
image and B) NDVI image.
Supp. Figure 2. Drone Features Open asset ↗pdf-raw-page:40 lines:1-46Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
Common beanField / plotSeed / grainSegmentationPigment / colour / senescence
Abstract Canning color retention is a key quality trait in dry bean ( Phaseolus vulgaris L.) breeding, influencing consumer acceptance and commercial value. Public breeding programs maintain canning quality as a selection trait of importance, but existing color evaluation methods such as visual rating are subjective, while instrument colorimetry is costly, provides limited throughput, and often struggles to accurately detect differences between genotypes. To address these challenges, we developed a high‐throughput phenotyping pipeline that integrates computer vision and deep learning to improve canning color quality assessment in dry beans. This pipeline combines the YOLOv8n (“You Only Look Once” version 8, nano variant) object detection model with the Segment Anything Model for precise bean segmentation. 525 black dry bean genotypes from Michigan State University preliminary and advanced yield trials over multiple years were evaluated using this pipeline. The final model was trained with over 1200 images of canned dry bean samples. The YOLOv8n model achieved near‐perfect detection performance, with precision reaching 0.99 and recall reaching 1 after 44 epochs of training. Comparative analysis showed that the image‐derived D ‐score (euclidean distance–based image‐derived color score) consistently outperformed visual ratings and colorimetry methods, with lower prediction error in regression models. The D ‐score metric offered high resolution in color assessment, enabling the distinction of subtle, genotype‐level differences. Additionally, the D ‐score provides a ranking criterion that enables breeders to make informed decisions and select superior genotypes. This pipeline was deployed to Michigan State University high‐performance computing center and can process 200 high‐resolution images in under 5 min, making it practical for large‐scale breeding applications while eliminating subjective bias and reducing costs.
Why it matches plant phenotyping methods乾燥インゲンの缶詰色保持という植物品質形質を対象に、画像認識・深層学習・セグメンテーションによる高スループット表現型取得パイプラインを開発・評価しており、方法が研究の中心です。
abstractwe developed a high‐throughput phenotyping pipeline that integrates computer vision and deep learning to improve canning color quality assessment in dry beans.
Common bean (Phaseolus vulgaris L.) seed phenotyping is essential for characterizing genetic diversity and identifying superior traits to support breeding for climate resilience and nutritional quality. Traditional manual techniques are increasingly being replaced by high-throughput, image-based digital phenotyping to ensure precision and efficiency in large-scale morphometric analysis. In this study, 30 common bean accessions from the Rural Development Administration (RDA) Gene Bank, South Korea, were phenotyped in 2025 using high-resolution image-based analysis to quantify key traits, including area, solidity, circularity, major/minor axis lengths, aspect ratio, and Feret diameter. One-way ANOVA revealed highly significant differences among accessions for all measured traits (p < 0.001), confirming substantial genotypic variability. Seed area ranged from 93.41 mm2 (IT160310) to 39.00 mm2 (IT337943), while roundness varied from 0.708 to 0.462, indicating pronounced morphological diversity. Spearman’s rank correlation showed a strong positive relationship between seed area and Feret diameter (r = 0.94), whereas aspect ratio and roundness exhibited a perfect negative correlation (r = −1.0). Hierarchical clustering and PCA effectively grouped accessions, with the first two components explaining 96.3% of total variation (PC1 and PC2). Validation against manual methods showed strong correlations (r = 0.95 for area; r = 0.94 for length), confirming ImageJ’s reliability. These findings provide a robust phenotypic foundation for breeding programs, enabling trait-based selection and supporting the integration of high-throughput pipelines into germplasm screening and future genomic studies, such as marker-trait association and genomic selection.
Why it matches plant phenotyping methods画像ベースで種子形態形質を高スループットに抽出し、手動測定との相関で検証しており、表現型取得手法が研究の中心である。
abstractTraditional manual techniques are increasingly being replaced by high-throughput, image-based digital phenotyping to ensure precision and efficiency in large-scale morphometric analysis.
Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems. The system achieved a global energy resolution of 11.93 ± 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants ( N = 3) over 180 min following 11 CO 2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments ( p < 0.001). Hierarchical variability analysis showed that within-plant spatial organization ( CV TTP = 0.03) is significantly more stable than inter-plant variation ( CV TTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.
Why it matches plant phenotyping methods植物根圏における炭素動態を非侵襲・時系列で測定する専用PETシステムを開発し、性能・再現性・空間解析能力を検証した研究であり、植物状態の取得方法が中心である。
abstractWe present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems.
Abstract Meloidogyne incognita (root-knot nematode) is one of the most damaging soilborne pathogens affecting the common bean. Control relies primarily on resistant cultivars, making accurate resistance phenotyping a key component of breeding programs. Here, we developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population. For initial screening, 361 RILs were evaluated with three replications for galling index (GI), number of galls (NG), and egg masses (EM) at 60 days after inoculation (DAI). A subset of 24 segregating RILs was further assessed in a second trial for GI, NG, EM, and reproduction factor (RF), with seven replications at 30 and 60 DAI. A multi-trait factor analytic mixed model was used to derive an overall resistance index (ORI) for genotype classification into moderately resistant (MR), intermediate (I) and susceptible (S) classes. We also assessed the potential of a qPCR-based phenotyping protocol using two contrasting RILs from the segregating subset. High heritability (> 0.8) and strong genotypic correlations among resistance components were observed in the RIL segregants, indicating a robust genetic basis for selection. MR genotypes consistently exhibited reduced GI, NG, EM, and RF, and transgressive segregants were identified within the MR group, confirming that the ORI framework effectively distinguished resistance levels. Moreover, later evaluation improved genotype classification and revealed resistance shifts. qPCR-based phenotyping consistently discriminated MR and S lines in agreement with classical phenotyping, supporting its use as a complementary evaluation tool. Overall, our results validate an integrative multi-trait strategy for more precise resistance phenotyping and genotype selection.
Why it matches plant phenotyping methodsマメの線虫抵抗性を対象に、複数の表現型指標、統合モデル、qPCRプロトコルを組み合わせた抵抗性フェノタイピング手法を開発・検証しており、手法が研究の中心である。
abstractwe developed an integrated phenotyping approach to identify resistant genotypes in a recombinant inbred line (RIL) population.
Common beanLeafClassificationObject detectionDisease symptoms / severity
The effective operation of automated plant disease diagnosis systems is a critical factor in modern precision agriculture, requiring robust solutions for early detection of pathologies. Traditional diagnostic methods based on visual inspection are labor-intensive, subjective, and difficult to scale. This paper presents an improved method for classifying pathologies of agricultural plant leaves based on deep learning technologies, specifically aimed at increasing diagnostic accuracy and optimizing computational performance in high-load environments. A modified five-block Convolutional Neural Network (CNN) architecture, derived from the VGG16 baseline, is proposed. The key architectural innovation involves the deep integration of batch normalization mechanisms after convolutional layers and dropout regularization in the fully connected layers. These modifications successfully addressed the issue of overfitting on limited datasets, ensuring the model's robustness to variations in input data and improving feature extraction capabilities for complex disease patterns. To ensure the efficiency of experimental studies involving large-scale image datasets, a distributed parallel training technology was implemented. This approach relies on the principle of data parallelism with synchronous gradient updates across multiple computing nodes. The implementation allowed for a significant reduction in model training time and provided horizontal scalability of the system, making it suitable for processing big data. Furthermore, the paper describes the technological aspects of software creation, emphasizing the use of declarative configuration and a comprehensive versioning system (following MLOps principles). This approach guarantees the full reproducibility of experiments, systematic documentation of the development process, and reliability of the obtained results. Experimental studies were conducted using a representative dataset of bean leaf images classified into four distinct categories. The results established that the proposed method achieves a classification accuracy of 91.2%, outperforming the baseline model by 4%. The study also proved the critical impact of data augmentation techniques on the model's generalization ability, particularly under conditions of variable lighting and diverse shooting angles. The obtained results confirm the practical value of the method for developing scalable automated diagnostic systems.
Why it matches plant phenotyping methods植物葉の病理状態を画像から分類するCNN手法を開発・改良し、データセット上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。
abstractThis paper presents an improved method for classifying pathologies of agricultural plant leaves based on deep learning technologies
Common beanPigeon peaLaboratory / benchtopMultispectral / hyperspectralSeed / grainClassification
Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible-near-infrared-shortwave infrared (VIS-NIR-SWIR) hyperspectral imaging (HSI; 449.54-2399.17 nm; 563 bands) was used to classify 32 grain-legume accessions ( n = 3200 seeds; 100 seeds per accession), comprising 30 common bean ( Phaseolus vulgaris L.) landraces plus two outgroup legumes ( Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession ( n = 2240) reserved for fully independent training and 30 seeds per accession ( n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R 2 of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54-456.30 nm and 577.02-597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification.
Why it matches plant phenotyping methods豆類種子の識別・分類を目的に、ハイパースペクトル画像取得、波長選択、機械学習・深層学習をベンチマークしており、種子形質の計測・抽出手法が中心である。
abstractOverall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts
Reliable agricultural statistics support food security monitoring and evidence-based decision making. In Mozambique, official agricultural statistics are primarily derived from the Integrated Agricultural Survey (IAI), an enumerator-based field survey that provides essential contextual information on agricultural production but remains labour-intensive, costly and spatially and temporally constrained, particularly in remote rural areas. While satellite remote sensing offers complementary, wall-to-wall coverage, its spatial resolution is often insufficient to directly capture the fragmented fields, mixed and intercropping patterns, shifting cultivation and strong sub-field variability typical of smallholder farming systems. Consequently, consistent estimation of crop area and crop type derived from enumerator-based crop cover assessments remains challenging in these landscapes.This study investigates the potential of high-resolution multispectral data acquired with Uncrewed Aerial Vehicles (UAVs) to complement field surveys by providing spatially explicit and internally consistent crop cover and crop fraction estimates at the field and sub-field scale. By resolving individual crops and dominant intercropping systems, UAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates, while providing a basis for future integration with coarser-resolution satellite remote sensing. High-resolution RGB and multispectral imagery (green, red, red edge, and near-infrared; ≤5 cm ground sampling distance) was collected using a DJI Mavic 3M with RTK over 30 sampling areas of 500 × 500 m in Manica Province during the 2025 agricultural season. In parallel, a field survey recorded standardized observations of agricultural activity, including crop type (of most field and tree crops), intercropping combinations and enumerator-based estimates of fractional crop cover. UAV images were processed using a workflow tailored to heterogeneous smallholder landscapes to produce orthomosaics, digital surface models (DSMs), and vegetation indices. These products were linked to field observations through segments representing relatively homogeneous land units, enabling direct comparison between UAV-derived and survey-based crop cover estimates.For crop classification, training polygons were delineated on RGB orthomosaics for single-crop fields (e.g. maize, beans, sorghum and cassava) and common intercropping combinations (e.g. maize–beans). Annotated mosaics were tiled and augmented and used to train convolutional neural network models (e.g. UNet++), incorporating multispectral vegetation indices and DSM-derived height information as additional input channels. Model performance was evaluated using Intersection over Union, Dice coefficients, and regression metrics for fractional cover accuracy.A comparison framework was implemented to relate UAV-derived crop type, crop combinations and fractional cover to field survey observations while explicitly accounting for measurement uncertainty. Model II regression quantified systematic bias and proportional differences between the two methods. Initial results indicate that UAV-derived estimates provide spatially consistent crop cover information in fields with complex intercropping structures. Ongoing work focuses on refining segmentation accuracy, analysing residual discrepancies and assessing how UAV-derived crop cover information can be integrated to expand the spatial coverage and reliability of agricultural statistics in smallholder landscapes.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物種・混植構造・作物被覆率を抽出し、CNN分類と分画被覆推定を検証する方法が研究の中心である。
abstractUAV-based observations support the interpretation of farmer-reported crop cover proportions, improve consistency across enumerators, and enable post-survey correction of crop area estimates
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
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Early identification of bean leaf diseases, particularly Angular Leaf Spot and Bean Rust, is vital for ensuring crop productivity and global food security, especially within smallholder farming systems where disease outbreaks can rapidly escalate and cause severe yield losses. Conventional disease identification through visual inspection is labor-intensive, subjective, and highly dependent on expert knowledge, making it impractical for large-scale agricultural monitoring. Although recent deep learning-based approaches have demonstrated impressive accuracy in plant disease classification, their inherent “black-box” nature significantly limits real-world adoption, as farmers and agronomists often lack the ability to understand, trust, or act upon unexplained predictions. To address these challenges, this study proposes an automated and explainable disease diagnostic framework based on a Vision Transformer (ViT-B/16) architecture optimized through transfer learning from ImageNet. Unlike traditional convolutional neural networks that primarily focus on localized features, the Vision Transformer processes images as a sequence of flattened patches and leverages self-attention mechanisms to capture long-range dependencies and global contextual patterns across the entire leaf surface. This global representation enables the model to detect subtle and spatially distributed disease symptoms that are often overlooked by CNN-based approaches. To further enhance transparency and interpretability, GradCAM + + is integrated into the framework as an explainable artificial intelligence (XAI) mechanism. This method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions, thereby establishing a human-interpretable validation loop for farmers, agronomists, and domain experts. The proposed framework was evaluated on the publicly available I-Bean dataset, achieving a validation accuracy of 97.52% along with strong precision, recall, and F1-score performance. The generated GradCAM + + visualizations consistently demonstrate the model’s sensitivity to true diseased regions, reinforcing both the reliability and trustworthiness of its predictions. By combining high-capacity global feature learning with visual explainability, the proposed approach offers a scalable, transparent, and practical solution for real-world precision agriculture. This framework not only enhances diagnostic accuracy but also bridges the critical gap between model performance and user trust, enabling informed decision-making and timely disease management in modern farming environments.
Why it matches plant phenotyping methods画像から豆葉の病害症状を分類・可視化する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格。
abstractThis method generates class-specific heatmaps that visually highlight the exact pathological regions influencing the model’s predictions
Reproduction assets foundThe paper uses the publicly available I-Bean bean leaf disease image dataset (Healthy, Angular Leaf Spot, Bean Rust) and points to it via a Data availability DOI (10.21227/4k7y-vs03), which is an allowed URL. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPSP and SNT: Problem Formulation and MethodologyAS and DS: Implementation and VisualizationMVV PK and KB: Original Draft and Supervision.
Funding
Open access funding provided by Symbiosis International (Deemed University). This research received no external funding.
Data availability
[https://dx.doi.org/10.21227/4k7y-vs03]
Declarations
Competing interests
The authors declare no competing interests.
The authors declare that they have no conflict of interest.
References
1.
Wang Y Wang Q Su Y Jing B Feng M
Detection of kidney bean leaf spot disease based on a hybrid deep learning model
Sci. Rep. 2025 15 1 11185
10.1038/s41598-025-93742-7
40169647
POpen asset ↗10.21227/4k7y-vs03lines:325-415Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Feb 2026Computers and Electronics in Agriculture.
Thermal imaging is becoming a valuable tool for monitoring plant canopy temperature, which can serve as an indicator of crop water stress. However, specialized thermal sensors are often cost-prohibitive. This study explored strategies for supplementing crop water stress monitoring by generating synthetic thermal images from standard Red-Green-Blue (RGB) imagery captured using an unmanned aerial vehicle system (UAVs) equipped with a Zenmuse XT2 sensor and leveraging deep learning models. UAV-based RGB and thermal images were collected from 32 experimental plots of sweet corn and green beans over three growing seasons from 2020 to 2023. Each crop was subjected to one full and three deficit irrigation treatments, replicated four times. A total of 3,400 UAV images were collected over three seasons. Image processing was done in Pix4D software, and orthomosaic RGB and thermal maps were spatially aligned using ground control points (GCPs). The UAV RGB and thermal map data were split into 80 % and 20 % for training and testing, respectively. Two image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs. Image quality evaluation metrics, i.e., correlation coefficients (r), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), were used to evaluate the models’ performance. Crop water stress index (CWSI) values were also computed from measured and generated thermal imageries to assess practical applicability. Generated thermal canopy temperature outputs from the Pix2PixGAN model showed a strong correlation with the measured data using a thermal camera (r >0.95). Moreover, Pix2PixGAN resulted in lower MSE (5.63) and higher PSNR (42.98) than CycleGAN (MSE = 7.09, PSNR = 40.56), whereas CycleGAN had a slightly higher SSIM (0.44) than Pix2PixGAN (0.31). CWSI values derived from the generated thermal images reflected the expected gradients of water stress across irrigation treatments, with the highest CSWI observed from deficit irrigation treatments compared to the full irrigation. These results demonstrate that RGB-to-synthetic-thermal image translation using GAN models could be used to support crop water stress assessment and irrigation scheduling.
Why it matches plant phenotyping methodsRGB画像から合成熱画像を生成し、作物キャノピー温度と水ストレス指標を推定するGAN手法の開発・比較・性能評価が中心であり、植物状態の表現型取得に直接関係する。
abstractTwo image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs.
Common beanStem / branchObject detectionPhysiological trait estimationGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force measurement systems have limited capacity to capture weak forces in freely moving plant organs-such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Unlike many force measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing extraction of the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (e.g. growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with Phaseolus vulgaris shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not- an open question since Darwin's first observations.
Why it matches plant phenotyping methods自由に動く植物器官が発生する微弱な力を、カメラ追跡と力抽出により定量する測定システムを開発・実証しており、植物表現型の取得方法が研究の中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Reproduction assets foundThe authors deposited the full analysis workflow (data and code) for five example force-measurement trajectories on Zenodo, publicly accessible via DOI 10.5281/zenodo.15545548. This directly reproduces the paper's camera-based plant force phenotyping measurements and computational analysis. Other experimental data are仅Dataset · publicof interest
None declared.
Funding
YM acknowledges support from the Israel Science Foundation Research
Grant (ISF) no. 2307/22, and ERC grant GROWsmart 101165101. AO
acknowledges support from the Colton Foundation scholarship.
Data availability
We have put the full workflow for five example trajectories on a Zenodo
repository (https://doi.org/10.5281/zenodo.15545548; Ohad and
Meroz, 2025). Other experimental data are available upon request.
References
Autumn K, Liang YA, Tonia Hsieh S, Zesch W, Chan WP, Kenny TW,
Fearing R, Full RJ. 2000. Adhesive force of a single gecko foot-hair.
Nature 405, 681–685.
Backholm M, Bäumchen O. 2019. Micropipette force sensors for in vivo
force measurementsOpen asset ↗Zenodo · 10.5281/zenodo.15545548pdf-raw-page:9 lines:1-95Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method's performance against ground-truth models using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価し、植物形態の非破壊・高スループット表現型解析への利用可能性を検証しており、方法が研究の中心です。
abstractThis study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset.
Background: The purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields. Traditional agricultural procedures often fall short in delivering timely and accurate monitoring, necessitating the adoption of innovative techniques. Methods: The study considers vegetative indicators such as NDVI, GNDVI and canopy cover to track the growth of three legume crops-peanut, soybean and common bean. Machine learning models, including random forest, support vector machines and multiple linear regression, were developed to predict agricultural production using remote sensing data. Statistical analysis was performed to verify the trustworthiness of vegetation indicators against ground-truth measurements. Result: The models achieved high accuracy, with R² values reaching up to 0.92. Statistical analysis confirmed strong relationships between vegetation indicators and ground-truth data. Among the studied crops, soybeans exhibited the highest growth vigor and yield. The study demonstrates that integrating machine learning with drone photography can enhance precision agriculture, making it more scalable and sustainable. Future research is recommended to explore different crop varieties and environmental conditions to further optimize the application of these technologies.
Why it matches plant phenotyping methodsドローンリモートセンシングと機械学習を用いて作物生育指標および収量を推定し、地上実測値で検証することが研究の中心である。
abstractThe purpose of this project is to evaluate the prospective use of drone-based remote sensing for assessing legume crop development and predicting their yields.
Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル計測、キャノピー構造指標、特徴選択・回帰モデルを統合し、植物レベルの収量を非破壊推定する方法が研究の中心である。
titleNon-Destructive Yield Prediction in Common Bean Using UAV-Based Spectral and Structural Metrics
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Common beanLeafClassificationDisease symptoms / severity
Background: Agriculture has always been the source of global food security but is challenged by crop diseases. Beans, a vital protein source, are particularly vulnerable to the diseases whch may occur due to varous pathogens ncludng bacteria fung etc. Accurate disease identification has become critical in order to maintain increasing demands of growing population. Methods: This study utilized the AlexNet convolutional neural network (CNN) to classify bean leaf images into three classes (two disease classes i.e Angular Leaf Spot and Rust and one Healthy class). An open dataset containing leaf mages of beans belonging to all the three classes was used to train the model. The mages were first preprocessed and resized to 224x224 pixels for optimal model performance. The AlexNet model was trained for 25 epochs using cross-entropy loss, ReLU activation, max-pooling and dropout regularization. Result: An accuracy of 95.4% was achieved while the validation accuracy was 78.2%. Other performance metrics, such as precision, recall and F1-score, highlighted strengths in identifying healthy leaves, with an overall accuracy of 82.81%. However, some misclassifications occurred between disease classes due to visual similarities. The results demonstrate AlexNet’s potential for automated plant disease detection, providing a scalable solution for enhancing agricultural practices and food security. Further optimization and integration with field applications are recommended for improved accuracy and usability.
Why it matches plant phenotyping methods豆葉画像から健全・病害状態をCNNで推定する画像ベースの植物表現型手法が研究の中心であり、分類性能も評価しているため。
abstractThis study utilized the AlexNet convolutional neural network (CNN) to classify bean leaf images into three classes (two disease classes i.e Angular Leaf Spot and Rust and one Healthy class).
Common beanPotatoTomatoField / plotLeafObject detectionDisease symptoms / severity
In order to overcome the key challenges associated with detecting tomato leaf disease in complex agricultural environments, such as leaf occlusion, variation in lesion size and light interference, this study presents a lightweight detection model called ToMASD. This model integrates multi-scale feature decoupling and an adaptive alignment mechanism. The model innovatively comprises a dual-branch adaptive alignment module (TAAM) that achieves cross-scale lesion semantic alignment via a dynamic feature pyramid, a local context-aware gated unit (Faster-GLUDet) that uses a spatial attention mechanism to suppress background noise interference, and a multi-scale decoupling detection head (MDH) that balances the detection accuracy of small and diffuse lesions. On a dataset containing six types of disease under various weather conditions, ToMASD achieves an average precision of 84.3%,.by a margin of 4.7% to 12.1% over thirteen mainstream models. The computational load is compressed to 7.1 GFLOPs. Through the introduction of a transfer learning paradigm, the pre-trained weights of the tomato disease detection model can be transferred to common bean and potato detection tasks. Through domain adaptation layers and adversarial feature decoupling strategies, the domain shift problem is overcome, achieving an average precision of 92.7% on the target crop test set. False detection rates in foggy and strong light conditions are controlled at 6.3% and 9.8%, respectively. This study achieves dual breakthroughs in terms of both high-precision detection in complex scenarios and the cross-crop generalization ability of lightweight models. It provides a new paradigm for universal agricultural disease monitoring systems that can be deployed at the edge.
Why it matches plant phenotyping methodsトマト葉の病斑・病害状態を画像から検出するモデルを開発し、複数モデル比較、悪条件評価、他作物への汎化検証を行っており、植物病害フェノタイピング手法が中心である。
abstractthis study presents a lightweight detection model called ToMASD.
Reproduction assets foundThe paper's data availability statement points to a public potato disease dataset hosted on GitCode, which was used in the study's cross-crop transfer experiments (potato disease detection). The tomato dataset is from Roboflow (third-party platform, no direct URL given), and no author analysis code or trained model is,Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://gitcode.com/open-source-toolkit/829ec .Open asset ↗gitcode.com/open-source-toolkit/829eclines:649-666Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Common beanLaboratory / benchtopRaman / spectroscopySeed / grainPhysiological trait estimation
Common bean ( Phaseolus vulgaris L.) is the world's most important legume crop and a vital staple food for millions of people in Latin America and Africa. Given the increasing trend in bean consumption and its importance for nutrition and food security in these regions, there is an urgent need to enhance common bean seeds' nutritional value through breeding. This requires rapidly assessing large and diverse germplasm collections to uncover key nutritional traits in the available genetic diversity. To address this challenge, Near-Infrared Spectroscopy (NIRS) offers a large-scale, cost-effective and non-destructive approach for accurately predicting nutrient content in intact common bean seeds. This study describes the development of predictive models based on NIRS to predict nitrogen (N), iron (Fe) and zinc (Zn) content, using whole common bean seeds from a germplasm core collection held at the International Center for Tropical Agriculture. Spectra were captured for 1754 accessions (wild and domesticated), and reference values for N, Fe, and Zn content were measured with conventional destructive methods in a panel of 401 accessions. Prediction models of N content achieved a concordance correlation coefficient (CCC) of 0.84, while for Fe and Zn, CCC was 0.4. NIRS quantification detected higher N content in wild accessions than in domesticated accessions. These results demonstrate that NIRS can effectively estimate the N content of common bean seeds in a non-destructive manner, while providing valuable nutritional information to enhance access to large genebank collections for bean breeding.
Why it matches plant phenotyping methodsNIRSによるインタクトなインゲン種子の栄養形質推定モデルを開発・検証しており、方法が研究の中心である。
abstractThis study describes the development of predictive models based on NIRS to predict nitrogen (N), iron (Fe) and zinc (Zn) content
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques—Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D—using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method’s performance against ground-truth scans using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価しており、植物形態の取得と高スループット表現型解析への応用が中心である。
abstractThis study systematically evaluates six advanced generative techniques
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するデータセットが研究の中心であり、植物病害フェノタイピング用の画像データセットとして適格です。
abstractThe PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
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Value of the Data
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The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Plant disease epidemiologists often work with datasets smaller than ideal for data-hungry machine-learning (ML) algorithms, thereby risking overfitting. We demonstrate how an interpretation-guided modeling approach, leveraging complex ML primarily for insight generation, can overcome this challenge, using white mold (caused by Sclerotinia sclerotiorum ) in snap beans ( Phaseolus vulgaris ) as a case study. An observational dataset of white mold prevalence across 356 commercial snap bean fields in central and western New York State (2006 to 2008) was augmented by merging georeferenced observations with POLARIS soils data and engineered features from downscaled ERA5-Land environmental data. Functional data analysis identified weather periods associated with white mold risk, and random forests (RFs), used interpretatively, identified key predictors. Although RF models showed high apparent performance, they exhibited significant overfitting and poor calibration. Insights from RF interpretation (via SHapley Additive exPlanations analysis) guided the development of a simpler, four-predictor logistic regression model using restricted cubic splines. This simpler model was better calibrated and had acceptable discrimination (internally validated C statistic = 0.77). For smaller epidemiological datasets, our results advocate for using ML primarily as an interpretive tool to guide the development of simpler, less data-intensive, yet robust predictive models better suited for practical disease management decisions.
Why it matches plant phenotyping methods植物の白絹病 prevalence を対象に、環境データ統合、解釈可能な機械学習、モデル比較・検証を用いて病害状態の予測手法を開発しており、病害表現型の推定が中心である。
abstractWe demonstrate how an interpretation-guided modeling approach, leveraging complex ML primarily for insight generation, can overcome this challenge, using white mold (caused by Sclerotinia sclerotiorum ) in snap beans ( Phaseolus vulgaris ) as a case study.
Abstract This proof-of-concept study explores the potential of hyperspectral imaging (HSI) for nondestructive characterization of feeding damage by two invasive stink bug species on bean pods under controlled laboratory conditions. We compared spectral signatures of feeding sites caused by Halyomorpha halys and Nezara viridula on Phaseolus vulgaris seven days postinfestation. Using a limited dataset of 45 observations from 20 pods (12 individual plants), we identified distinct spectral modifications induced by both species. H. halys feeding increased visible spectrum reflectance (450-700 nm) with reduced near-infrared (NIR) reflectance, while N. viridula caused more severe spectral changes with substantial NIR decreases (up to 31% at 800 nm). Difference spectra revealed species-specific sensitivity patterns with maximum responses at 540 nm (green) and 740 nm (NIR) for both species, though N. viridula showed consistently larger deviations from healthy tissue. Key wavelengths for damage characterization were identified at 660-680 nm and 730-780 nm for both species. While these initial findings suggest potential spectral discrimination between stink bug feeding damage and healthy tissue, we emphasize that this preliminary study requires extensive validation with larger sample sizes, multiple cultivars, comparison with other herbivores, and field conditions before any practical application. The results provide first insights into spectral responses to stink bug feeding that warrant comprehensive follow-up investigation practical hyperspectralbased pest symptom monitoring tools that could enable more targeted management strategies against these economically significant pests.
Why it matches plant phenotyping methods豆莢の吸汁被害という植物状態をハイパースペクトル画像で非破壊評価する方法の概念実証であり、スペクトル特徴と識別波長の抽出が研究の中心です。
abstractThis proof-of-concept study explores the potential of hyperspectral imaging (HSI) for nondestructive characterization of feeding damage by two invasive stink bug species on bean pods under controlled laboratory conditions.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Introduction Early detection of diseases on bean leaves is essential for preventing declines in agricultural productivity and mitigating broader agricultural challenges. However, some bean leaf diseases are difficult to detect even with the human eye, posing significant challenges for machine learning methods that rely on precise feature extraction. Methods We propose a novel approach, DCT-Transformers, which combines a preprocessing technique, dynamic range enhanced discrete cosine transform (DRE-DCT) with Transformer-based models. The DRE-DCT method enhances the dynamic range of input images by extracting high-frequency components and subtle details that are typically imperceptible while preserving overall image quality. Transformer models were then used to classify bean leaf images before and after applying this preprocessing step. Results Experimental evaluations demonstrate that the proposed DCT-Transformers method achieved a classification accuracy of 99.56% (precision: 0.9916, recall: 0.9912, F1-score: 0.9912) when using preprocessed images, compared to 95.92% when using non-preprocessed images. Moreover, the method outperformed state-of-the-art approaches (all below 94%) and similar studies (all below 98.5%). Discussion These findings indicate that enhancing feature extraction through DRE-DCT significantly improves disease classification performance. The proposed method offers an efficient solution for early disease detection in agriculture, contributing to improved disease management strategies and supporting food security initiatives.
Why it matches plant phenotyping methods豆の葉画像から病害状態を推定する画像・計算手法を提案し、前処理とTransformerの性能を比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractWe propose a novel approach, DCT-Transformers, which combines a preprocessing technique, dynamic range enhanced discrete cosine transform (DRE-DCT) with Transformer-based models.
Reproduction assets foundThe paper's data availability statement explicitly links the iBean leaf disease image dataset used for all experiments and the authors' public GitHub repository containing the study's implementation code.Dataset · publicThe Makere iBean dataset can be downloaded from the following link: https://github.com/AI-Lab-Makerere/ibean/ .Open asset ↗https://github.com/AI-Lab-Makerere/ibean/lines:463-478Code · publicThe code implemented in this study can be accessed via: https://github.com/harisushehu/bean-leaf-diseases-detection .Open asset ↗https://github.com/harisushehu/bean-leaf-diseases-detectionlines:463-478Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
Abstract The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.
Why it matches plant phenotyping methods植物葉の病害状態を画像から認識する新規深層学習手法を提案・比較評価しており、植物フェノタイピング手法が中心である。
abstractIn this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants.
Common beanField / plotClassificationStress / disease detectionDisease symptoms / severity
Common bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robustness under real-world farm conditions. A dataset of 100,000 annotated images augmented with geometric, color, and FGSM-based perturbations, simulating field variability. FGSM was selected for its computational efficiency in low-resource settings. The model, fine-tuned using transfer learning and validated through cross-validation, achieved an accuracy of 99.4%. Results highlight the effectiveness of integrating adversarial robustness to enhance model reliability for mobile-based plant disease detection in resource-constrained environments.
Why it matches plant phenotyping methods植物の病徴を画像から検出するVision Transformer手法の開発・頑健性検証が中心であり、植物病害状態の表現型推定に該当する。
abstractThis study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robustness under real-world farm conditions.
Reproduction assets foundThe paper's field-collected common bean disease image dataset (59,072 images, annotated, four classes) was published on Zenodo, with the exact URL given in the data availability statement and footnotes. This is a paper-specific, public, directly actionable asset. No code or trained model deposit is explicitly stated.Dataset · publicbility with farmers and agricultural experts will be essential for real-world application.
Funding Statement
The author(s) declare that financial support was received for the research and/or publication of this article. The data collection was funded by The Organization for Women in Science for the Developing World.
Footnotes
1
https://zenodo.org/api/records/8286126/files-archive
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://zenodo.org/api/records/8286126/files-archive .
Author contributions
UM: Validation, Writing – review & editing, Formal analysOpen asset ↗Zenodo · 8286126lines:291-310Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
PURPOSE: Bacterial blight poses a significant threat to red kidney bean growth, often leading to substantial yield losses. Real-time monitoring of this disease is crucial for effective prevention and control, ensuring optimal yield. This study proposes a hyperspectral-based approach to assess the the bacterial blight disease in red kidney beans. METHODS: In this study, canopy hyperspectral data from two experimental areas were collected, and five spectral preprocessing methods—multiple scattering correction (MSC), standard normal variation (SNV), first-order derivative (FD), second-order derivative (SD), and logarithmic transformation (LOG)—were applied to the raw spectra (R). Feature bands were extracted using a combination of the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA). Disease severity classification models were constructed using support vector machine (SVM) and partial least squares discriminant analysis (PLS-LDA), while estimation models were developed using support vector regression (SVR) and partial least squares regression (PLSR). RESULTS: Results demonstrated that FD preprocessing most effectively enhanced spectral features for bacterial blight detection, with sensitive bands optimally extracted using the CARS-SPA algorithm. The bands centered at 750 nm and 945 nm were identified as the most sensitive across all preprocessing methods. For condition index estimation, the PLSR model performed best, with FD preprocessing achieving R² values of 0.883 (modeling set) and 0.863 (validation set), and RMSE values of 0.072 and 0.083, respectively. CONCLUSIONS: These findings highlight the potential of hyperspectral technology, combined with feature extraction and machine learning algorithms, for efficient and accurate detection of bacterial blight in red kidney beans. This study provides a methodological and technical framework for monitoring other crops and diseases.
Why it matches plant phenotyping methodsハイパースペクトルデータからインゲンマメの病害状態・重症度を推定し、前処理、特徴帯抽出、分類・回帰モデルを検証すること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。
abstractThis study proposes a hyperspectral-based approach to assess the the bacterial blight disease in red kidney beans.
Common beanAerial / UAVField / plotMultispectral / hyperspectralLeafPhysiological trait estimation
Leaf nitrogen assessment is crucial for optimizing crop management, driving remote sensing use. This study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features. Research conducted over two years (2021–2022) evaluated various nitrogen rates across critical growth stages (V4, R5, and R7). Machine learning models combining spectral and textural information significantly outperformed single-index approaches, achieving root mean square error (RMSE) values of 1.80 g kg⁻¹ (relative root mean square error – RRMSE = 2.93 %) at V4 stage using support vector machine with VIs, and 2.79 g kg⁻¹ (RRMSE = 5.20 %) at R5 stage using random forest with VIs. For later growth stages (R7) and across the entire season (all growth stages), the combination of VIs and texture metrics proved most effective, with random forest achieving RMSE values of 3.42 and 3.96 g kg⁻¹ (RRMSE = 7.40 and 7.32 %), respectively. Texture analysis in across-row directions (90° and 135°) provided superior performance compared to traditional diagonal approaches for row-planted crops. Linear regression analysis showed that normalized difference texture indices incorporating correlation and homogeneity explained up to 71 % of leaf nitrogen content variability at R7 stage. The optimal nitrogen rate of 91 kg ha⁻¹, validated through both yield response and leaf nitrogen measurements, provides a robust benchmark for nitrogen management in common bean production. This methodology offers a practical framework for real-time, site-specific nitrogen management that improves upon current recommendation systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植体の葉窒素含量を推定するため、スペクトル・テクスチャ特徴と機械学習を比較評価しており、植物形質取得・推定手法が研究の中心である。
abstractThis study demonstrates the effectiveness of unmanned aerial vehicles (UAVs) multispectral imagery for enhancing leaf nitrogen content estimation in common bean (Phaseolus vulgaris L.) through the integration of vegetation indices (VIs) and texture features.
Common beanGreenhouseThermalTissueStress / disease detectionDisease symptoms / severityPlant / canopy temperature
Abstract The common bean ( Phaseolus vulgaris L.) is of great socioeconomic importance in Brazil, being widely cultivated by family farmers who preserve traditional varieties adapted to regional conditions. These varieties represent a strategic source of genetic variability for breeding programs. Among the main phytosanitary obstacles to cultivation, common bacterial blight (CBB), caused by Xanthomonas phaseoli pv. phaseoli stands out as it compromises bean productivity. This study aimed to evaluate 54 traditional genotypes for resistance to CBC, using visual severity scales and infrared thermography as a complementary tool. The experiment was carried out in a greenhouse, in a randomized block design with three replicates, in two seasons (May and October 2019). Inoculation was performed by two methods (cutting with scissors at 10⁷ CFU·mL -1 and infiltration with a syringe at 10⁶ CFU·mL -1 ) with the strain Xpp ‘139-y’. The variables analyzed included area under the disease progress curve (AUDPC), incubation period (IP), and final score (FS). Thermal images were obtained up to three days after inoculation, allowing the calculation of the mean temperature difference (MTD) between healthy and infected tissues. Thermographic analysis enabled early detection of infection, before the appearance of visual symptoms, distinguishing resistant genotypes such as BAC-6 and UENF 2599. The results highlight the potential of thermography as a fast, accurate, and non-destructive method for selecting resistant genotypes, contributing to the modernization and sustainability of bean breeding programs.
Why it matches plant phenotyping methods赤外線サーモグラフィーで感染植物組織の温度差を測定し、視覚症状前の病害状態を推定する方法を、抵抗性選抜へ実質的に適用しているため含める。
abstractusing visual severity scales and infrared thermography as a complementary tool
Abstract Common bean ( Phaseolus vulgaris L.) can fix atmospheric nitrogen (N) through symbiosis with Rhizobia species. This trait is often underutilized by growers and overlooked by breeders due to the laborious and costly evaluation techniques involved. There is a critical need for the development of new screening tools to enhance nitrogen fixation efficiency. Remote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation. In this study, we investigated the use of vegetation indices and machine learning (ML) methods in estimating symbiotic nitrogen fixation (SNF). Forty‐two black bean breeding lines from the Dry Bean Breeding Program at Michigan State University were grown and compared under both high and low N conditions. A random forest model developed to predict percent nitrogen derived from the atmosphere (%Ndfa) using remote sensing (RS) data resulted in an average accuracy of R 2 = 0.86. A 3‐year evaluation of these trials in Michigan demonstrated how seed yield under unfertilized conditions could be used as an indirect indicator of SNF ability. Two accurate prediction models for yield were developed using stepwise general linear modeling (StepwiseGLM) and Bayesian regularized artificial neural network (BRNeural Network) (stepwise general linear model r = 0.64; Bayesian regularized neural network r = 0.65). These results suggest that seed yield and RS data coupled with ML offer a promising tool to efficiently implement indirect selection for SNF in common bean.
Why it matches plant phenotyping methodsUASリモートセンシングと機械学習により、共生窒素固定という植物形質を推定するスクリーニング手法を開発・評価しており、表現型取得と予測モデルが研究の中心である。
abstractRemote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation.
Reproduction assets foundThe paper's data availability statement explicitly says the code and methodologies used in this study are available in the authors' public GitHub repository (msudrybeanbreeding). No phenotype dataset, imagery, or model checkpoint deposit is stated in the supplied blocks.Code · publicte helpful conversations and comments from J.D.
