Drought stress severely limits foxtail millet yield and quality, yet current drought-resistance indices are exclusively yield-oriented and ignore grain-filling quality. Our two-year (2024–2025) experiments with 24–48 varieties revealed that yield and blighted grain rate (BGR) are partially decoupled (e.g., Zhangzagu 18: yield 2307 kg/ha, BGR 0.444; Zhonggu 19: yield 1622 kg/ha, BGR 0.280). We therefore constructed the Yield–Quality Synergy Index (YQSI = DYI − BGR), which penalizes varieties with poor grain filling. The YQSI tied for first place with DYI in comprehensive screening performance and achieved the highest inter-annual stability (Spearman ρ = 0.823, Jaccard = 0.438, composite score = 1.261). Sensitivity analysis confirmed robustness of the equal-weight formula across a 4-fold range of quality-penalty weights. Six strongly drought-resistant germplasms with balanced yield and quality were identified. Using UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening. Feature-importance analysis highlighted NDVI, WDRVI, and red-edge texture as key predictors. This study provides a quality-constrained drought-resistance evaluation framework and demonstrates the potential of UAV-based high-throughput phenotyping for foxtail millet breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データから干ばつ耐性指標を予測する高スループット表現型解析手法が研究の中心であり、モデル性能も評価している。
abstractUsing UAV multimodal data (RGB, multispectral, and thermal infrared) acquired during grain filling, a Random Forest model predicted a YQSI with overall R2 = 0.819 and an F1 score of 0.933 for variety screening.
Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.
Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。
abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.
Accurate prediction of foxtail millet yield is essential for effective field management and high-throughput breeding. Despite advances in UAV-based yield prediction for major crops, existing studies predominantly rely on single-temporal features (SFs) extracted at noon, overlooking significant diurnal dynamic signals that characterize crop responses to water stress. To address this research gap, we propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy under different irrigation regimes. During the flowering and grain-filling stages, UAV images were acquired across eight time slots (T1–T8) within a single day to capture the complete diurnal trajectory of canopy physiological responses. SFs were extracted at each time slot, and CFs were derived through summation, averaging, and range operations across multiple slots. A systematic four-step workflow was developed to determine the optimal UAV flight frequency and timing by balancing prediction accuracy with operational costs. Three ensemble learning algorithms (Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost)) were evaluated using multiple feature sets incorporating SFs, CFs, and their integration. Results demonstrated that CFs more comprehensively captured dynamic crop responses to water stress than SFs. Canopy features from afternoon combinations generally exhibited stronger yield correlations than morning combinations. The [T5, T8] combination was identified as optimal, providing a practical balance between prediction accuracy and operational cost. Model comparison revealed that RF exhibited greater robustness across different water treatments, whereas AdaBoost achieved higher accuracy on the test set. Feature importance analysis confirmed the dominance of CFs, with ∑VSWI ranking first across both models and growth stages. This study provides a systematic framework for utilizing diurnal dynamic signals in crop yield prediction, offering new methodological insights for precision agriculture and high-throughput phenotyping of foxtail millet and other dryland crops.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から作物特徴量を抽出し、収量という植物形質を推定する手法と、撮影頻度・時刻を最適化するワークフローが研究の中心であるため。
abstractwe propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy
MilletNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Finger millet ( Eleusine coracana (L.) Gaertn.) is a small seeded cereal known for its health benefits and its ability to grow in water-limited and saline environments. Despite its health and adaptation advantages, there is no standardized BBCH scale developed for finger millet, precluding standardization of a defined growth and development scale. The initial step in any breeding program and related research, is the precise, consistent and standardized description of crop growth stages. Therefore, we present a standardized phenological scale to describe and compare different stages of growth and development. In this study, we used four-finger millet accessions from the U.S.D.A. National Plant Germplasm System (NPGS) collection. Using a standardized BBCH chart as the baseline, we describe nine main stages of development to provide a simplified and user-friendly phenological growth staging scale for finger millet. A novel feature of this study was the use of Neural Radiance Fields (NeRF) to generate three-dimensional (3-D) renderings that align growth stages with 3-D imaging, paving the way for future phenological staging studies to incorporate 3-D visualization as a tool for standardization, researcher training, and reduction of observer bias across environments.