Kelly, which improved the quality of our final manuscript.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Code and methodologies used in this study are available in the
GitHub repository: https://github.com/msudrybeanbreeding
O RC I D
MasonJackson https://orcid.org/0009-0004-7635-0418
LeonardoVolpato https://orcid.org/0000-0003-1119-0615
EvanM. Wright https://orcid.org/0009-0003-7512-0963
ValerioHoyos-Villegas https://orcid.org/0000-0003-1080-9148
FranciscoE. Gomez https://orcid.org/0000-0002-2862-7118
R E F E R E N C E S
Ahamed, T., Tian, L., ZhangOpen asset ↗msudrybeanbreedingpdf-raw-page:13 lines:1-83Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Dry bean, the fourth-largest pulse crop in Canada is increasingly impacted by climate variability, needing efficient methods to support cultivar development. This study investigates the potential of unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) and multispectral imaging (MSI) for high-throughput phenotyping of dry bean traits. Image data were collected across two dry bean field trials to assess plant height, lodging and seed yield. Multiple LiDAR-derived features accessing canopy height, crop lodging and digital biomass were evaluated against manual height measurements, visually rated lodging scale and seed yield, respectively. At the same time, three MSI-derived data were used to estimate seed yield. Classification- and regression-based machine learning models were used to estimate key agronomic traits using both LiDAR and MSI-based crop features. The canopy height derived from LiDAR showed a good correlation (R2 = 0.86) with measured plant height at the mid-pod filling (R6) stage. Lodging classification was most effective using Gradient Boosting, Random Forest and Logistic Regression, with R8 (physiological maturity stage) canopy height being the dominant predictor. For seed yield prediction, models integrating LiDAR and MSI outperformed individual datasets, with Gradient Boosting Regression Trees yielding the highest accuracy (R2 = 0.64, RMSE = 687.2 kg/ha and MAE = 521.6 kg/ha). Normalized Difference Vegetation Index (NDVI) at the R6 stage was identified as the most informative spectral feature. Overall, this study demonstrates the importance of integrating UAV-based LiDAR and MSI for accurate, non-destructive phenotyping in dry bean breeding programs.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル画像と機械学習を用いて、乾燥インゲンの草丈・倒伏・収量を非破壊推定し、手測定等と検証しており、表現型取得手法が研究の中心です。
abstractThis study investigates the potential of unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) and multispectral imaging (MSI) for high-throughput phenotyping of dry bean traits.
Common beanStem / branchObject detectionTrackingGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force-measurement systems have limited capacity to capture weak (sub-mN) forces in freely moving plant organs - such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Crucially, unlike many force-measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods such as cantilevers. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing to extract the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (such as growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with bean ( Phaseolus vulgaris ) shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not - an open question since Darwin’s first observations.
Why it matches plant phenotyping methods自由に成長する植物器官が発生する力を、非拘束・二軸で測定し、カメラ画像から3D位置と力を抽出する新規計測システムの開発・実証であり、植物の機械的状態を測る方法が中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Common beanLeafRootStem / branchGrowth / development / phenologyPhotosynthesis / fluorescenceStress response / tolerance
In the course of climate change, drought is becoming one of the most important abiotic stress factors in agroecosystems and significantly affects agricultural productivity. Common bean (Phaseolus vulgaris L.), one of the most important legumes with a high protein content for human consumption, is very sensitive to water deficit. Thus, it is important to understand the physiological and developmental effects of water deficit on the bean. Thanks to technological advances, traditional phenotyping methods have evolved towards high-throughput phenotyping (HTP), which utilizes various imaging technologies for rapid and non-destructive monitoring of plant traits. This review examines the effects of water deficit on bean morphology (roots, leaves, stems, and generative organs), physiology (photosynthesis, antioxidant activity, phytohormones), and gene expression. We will also describe the HTP techniques used to quantify this water deficit-induced response through different imaging techniques and evaluate their applicability for the generation of reliable phenotypic data and the selection of drought-tolerant genotypes for further breeding and genetic progress.
Why it matches plant phenotyping methods乾燥ストレス下のインゲンマメ形質を定量するハイスループット画像技術をレビューし、表現型データの信頼性と適用性を評価するため、方法レビューとして中心的に該当する。
abstractThis review examines the effects of water deficit on bean morphology (roots, leaves, stems, and generative organs), physiology (photosynthesis, antioxidant activity, phytohormones), and gene expression.
This study investigated the potential of using remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress. Two field experiments were conducted with three irrigation regimes: 100% (B100), 75% (B75), and 50% (B50) of the full irrigation requirements. Various measured parameters including, wet biomass (WB), dry biomass (DB), canopy moisture content (CMC), soil plant analysis development (SPAD), and soil water content (SWC) as well as seed yield (SY) were evaluated. The results showed that the highest values for WB, DB, CMC, SWC, and SY were achieved under B100, while the highest SPAD values were achieved under B75. The study also found that most of the RGB image indices (RGBIs) and spectral reflectance indices (SRIs) exhibited a linear relationship with the measured parameters and SY, with R² values ranging from 0.34 to 0.95. In contrast, SPAD showed a significant quadratic relationship, with R² values ranging from 0.34 to 0.79. Additionality, the newly developed SRIs demonstrated 5-40% higher correlations compared to the best-performing published SRIs across all measured parameters and SY. ANNs using RGBIs and SRIs separately demonstrated high prediction accuracy with R 2 values ranging from 0.79 to 0.97 and 0.86 to 0.97, respectively. Combining the RGBIs and SRIs, the ANNs achieved higher prediction accuracy, with R² values ranging from 0.88 to 0.99 across different parameters. In conclusion, this study demonstrates the effectiveness of using SRIs and RGBIs with ANNs as practical tools for managing the growth and production of dry bean crops under deficit irrigation.
Why it matches plant phenotyping methodsRGB画像指標・スペクトル反射指標とANNを用いて、乾燥豆のバイオマス、水分、SPAD、収量などの植物形質を定量・予測する手法を開発し、相関および予測精度を評価している。形質取得・推定法が研究の中心である。
abstractusing remote sensing indices with artificial neural networks (ANNs) to quantify the responses of dry bean plants to water stress
Common beanPepper / chilliTomatoLeafClassificationStress / disease detectionDisease symptoms / severity
Sustainable agriculture holds the key in meeting food production requirements for a rapidly growing population without exacerbating environmental degradation. Plant leaf diseases pose a critical threat to crop yield and quality. Existing inspection methods are labor-intensive and prone to human errors, while lacking support for large-scale agriculture. This research aims to enhance plant health by developing advanced deep learning models for the detection and classification of plant diseases across a variety of species. A deep learning model based on the paradigm of the MobileNet architecture is proposed, which employs a dedicated design through deeper convolutional layers, dropout regularization, and fully connected layers. This results in significant improvements in disease classification in tomato, bean, and chili plants, with accuracy rates of 97.90%, 98.12%, and 97.95%, respectively. Moreover, Grad-CAM is used to shed light on the decision-making process of the proposed model. The work contributes to the advancement of precision farming and sustainable agricultural practices, supporting timely and accurate plant disease diagnosis.
Why it matches plant phenotyping methods植物葉の病害状態を画像から検出・分類する深層学習法の開発と評価が研究の中心であり、植物フェノタイピング手法に該当します。
abstractThis research aims to enhance plant health by developing advanced deep learning models for the detection and classification of plant diseases across a variety of species.
Common beanLeafStress / disease detectionDisease symptoms / severity
Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages, are computationally expensive and do not always yield optimal results. Moreover, reliable datasets for kidney bean leaf spot disease remain scarce. To address these challenges, this study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset, filling a significant gap in the field. Based on this dataset, a novel hybrid deep learning model framework is proposed, which integrates deep learning models (EfficientNet-B7, MobileNetV3, ResNet50, and VGG16) for feature extraction with machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, and Stochastic Gradient Boosting) for classification. By leveraging the Optuna tool for hyperparameter optimization, 16 combined models were evaluated. Experimental results show that the hybrid model combining EfficientNet-B7 and Stochastic Gradient Boosting achieves the highest detection accuracy of 96.26% on the KBLD dataset, with an F1-score of 0.97. The innovations of this study lie in the construction of a high-quality KBLD dataset and the development of a novel framework combining deep learning and machine learning, significantly improving the detection efficiency and accuracy of kidney bean leaf spot disease. This research provides a new approach for intelligent diagnosis and management of crop diseases in precision agriculture, contributing to increased agricultural productivity and ensuring food security.
Why it matches plant phenotyping methods腎豆葉の病斑を画像から検出・分類するデータセット構築と深層学習手法開発が研究の中心であり、植物の病害状態を直接推定するため。
abstractthis study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset
Unmanned aerial system (UAS)-based remote sensing technologies have seen increasing application in precision agriculture and crop management. By collecting and analyzing remote imagery of crops, we can extract valuable information about the nutrient, structure, and growth variation of crops at scales 0.74, rRMSE < 0.13) by the full bloom stage, at least a week before harvest. This early prediction capability is crucial for effective agricultural planning and management. Our findings underscore the potential of combining multiple UAS-based remote sensing technologies for efficient and accurate yield assessment and prediction in agriculture.
Why it matches plant phenotyping methodsUAS-LiDARとマルチスペクトル画像を統合し、作物の収量を早期推定するセンシング・解析手法が中心であり、植物形質の技術的評価に該当する。
titleEnhancing snap bean yield prediction through synergistic integration of UAS-Based LiDAR and multispectral imagery
The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R 2 (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.
Why it matches plant phenotyping methods植物葉のMn濃度という生理・元素形質を、携帯型XRFで非破壊測定する方法の性能評価と検証が中心であり、単なる生物学的実験での routine 測定ではない。
abstractThe direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%).
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
1
Value of the Data
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The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
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Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
4
Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
As a critical indicator of plant growth and water use, accurately and promptly estimating leaf area index (LAI) is essential for improved crop production. However, measuring LAI using destructive methods is labor-intensive and time-consuming. The main objective of this study was to leverage vegetation indices (VIs) generated from unmanned aerial vehicle (UAV)-based images and machine learning (ML) algorithms for LAI estimation of green beans and sweet corn. The research experiments were conducted for three consecutive years during the winter (dry) seasons of 2020 through 2023 at the Tropical Research and Education Center (TREC), University of Florida, Homestead, Florida. The experiment consists of 32 plots with four irrigation treatments, i.e., 100% full irrigation (FI), 75%, 50%, and 25% FI, with four replications. Destructive leaf samples were collected by cutting plants from 30cm row length of two inner plot rows. The leaf area (LA) of each green beans and sweet corn sample was measured using the LI-3000C transparent belt conveyor. The plant height and width of these crops were also measured bi-weekly. The LAI of the plants was calculated using the plant density method along with measured LA. The DSSAT model calibrated in a previous study for simulating plant growth and yield was used to simulate the LAI of both crops. Moreover, a UAV-based RedEdge-MX sensor was employed throughout the seasons to collect high-resolution multispectral imageries that consist of five bands. Twelve VIs were generated from UAV-based multispectral images. The DSSAT model simulated LAI for both crops was validated against plant-estimated LAI, and results depicted a good correlation for green beans and reasonable for sweet corn. Furthermore, validated DSSAT LAI values were compared with the twelve VIs to build a relationship between LAI and VIs. Out of 12 indices, six VIs, i.e., EVI2, NDVI, NGRDI, NIRRENDVI, RENDVI, and SAVI showed a good agreement with LAI for both crops. Additionally, ML algorithms, i.e., random forest (RF), eXtreme gradient boosting (XGB), and light gradient boosting (LGB) models, were trained to predict the LAI of green beans and sweet corn using VIs as input features. The LGB, RF, and XGB models predicted LAI with acceptable accuracy, achieving r² values of 0.78, 0.90, and 0.90 with RMSE values of 0.43, 0.29, and 0.28 respectively for sweet corn and r² of 0.72, 0.79, and 0.80 and RMSE of 1.01, 0.86, and 0.85 respectively for green beans. Therefore, it is concluded that ML models can predict the LAI with good accuracy for green beans and sweet corn using UAV multispectral image-based VIs as input features.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて作物のLAIを推定する手法が研究の中心であり、植物形質の取得・推定方法を技術的に評価している。
abstractThe main objective of this study was to leverage vegetation indices (VIs) generated from unmanned aerial vehicle (UAV)-based images and machine learning (ML) algorithms for LAI estimation of green beans and sweet corn.
Common beanMultispectral / hyperspectralWhole plant / canopy / plot / fieldObject detectionImage / point-cloud registrationGrowth / development / phenology
Green roofs offer ecological benefits but harsh rooftop environment stressors like heat, water scarcity, and wind can limit optimal plant growth. Monitoring plant health is crucial for optimizing green roof performance and remote sensing provides a non-destructive approach, yet challenges persist in tight, urban settings. This allows for close-range ground monitoring as a viable solution in these emerging environments. Recent studies explored automatic image registration techniques, showing promise in homogeneous settings, but faced uncertainties in mixed species systems or early plant growth stages. Feature detection techniques such as Speeded up Robust Features (SURF) using fixed and moving image spaces have been proposed for alignment. This study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season. This study developed a sophisticated monitoring system using close-range multispectral sensors for aligning bands in mixed species plant communities, contributing to enhanced green roof management and sustainability. The results of the study showed no significant differences in band alignment accuracy between growth stages for mixed species bush bean/sedum and single species sedum modules, but significant differences were found in single species bush bean systems. Additionally, mixed species modules showed better accuracy compared to single species bush bean modules, indicating that heterogenous systems provide better alignment accuracy due to more diverse feature extractions.
Why it matches plant phenotyping methods混合集団植物の近接マルチスペクトル画像について、特徴検出・バンドマッチング・画像位置合わせを開発・評価する研究であり、植物モニタリング用の画像取得・解析手法が中心である。
abstractThis study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season.
Agriculture greatly impacts Bangladesh's economy, and vegetable cultivation plays a significant role in Agriculture by providing nourishment, and food security as well as improving the economy. The necessity of food production is growing similarly to the population growth. The farmers of Bangladesh are working hard to meet this need for food production and to gain yields. However, every year the farmers face a significant amount of loss in production due to the attack of different diseases and viruses due to the lack to technological development. The reason behind most of these losses is the lack of knowledge about diseases and being unable to detect the diseases early. Therefore, the early detection of plant disease is significant in balancing the country's economy and preventing undesirable losses. To bring a solution to this problem our dataset provides a total of 4467 images of Beans and Cowpeas leaf images which include different disease classes and fresh leaves. The dataset comprises 2,273 images of Bean and 2,194 images of Cowpea plants where each plant provides 4 classes of different disease along with the healthy leaves. This dataset will assist researchers in identifying plant diseases and farmers as well as contribute to the economy of the country.
Why it matches plant phenotyping methods豆類葉の画像から病害状態を推定する画像データセットが研究の中心であり、植物病害表現型のデータ資源として収録対象です。
titleComprehensive smartphone image dataset for bean and cowpea plant leaf disease detection and freshness assessment from Bangladesh vegetable fields
Reproduction assets foundThe paper is a Data in Brief article describing a smartphone image dataset of bean and cowpea leaf disease/freshness. The authors' own dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-specific, publicly actionable asset. The Kaggle bean disease dataset is cited priorDataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/ykvcrjffzd.1
Direct URL to data: https://data.mendeley.com/datasets/ykvcrjffzd/1Open asset ↗Mendeley Data · 10.17632/ykvcrjffzd.1lines:1-51Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Common beanAerial / UAVField / plotLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldCountingMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenology
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Why it matches plant phenotyping methodsドローン画像と深層学習を用いて成熟度、株数、草丈を推定する手法を開発・評価し、パイプラインとオープンソースソフトウェアも提示しており、植物表現型取得が研究の中心である。
abstractIn this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH)
Reproduction assets foundThe paper explicitly states that all R/Python analysis code, apps, and the complete datasets (orthomosaics, shapefiles, ground notes, clipped plots) are publicly available via the authors' GitHub organization and three Zenodo deposits for RM, SC, and PH.Dataset · publicof the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
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SS , Bales
S
.
Dry beans ( Phaseolus vuOpen asset ↗zenodo · 10.5281/zenodo.7922565lines:677-730Dataset · publicauthors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainabOpen asset ↗zenodo · 10.5281/zenodo.7922584lines:677-730Dataset · publicrests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review
. LeguOpen asset ↗zenodo · 10.5281/zenodo.7922589lines:677-730Code · publics from each individual breeding plot were extracted from the time series of images (6 and 9 flights date), and the RM was estimated using an optimized threshold value of 0.06. To perform the VI extractions from each breeding plot in the field, an open-source Streamlit app in Python was implemented and can be accessed online at: https://msudrybeanbreeding-vegetation-index--vi-extractions-v0-3-9knpzt.streamlit.app/ . Additionally, to accommodate user preferences, an R script is available to perform VI extractions analysis (Data S7 ).
SC DL model
The SC pipeline deployed in this study comprised 6 distinct steps, starting from the raw images and annotations, and ending with the final SC predictiOpen asset ↗lines:139-147Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 7 Sept 2026
Published7 Nov 2024The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Legume seeds are essential for nutrition and play crucial roles in food security, climate change mitigation, biodiversity conservation, and sustainability. Among them, the common bean (Phaseolus vulgaris L.) is the most widely cultivated and vital to the food value chain. Originating in Mesoamerica and independently domesticated there and in the Andes about 8,000 years ago, common beans have adapted to diverse environments globally following the Columbian exchange. Ancient DNA (aDNA) sequencing offers insights into the bean’s adaptation and evolution, but traditional methods struggle with detailed 3D phenotypic analysis due to the beans’ small size and the destructive nature of aDNA extraction. Expensive high-detail optical instruments are not commonly available in genetic labs. This research proposes macro-photogrammetry as a low-cost technique for creating detailed 3D digital replicas of beans. By using samples from archaeological sites in northern Peru, this method preserves phenotypic information despite the destruction of physical samples during DNA extraction. The study details the process of capturing high-detail images with specialized equipment to build a digital phenotype library, preserving the morphological features of ancient beans for further study and future comparison with modern varieties.
Why it matches plant phenotyping methodsマクロ写真測量による小型マメ種子の3D形態取得とデジタル表現型ライブラリ構築が研究の中心であり、植物形態の保存・比較を目的とするため。
abstractThis research proposes macro-photogrammetry as a low-cost technique for creating detailed 3D digital replicas of beans.
Common beanAerial / UAVField / plotMultispectral / hyperspectralThermalWhole plant / canopy / plot / fieldPhysiological trait estimationWater status / transpiration
Evapotranspiration (ET) estimation by remote sensing is an innovative and promising option, due to its low cost and operation. It is an important tool for estimating ET and can be used to support decision-making. The origin and quality of images are fundamental for quality of information, as low spatial and temporal resolution of satellites directly impacts these customers. In this context, the objective of this study was to estimate the evapotranspiration of common bean crop using the SAFER algorithm in three different sources of albedo. The study was carried out in a bean cultivation area irrigated by central pivot, located in Itaberaí-GO Brazil in 2021. Images from a MicaSense Altum multispectral and thermal camera coupled to a drone and albedo images from Landsat 8 and Sentinel 2A satellites were used for ETa estimation. The data were compared with ET met by FAO method, Embrapa and climatological water balance by statistical indices. The correlation with standard methods was satisfactory, especially with FAO, and in general, the MSE (mean square error) and MAE (mean absolute error) adopted values smaller than 0.4mm day-1. The confidence index ranges from 0.91 to 0.97. The comparison of the ET values calculated from the multispectral and thermal camera and the three ways of calculating the surface albedo was considered satisfactory. Thus, the adaptation adopted in the SAFER algorithm for obtaining the albedo was efficient. The use of multispectral and thermal camera images with SAFER is an efficient tool in estimating the evapotranspiration of bean crop, and is capable of replacing the use of orbital images, which are limited by meteorological conditions and imaging frequency.
Why it matches plant phenotyping methodsマルチスペクトル・熱画像とSAFERアルゴリズムを用いて豆作物の蒸発散量を推定し、FAO等の標準法と比較検証しているため、作物の生理状態を測定する手法が中心です。
abstractThe data were compared with ET met by FAO method, Embrapa and climatological water balance by statistical indices.
Common beanLeafClassificationSegmentationDisease symptoms / severity
Detecting plant diseases is a challenging and time-consuming task that requires expertise and laboratory conditions. Deep learning methods have been proposed as a solution to this problem, and their effectiveness in plant disease detection has become a popular research topic in recent years. This study aimed to investigate the performance of the U-Net architecture, which has been successful in medical image segmentation, in the segmentation of agricultural images. Sixty images, including angular leaf spot and bean rust diseases commonly found in bean plants, were used in the study. The images were segmented with U-Net, and then, in the first stage, only the images containing diseases were classified. In the second stage, classification was performed using raw images. Deep learning methods VGG16, AlexNet, MobileNet-v2, and DenseNet201 were used for the classifications. The results showed that the classification accuracy was higher for the segmented images than for the raw images. The highest accuracy rate, 100%, was achieved with DenseNet201 in the classification carried out by removing the segmented diseased regions. Using the U-Net architecture, which has demonstrated good performance with relatively few medical images, promising results were achieved in segmenting plant diseases. A software was developed to obtain only the images of the diseased areas by overlapping the original images with the segmented images. The proposed end-to-end system achieved higher classification accuracy by focusing deep learning architectures only on the desired regions. Finally, 100% classification accuracy was achieved with the DenseNet201 architecture using only segmented diseased images.
Why it matches plant phenotyping methods植物葉の病変領域を画像からセグメンテーションし、病害状態を推定する手法が研究の中心であり、単なる病害測定ではなくU-Netと分類ワークフローを評価しているため。
abstractThis study aimed to investigate the performance of the U-Net architecture, which has been successful in medical image segmentation, in the segmentation of agricultural images.
The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis, thereby enhancing management efficiency.
Why it matches plant phenotyping methodsドローン搭載センサーと画像解析により、植物の食害・葉面積減少（defoliation）を定量化し、地上検証と比較評価しているため、植物表現型取得手法が中心である。
abstractThis study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis.
Common beanField / plotThermalLeafStress / disease detectionPlant / canopy temperature
Among the factors causing yield losses in agricultural fields, plant diseases are known to be one of the most significant. For many years, pesticides have been used to combat these diseases. However, due to the unintended toxic effects of pesticides on non-target organisms in recent years, there have been restrictions on their usage. Therefore, there has been an increased interest in alternative methods to chemical control in combating plant diseases. Among these alternative methods, thermal imaging, widely used within the scope of precision agriculture practices, holds a significant position. This study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods. According to the obtained results, it has been determined that leaves infected with the pathogen have a temperature approximately 2 ºC lower than healthy leaves. Surface temperatures of healthy and infected leaves were measured at 60-minute intervals for three weeks. Throughout this three-week period, it was observed that the average daily temperatures of infected leaves and healthy leaves were below ambient temperatures. Thermal imaging is considered to play a crucial role in the potential early detection of plant diseases.
Why it matches plant phenotyping methods熱画像を用いて感染葉と健全葉の温度差から植物病害を早期検出する方法を評価しており、植物状態の取得・判定が研究の中心です。
abstractThis study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods.
Introduction Soil-borne pathogens cause considerable crop losses and food insecurity in smallholder systems of sub-Saharan Africa. Soil and crop testing is critical for estimating pathogen inoculum levels and potential for disease development, understanding pathogen interactions with soil nutrient and water limitations, as well as for developing informed soil health and disease management decisions. However, formal laboratory analyses and diagnostic services for pathogens are often out of reach for smallholder farmers due to the high cost of testing and a lack of local laboratories. Methods To address this challenge, we assessed the performance of a suite of simplified soil bioassays to screen for plant parasitic nematodes (e.g., Meloidogyne , Pratylenchus ) and other key soil-borne pathogens ( Pythium and Fusarium ). We sampled soils from on-farm trials in western Kenya examining the impact of distinct nutrient inputs (organic vs. synthetic) on bean production. Key soil health parameters and common soil-borne pathogens were evaluated using both simple bioassays and formal laboratory methods across eleven farms, each with three nutrient input treatments (66 samples in total). Results and discussion The soil bioassays, which involved counting galls on lettuce roots and lesions on soybean were well correlated with the abundance of gall forming ( Meloidogyne ) and root lesion nematodes (e.g., Pratylenchus ) recovered in standard laboratory-based extractions. Effectiveness of a Fusarium bioassay, involving the counting of lesions on buried bean stems, was verified via sequencing and a pathogenicity test of cultured Fusarium strains. Finally, a Pythium soil bioassay using selective media clearly distinguished pathogen infestation of soils and infected seeds. When examining management impact on nematode communities, soils amended with manure had fewer plant parasites and considerably more bacterivore and fungivore nematodes compared to soils amended with synthetic N and P. Similarly, Pythium presence was 35% lower in soils amended with manure, while the Fusarium assays indicated 23% higher Fusarium infection in plots with amended manure. Our findings suggest that relatively simple bioassays can be used to help farmers assess soil-borne pathogens in a timely manner, with minimal costs, thus enabling them to make informed decisions on soil health and pathogen management.
Why it matches plant phenotyping methods植物根のこぶ・病斑を用いて土壌病原体による植物病害状態を評価する簡易バイオアッセイを開発・検証しており、病害フェノタイピング手法が中心である。
abstractwe assessed the performance of a suite of simplified soil bioassays to screen for plant parasitic nematodes
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Common beanMaizeSoybeanGrowth chamberRoot2D/3D reconstructionBiomass / plant weightRoot system architecture
Abstract Background Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( $$R^2 = 0.82$$ R 2 = 0.82 ) and root surface area ( $$R^2 = 0.8$$ R 2 = 0.8 ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.
Why it matches plant phenotyping methodssEITによる根系形質の非侵襲的・空間分解測定と、測定設計および再構成性能の評価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。
titleQuantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design
Common beanRaman / spectroscopyLeafPhysiological trait estimation
Beans are the main direct source of protein consumed by humans in the world and their productivity is directly linked to nitrogen. The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy, comparing predictions between complete spectral data and only intervals obtained with the variable importance in projection (VIP). Doses of 0, 50, 100, and 150 kg ha−1 of N were applied and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the variable importance in projection (VIP). The intervals of 700–740 nm and 983–995 nm were considered the most important for the study of nitrogen. More efficient predictions were verified for RF and KNN models (R2 = 0.89, RMSE = 2.23 g kg−1; and R2 = 0.80, RMSE = 2.89 g kg−1, respectively) when only the most important spectral regions were included. The efficiency of nitrogen prediction based on NIR reflectance combined with machine learning was verified, which can serve as an important tool in precision agriculture.
Why it matches plant phenotyping methods葉のN濃度という植物形質をNIR分光と機械学習で推定し、複数モデルと波長域を比較評価しており、表現型取得・推定手法が研究の中心である。
abstractwe evaluated the performance of four machine learning algorithms in nitrogen estimation using NIR spectroscopy
Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers' ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.
Why it matches plant phenotyping methodsインゲンマメ葉・莢の病徴を画像から検出・分類する深層学習手法を開発・比較し、未知データとAndroidアプリで検証しており、植物病害状態の取得手法が中心である。
abstractwe present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies.
Common beanRaman / spectroscopyLeafPhysiological trait estimation
Beans are the most widely used protein source in the world and their productivity is directly linked to nitrogen (N). The short crop cycle imposes the need for fast methodologies for N quantification. In this work, we evaluated the performance of four machine learning algorithms in nitrogen prediction using NIR spectroscopy. Increasing doses of nitrogen were applied to the plants and leaf reflectance was collected. Weka software was used to test the algorithms. The selection of the most effective spectral zones was made with the VIP. Considering predictions with the whole NIR, the best results were achieved with RF (R2 = 0,84 and RMSE = 2,69 g kg-1) and KNN (R2 = 0,77 and RMSE = 3,86 g kg-1). The intervals of 700-740 nm and 983-995 nm were considered the most important for the study of N. More efficient predictions were verified when only spectral regions screened by VIP were included, increasing the accuracy of the RF, KNN and M5 models by 6%, 4% and 8%, respectively. The efficiency of N prediction based on NIR reflectance combined with machine learning was verified. This approach can optimize the management of nitrogen fertilization, serving as an important tool in precision agriculture.
Why it matches plant phenotyping methodsNIR葉反射と機械学習による植物窒素量推定の性能比較・検証が研究の中心であり、植物の生理状態を定量するフェノタイピング手法に該当する。
abstractIn this work, we evaluated the performance of four machine learning algorithms in nitrogen prediction using NIR spectroscopy.
High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.
Why it matches plant phenotyping methods画像処理・形態計測・色彩計測と機械学習を用いて遺伝資源の表現型記述子を開発・比較し、遺伝資源管理ワークフローへ統合することが中心であるため。
abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms.
Reproduction assets foundThe paper's Data Availability statement explicitly points to a public GitHub repository containing the phenomics and traditional descriptor data underlying the study, which directly reproduces the paper's plant-phenotyping measurements. Figures and tables in the article are not treated as separate assets.Dataset · publicData Availability: The data underlying the results presented in the study are available from https://github.com/agrocompuepidemlab/Digital-descriptors-genebank The data of the phenomics and traditional descriptors of the evaluated accessions are associated to this one.Open asset ↗agrocompuepidemlab/Digital-descriptors-genebanklines:143-155Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device-LysipheN-assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.
Why it matches plant phenotyping methods個体ごとの蒸散と関連生理形質を高頻度に測定するIoTデバイスを開発・試験しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time.
Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.
Why it matches plant phenotyping methods携帯型および自動クロロフィル蛍光フェノタイピングによる光合成効率の反復測定と、収量予測への技術適用が研究の中心であるため。
abstractUnder fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping.
Reproduction assets foundThe paper's field MultispeQ chlorophyll fluorescence phenotyping data (Fq'/Fm' with PPFR and environmental covariates for the Dar18B, Dar19B, and Pal19D trials) are publicly available on the PhotosynQ platform via three author-provided project URLs. Glasshouse ChlF/biomass data are only in supplementary files without aDataset · publicThe MultispeQ data are also available on the PhotosynQ data base after creating an account (Darién 2018: https://photosynq.org/projects/climbers-in-darien-2018Open asset ↗PhotosynQ · climbers-in-darien-2018lines:374-422Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Common beanStereoWhole plant / canopy / plot / field2D/3D reconstructionTrackingGrowth / development / phenology
Climbing plants, such as common beans ( Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86-98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.
Why it matches plant phenotyping methodsクライミング植物の先端位置とナテーション運動を抽出するステレオビジョン手法を開発し、抽出率と3D定位誤差を評価しており、植物フェノタイピング手法が研究の中心である。
abstractwe introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision.
Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI) using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff timing to allow the crop to dry down for harvest. Therefore, the goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to process and transmit CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farm fields, respectively. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm in each day. Initially, the overall trend of CC development increased over time but fluctuations in daily readings were noticed due to changing lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCₐₙₒₚyCAM) were compared with CC obtained from a LI-COR Plant Canopy Analyzer (CCLAI). The average error between CCCₐₙₒₚyCAM and CCLAI was 2.3 %, and RMSE and R² were 2.95 % and 0.99, respectively. In addition, maximum CC (CCₘₐₓ) and duration of the maximum CC (tₘₐₓ_cₐₙₒₚy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCₘₐₓ and tₘₐₓ_cₐₙₒₚy (R² = 0.77, Adjusted R² = 0.62) as compared to yield versus CCₘₐₓ (R² = 0.58) or yield versus tₘₐₓ_cₐₙₒₚy (R² = 0.45) only. This edge-computing, IoT enabled CanopyCAM, provided accurate and continuous CC readings for dry edible beans which could be used by growers and researchers for different purposes.
Why it matches plant phenotyping methods乾燥インゲンの群落被覆率を画像から連続推定するCanopyCAMを開発し、既存測定器との比較検証および精度評価を行っており、植物形質取得法が中心的である。
abstractTherefore, the goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously.
Common beanLeafStress / disease detectionDisease symptoms / severity
This study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of angular leaf spot (ALS), caused by the fungus Pseudocercospora griseola, in the leaflets of common bean plants. For the elaboration of SADs, the minimum and maximum disease severity limits in the leaflets were considered, along with the intermediate levels following linear increments. The SADs had 10 levels of severity as follows: 0.5%, 2.2%, 4.5%, 9.6%, 20.1%, 30.8%, 41.2%, 52.1%, 67.4%, and 77.3%. The validation was performed by 20 raters, 10 inexperienced and 10 experienced in quantifying severities of foliar diseases. The severity estimates were obtained in 150 leaflets displaying different levels of severity without and using the proposed SADs. Accuracy, precision and reliability of the estimates were improved using SADs. The SADs obtained in the present study proved to be adequate to estimate the severity of ALS in the leaflets of common bean plants with the potential of use in any study in which it is crucial to quantify the severity of this foliar disease to discriminate treatments with precision and accuracy.
Why it matches plant phenotyping methodsインゲン葉の角斑病重症度を定量する標準面積図セットを開発し、評価者実験で精度・再現性を検証しており、植物表現型測定法が研究の中心である。
abstractThis study aimed to develop and validate a standard area diagram set (SADs) to estimate the severity of angular leaf spot (ALS)
Bean is one of the widely grown crop in the world.This crop is easily prone to various diseases such as Alternaria alternata, Bacterial blight, Cercospora yellow spot and Red spider Mite.Among these diseases, spider mites are most dangerous and widely occurring disease, hence this paper mainly aims at classification and identification of spider mite disease caused by spider mite.These diseases cause damage to the plants by feeding on green content of leaf leading to aging and earliest fruitless end of the crop.In the existing situation farmer identify symptoms of the diseases by his vision, but he cannot differentiate types of the disease at its earliest stage of development.To know type of disease farmer need to get guidance from the expert which is time and cost fetching process.In order to control disease, it should be detected at its primary stage of development and pesticide is sprayed to the diseased plants.If the growth of disease extends its earliest stage of development, it cannot be controlled easily.In order to solve the problems faced by the existing system, a novel automated computer vision based system is proposed for classification and early detection of diseases on bean crop using image processing and sending diseased information to the farmer using mobile computing.The experiment is conducted over 400 images on the underside surface of leaves of bean crop.The Precision, Recall, Error Rate and Average Accuracy obtained by the proposed system in detecting red spider mite disease are 73.6%,81.2%, 15% and 84% respectively.
Why it matches plant phenotyping methods豆類葉の病害状態を画像から自動検出・分類するコンピュータビジョン手法が研究の中心であり、性能指標による評価も行われているため、植物病害フェノタイピング手法として採用する。
abstracta novel automated computer vision based system is proposed for classification and early detection of diseases on bean crop using image processing
Dry bean (Phaseolus vulgaris L.) is the third largest pulse crop grown in Canada. Due to climate change and extreme weather, dry bean varieties are subjected to abiotic and biotic stresses, which affect yield stability and seed quality. Development of resilient cultivars is the most effective strategy to ensure productivity and environmental sustainability of dry bean crop. In this project, key phenotypic traits will be extracted for genetic improvement and development of elite cultivars with early maturity and high yield. Traditional phenotyping approaches are rigorous, time-consuming, and subject to human errors. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) has been changing the way of doing large-scale phenotyping in plant breeding. The use of aerial imaging systems offers a potential solution to provide an intensive tool for complex traits assessment to evaluate a large number of dry bean genotypes. By this, HTP technique will be optimized to improve selection efficiency of agronomic, physiological and disease resistance traits. In this study, two dry bean field trials, Advanced Yield Trial (AYT) consisting of F7 generation [yellow bean (5 entries), Pinto bean (20 entries)], and Performance Yield Trial (PeYT) of F8-F10 generation (49 entries) were grown in a randomized-block design at the Fairfield Research Farm at AAFC Lethbridge, AB. Both field trials were imaged at the specific developmental stages (vegetative, flowering, maturity) using UAV mounted RGB and multispectral sensors. The acquired imagery have been processed to accurately overlay images from different dates (time-series data comparison). We analyzed three-time point RGB and multispectral images to identify valuable traits such as canopy height, crop lodging, physiological maturity and accumulation of crop biomass over time. With the preliminary results, we found the utilization of UAV-based HTP has significant advantage in non-destructive measurements of canopy-level functional traits. Assessment of these traits at same climatic region can be used to identify crop characteristics that are important for screening of high-quality dry bean experimental lines and cultivars in field conditions. In the long term, it will provide a consistent and reliable information system to rapidly screen thousands of breeding populations individually that need to be genotyped for morphological and physiological functional traits.
Why it matches plant phenotyping methodsUAV搭載RGB・マルチスペクトル画像を用いた作物形質抽出とHTP最適化が研究の中心であり、乾燥豆育種試験で形質測定・スクリーニングに実質的に適用している。
abstractHTP technique will be optimized to improve selection efficiency of agronomic, physiological and disease resistance traits.
Common beanObject detectionStress / disease detectionDisease symptoms / severity
The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.
Why it matches plant phenotyping methodsインゲンマメ葉の褐斑病領域を画像から検出するYOLOv5改良モデルを開発し、複数モデルとの性能比較・評価を行っており、植物病害状態の表現型抽出が中心である。
abstractIn this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Abstract In many farm locations in Ethiopia, common bacterial blight (CBB), one of the common bean's known diseases, impacts the crop, lowers yield by up to 45 percent of the production, and also has an impact on seed quality. Recently, many researchers have tried to overcome this disease by conducting field surveys, crop management activities, crop rotation, chemical treatment, proper cultural practices, and integrated disease management. However, this method is a very tedious, time-consuming, and costly technique that requires more experts. To overcome these issues, a modern deep learning approach is proposed for the early detection of common bean disease. The three main phases are followed for the proposed approach. Firstly, the collection of healthy and diseased common bean images with the help of domain experts from different agricultural research centers is done. Then the design of a modern convolutional neural network that can detect and classify the input image as diseased or healthy is done. Lastly, the designed model is trained and evaluated. During the classification, the designed model is assessed against the performance of two pre-trained models (VGG16 and InceptionV3) to accurately detect the common bean disease. The performance of the proposed model is evaluated using a dataset that contains total images of 3,135 with two classes of healthy and common bacterial blight disease. We have used data augmentation techniques to generate more images to fit the proposed model. Based on our experimental results, the best performance is achieved using a proposed model with a classification accuracy of 98.2% for early detection of common bacterial blight disease on a common bean leaf. The result of this study can help farmers and domain experts by detecting CBB disease at an early stage for the necessary treatments.
Why it matches plant phenotyping methodsインゲン葉の画像から病徴（健全／斑点細菌病）を推定する深層学習モデルを開発・評価しており、植物病害状態の取得・抽出が研究の中心である。
abstracta modern deep learning approach is proposed for the early detection of common bean disease
Common beanCowpeaSorghumField / plotThermalLeafPhysiological trait estimationStomatal traitsWater status / transpiration
Stomatal conductance ( g s ) is a critical plant biophysical variable that reflects plant regulation of CO 2 uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g s indirectly through leaf temperature ( T leaf ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T leaf measurements to instead determine parameters of a model for g s that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g s model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T leaf , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g s parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.
Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、ガス交換データで検証しており、植物フェノタイピング手法が中心である。
abstractOur objective was to develop an automated pipeline for g s model parameterization using thermal data
Common beanCowpeaSorghumField / plotThermalLeafPhysiological trait estimationStomatal traitsPlant / canopy temperature
Stomatal conductance ( g ) is a critical plant biophysical variable that reflects plant regulation of CO uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g indirectly through leaf temperature ( T ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T measurements to instead determine parameters of a model for g that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.
Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、葉レベルのガス交換データで検証しており、植物表現型取得法が中心である。
abstractOur objective was to develop an automated pipeline for g model parameterization using thermal data
Abstract Plant phenotyping is the science to quantify the quality, photosynthesis, development, growth, and biomass productivity of different crop plants. In the past, plant phenotyping employed methods such as grid count and regression models. However, the grid count method proved to be labor‐intensive and time‐consuming, while the regression model lacked accuracy in calculating leaf area. To address these challenges, a portable automatic platform was developed for precise ground‐based imaging of field plots. This platform consisted of a frame, an RGB camera, a stepper motor, a control board, and a battery. The RGB camera captured images, which were then processed using MATLAB software. Statistical analysis was performed to compare the results obtained from the grid count, regression model, and image processing techniques. The correlation coefficient (r) between the image processing technique and the regression model for sunflower was found to be 0.98 and 0.97, respectively, whereas for kidney bean it was 0.99 and 0.96, respectively. The minimum and maximum values for leaf area density (LAD) of all selected sunflower leaves were determined to be 0.132 and 0.714 m²/m³, respectively. For kidney bean leaves, the minimum and maximum mean LAD values were found to be 0.081 and 0.239 m²/m³, respectively. Ergonomic aspects of the developed automatic system were studied. The developed system had lower physiological parameters, such as working heart rate of 99 beats/min, work pulse of 18 beats/min, oxygen consumption of 786 mL/min, and energy consumption of 11.5 kJ/min compared to the grid count method. Thus, developed automatic ground‐based imaging system would significantly reduce physiological workload and associated hazards. Therefore, the developed method proved satisfactory in comparison to other techniques, offering a quick, efficient, and user‐friendly approach for determining plant phenotypes.
Why it matches plant phenotyping methods圃場作物の葉面積密度などの表現型を画像から推定する自動撮像プラットフォームを開発し、既存手法と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。
abstracta portable automatic platform was developed for precise ground‐based imaging of field plots
Common beanField / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisGrowth / development / phenology
Societal Impact Statement As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation. Summary There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments. Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments. Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E. We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.