Why it matches plant phenotyping methods指のキビの生育段階を標準化する手法を開発し、NeRFによる3D画像化を生育ステージ判定・標準化に組み込んでいるため、植物フェノタイピング手法が中心である。
titlePhenological growth stages of Finger millet (Eleusine coracana (L.)) using 3-D phenotyping with relevance to breeding applications
Pearl millet is an important crop in arid regions, but its yield is reduced by foliar diseases like Downy Mildew and Rust. Traditional and deep learning methods struggle with accurate lesion detection, severity estimation, and robustness under complex field conditions, and often lack interpretability for practical agricultural deployment. To address these challenges, this study proposes the Adaptive Severity-Aware Swin Attention Network (ASA-SAN), an integrated framework designed for disease segmentation, classification, and severity estimation in pearl millet leaves. The proposed architecture combines a Swin Transformer encoder for hierarchical feature extraction with a ResUNet++ decoder for accurate lesion segmentation. This is further enhanced using Adaptive Channel Attention to improve feature discrimination and a dual-stream classification network to jointly capture local lesion characteristics and global contextual information. Additionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations. Experimental evaluations conducted on a pearl millet leaf dataset demonstrate that the proposed method achieves a Dice score of 97.8%, IoU of 95.6%, classification accuracy of 98.3%, and F1-score of 98.2%, outperforming several state-of-the-art methods. Furthermore, Grad-CAM visualizations enhance model interpretability by highlighting disease-relevant regions. Overall, the ASA-SAN framework provides a robust, interpretable, and severity-aware solution for automated pearl millet disease analysis, enabling early detection and supporting precision agriculture practices for improved crop protection and yield optimization.
Why it matches plant phenotyping methods真珠粟葉の病斑を画像から分割・分類し、病害重症度を定量推定する手法を中心に開発・評価しているため、植物表現型計測手法として含める。
abstractAdditionally, an Adaptive Disease Severity Index (ADSI) is introduced to quantitatively assess disease progression based on lesion area ratio, color degradation, edge irregularity, and texture variations.
Reproduction assets foundThe paper's phenotyping inputs are drawn from a public, open-access image dataset: the Pearl Millet Leaf Disease dataset (Version 2) hosted on Roboflow Universe, containing annotated images of Downy Mildew, Rust, and healthy pearl millet leaves. This is a paper-specific, publicly available asset directly used for the作者Dataset · publicThe dataset used in this research was taken from the publicly available open-access Pearl
Millet Leaf Disease dataset hosted on Roboflow Universe, which has images of Downy
Mildew, Rust and healthy pearl millet leaves annotated publicly available [26]. To ensure
experimental consistency and reproducibility, all experiments were conducted with Version
2 of the open access dataset.Open asset ↗Roboflow Universepdf-raw-page:10 lines:1-28Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pearl millet is a vital crop in arid and semi-arid regions, but its productivity is significantly impacted by fungal and bacterial diseases. Traditional disease detection methods, including manual inspection and conventional machine learning techniques, often suffer from inefficiencies due to subjectivity, time consumption, and limited feature extraction capabilities. Deep learning-based segmentation models such as U-Net, SegNet, DeepLabV3, and FCN have been employed to automate disease identification, but they exhibit limitations in accurately localizing disease-affected regions, handling complex spatial patterns, and maintaining high segmentation precision. To address these challenges, this study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection. The novel framework enhances disease localization by utilizing hierarchical multi-scale feature learning, where low, mid, and high-level features contribute to superior classification accuracy. The attention mechanism prioritizes critical disease regions, reducing false positives, while Mask R-CNN enables pixel-wise segmentation, refining disease boundary identification. The proposed model is implemented using Python and TensorFlow with high-resolution image datasets. Performance evaluation demonstrates HAMNet-Mask R-CNN achieving a 99.35% Dice score, 98.80% IoU, 99.75% Precision, 99.78% Recall, and 99.65% Accuracy, outperforming U-Net (95.03% Dice, 90.53% IoU), SegNet (94.58% Dice, 89.73% IoU), DeepLabV3 (98.31% Dice, 96.69% IoU) and FCN(98.47% Dice score, 97.00% IoU). The results confirm the model's superiority in disease segmentation and classification, offering a robust and scalable solution for real-time disease monitoring in smart agriculture. The integration of hierarchical attention and spectral-spatial fusion significantly enhances detection accuracy, ensuring reliable disease identification and contributing to improved agricultural productivity.
Why it matches plant phenotyping methods真珠粟の病変領域を画像から画素単位で抽出・分類する深層学習手法を開発し、既存モデルと性能比較しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study proposes HAMNet-Mask R-CNN, an AI-powered hierarchical multi-scale attention network integrated with a spectral-spatial fusion module for precise pearl millet disease detection.