Why it matches plant phenotyping methods開花時期という植物形質を予測する機械学習フレームワークの開発・比較が研究の中心であり、育種への再利用可能な表現型推定手法に該当する。
abstractwe develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean
Abstract Crops’ health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs’ has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs.
Why it matches plant phenotyping methodsインゲン葉の画像から病害状態を分類するCNN手法を比較・構築し、性能評価しており、植物病害表現型の抽出が研究の中心です。
abstractAn ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Common beanSeed / grainFruit / seed / panicle traits
AbstractIn the early twentieth century, Wilhelm Johannsen's breeding experiments on pure lines of beans provided empirical support for his groundbreaking distinction between phenotype and genotype, the foundation stone of classical genetics. In contrast with the controversial history of the genotype concept, the notion of phenotype has remained essentially unrevised since then. The application of the Johannsenian concept of phenotype to modularly built, nonunitary plants, however, needs reexamination. In the first part of this article it is shown that Johannsen's appealing solution for dealing with the multiplicity of nonidentical organs produced by plant individuals (representing individual plant phenotypes by arithmetic means), which has persisted to this day, reflected his intellectual commitment to nineteenth-century typological thinking. Revisitation of Johannsen's results using current statistical tools upholds his major conclusion about the nature of heredity but at the same time falsifies two important ancillary conclusions of his experiments-namely, the alleged homogeneity of pure lines (genotypes) regarding seed weight variability and the lack of transgenerational effects of within-line (within-genotype) seed weight variation. The canonical notion of individual plant phenotypes as arithmetic means should therefore be superseded by a concept of phenotype as a dual property, consisting of central tendency and variability components of organ trait distribution. Phenotype duality offers a unifying framework applicable to all nonunitary organisms.
Why it matches plant phenotyping methods植物個体の表現型を器官形質の分布として表現・解析する統計的枠組みを中心に提案しており、種子重量の中心傾向と変動性を含む表現型定義の方法論的発展に該当する。
abstractThe canonical notion of individual plant phenotypes as arithmetic means should therefore be superseded by a concept of phenotype as a dual property, consisting of central tendency and variability components of organ trait distribution.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Common beanWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisStress response / toleranceWater status / transpiration
Abstract Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device - LysipheN - assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.
Why it matches plant phenotyping methods個体ごとの蒸散量・蒸散効率などの植物形質を高頻度に取得するIoT重量計測装置の開発と試験が中心であり、植物フェノタイピング手法として明確に該当する。
abstractThis study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time.
Abstract Background Significant effort has been made in manually tracking plant maturity and to measure early-stage plant density, and crop height in experimental breeding plots. Agronomic traits such as relative maturity (RM), stand count (SC) and plant height (PH) are essential to cultivar development, production recommendations and management practices. The use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost. Recent advances in deep learning (DL) approaches have enabled the development of automated high-throughput phenotyping (HTP) systems that can quickly and accurately measure target traits using low-cost RGB drones. In this study, a time series of drone images was employed to estimate dry bean relative maturity (RM) using a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for features extraction and capturing the sequential behavior of time series data. The performance of the Faster-RCNN object detection algorithm was also examined for stand count (SC) assessment during the early growth stages of dry beans. Various factors, such as flight frequencies, image resolution, and data augmentation, along with pseudo-labeling techniques, were investigated to enhance the performance and accuracy of DL models. Traditional methods involving pre-processing of images were also compared to the DL models employed in this study. Moreover, plant architecture was analyzed to extract plant height (PH) using digital surface model (DSM) and point cloud (PC) data sources. Results The CNN-LSTM model demonstrated high performance in predicting the RM of plots across diverse environments and flight datasets, regardless of image size or flight frequency. The DL model consistently outperformed the pre-processing images approach using traditional analysis (LOESS and SEG models), particularly when comparing errors using mean absolute error (MAE), providing less than two days of error in prediction across all environments. When growing degree days (GDD) data was incorporated into the CNN-LSTM model, the performance improved in certain environments, especially under unfavorable environmental conditions or weather stress. However, in other environments, the CNN-LSTM model performed similarly to or slightly better than the CNN-LSTM + GDD model. Consequently, incorporating GDD may not be necessary unless weather conditions are extreme. The Faster R-CNN model employed in this study was successful in accurately identifying bean plants at early growth stages, with correlations between the predicted SC and ground truth (GT) measurements of 0.8. The model performed consistently across various flight altitudes, and its accuracy was better compared to traditional segmentation methods using pre-processing images in OpenCV and the watershed algorithm. An appropriate growth stage should be carefully targeted for optimal results, as well as precise boundary box annotations. On average, the PC data source marginally outperformed the CSM/DSM data to estimating PH, with average correlation results of 0.55 for PC and 0.52 for CSM/DSM. The choice between them may depend on the specific environment and flight conditions, as the PH performance estimation is similar in the analyzed scenarios. However, the ground and vegetation elevation estimates can be optimized by deploying different thresholds and metrics to classify the data and perform the height extraction, respectively. Conclusions The results demonstrate that the CNN-LSTM and Faster R-CNN deep learning models outperforms other state-of-the-art techniques to quantify, respectively, RM and SC. The subtraction method proposed for estimating PH in the absence of accurate ground elevation data yielded results comparable to the difference-based method. In addition, open-source software developed to conduct the PH and RM analyses can contribute greatly to the phenotyping community.
Why it matches plant phenotyping methodsRGBドローン画像と深層学習を用いて、乾燥豆の成熟期、株数、草高を推定する手法を開発・比較・検証しており、表現型取得が研究の中心である。
abstractThe use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost.
Reproduction assets foundThe preprint explicitly states that the authors' open-source phenotyping software (RM, SC, PH pipelines) is available on GitHub, with specific tools (matuRity, Vegetation index calculator, PlantHeightR, draw-plots-qgis) hosted at public URLs, and that the datasets (orthomosaics, shapefiles, ground notes, clipped plots,Code · publicle 2: Data S1). The GCPs were input and identified into the Pix4D project using the basic manual editor before initial processing.
R [ 57 ] software integrated with QGIS [ 58 ] was used to generate the polygon shapefiles according to plot boundary delimitation using the function ‘Draw plots from clicks’ available at https://github.com/diegojgris/draw-plots-qgis (Fig. 1 -b). Shapefiles were defined using images collected from the first flight available from each location. GDAL (Geospatial Data Abstraction Library) tool plugin in QGIS was used to spatial polygon vectors (or shapefiles) adjustments with a buffer zone for each plot to prevent any influence of neighboring plots. AdditOpen asset ↗diegojgris/draw-plots-qgislines:82-143Code · public3
4. DISCUSSION
The available open source HTP tools, matuRity [ 69 ], PlantHeightR [ 93 ], and Vegetation index calculator provided in this study, have the potential to facilitate and increase the data analysis performance in plant breeding and related areas. The user can either access them on-line or download the repository at https://github.com/msudrybeanbreeding?tab=repositories . Additionally, the step-by-step pipelines deployed in this study using DL methods are available at the GitHub repositories, as well as the complete data set used to perform the analysis including orthomosaics, shapefiles, ground notes, clipped plots, and programming codes. Thus, researchers may be able to replicaOpen asset ↗msudrybeanbreedinglines:572-648Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
The common bean has received attention as a model plant for legume studies, but little information is available about the morphology of its pods and the relation of this morphology to the loss of seed dispersal and/or the pod string, which are key agronomic traits of legume domestication. Dehiscence is related to the pod morphology and anatomy of pod tissues because of the weakening of the dorsal and ventral dehiscence zones and the tensions of the pod walls. These tensions are produced by the differential mechanical properties of lignified and non-lignified tissues and changes in turgor associated with fruit maturation. In this research, we histologically studied the dehiscence zone of the ventral and dorsal sutures of the pod in two contrasting genotypes for the dehiscence and string, by comparing different histochemical methods with autofluorescence. We found that the secondary cell wall modifications of the ventral suture of the pod were clearly different between the dehiscence-susceptible and stringy PHA1037 and the dehiscence-resistant and stringless PHA0595 genotypes. The susceptible genotype had cells of bundle caps arranged in a more easily breakable bowtie knot shape. The resistant genotype had a larger vascular bundle area and larger fibre cap cells (FCCs), and due to their thickness, the external valve margin cells were significantly stronger than those from PHA1037. Our findings suggest that the FCC area, and the cell arrangement in the bundle cap, might be partial structures involved in the pod dehiscence of the common bean. The autofluorescence pattern at the ventral suture allowed us to quickly identify the dehiscent phenotype and gain a better understanding of cell wall tissue modifications that took place along the bean's evolution, which had an impact on crop improvement. We report a simple autofluorescence protocol to reliably identify secondary cell wall organization and its relationship to the dehiscence and string in the common bean.
Why it matches plant phenotyping methods豆莢の裂開性・string形質を対象に、自己蛍光による組織構造の迅速判定プロトコルを提示しており、表現型取得法が研究の中心である。
abstractThe autofluorescence pattern at the ventral suture allowed us to quickly identify the dehiscent phenotype
Common bean has received attention as a model plant for legume studies, but little information is available about the morphology of its pods and the relation of this morphology to loss of seed dispersal and/or the pod string, which are key agronomic traits of legume domestication. Dehiscence is related to pod morphology and the anatomy of pod tissues because of the weakening of the dorsal and ventral dehiscence zones and the tensions of the pod walls. These tensions are produced by the differential mechanical properties of lignified and non-lignified tissues and changes in turgor associated with fruit maturation. In this research, we histologically studied the dehiscence zone of the ventral and dorsal sutures of the pod in two contrasting genotypes for dehiscence and string, by comparing different histochemical methods with autofluorescence. We found that the secondary cell wall modifications of the ventral suture of the pod were clearly different between the dehiscence-susceptible and stringy PHA1037 and the dehiscence-resistant and stringless PHA0595 genotypes. The susceptible genotype had cells of bundle caps arranged in a more easily breakable bowtie knot shape. The resistant genotype had a larger vascular bundle area and fibre cap cells (FCCs), and due to their thickness, the external valve margin cells were significantly stronger than those from PHA1037. Our findings suggest that the FCC area, and the cell arrangement in the bundle cap, might be a partial structure involved in the pod dehiscence of common bean. The autofluorescence pattern at the ventral suture allowed us to quickly identify the dehiscent phenotype and gain a better understanding of cell wall tissue modifications that took place along the bean's evolution, which had an impact on crop improvement. We report a simple autofluorescence protocol to reliably identify secondary cell wall organization and its relationship to dehiscence and string in common bean.
Why it matches plant phenotyping methodsインゲン豆の莢の脱粒性・構造形質を識別するための自家蛍光プロトコルを開発し、組織学的手法と比較して信頼性を評価しており、表現型取得法が中心である。
abstractWe report a simple autofluorescence protocol to reliably identify secondary cell wall organization and its relationship to dehiscence and string in common bean.
The datasets comprised were collected from the MSU dry bean breeding program during 2020, 2021 and 2022 planting seasons at SVREC and HURON locations. The dataset includes the orthomosaic (.tif) generated using the raw images, plot boundary delimitations (.shp), clipped plots using two image sizes, numpy data, the CNN-LSTM deep learning model, and the ground-truth (GT) notes to predict relative maturity (RM). ############################################################################# /2020/SVREC_Mat: This directory contains subfolders /a._GCPs_coodinates: 1. Gomez_Saginaw.csv Ground Control Points coordinates collected in 2020 at SVREC location /b._Orthomosaics: Temporal flights to track plant maturity 1. Image naming structure: `Date-of-flight _ _ _ /` Orthomosaic (TIFF image) collected throughout maturity growth stage containing readable EXIF headers with image metadata. /c._Shapefiles Plot boundaries files (.shp) and field area from 2020 SVREC location containing plot level information using the breeding program metadata. /d._Ground_notes 1. 2020_SVREC_BN_ground.csv Ground truth notes collected in 2020 at SVREC location for Black and Navy bean market classes. /e._ ClipPlots_BN_256_64 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2020 SVREC location using 256x64 image size. /e._ClipPlots_BN_512_128 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2020 SVREC location using 512x128 image size. /f._numpy_data Numpy data (.npy) obtained in 2020 at SVREC location using 6 flights to Black and Navy bean market classes at two image sizes (256x64 and 512x128), as well as the ground-truth data (GT) and weather data using the Growing Degree Days (GDD). ############################################################################# /2021_HURON: This directory contains subfolders /a._GCPs_coodinates: 1. Huron GCPs1_final02.csv Ground Control Points coordinates collected in 2021 at HURON location /b._Orthomosaics: Temporal flights to track plant maturity 1. Image naming structure: `Date-of-flight _ _ _ /` Orthomosaic (TIFF image) collected throughout maturity growth stage containing readable EXIF headers with image metadata. /c._Shapefiles Plot boundaries files (.shp) and field area from 2021 HURON location containing plot level information using the breeding program metadata. /d._Ground_notes 1. 2021_HUR_Ground_data.csv Ground truth notes collected in 2021 at HURON location for Black and Navy bean market classes. /e._ClipPlots_BN_256_64 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2021 HURON location using 256x64 image size. /e._ClipPlots_BN_512_128 PNG images clipped from the orthomosaic of 2021 HURON location using 512x128 image size. /f._numpy_data Numpy data (.npy) obtained in 2021 at HURON location using 9 flights to Black and Navy bean market classes at two image sizes (256x64 and 512x128), as well as the ground-truth data (GT) and weather data using the Growing Degree Days (GDD). ############################################################################# /2021_SVREC: This directory contains subfolders /a._GCPs_coodinates: 1. SVREC_GCPs_7_21_2021_UTM_WGS84.csv Ground Control Points coordinates collected in 2021 at SVREC location /c._Shapefiles Plot boundaries files (.shp) and field area from 2021 SVREC location containing plot level information using the breeding program metadata. /d._Ground_notes 1. 2021_SVREC_BN_ground_P01.csv Ground truth notes collected in 2021 at SVREC location for Black and Navy bean market classes. /e._ ClipPlots_BN_256_64 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2021 SVREC location using 256x64 image size. /e._ClipPlots_BN_512_128 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2021 SVREC location using 512x128 image size. /f._numpy_data/SVREC_P1 Numpy data (.npy) obtained in 2021 at SVREC location using 9 flights to Black and Navy bean market classes at two image sizes (256x64 and 512x128), as well as the ground-truth data (GT) and weather data using the Growing Degree Days (GDD). ############################################################################# /2022_HURON: This directory contains subfolders /a._GCPs_coodinates: 1. 2022_HUR_GCPs_points_01.csv Ground Control Points coordinates collected in 2022 at HURON location /b._Orthomosaics: Temporal flights to track plant maturity 1. Image naming structure: `Date-of-flight _ _ _ /` Orthomosaic (TIFF image) collected throughout maturity growth stage containing readable EXIF headers with image metadata. /c._Shapefiles Plot boundaries files (.shp) and field area from 2022 HURON location containing plot level information using the breeding program metadata. /d._Ground_notes 1. Ground_Notes_HUR_22_table Ground truth notes collected in 2021 at HURON location for Black and Navy bean market classes. /e._ClipPlots_BN_256_64 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2022 HURON location using 256x64 image size. /e._ClipPlots_BN_512_128 PNG images clipped from the orthomosaic of 2022 HURON location using 512x128 image size. /f._numpy_data Numpy data (.npy) obtained in 2022 at HURON location using 9 flights to Black and Navy bean market classes at two image sizes (256x64 and 512x128), as well as the ground-truth data (GT) and weather data using the Growing Degree Days (GDD). ############################################################################# /2022_SVREC: This directory contains subfolders /a._GCPs_coodinates: GCPs_SVREC_2022_UTM_WGS84_Meters_01.csv Ground Control Points coordinates collected in 2022 at SVREC location /c._Shapefiles Plot boundaries files (.shp) and field area from 2022 SVREC location containing plot level information using the breeding program metadata. /d._Ground_notes 1. 22_SVREC_BN_ground.csv Ground truth notes collected in 2022 at SVREC location for Black and Navy bean market classes. /e._ ClipPlots_BN_256_64 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2022 SVREC location using 256x64 image size. /e._ClipPlots_BN_512_128 Image naming structure: `Location-Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2022 SVREC location using 512x128 image size. /f._numpy_data Numpy data (.npy) obtained in 2022 at SVREC location using 6 flights to Black and Navy bean market classes at two image sizes (256x64 and 512x128), as well as the ground-truth data (GT) and weather data using the Growing Degree Days (GDD). ############################################################################# /2021_SVREC_ortho1 and /2021_SVREC_ortho2 Image naming structure: `Date-of-flight _ _ _ /` Orthomosaic (TIFF image) collected throughout maturity growth stage containing readable EXIF headers with image metadata in 2021 at SVREC location. ############################################################################# /2022_SVREC_ortho1, /2022_SVREC_ortho2 and /2022_SVREC_ortho3 Image naming structure: `Date-of-flight _ _ _ /` Orthomosaic (TIFF image) collected throughout maturity growth stage containing readable EXIF headers with image metadata in 2022 at SVREC location.
Why it matches plant phenotyping methods乾燥インゲンの成熟期・草丈・立株数をRGBドローン画像と深層学習で推定するデータセットおよびモデルを提供しており、植物形質の取得・推定方法が中心である。
titleDigital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans via RGB Drone-Based Imagery and Deep Learning Approaches
The datasets comprised were collected from the MSU dry bean breeding program in 2022 planting season at SVREC location. The dataset includes the orthomosaic (.tif) generated using the raw images, raw images (.jpeg) from two flight altitude, plot boundary delimitations (.shp), clipped plots, annotations, Faster R-CNN deep learning model, and the ground-truth (GT) notes to predict stand count (SC). /a._Orthomosaics: This directory contains 1 file 1. Image naming structure: `Date-of-flight _ _ /` Orthomosaic (TIFF image) collected around VC growth stage containing readable EXIF headers with image metadata. /b._Shapefiles Plot boundaries files (.shp) and field area from 2022 SVREC location containing plot level information using the breeding program metadata. /c._ClipPlots Image naming structure: `Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of 2022 SVREC location. /d._Annotations__1 VGG project containing the bean plant annotations via boundary boxes with x, y, height and width coordinates. /e._Resize_img_annot__2 VGG project containing the bean plant annotations via boundary boxes with x, y, height and width coordinates. /f._HyperTunning__3 Model used to perform the hyperparameter tunning. /g._PseudoLab__4: Pseudo labeling using repetitions 3 and 4 from the 2022 SVREC location. This folder contains a subfolder: Image naming structure: `Experiment-PlotID _ _ ` PNG images clipped from the orthomosaic of rep 2 and 4 of the 2022 SVREC location. /h._TrainModel__5 Model used to perform the training. /i._TestingModel1: This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png) and the inference model to early flight date. /i._TestingModel2 This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png), new set of annotations, and the inference model to lower flight altitude (6 meters). /i._TestingModel3 This folder contains orthomosaic (.tif), shapefile (.shp), clipped plots (.png) and the inference model to higher flight altitude (10 meters). /j._Mask_count_Seg-CNN Traditional methods using mask via R and Python programing to perform stand count (SC). Also, the CNN segmented model is available to classify between soil and vegetation. /Raw_img3_6_13_22_SVREC_RGB_SC_7m_1: Raw images (.jpeg) collected at 7 meters of flight altitude. /Raw_img3_6_13_22_SVREC_RGB_SC_7m_2: Cont. Raw images (.jpeg) collected at 7 meters of flight altitude. /Raw_img4_6_13_22_SVREC_RGB_SC_10m_1: Raw images (.jpeg) collected at 10 meters of flight altitude. /Raw_img4_6_13_22_SVREC_RGB_SC_10m_2: Cont. Raw images (.jpeg) collected at 10 meters of flight altitude.
Why it matches plant phenotyping methodsRGBドローン画像と深層学習・セグメンテーションにより、乾燥豆のスタンド数、成熟度、草丈を推定するデータセットおよび解析手法が中心である。
titleDigital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans via RGB Drone-Based Imagery and Deep Learning Approaches
The datasets included were collected from the MSU dry bean breeding project sites in planting seasons 2020, 2021 and 2022. Specific planting season includes the digital surface models (DSM), digital terrain model (DTM) or point cloud (PC) from above and soil level generated using the raw images, plot boundary delimitations (.shp), and the ground-truth notes to plant height (PH) estimation. /2020: This directory contains 4 folders /a._Ground_notes 1. 2020 N&B Raw.xlsx Ground truth notes collected in 2020 at SVREC location for Black and Navy bean market classes. /b._Shapefiles Plot boundaries files (.shp) and field area from 2020 SVREC location containing plot level information using the breeding program metadata. /c._DSM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Surface Model (TIFF images) collected from vegetation containing readable EXIF headers with image metadata. /d._PC 1. Image naming structure: `Date-of-flight _ _ /` Point Cloud (LAZ files) collected from the vegetation containing data points from above ground used to perform the plot reconstruction analysis. ################################################################################### /2021_HURON: This directory contains 6 folders /a._Ground_notes 1. 2021 N&B Raw.xlsx Ground truth notes collected in 2021 at HURON location for Black and Navy bean market classes. /b._Shapefiles Plot boundaries files (.shp) and field area from 2021 HURON location containing plot level information using the breeding program metadata. /c._DSM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Surface Model (TIFF images) collected from vegetation containing readable EXIF headers with image metadata. /d._DTM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Terrain Model (TIFF image) collected before vegetation established containing readable EXIF headers with image metadata. /e._PC_veg 1. Image naming structure: `Date-of-flight _ _ _ /` Point Cloud (LAZ files) collected from the vegetation containing data points from above ground used to perform the plot reconstruction analysis. /f._PC_soil 1. Image naming structure: `Date-of-flight _ _ _ /` Point Cloud (LAZ files) collected from the bare soil containing data points from ground level used to perform the plot reconstruction analysis. ################################################################################### /2021_SVREC: This directory contains 6 folders /a._Ground_notes 1. 2021 N&B Raw.xlsx Ground truth notes collected in 2021 at SVREC location for Black and Navy bean market classes. /b._Shapefiles Plot boundaries files (.shp) and field area from 2021 SVREC location containing plot level information using the breeding program metadata. /c._DSM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Surface Model (TIFF images) collected from vegetation containing readable EXIF headers with image metadata. /d._DTM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Terrain Model (TIFF image) collected before vegetation established containing readable EXIF headers with image metadata. /e._PC_veg 1. Image naming structure: `Date-of-flight _ _ _ /` Point Cloud (LAZ files) collected from the vegetation containing data points from above ground used to perform the plot reconstruction analysis. /f._PC_soil 1. Image naming structure: `Date-of-flight _ _ _ /` Point Cloud (LAZ files) collected from the bare soil containing data points from ground level used to perform the plot reconstruction analysis. ################################################################################### /2022/SVREC: This directory contains 4 folders /a._Ground_notes 1. 2022 N&B Raw.xlsx Ground truth notes collected in 2022 at SVREC location for Black and Navy bean market classes. /b._Shapefiles Plot boundaries files (.shp) and field area from 2022 SVREC location containing plot level information using the breeding program metadata. /c._DSM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Surface Model (TIFF images) collected from vegetation containing readable EXIF headers with image metadata. /d._PC 1. Image naming structure: `Date-of-flight _ _ /` Point Cloud (LAZ files) collected from the vegetation containing data points from above ground used to perform the plot reconstruction analysis. ################################################################################### /2022/HURON: This directory contains 4 folders /a._Ground_notes 1. 2022 N&B Raw.xlsx Ground truth notes collected in 2022 at HURON location for Black and Navy bean market classes. /b._Shapefiles Plot boundaries files (.shp) and field area from 2022 HURON location containing plot level information using the breeding program metadata. /c._DSM 1. Image naming structure: `Date-of-flight _ _ _ /` Digital Surface Model (TIFF images) collected from vegetation containing readable EXIF headers with image metadata. /d._PC 1. Image naming structure: `Date-of-flight _ _ /` Point Cloud (LAZ files) collected from the vegetation containing data points from above ground used to perform the plot reconstruction analysis. For general information or questions about the UAS-based imagery analysis to estimate plant height (PH), please contact leo.agroufv@gmail.com (Leonardo Volpato). A pipeline software tool and scripts for data extraction and analysis were developed to accomplish the activities ranging from image capture to statistical analysis of extracted features. The plant height (PlantHeightR) R shiny software can be accessed at https://github.com/msudrybeanbreeding/PlantHeightR The UAS-based PH date scripts and processes used to perform the image analyses and trait extract are available at https://github.com/msudrybeanbreeding?tab=repositories.
Why it matches plant phenotyping methodsRGBドローン画像・DSM・点群から植物高などを推定するデータセットと解析パイプライン、R Shinyソフトウェアを提供しており、植物表現型の取得・抽出手法が中心である。
titleDigital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans via RGB Drone-Based Imagery and Deep Learning Approaches
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
BACKGROUND: Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS: We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS: TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
Why it matches plant phenotyping methodsTSWIFTという移動式タワー型ハイパースペクトル計測システムの技術詳細と、連続的な植物構造・生理形質モニタリングへの適用を中心に扱っており、植物フェノタイピング手法が明確に中心である。
abstractHere, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Drought is a significant constraint in bean production. In this study, we used high-throughput phenotyping methods (chlorophyll fluorescence imaging, multispectral imaging, 3D multispectral scanning) to monitor the development of drought-induced morphological and physiological symptoms at an early stage of development of the common bean. This study aimed to select the plant phenotypic traits which were most sensitive to drought. Plants were grown in an irrigated control (C) and under three drought treatments: D70, D50, and D30 (irrigated with 70, 50, and 30 mL distilled water, respectively). Measurements were performed on five consecutive days, starting on the first day after the onset of treatments (1 DAT-5 DAT), with an additional measurement taken on the eighth day (8 DAT) after the onset of treatments. Earliest detected changes were found at 3 DAT when compared to the control. D30 caused a decrease in leaf area index (of 40%), total leaf area (28%), reflectance in specific green (13%), saturation (9%), and green leaf index (9%), and an increase in the anthocyanin index (23%) and reflectance in blue (7%). The selected phenotypic traits could be used to monitor drought stress and to screen for tolerant genotypes in breeding programs.
Why it matches plant phenotyping methodsクロロフィル蛍光、マルチスペクトル画像、3Dスキャンを用いた高スループット表現型計測が中心で、干ばつ症状の早期検出と感受性形質の選定を技術的に評価している。
abstractwe used high-throughput phenotyping methods (chlorophyll fluorescence imaging, multispectral imaging, 3D multispectral scanning) to monitor the development of drought-induced morphological and physiological symptoms
Common beanRootSegmentationStress / disease detectionDisease symptoms / severity
Premise Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. Methods Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. Results Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values ( R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. Discussion Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
Why it matches plant phenotyping methodsPlantCVを用いて根の病害重症度と根面積を自動画像推定するRootDSパイプラインを開発し、手動評価との相関で検証しており、植物表現型取得法が研究の中心です。
abstractUsing PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software.
Reproduction assets foundThe authors publicly released the RootDS Python analysis code and a subset of the root images on GitHub; the full dataset is available only upon request.Code · publicThe available code and a subset of the images are available on GitHub ( https://github.com/HausMJ/RootDS_PythonCode ).Open asset ↗HausMJ/RootDS_PythonCodelines:95-140Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 7 Sept 2026
Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits ( R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.
Why it matches plant phenotyping methodsハイパースペクトルセンシングとPLS回帰により、植物の生理形質を推定し、遺伝子型間の干ばつ応答を表現型解析する手法を実証しており、方法が中心的である。
abstractProximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
A dry beans (Phaseolus vulgaris L.) cultivar must fit the environment in which it will be grown. Therefore, days to maturity (DM) is the most important physiological component affecting yield and grain quality outcomes. Additionally, dry bean stand count (SC) at early growth stages estimation provides useful information for agronomic decision-making and can measure root rot loss due to damping-off. The visual inspection to determine the accurate maturity date and the final number of emerged plants is labor-intensive, time-demanding, and tedious. Therefore, there is an increasing demand for alternative approaches to estimating DM and SC in a high-throughput phenotyping mode (HTP). In this study, we developed a Deep Learning (DL) HTP pipeline to capture the sequential behavior of time series data for estimating DM and to identify target plants in the early growth stage for SC estimation using field dry bean data obtained from aerial RGB images at the plot-level. A state-of-the-art hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) was used to extract DM features and capture the sequential behavior of time series data. Faster R-CNN object detection method was deployed to SC. The DL model to estimate DM was tested on five different environments across years, and SC was done comparing different ground sample resolutions in two trials. Results suggest the effectiveness of the CNN-LSTM and Faster R-CNN models employed compared to traditional methods. Furthermore, this study highlighted the technical parameters that can influence the DL model results in the breeding program decision-making.
Why it matches plant phenotyping methods航空RGB画像と深層学習を用いて、成熟期と個体数という植物形質を高スループットに推定する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractThe DL model to estimate DM was tested on five different environments across years, and SC was done comparing different ground sample resolutions in two trials.
Common beanField / plotRootMorphology / geometry measurementRoot system architecture
Abstract The objective of this work was to evaluate root phenotyping methods and the ideal phenological stage to quantify the root system of fixed and segregating common bean populations, in order to select superior genotypes. The experiment was carried out in two municipalities in the state of Santa Catarina, Brazil, and the treatments consisted of six genotypes, the Shovelomics and WinRHIZO root phenotyping methods, and the V4-4, R6, and R8 phenological stages. The simple lattice experimental design was used to evaluate the following variables: basal root angle, vertical root length, left and right horizontal root length, total root length, projected area, and root average volume and diameter. For all variables, there was a significant interaction between phenotyping methods and phenological stages, showing their influence on root system evaluation. The Shovelomics and WinRHIZO phenotyping methods are efficient in quantifying the root system of common bean plants and show specificity for phenological stages, regardless of the genotype. The quantification of the root system of fixed and segregating genotypes is analogous in both methods. The Shovelomics method is more efficient in evaluating the root system of common bean at the R8 stage, and the WinRHIZO method, at the R6 stage.
Why it matches plant phenotyping methods根系表現型測定法（Shovelomics と WinRHIZO）の比較評価と、測定に適した生育段階の検証が研究の中心であるため。
abstractThe objective of this work was to evaluate root phenotyping methods and the ideal phenological stage to quantify the root system of fixed and segregating common bean populations
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).
Introduction Evaluations of interspecific hybrids are limited, as classical genebank accession descriptors are semi-subjective, have qualitative traits and show complications when evaluating intermediate accessions. However, descriptors can be quantified using recognized phenomic traits. This digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid. In this study, a line of P. vulgaris , P. acutifolius and P. parvifolius accessions and their crosses were sown in the mesh house according to CIAT seed regeneration procedures. Methodology Three accessions and one derived breeding line originating from their interspecific crosses were characterized and classified by selected phenomic descriptors using multivariate and machine learning techniques. The phenomic proportions of the interspecific hybrid (line INB 47) with respect to its three parent accessions were determined using a random forest and a respective confusion matrix. Results The seed and pod morphometric traits, physiological behavior and yield performance were evaluated. In the classification of the accession, the phenomic descriptors with highest prediction force were Fm', Fo', Fs', LTD, Chl, seed area, seed height, seed Major, seed MinFeret, seed Minor, pod AR, pod Feret, pod round, pod solidity, pod area, pod major, pod seed weight and pod weight. Physiological traits measured in the interspecific hybrid present 2.2% similarity with the P. acutifolius and 1% with the P. parvifolius accessions. In addition, in seed morphometric characteristics, the hybrid showed 4.5% similarity with the P. acutifolius accession. Conclusions Here we were able to determine the phenomic proportions of individual parents in their interspecific hybrid accession. After some careful generalization the methodology can be used to: i) verify trait-of-interest transfer from P. acutifolius and P. parvifolius accessions into their hybrids; ii) confirm selected traits as "phenomic markers" which would allow conserving desired physiological traits of exotic parental accessions, without losing key seed characteristics from elite common bean accessions; and iii) propose a quantitative tool that helps genebank curators and breeders to make better-informed decisions based on quantitative analysis.
Why it matches plant phenotyping methodsインタースペシフィック雑種の形質を定量化・分類し、ランダムフォレストと混同行列で親由来のフェノミック形質割合を検証する方法論が研究の中心である。
abstractThis digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid.
Reproduction assets foundThe paper's MultispeQ physiological phenotyping measurements (1,022 observations) are publicly available on the PhotosynQ platform as the authors' own project 'domestication-syndrome' (ID 5685). No author analysis code or trained model deposit is stated; the data availability statement only promises raw data on requestDataset · publicThe classical protocol was used: Leaf Photosynthesis MultispeQ V1.0 (the raw data are available at: https://photosynq.org/projects/domestication-syndrome ; ID 5685).Open asset ↗PhotosynQ · ID 5685lines:319-327Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Seed phenotyping is routinely done using visual assessment that suffers from subjectivity. In the present study we phenotyped 278 Western Himalayan bean accessions using a low cost spectroscopic method based on quantification of seed colour in terms of L*,a*,b* and δ E. There was substantial variation for L*, a*, b* and δ E parameters for all seven colour classes. The mean value for L* was lowest for red (31.31) and highest for white (78.17) and reverse in case of a*. Similarly, mean value for b* was lowest for black (-0.29) and highest for yellow (40.86). In terms of deviations from standard colours depicted by δ E, highest mean value was observed in red colour (6012.16) and lowest was recorded for green (18.36) with a mean of 2525.72 across all colours. The first two principal components accounted for 86.74% of variation. contributed by colour and L* in PC1 and b*, L* and a* in PC2. In the present study, based on the factor loading graph, colour is strongly correlated with L* as is evident from its significant contribution in both PC1 and PC2. The multivariate analysis clearly delineates the diversity panel of 278 genotypes into distinct colour groups as shown by concentration of genotypes of similar colour class into specific regions of four coordinates of biplot based on L*, a* and b* values and their observed relationship with colour scores. The method removes the subjectivity in visual colour specifications, is quantitative and can help in exact quantification of varietal differences.
Why it matches plant phenotyping methods種子表面色という植物形質を、主観的な目視評価に代わる低コスト・高スループット分光法で定量化することが研究の中心であり、方法開発と大規模適用に該当する。
titleA Low cost and high throughput spectroscopic method for quantification of seed coat colour differences in plain seeded bean (Phaseolus vulgaris L.) germplasm from Western Himalayan Kashmir
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
The development of automated, image-based, high-throughput plant phenotyping enabled the simultaneous measurement of many plant traits. Big and complex phenotypic datasets require advanced statistical methods which enable the extraction of the most valuable traits when combined with other measurements, interpretation, and understanding of their (eco)physiological background. Nutrient deficiency in plants causes specific symptoms that can be easily detected by multispectral imaging, 3D scanning, and chlorophyll fluorescence measurements. Screening of numerous image-based phenotypic traits of common bean plants grown in nutrient-deficient solutions was conducted to optimize phenotyping and select the most valuable phenotypic traits related to the specific nutrient deficit. Discriminant analysis was used to compare the efficiency of groups of traits obtained by high-throughput phenotyping techniques (chlorophyll fluorescence, multispectral traits, and morphological traits) in discrimination between nutrients [nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), and iron (Fe)] at early and prolonged deficiency. Furthermore, a recursive partitioning analysis was used to select variables within each group of traits that show the highest accuracy for assigning plants to the respective nutrient deficit treatment. Using the entire set of measured traits, the highest classification success by discriminant function was achieved using multispectral traits. In the subsequent measurements, chlorophyll fluorescence and multispectral traits achieved comparably high classification success. Recursive partitioning analysis was able to intrinsically identify variables within each group of traits and their threshold values that best separate the observations from different nutrient deficiency groups. Again, the highest success in assigning plants into their respective groups was achieved based on selected multispectral traits. Selected chlorophyll fluorescence traits also showed high accuracy for assigning plants into control, Fe, Mg, and P deficit but could not correctly assign K and N deficit plants. This study has shown the usefulness of combining high-throughput phenotyping techniques with advanced data analysis to determine and differentiate nutrient deficiency stress.
Why it matches plant phenotyping methods複数のハイスループット表現型計測（マルチスペクトル、3D、クロロフィル蛍光）と高度なデータ解析を組み合わせ、栄養欠乏の識別に有効な形質を選定・評価することが研究の中心である。
abstractScreening of numerous image-based phenotypic traits of common bean plants grown in nutrient-deficient solutions was conducted to optimize phenotyping and select the most valuable phenotypic traits related to the specific nutrient deficit.
Common beanWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
Background Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.
Why it matches plant phenotyping methods遺伝子型・環境データから開花時期という植物形質を予測する動的モデルを構築・比較しており、形質推定手法が研究の中心である。
abstractWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.).
Reproduction assets foundThe authors publicly deposited the paper's MET phenotypic/meteorological observational data, synthetic data, and the R/FORTRAN analysis code (dynamic mixed-effects flowering model) on figshare (DOI 10.6084/m9.figshare.19692628), and separately deposited the RI family genotype data at a figshare link given in Methods.Dataset · publicComputer codes are available in the Supplementary Materials file, and observational and synthetic data in Additional file 1 , which have been uploaded to the figshare database repository ( https://doi.org/10.6084/m9.figshare.19692628 ).Open asset ↗figshare · 10.6084/m9.figshare.19692628lines:167-260Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Common beanPepper / chilliField / plotSegmentation
Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.
Why it matches plant phenotyping methods画素レベル画像セグメンテーション手法そのものを開発し、植物・農業およびパプリカ病害データセットで性能評価しているため、植物画像から状態・領域を抽出する方法研究として中心的です。
abstractThis paper presents a novel method for efficiently creating pixel-level fine segmentation labelling
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub. The newly constructed plant datasets (Bean-Field, Paprika-Disease, Circle) are only available on request, so they qualify as request_only, not public.Code · publicCode availability
The code is available at https://github.com/hololee/coarse-to-fine-segmentation-labelling .Open asset ↗hololee/coarse-to-fine-segmentation-labellinglines:226-263Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Seed weight and size are important yield components. Thus, selecting for large seeds has been a key objective in crop domestication and breeding. In common bean, seed shape is also important since it influences industrial processing and plays a vital role in determining the choices of consumers and farmers. In this study, we performed genome-wide association studies on a core collection of common bean accessions to dissect the genetic architecture and identify genomic regions associated with seed morphological traits related to weight, size, and shape. Phenotypic data were collected by high-throughput image-based approaches, and utilized to test associations with 10,362 single-nucleotide polymorphism markers using multilocus mixed models. We searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation. The collection exhibited high variability for the entire set of seed traits, and the Andean gene pool was found to produce larger, heavier seeds than the Mesoamerican gene pool. Strong pairwise correlations were verified for most seed traits. Genome-wide association studies identified marker-trait associations accounting for a considerable amount of phenotypic variation in length, width, projected area, perimeter, and circularity in 4 distinct genomic regions. Promising candidate genes were identified, e.g. those encoding an AT-hook motif nuclear-localized protein 8, type 2C protein phosphatases, and a protein Mei2-like 4 isoform, known to be associated with seed size and weight regulation. Moreover, the genes that were pinpointed are also good candidates for functional analysis to validate their influence on seed shape and size in common bean and other related crops.
Why it matches plant phenotyping methods高スループット画像法による種子形状・サイズ形質の取得が明示され、GWASのための中心的な表現型データ取得手法として扱われているため。
abstractPhenotypic data were collected by high-throughput image-based approaches
As a common problem in snap beans, hard seed has seriously affected the large-scale industrial planting and yield of snap bean. To realize accurate, quick and non-destructive identifying the hard seeds of snap bean is of great significance to avoiding the effects of hard seeds on germination and growth. This research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean. This study obtained the characteristic spectra from the hyperspectral image of a single seed, and then combined the synthetic minority over-sampling technique (SMOTE) and Tomek links to balance the numbers of hard and non-hard seed samples. The characteristic wavelengths were extracted from the average spectrum. Then the average spectrum was processed by first derivative (1D). After that, the characteristic wavelengths could be extracted using successive projections algorithm (SPA). Finally, a radial basis function-support vector machine (RBF-SVM) model was established to realize the intelligent detection of hard seeds, and the detection accuracy rate reached 89.32%. The research results showed that HSI technology could achieved accurate, fast and non-destructive testing of the hard seeds of snap bean, which is of great significance to the large-scale and standardized planting of snap bean and increase the yield per unit area.