Reproduction assets foundThe paper's authors explicitly state that the custom HAMNet–Mask R-CNN implementation code (preprocessing, training, evaluation, visualization) is publicly available on GitHub. The pearl millet leaf image dataset used as phenotyping input is also publicly hosted on Roboflow Universe and cited as the data source. The roCode · publicThe custom code developed for the implementation of the proposed HAMNet–Mask R-CNN framework, including data preprocessing, model training, evaluation, and visualization scripts, is publicly available in a GitHub repository. The code can be accessed at: https://github.com/ramyalaksha/Hamnet.gitOpen asset ↗https://github.com/ramyalaksha/Hamnet.gitlines:267-297Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。
abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.
Introduction Pearl millet is a high nutritional cereal recognised for its agro-climatic resilience, making it relevant for food security under climate change scenarios. Phenotypic traits are indicative of crop performance, stability and adaptability, yet the potential of metabolomics to predict these traits has not been explored. Objectives This study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models. Methods Grains metabolic profiles were obtained using untargeted UHPLC-LTQ-Orbitrap-HRMS. Phenotypic data were sourced from standardised evaluations conducted by Embrapa across different years and field trials within the Sete Lagoas experimental station (Minas Gerais, Brazil). Generalised linear modelling with penalisation (GLM) and Random Forest was applied to explore the correlation between metabolism and 21 phenotypic traits. Results GLM successfully predicted eight qualitative and seven quantitative traits. Prediction accuracy was higher for qualitative traits, reflecting their comparatively simpler genetic architecture, whereas quantitative traits also achieved satisfactory performance (R² ≥ 0.6). Key predictors included phenolic compounds, amino acids, fatty acids, and carbohydrates. Notably, several associations corresponded to metabolites involved in nitrogen metabolism and vegetative growth, underscoring biologically meaningful links between metabolic profiles and trait variation. Conclusions This exploratory study presents the first metabolome characterisation of a pearl millet germplasm bank, coupled with predictive modelling of phenotypic traits. However, our findings are constrained by the single-environment design and the absence of population-structure assessment. To establish the stability and biological relevance of these results, future work should incorporate multi-environment trials and pathway-level analyses accounting for population structure.
Why it matches plant phenotyping methodsメタボロームを入力として機械学習で植物の表現型形質を予測し、複数形質で予測精度を評価しているため、単なる生物学的測定ではなく形質推定手法の検証が中心です。
abstractThis study aimed to identify metabolite-trait associations in the Brazilian germplasm core collection, comprising 203 pearl millet genotypes, by combining untargeted metabolomics with machine-learning models.
Reproduction assets foundThe paper deposits its metabolomics and phenotypic metadata in a public repository (Recherche Data Gouv, DOI 10.57745/GU6WDG). No author analysis code or trained model deposit is stated; supplementary materials are not linked to a qualifying URL.Dataset · public.623/2023; 26/210.152/2023; 26/201.317/2022), National Council for Scientific and Technological Development (CNPq) (407350/2023-3; 314100/2023-7), Coordination for Improvement of Personnel with Higher Education (CAPES) (financial code 001).
Data availability
The metabolomics and metadata reported in this paper are available via https://doi.org/10.57745/GU6WDG.
Declarations
Competing interests
The authors declare no competing interests.
References
Alonso-Blanco C Méndez-Vigo B
Genetic architecture of naturally occurring quantitative traits in plants: An updated synthesis
Current Opinion in Plant Biology 2014 18 37 43
10.1016/j.pbi.2014.01.002
24565952
Alonso-Blanco, C., & Méndez-VigoOpen asset ↗10.57745 · GU6WDGlines:121-160Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
ArabidopsisMaizeMilletRootPhysiological trait estimationRoot system architecture
Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method “cut and flow” is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.
Why it matches plant phenotyping methods根系の水理特性を解析・予測し、表現型化するモデルとオープンソースソフトウェアを開発・提示しており、植物フェノタイピング手法が中心である。
abstractHydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA.