Why it matches plant phenotyping methodsインゲン豆種子の硬実状態を対象に、ハイパースペクトル画像とスペクトル処理・機械学習による非破壊検出法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis research was based on hyperspectral imaging (HSI) to achieve accurate detection of hard seeds of snap bean.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Common beanField / plotMultispectral / hyperspectralLeafSegmentationLeaf traits
Detecting and identifying plants using image analysis is a key step for many applications in precision agriculture (from phenotyping to site specific weed management). Instance segmentation is usually carried on to detect entire plants. However, the shape of the detected objects changes between individuals and growth stages. A relevant approach to reduce these variations is to narrow the detection on the leaf. Nevertheless, segmenting leaves is a difficult task, when images contain mixes of plant species, and when individuals overlap, particularly in an uncontrolled outdoor environment. To leverage this issue, this study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment. It combines “deep contour aware” (to separate the inner of big leaves from its edges), “Leaf Segmentation trough classification of edges” (to separate instances with a specific inner edges) and “Pyramid CNN for Dense Leaves” (to consider edges at different scales). But the segmentation output is also refined using a Watershed and a method to compute optimized vegetation indices (DeepIndices). The method is compared to others running the leaf segmentation challenge (provided by the International Network on Plant Phenotyping) and applied on an external dataset of Komatsuna plants. In addition, a new multispectral dataset of 300 images of bean plants is introduced (with dense foliage, individuals overlapping, mixes of species and natural lighting conditions). The ground truth (e.g. the leaves boundaries) is defined by labelled polygons and can be used to train and assess the performance of various algorithms dedicated to leaf detection or crop/weed classification. On the usual datasets, the performances of the proposed method are similar to those of the usual methods involved in the leaf segmentation challenges. On the new dataset, their results are strongly better than those of the usual RCNN method. Remaining errors are bad fusion between neighboring areas and over segmentation of multi-foliate leaves. Structural analysis methods could be studied in order to overcome these deficiencies.
Why it matches plant phenotyping methods葉のインスタンス分割による植物器官形状の抽出手法を開発・比較検証し、植物画像データセットも提供しているため、植物フェノタイピング手法が中心である。
abstractthis study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment.
Common beanWhole plant / canopy / plot / fieldGrowth / time-series analysisGrowth / development / phenology
Abstract BackgroundPredicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. ResultsWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean ( Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype , Environment , and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.ConclusionsThe dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.
Why it matches plant phenotyping methods遺伝子型・環境データから開花時期という植物形質を予測する動的モデルを構築し、既存モデルと比較検証しており、形質推定手法が研究の中心である。
abstractWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean
Common beanChlorophyll fluorescenceLeafClassificationStress / disease detectionDisease symptoms / severity
Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant−pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and X. citri pv. fuscans is a major threat to common bean. The pathogenicity of these bacteria is variable among strains and depends mainly on a type III secretion system and associated type III effectors such as transcription activator-like effectors. Because the impact of a single gene is often small and difficult to detect, a discriminating methodology is required to distinguish the slight phenotype changes induced during the progression of the disease. Here, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains. Interestingly, rub inoculation of the first leaves combined with symptom assessment by machine learning-based imaging allowed significant distinction between wild-type and mutant strains. By contrast, dip inoculation of first-trifoliate leaves combined with chlorophyll fluorescence imaging did not differentiate the strains. Furthermore, the new method developed here led to the miniaturization of pathogenicity tests and significant time savings.
Why it matches plant phenotyping methods機械学習画像による病徴評価法を開発・比較検証し、病原性試験の小型化と時間短縮にもつなげており、植物病害表現型の取得法が研究の中心です。
abstractHere, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains.
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Plant phenotyping relevance match · UnverifiedbioRxiv · checked 8 Sept 2026
Common beanLeafSeed / grainStress / disease detectionDisease symptoms / severity
Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are typically given pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then employed machine learning to investigate if we could use genomic data to predict virulence on bean. We used gradient boosted decision trees to model the virulence using whole genome kmers, type III secreted effector kmers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study demonstrates the power of machine learning for predicting host specific adaptation and strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains. AUTHOR SUMMARYPseudomonas syringae is a genetically diverse Gammaproteobacterial species complex responsible for numerous agronomically important crop diseases. Strains in the P. syringae species complex are frequently categorized into pathovars depending on pathogenic characteristics such as host of isolation and disease symptoms. Common bean pathogens from P. syringae are known to cause two major diseases: the halo blight disease, which is characterized by large necrotic lesions surrounded by a chlorotic zone or halo of yellow tissue; and the bacterial spot disease, which is characterized by brown leaf spots. While halo blight can cause serious crop losses, bacterial spot disease is generally of minor agronomic concern. The application of statistical genetic and machine learning approaches to genomic data has greatly increased our power to identify genes underlying traits of interest, such as host specificity. Machine learning models can be used to predict outcomes from new samples or to identify the genetic feature(s) that carry the most importance when predicting a particular phenotype. Here, we implemented a rapid method for screening a proxy of virulence for P. syringae isolates on common bean, and used this screen to assess virulence of P. syringae strains on bean. We found that halo blight pathogens display a stronger degree of host specificity compared to brown spot pathogens, and that genomic kmers and virulence factors can be used to predict the virulence of P. syringae isolates on bean using machine learning models.
Why it matches plant phenotyping methods豆での病原性という植物の病態を測定する迅速な感染アッセイを開発し、その測定結果を用いた機械学習予測と機能検証を行っており、表現型取得法が中心的です。
abstractHere we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris).
The development of RGB (red, green, blue) sensors has opened the way for plant phenotyping. This is relevant because plant phenotyping allows us to visualize the product of the interaction between the plant ontogeny, anatomy, physiology, and biochemistry. Better yet, this can be achieved at any stage of plant development, i.e., from seedling to maturity. Here, we describe the use of phenotyping, based on the stay-green trait, of common bean (Phaseolus vulgaris L.) plant, as a model, stressed by water deficit, to elucidate the result of that interaction. Description is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software. These images allow us to obtain a data group related to the color parameters that quantify the changes and alterations in each plant growth and development.
Why it matches plant phenotyping methodsRGB画像をフェノミックプラットフォームと専用ソフトで解析し、インゲンのstay-green形質や生育変化を定量化する手法の応用が中心である。
abstractDescription is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software.
The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points.
Why it matches plant phenotyping methodsUAS-SfMとLiDARによる作物形状計測を比較・検証し、作物高と畝幅という植物形質の精度を評価しており、表現型取得手法が中心である。
abstractWe also evaluated the crop height and the row width from the CHMs and compared them with field measurements
Common beanAerial / UAVField / plotGreenhouseMultispectral / hyperspectralRaman / spectroscopyFruitRootSeed / grainWhole plant / canopy / plot / field
Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.
Why it matches plant phenotyping methodsUASハイパースペクトル画像からスナップ豆の pod weight と seed length を推定する手法を開発・評価し、特徴選択ライブラリも提示しており、植物形質取得が研究の中心である。
abstractOur principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS.
Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像からインゲンの植物高などの生育形質を予測する手法が研究の中心であり、画像ベースの形質推定に該当する。
abstractthe prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV
Common beanLeafClassificationSegmentationDisease symptoms / severity
The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision.
Why it matches plant phenotyping methods豆葉の病害状態を画像から検出・分類する深層学習およびロボットビジョン手法が研究の中心であり、植物の病害表現型を直接推定している。
abstracta modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed
Common beanSoybeanRootClassificationDisease symptoms / severity
Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.
Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習手法の開発が研究の中心であり、植物病害フェノタイピング手法として適格です。
abstractwe have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification.
In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way. If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.
Why it matches plant phenotyping methods植物の葉の症状に基づく病害分類を対象とし、複数のニューラルネットワーク比較と新規ModCNNの提案・評価が研究の中心であるため、植物病害表現型の計算的取得手法として含める。
abstractFor detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult.
Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71–0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium , plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.
Why it matches plant phenotyping methods根腐病抵抗性や植物生育を遺伝情報から推定するゲノム予測を中心に、温室・圃場データ間および複数集団で予測性能を評価しており、単なる生物学的測定ではなく植物形質推定法の検証・応用である。
abstractGenomic predictions were evaluated across and within further published data sets on root rots in other panels.
Reproduction assets foundThe paper's data availability statement explicitly deposits the SNP marker matrix and raw and modeled phenotypic data of the RR panel (root rot phenotyping measurements) on Harvard Dataverse, a public, paper-specific, actionable asset. No author analysis code repository is mentioned.Dataset · publicThe SNP marker matrix, the raw and modeled phenotypic data of the RR panel used in this study are available for download at Harvard Dataverse: https://doi.org/10.7910/DVN/SVA5CJ .Open asset ↗Harvard Dataverse · 10.7910/DVN/SVA5CJlines:500-547Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Common beanWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
ABSTRACT Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Current phenotype prediction models do not adequately capture all of these fluctuations or effectively use genotype information. Instead, we have developed a dynamic modular approach that captures the genotype, environment, and Genotype-by-Environment effects to express the time-to-flowering phenotype in real time in Phaseolus vulgaris . The module we describe can be applied to different plant processes and can gradually replace processes in existing crop models. Our model can enable accelerated progress in diverse breeding programs, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.
Why it matches plant phenotyping methods遺伝型・環境データから開花時期という植物表現型をリアルタイム予測する動的モデルの開発が中心であり、単なる生物学的実験ではない。
abstractInstead, we have developed a dynamic modular approach that captures the genotype, environment, and Genotype-by-Environment effects to express the time-to-flowering phenotype in real time in Phaseolus vulgaris .
Common beanField / plotFlowerGrowth / time-series analysisGrowth / development / phenologyYield / yield components
Abstract Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as “genetic coefficients.” These GSPs are estimated using phenotypic observations and may not represent “true” genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean ( Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTL×E interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G×E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.
Why it matches plant phenotyping methods遺伝子型と環境データから開花時期という植物形質を予測する動的遺伝子ベース計算モジュールを開発し、既存モデルと比較検証しており、形質推定法が研究の中心である。
abstractThe goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering
In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.
Why it matches plant phenotyping methods葉画像から作物の種・品種を識別する画像解析手法を提案・評価しており、植物表現型の取得・分類方法が研究の中心です。
abstractThe method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside.
Abstract Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as ‘genetic coefficients’. These GSPs are estimated using phenotypic observations and may not represent ‘true’ genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study that included 187 recombinant inbred lines (RILs) from a bi-parental bean family was conducted in 2011 and 2012 to measure the effects of quantitative trait loci (QTLs), environment (E) and QTL × E interactions across five sites. A dynamic mixed linear model was modified in this study to create a dynamic module that was then integrated into the Cropping System Model (CSM)-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic make-up of each genotype that is simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G × E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.
Why it matches plant phenotyping methods遺伝子ベースの開花期推定モジュールを開発し、実測値との比較検証を行う計算的な植物形質推定法が研究の中心である。
abstractThe goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model.
Common beanMaizeField / plotRootClassificationMorphology / geometry measurementRoot system architecture
A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T -test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.
Why it matches plant phenotyping methods根系長分布と根系構造を推定する土壌コア採取プロトコルを開発し、シミュレーション・圃場データ・トレンチで比較検証しており、植物表現型取得法が研究の中心である。
abstractA soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean.
Reproduction assets foundThe authors publicly deposited the field and simulation phenotype data, OpenSimRoot parameterizations/outputs, Voronoi R code, and analysis scripts on Zenodo (DOI 10.5281/zenodo.3952179), explicitly stated in the Data Availability section and Methods.Dataset · publicThe field and simulation data, model parameterization, R package to calculate Voronoi-adjusted root length distribution, and R scripts used to analyze data are available at Zenodo ( https://doi.org/10.5281/zenodo.3952179 ).Open asset ↗Zenodo · 10.5281/zenodo.3952179lines:95-122Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Common beanLiDAR / point cloudWhole plant / canopy / plot / fieldSegmentation
In recent years laser scanning platforms have been proven to be a helpful tool for plants traits analysing in agricultural applications.Three-dimensional high throughput plant scanning platforms provide an opportunity to measure phenotypic traits which can be highly useful to plant breeders.But the measurement of phenotypic traits is still carried out with labor-intensive manual observations.Thanks to the computer vision techniques, these observations can be supported with effective and efficient plant phenotyping solutions.However, since the leaves and branches of some plant types overlap with other plants nearby after a certain period of time, it becomes challenging to obtain the phenotypical properties of a single plant.In this study, it is aimed to separate bean plants from each other by using common clustering algorithms and make them suitable for trait extractions.K-means, Hierarchical and Gaussian mixtures clustering algorithms were applied to segment overlapping beans.The experimental results show that K-means clustering is more robust and faster than the others.
Why it matches plant phenotyping methods豆類植物の重なりをクラスタリングで分離し、形質抽出に適したセグメンテーション手法を開発・比較しており、植物フェノタイピング手法が中心である。
abstractIn this study, it is aimed to separate bean plants from each other by using common clustering algorithms and make them suitable for trait extractions.
Key message Several QTL governing color retention in processed black beans were identified by traditional and novel phenotyping methods applied to two black bean mapping populations. When black beans are hydrothermally processed prior to consumption, water-soluble anthocyanins are released from the seed coat, resulting in an undesirable faded brown color in the cooked product. The aim of this research was to develop mapping populations with different genetic sources of color retention in order to identify regions of the bean genome associated with canning quality traits. Two half-sibling black bean recombinant inbred line (RIL) populations segregating for post-processing color retention were developed. These RIL populations were phenotyped for canning quality traits over two years and genotyped using the BARCBean6k_3 BeadChip. In addition to traditional phenotyping by trained panelists, cooked beans were also phenotyped using a novel digital image analysis pipeline. Measurements of post-processing seed coat color from both phenotyping methods were compared, and the digital image analysis was shown to outperform the trained panelists. Quantitative trait loci (QTL) for post-processing color retention were detected on six chromosomes, with QTL on Pv08 and Pv11 consistently detected across phenotyping methods, populations, and years. Color retention QTL on Pv08 explained up to 32% of phenotypic variation but were significant over a large physical interval due to low SNP marker coverage. However, color retention QTL on Pv11 also explained a substantial amount of phenotypic variation (r 2 ≈ 25%) and mapped to a small genomic region near 52.5 Mbp. The QTL and methods described in this study will be useful for dry bean breeders and food scientists to produce high quality black beans that meet consumer needs.
Why it matches plant phenotyping methods黒豆種皮の加工後色保持を測定するデジタル画像解析パイプラインを開発・適用し、従来法との比較検証まで行っているため、植物表現型取得法が中心的です。
abstractcooked beans were also phenotyped using a novel digital image analysis pipeline.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60-75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r = -0.67 to -0.91 in ADP; r = -0.55 to -0.72 in DDP), followed by seed yield (r = 0.51 to 0.73 in ADP; r = 0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r = 0.28-0.88). Finally, lower resolution satellite images (0.05 to 5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r = 0.52) and biomass (r = -0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping.
Why it matches plant phenotyping methodsUAS・衛星のマルチスペクトル／熱画像からNDVIや温度を抽出し、乾燥豆のバイオマス・収量などの形質推定性能を比較・検証する研究であり、フェノタイピング手法が中心です。
titleUnmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean
Soil biota have important effects on crop productivity, but can be difficult to study in situ. Laser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy, providing new opportunities to investigate interactions between roots and edaphic organisms. LAT was used for analysis of maize roots colonized by arbuscular mycorrhizal fungi, maize roots herbivorized by western corn rootworm, barley roots parasitized by cereal cyst nematode, and common bean roots damaged by Fusarium. UV excitation of root tissues affected by edaphic organisms resulted in differential autofluorescence emission, facilitating the classification of tissues and anatomical features. Samples were spatially resolved in three dimensions, enabling quantification of the volume and distribution of fungal colonization, western corn rootworm damage, nematode feeding sites, tissue compromised by Fusarium, and as well as root anatomical phenotypes. Owing to its capability for high-throughput sample imaging, LAT serves as an excellent tool to conduct large, quantitative screens to characterize genetic control of root anatomy and interactions with edaphic organisms. Additionally, this technology improves interpretation of root-organism interactions in relatively large, opaque root segments, providing opportunities for novel research investigating the effects of root anatomical phenes on associations with edaphic organisms.
Why it matches plant phenotyping methodsレーザーアブレーショントモグラフィーを用いて根の解剖学的形質と病害・生物相互作用による損傷を三次元定量化する手法を開発・実証しており、表現型取得が研究の中心である。
abstractLaser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy
Reproduction assets foundThe paper deposits its LAT scan videos and 3D reconstructions of root colonization (AMF, WCR, nematode, Fusarium) in a public Zenodo repository, which directly reproduces this paper's phenotyping imaging data. Supplementary figures/tables are hosted at JXB, not at an allowed URL, so only the Zenodo deposit qualifies.Dataset · publicereo-microscope.
Fig. S4. Comparison of images of common bean ( Phaseolus vulgaris ) roots damaged by Fusarium ( Fusarium virguliforme ) taken with a stereo-microscope and LAT.
erz271_suppl_Supplementary_Figures_S1-S4_Tables_S1-S4
Click here for additional data file.
Data deposition
The following videos are available at Zenodo: http://doi.org/10.5281/zenodo.1479847 .
Video S1. LAT scan of maize ( Zea mays ) root segment colonized with AMF.
Video S2. Three-dimensional reconstruction of AMF colonization in a maize ( Zea mays ) root segment, highlighting the spatial relationship between AMF (yellow) and aerenchyma (green).
Video S3. LAT scan of maize ( Zea mays ) root segment colonized withOpen asset ↗Zenodo · 10.5281/zenodo.1479847lines:158-220Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Common beanChlorophyll fluorescenceLeafRootStem / branchClassificationPhysiological trait estimation
Investigating the translocation behaviour of fluorescent markers is significant for the effective application of the markers in weed and crop differentiation. Snap bean was used as a model plant to study the systemic movement of Rhodamine B (Rh-B) in specialty crops for weed control. A fluorescence imaging system was developed to monitor the uptake and translocation of Rh-B from dyed snap bean seeds to bean plants. Bean samples were dyed using different concentrations of Rh-B solutions. As the concentration of Rh-B applied to the seeds increased, the fluorescent signal of the marker was at first enhanced, then weakened. After germination, this marker was observed from the stems of bean seedlings at stages of first leaf to multiple leaves over time. The fluorescence response on the hypocotyl was stronger than that on the epicotyl, while there was limited translocation observed in plant roots and leaves based on this fluorescence imaging system. The fluorescence peak at 590 nm, measured by a spectrometer (350–1050 nm), exhibited the greatest contrast between untreated and treated plant samples. The proposed crop signalling approach based on Rh-B emission was able to classify snap bean plants from different weeds (e.g. burning nettle, groundsel, and barley). The results demonstrate that fluorescence imaging technology is a rapid and effective approach to studying the real-time translocation behaviour of a signalling marker in a crop system. Based on the unique fluorescence property, visualisation of the marker in vivo specialty crops grown from Rh-B treated seeds provides potential for their successful application in early season weed discrimination.
Why it matches plant phenotyping methods蛍光イメージングシステムを開発し、植物体内でのマーカー移行という生理状態を可視化・定量しており、手法が研究の中心である。
abstractA fluorescence imaging system was developed to monitor the uptake and translocation of Rh-B from dyed snap bean seeds to bean plants.
Common beanLaboratory / benchtopChlorophyll fluorescenceLeafTissueClassificationPhotosynthesis / fluorescenceStress response / tolerance
The deficiency of macro (N, P, S, Ca, Mg and K) and micro (Zn, Cu, B, Mo, Cl, Mn and Fe) minerals has a major effect on plant development. The lack of some nutrient minerals especially of nitrogen, potassium, calcium, phosphorus and iron is a huge problem for agriculture and early warning and prevention of the problem will be very useful for agro-industry. Methods currently used to determine nutritional deficiency in plants are soil analysis, plant tissue analysis, or combined methods. But these methods are slow and expensive. In this study, a new method for determining nutrient deficiency in plants based on the prompt fluorescence of chlorophyll a is proposed. In this paper bean plants are grown on a complete nutrient solution (control) were compared with those grown in a medium, which lacked one of these elements - N, P, K, Ca and Fe. In this article the mineral deficiency in nutrient solution was evaluated by the stress response of the plants estimated by leaves photosynthetic activity. The photosynthetic activity was estimated by analysis of the chlorophyll fluorescence using JIP-test approach that reflects functional activity of Photosystems I and II and of electron transfer chain between them, as well as the physiological state of the photosynthetic apparatus as whole. Next the fluorescence transient recorded from plants grown in nutrient solution with deficiency of N, P, K, Ca and Iron, as an input data in Artificial Neural Network was used. This ANN was train to recognise deficiency of N, P, K, Ca and Iron in bean plants. The results obtained were of high recognition accuracy. The ANN of fluorescence transient was presented as a possible approach to identify/predict the nutrient deficiency using the fast chlorophyll fluorescence records.
Why it matches plant phenotyping methodsクロロフィル蛍光測定とANNを組み合わせ、植物の栄養欠乏状態を推定・識別する方法を提案しており、表現型取得・判定手法が研究の中心である。
abstractIn this study, a new method for determining nutrient deficiency in plants based on the prompt fluorescence of chlorophyll a is proposed.
Common bean (Phaseolus vulgaris L.) is a major source of dietary protein and minerals in Latin America and Africa. Drought causes severe losses in the yields of many common bean cultivars. In this study, to obtain phenotypic variation over time on common bean under water restriction, we used a phenotyping platform with a sensor for visible (RGB). We hypothesized that greenness identification or “stay green” by high-throughput images is an efficient indicator for identifying differences among the common bean phenotypes. Plants of cultivars V8025 and Canario-60 under greenhouse conditions were irrigated to field capacity until the beginning of the seed-filling stage. Under well-watered conditions, whole plant senescence differed between the cultivars. V8025 seemed to maintain a constant green color throughout the evaluation, whereas the Canario-60 began to show yellowing at 24 d into the evaluation. Images showed early strong symptoms of senescence in the V8025 plants under water restriction; even significant necrosis was observed. In contrast, Canario-60 showed fewer symptoms of senescence than V8025 for half of the evaluations. Although both cultivars showed low yield under water restriction, the Canario-60 seed weight increased significantly. We suggest a non-invasive framework for using an image-based phenotyping platform for common bean screening under stress conditions.
Why it matches plant phenotyping methodsRGBセンサー搭載の表現型解析プラットフォームを用い、画像から緑色度・老化や壊死を非侵襲的に評価する枠組みを提案しており、植物表現型の取得・抽出が研究の中心である。
abstractwe used a phenotyping platform with a sensor for visible (RGB).
Common beanLaboratory / benchtopRaman / spectroscopySeed / grainPhysiological trait estimation
Rationale Abscisic acid (ABA) and 12-oxo-phytodienoic acid (OPDA) play crucial roles in seed development. However, because of their low ionization efficiencies, visualization by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) has been difficult. In this study, we used on-tissue chemical derivatization (OTCD) with the derivatization reagent Girard's T (GirT) in MALDI-IMS to visualize ABA and OPDA. Methods Immature Phaseolus vulgaris L. seeds were homogenized, and frozen homogenate sections were prepared using a cryostat. The concentration of the trifluoroacetic acid (TFA) and spray volume of the GirT solution were optimized using the homogenate sections. Immature seed sections were prepared using a cryostat, and the OTCD efficiency under optimal conditions was measured using liquid chromatography/tandem mass spectrometry (LC/MS/MS). The GirT solution was sprayed on the seed sections, and then MALDI-IMS was performed. Results The optimal TFA concentration and spray volume were 2% and 500 μL, respectively. The OTCD efficiency rates were 61 ± 10% for ABA and 45 ± 5% for OPDA. The peaks corresponding to GirT-derivatized ABA (ABA-GirT) and OPDA (OPDA-GirT) standards were detected on the optimal OTCD-treated seed sections. ABA-GirT was mainly distributed in the embryo, while OPDA-GirT was localized in the external structures. These results are in agreement with our previously published results. Conclusions Our results show that ABA and OPDA in the immature seeds were exactly visualized using OTCD with GirT in MALDI-IMS. Therefore, OTCD with GirT in MALDI-IMS is a promising technique for future research on the biological roles of ABA and OPDA in various immature seeds.
Why it matches plant phenotyping methods植物組織内のホルモン分布という生理状態を可視化するMALDI-IMS法を中心に、誘導体化条件の最適化と効率評価、空間検証を行っているため。
abstractwe used on-tissue chemical derivatization (OTCD) with the derivatization reagent Girard's T (GirT) in MALDI-IMS to visualize ABA and OPDA
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Phenotyping traits in large field crop trials with numerous breeding lines is an arduous task. Unmanned aerial vehicle (UAV) based remote sensing is currently being investigated for high-throughput agricultural field phenotyping applications. The system is conducive for rapid assessment of crop response to the environment, at a desired spatio-temporal resolution. Therefore, objective of this study was to evaluate such technology towards monitoring responses of dry bean lines to drought and low nitrogen stress (i.e., two trials and two seasons) under field conditions. A semi-automated image processing protocol was developed to extract features such as: (i) average green normalized difference vegetation index (GNDVI); and (ii) canopy area (total number of plant pixels) from individual plots. The data were acquired at mid-pod fill and late-pod fill growth stages in 2014 season, and at flowering, mid-pod fill, and late-pod fill growth stages in 2015 season. The relationships between remotely sensed image features with that of crop response variables such as seed yield, days to flowering, days to harvest maturity, days to seed fill, and biomass rating (for drought trial only) were assessed temporally. Overall, in drought experiment, both average GNDVI and canopy area were significantly correlated with seed yield in all trials at 5% level of significance. The average GNDVI and canopy area at flowering growth stages and average GNDVI at mid-pod fill stage were consistently highly correlated (r > 0.73) with seed yield. The average GNDVI at flowering (r of −0.54 to −0.73) and mid-pod fill (r of −0.52 to −0.73) stages was highly correlated with biomass rating. Thus, average GNDVI could possibly be used as a viable phenotype for capturing biomass differences as well. A pilot thermal imaging of the sample breeding plots in drought trials also indicated its potential in capturing the temperature differences resulting from stress. For the nitrogen stress experiment, the correlations between remotely sensed image features and response variables were lower than in the drought experiment. The nitrogen from vegetative growth did not effeciently partition into seed production, which could have resulted in low correlations.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像を用いた圃場フェノタイピング基盤を評価し、半自動画像処理で個別区画のGNDVIとキャノピー面積を抽出しているため、植物形質取得法が研究の中心です。
abstractUnmanned aerial vehicle (UAV) based remote sensing is currently being investigated for high-throughput agricultural field phenotyping applications.
Common beanLeafGrowth / development / phenologyLeaf traits
Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.
Why it matches plant phenotyping methods葉面積・質量の成長軌跡から品種別成長パラメータを推定し、QTLマッピングへ統合する統計手法が研究の中心であり、植物形質の計算的推定法に該当する。
abstractWe implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework.
Common beanSoybeanRaman / spectroscopyTissuePhysiological trait estimation
Nitric oxide (NO) is a signaling molecule with multiple functions in plants. Given its critical importance and reactivity as a gaseous free radical, we have examined NO production in legume nodules using electron paramagnetic resonance (EPR) spectroscopy and the specific fluorescent dye 4,5-diaminofluorescein diacetate. Also, in this context, we critically assess previous and current views of NO production and detection in nodules. EPR of intact nodules revealed that nitrosyl-leghemoglobin (Lb2+NO) was absent from bean or soybean nodules regardless of nitrate supply, but accumulated in soybean nodules treated with nitrate that were defective in nitrite or nitric oxide reductases or that were exposed to ambient temperature. Consequently, bacteroids are a major source of NO, denitrification enzymes are required for NO homeostasis, and Lb2+NO is not responsible for the inhibition of nitrogen fixation by nitrate. Further, we noted that Lb2+NO is artifactually generated in nodule extracts or in intact nodules not analyzed immediately after detachment. The fluorescent probe detected NO formation in bean and soybean nodule infected cells and in soybean nodule parenchyma. The NO signal was slightly decreased by inhibitors of nitrate reductase but not by those of nitric oxide synthase, which could indicate a minor contribution of plant nitrate reductase and supports the existence of nitrate- and arginine-independent pathways for NO production. Together, our data indicate that EPR and fluorometric methods are complementary to draw reliable conclusions about NO production in plants.
Why it matches plant phenotyping methodsマメ科根粒のNO状態を測定するEPRと蛍光プローブを比較・検証し、アーティファクトや検出の信頼性まで評価しており、植物状態の取得方法が研究の中心である。
abstractwe have examined NO production in legume nodules using electron paramagnetic resonance (EPR) spectroscopy and the specific fluorescent dye 4,5-diaminofluorescein diacetate.
This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630-850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440-850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.
Why it matches plant phenotyping methods葉・キャノピーのハイパースペクトル反射から植物病害の重症度を推定する手法を開発・評価しており、病害表現型の取得と推定が研究の中心である。
abstractA system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra.
Common beanField / plotMultispectral / hyperspectralSeed / grainPhysiological trait estimation
The cooking time of dry bean varies widely by genotype and is also influenced by the growing environment, storage conditions, and cooking method. Thus high‐throughput phenotyping methods to assess cooking time would be useful to breeders interested in developing cultivars with a desired cooking time. The objective of this study was to evaluate the performance of hyperspectral imaging technology for predicting dry bean cooking time. Fourteen dry bean genotypes with a wide range of cooking times were grown in five environments over 2 yr. Hyperspectral images were taken from whole dry seeds, and partial least squares regression models based on the extracted hyperspectral image features were developed to predict water uptake and cooking time of soaked and unsoaked beans. Relatively good predictions of water uptake were obtained, as measured by the correlation coefficient for prediction ( R pred = 0.789) and standard error of prediction (SEP = 4.4%). Good predictions of cooking time for soaked beans (ranging between 19.9–95.5 min) were achieved giving R pred = 0.886 and SEP = 7.9 min. The prediction models for the cooking time of unsoaked beans (ranging between 80–147 min) were less robust and accurate ( R pred = 0.708, SEP = 10.6 min). This study demonstrated that hyperspectral imaging technology has potential for providing a nondestructive, simple, fast, and economical means for estimating the water uptake and cooking time of dry bean. Moreover, a totally independent set of 110 similar dry bean samples confirmed the suitability of the technique for predicting cooking time of soaked beans after updating the partial least squares model with 20 of the new samples, giving R pred = 0.872 and SEP = 3.7 min. However, due to the genotypic and phenotypic variability of water absorption and cooking time in dry bean, periodical updates of these prediction models with more samples and new bean accessions, as well as testing other multivariate prediction methods, are needed for further improving model robustness and generalization.
Why it matches plant phenotyping methods乾燥豆の調理時間・吸水量という植物（種子）形質を、ハイパースペクトル画像と回帰モデルで非破壊推定し、独立サンプルで性能検証しているため、フェノタイピング手法が中心です。
abstractThus high‐throughput phenotyping methods to assess cooking time would be useful to breeders interested in developing cultivars with a desired cooking time.
Flooding is a devastating abiotic stress that endangers crop production in the twenty-first century. Because of the severe susceptibility of common bean ( Phaseolus vulgaris L.) to flooding, an understanding of the genetic architecture and physiological responses of this crop will set the stage for further improvement. However, challenging phenotyping methods hinder a large-scale genetic study of flooding tolerance in common bean and other economically important crops. A greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages. The Middle-American diversity panel ( n = 272) of common bean was developed to capture most of the diversity exits in North American germplasm. This panel was evaluated for seven traits under both flooded and non-flooded conditions at two early developmental stages. A subset of contrasting genotypes was further evaluated in the field to assess the relationship between greenhouse and field data under flooding condition. A genome-wide association study using ~150 K SNPs was performed to discover genomic regions associated with multiple physiological responses. The results indicate a significant strong correlation ( r > 0.77) between greenhouse and field data, highlighting the reliability of greenhouse phenotyping method. Black and small red beans were the least affected by excess water at germination stage. At the seedling stage, pinto and great northern genotypes were the most tolerant. Root weight reduction due to flooding was greatest in pink and small red cultivars. Flooding reduced the chlorophyll content to the greatest extent in the navy bean cultivars compared with other market classes. Races of Durango/Jalisco and Mesoamerica were separated by both genotypic and phenotypic data indicating the potential effect of eco-geographical variations. Furthermore, several loci were identified that potentially represent the antagonistic pleiotropy. The GWAS analysis revealed peaks at Pv08/1.6 Mb and Pv02/41 Mb that are associated with root weight and germination rate, respectively. These regions are syntenic with two QTL reported in soybean ( Glycine max L.) that contribute to flooding tolerance, suggesting a conserved evolutionary pathway involved in flooding tolerance for these related legumes.
Why it matches plant phenotyping methods洪水耐性を評価する温室フェノタイピングプロトコルを開発し、圃場データとの相関で信頼性を検証しており、表現型取得法が研究の中心である。
abstractA greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe phenotypic responses of seven traits were measured in both non-flooded and flooded conditions (Supplementary Material, Data Sheet 1).Open asset ↗lines:55-103Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.
Why it matches plant phenotyping methods豆の草姿を形態形質からANNで分類・予測する手法が研究の中心で、年次データによる訓練・検証と予測性能評価を実施している。
abstractThe objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture.
The two-spotted spider mite (Tetranychus urticae Koch; TSSM) feeds on the under-surface of leaves, piercing the chloroplast-containing cells and affecting pigments as well as leaf structure. This damage could be spectrally detectable in the visible and near-infrared spectral regions. The aim was to spectrally explore the ability to assess TSSM damage levels in greenhouse-grown pepper (Capsicum annuum) and bean (Phaseolus vulgaris) leaves. Several vegetation indices (VIs) provided the ability to classify early TSSM damage using a one-way analysis of variance. Hyperspectral (400–1000 nm) and multispectral (five common bands) data were analysed and cross-validated independently by partial least squares-discriminant analysis models. These analyses resulted in 100% and 95% success in identifying early damage with hyperspectral data reflected from pepper and bean leaves, respectively, and in 92% with multispectral data reflected from pepper leaves. Although the TSSM activity occurred on the underside of leaves their damage can be spectrally detected by reflected data from the upper side. Early TSSM damage identification to greenhouse pepper and bean leaves, that their sole damage was by TSSM, can be obtained by VIs, hyperspectral data, and multispectral data. This study shows that by using sub leaf spatial resolution early damage by TSSM can be spectrally detected. It can be potentially applied for greenhouses as well as fields as an early detection method for TSSM management.
Why it matches plant phenotyping methods葉のスペクトル反射からハダニ被害という植物状態を検出・分類する手法を、ハイパースペクトル／マルチスペクトルデータと検証済みモデルで評価しており、表現型取得が中心です。
abstractThe aim was to spectrally explore the ability to assess TSSM damage levels in greenhouse-grown pepper (Capsicum annuum) and bean (Phaseolus vulgaris) leaves.
Seed shattering in crops is a key domestication trait due to its relevance for seed dispersal, yield, and fundamental questions in evolution (e.g., convergent evolution). Here, we focused on pod shattering in common bean ( Phaseolus vulgaris L.), the most important legume crop for human consuption in the world. With this main aim, we developed a methodological pipeline that comprises a thorough characterization under field conditions, including also the chemical composition and histological analysis of the pod valves. The pipeline was developed based on the assumption that the shattering trait itself can be treated in principle as a "syndrome" (i.e., a set of correlated different traits) at the pod level. We characterized a population of 267 introgression lines that were developed ad-hoc to study shattering in common bean. Three main objectives were sought: (1) to dissect the shattering trait into its "components," of level (percentage of shattering pods per plant) and mode (percentage of pods with twisting or non-twisting valves); (2) to test whether shattering is associated to the chemical composition and/or the histological characteristics of the pod valves; and (3) to test the associations between shattering and other plant traits. We can conclude the following: Very high shattering levels can be achieved in different modes; shattering resistance is mainly a qualitative trait; and high shattering levels is correlated with high carbon and lignin contents of the pod valves and with specific histological charaterstics of the ventral sheath and the inner fibrous layer of the pod wall. Our data also suggest that shattering comes with a "cost," as it is associated with low pod size, low seed weight per pod, high pod weight, and low seed to pod-valves ratio; indeed, it can be more exaustively described as a syndrome at the pod level. Our work suggests that the valve chemical composition (i.e., carbon and lignin content) can be used for a high troughput phenotyping procedures for shattering phenotyping. Finally, we believe that the application of our pipeline will greatly facilitate comparative studies among legume crops, and gene tagging.
Why it matches plant phenotyping methodsインゲンマメの莢裂開という植物形質を対象に、圃場評価・化学分析・組織学的解析を組み合わせた方法論的パイプラインを開発し、高スループット表現型解析への応用可能性を示しているため。
abstractwe developed a methodological pipeline that comprises a thorough characterization under field conditions, including also the chemical composition and histological analysis of the pod valves.
Common beanMaizeMultispectral / hyperspectralLeafPhysiological trait estimationLeaf traits
Rapid nondestructive measurements at leaf level of nitrogen concentration (%N) and leaf mass per area (LMA) are needed to improve crop simulation model development and calibration, and better understanding of in-season N management. Many contact reflectance-based techniques for %N and LMA estimations require calibration across species, cultivars, growing stages, and cultural practices. Narrowband (hyperspectral) reflectance spectroscopy, in combination with partial least square regression (PLSR) models, offers improved performance over vegetation indices derived from standard linear regression analysis with simple ratios or combined formulas. Little research on the application of contact spectroscopy data and PLSR techniques has been conducted for sweet corn (Zea mays L.) and snap bean (Phaseolus vulgaris L.). In this study, we sought to determine the optimum wavelength ranges for %N and LMA estimations and to develop and evaluate spectroscopic models in estimating %N and LMA in the two species. Best PLSR predictions utilized 1500 to 2400 nm for %N estimations and 450 to 2400 nm for LMA estimations in both species, with high averaged coefficient of determination (R²) values (0.90–0.95 for cross-validation, 0.52–0.71 for external validation) and low root mean square error (RMSE, reported as percentage of sample data range) (4.59–5.67% for cross-validation, 9.50–18.46% for external validation). The results indicate that narrowband reflectance spectroscopy (450–2500 nm) combined with PLSR analysis is a promising method for rapid and nondestructive estimates of %N and LMA at leaf level in sweet corn and snap bean across growth stages, N management, and years.
Why it matches plant phenotyping methods葉の窒素濃度とLMAという植物形質を、反射分光とPLSRで非破壊推定するモデルを開発・評価しており、取得・抽出手法が研究の中心である。
abstractwe sought to determine the optimum wavelength ranges for %N and LMA estimations and to develop and evaluate spectroscopic models in estimating %N and LMA in the two species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Common beanCowpeaField / plotLaboratory / benchtopRootMorphology / geometry measurementRoot system architecture
Low phosphorus (P) availability and drought are primary constraints to common bean and cowpea production in developing countries. Genetic variation of particular root architectural phenes of common bean is associated with improved acquisition of water and phosphorus. Quantitative evaluation of root architectural phenotypes of mature plants in the field is challenging Nonetheless, in situ phenotyping captures responses to environmental variation and is critical to improving crop performance in the target environment. The objective of this study was to develop flexible high-throughput root architectural phenotyping platforms for bean and cowpea, which have distinct but comparable root architectures. The bean phenotyping platform was specifically designed to scale from the lab to the field. Initial laboratory studies revealed cowpea does not have basal root whorls so the cowpea phenotypic platform was taken directly to field evaluation. Protocol development passed through several stages including comparisons of lab to field quantification systems and comparing manual and image-based phenotyping tools of field grown roots. Comparing lab-grown bean seedlings and field measurements at pod elongation stage resulted in a R2 of 0.66 for basal root whorl number (BRWN) and 0.92 for basal root number (BRN) between lab and field observations. Visual ratings were found to agree well with manual measurements for 12 root parameters of common bean. Heritability for 51 traits ranged from zero to eighty-three, with greatest heritability for BRWN and least for disease and secondary branching traits. Heritability for cowpea traits ranged from 0.01 to 0.80 to with number of large hypocotyl roots (1.5A) being most heritable, nodule score (NS) and tap root diameter at 5cm (TD5) being moderately heritable and tap root diameter 15cm below the soil level (TD15) being least heritable. Two minutes per root crown were required to evaluate 12 root phene descriptors manually and image analysis required 1h to analyze 5000 images for 39 phenes. Manual and image-based platforms can differentiate field-grown genotypes on the basis of these traits. We suggest an integrated protocol combining visual scoring, manual measurements, and image analysis. The integrated phenotyping platform presented here has utility for identifying and selecting useful root architectural phenotypes for bean and cowpea and potentially extends to other annual legume or dicotyledonous crops.
Why it matches plant phenotyping methodsマメ科作物の根系形態を対象に、圃場対応のハイスループット表現型解析プラットフォームを開発し、手動・画像ベース手法、実験室と圃場測定を比較検証しているため、表現型取得法が中心である。
abstractThe objective of this study was to develop flexible high-throughput root architectural phenotyping platforms for bean and cowpea