MilletPeaField / plotGreenhouseNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstructionVisualization / data managementGrowth / development / phenology
Plant phenotyping in precision agriculture increasingly requires high-fidelity three-dimensional reconstruction and accessible visualization methods. This study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages. We collected multi-view imagery of finger millet, proso millet, mungbean, and field pea under controlled greenhouse conditions, aligning data acquisition with standardized BBCH phenological scales. Camera pose estimation was performed using GLOMAP, followed by reconstruction via both Nerfacto and G-Splat implementations. Quantitative evaluation using PSNR, SSIM, and LPIPS metrics revealed complementary strengths of the two approaches: G-Splat achieved superior structural fidelity, while NeRF provided enhanced perceptual realism. Both reconstruction methods were successfully integrated into an immersive VR greenhouse environment deployed on Meta Quest headsets, maintaining consistently high framerates. This framework establishes a practical foundation for incorporating neural reconstruction and immersive technologies into agricultural phenotyping workflows, supporting both research applications and educational engagement.
Why it matches plant phenotyping methods植物の多視点画像からNeRFと3D Gaussian Splattingで3D形状を再構成し、画質指標で比較評価する統合フェノタイピング基盤の開発・検証が中心である。
abstractThis study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages.
MilletSorghumRootTissueSegmentationRoot system architecture
Root anatomical features are critical for plant performance characterization, yet phenotyping at the anatomical scale remains limited by the extreme annotation burden of cellular segmentation. We present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions. Our approach decomposes multi-class segmentation into species-agnostic tissue identification followed by tissue type classification. By designing robust input representations invariant to imaging artifacts and morphological variations, our framework enables rapid adaptation to new species with fewer than 40 labeled images. Additionally, the first stage automatically generates tissue boundaries, transforming tedious manual tracing into simple tissue labeling. We validate our method on pearl millet, and sorghum root cross-sections from different imaging protocols, achieving state-of-the-art performance while dramatically reducing deployment time. This efficiency breakthrough enables scalable root phenotyping across diverse crop species, accelerating the development of climate-resilient varieties for global food security.
Why it matches plant phenotyping methods植物根の解剖学的形質を対象とする画像セグメンテーション手法を開発し、複数種・撮像条件で検証しているため、方法が研究の中心である。
abstractWe present a two-stage segmentation framework that greatly reduces annotation requirements while maintaining high accuracy across diverse plant species and imaging conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the annotated root image dataset (Zenodo 17726414), trained segmentation models (Zenodo 17737703), and the authors' source code (GitHub janetkok/Root-Segmentation-Beyond-Species-Boundaries), all directly reproducing this paper's root anatomical phenotyping andDataset · publicThe dataset and models are available at https://doi.org/10.5281/zenodo.17726414 and https://doi.org/10.5281/zenodo.17737703 , respectively.Open asset ↗Zenodo · 10.5281/zenodo.17726414lines:242-251Code · publicThe source code is hosted at https://github.com/janetkok/Root-Segmentation-Beyond-Species-Boundaries .Open asset ↗GitHub · janetkok/Root-Segmentation-Beyond-Species-Boundarieslines:242-251Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Clustering algorithms are widely used for phenotypic characterization and germplasm management, particularly in data-scarce crops such as neglected and underutilized species (NUS) that lack genomic resources. However, their performance under biologically realistic conditions remains poorly understood. Standard clustering methods commonly applied in crop research often assume distinct, isotropic, and homogeneous clusters, assumptions rarely satisfied in real-world phenotypic datasets. We developed a flexible and empirically calibrated simulation framework, using phenotypic data from West African fonio (Digitaria exilis), to benchmark the performance of eleven clustering algorithms under both idealized and realistic scenarios. Our simulations integrated heterogeneous trait distributions (normal, gamma), strong inter-trait correlations (up to r = -0.84), heteroscedasticity, and moderate population structure (mean Pst = 0.16 ± 0.001, achieved through iterative calibration). Each scenario was replicated 100 times, with clustering accuracy evaluated using external (ARI, NMI) and internal (Silhouette, Davies-Bouldin) validation metrics under standardized conditions. The results revealed consistently poor algorithm performance under realistic conditions (e.g., ARI < 0.07), including for widely used methods in Neglected and Underutilized Species (NUS) research such as K-means, GMM, and PAM. Notably, conventional validation metrics failed to detect biologically meaningful structure revealed by geometric diagnostics, highlighting a critical methodological limitation. Performance markedly improved under idealized conditions, validating our simulation framework. These findings highlight the risk of overinterpreting clustering outputs from weakly structured phenotypic datasets and expose key limitations in current biodiversity analysis practices, particularly those guiding plant genetic resource conservation programs. We provide an open-source R-based diagnostic tool, with parameter specifications to assist practitioners in selecting reproducible and interpretable clustering approaches for germplasm management and biodiversity assessment in data-scarce crops.
Why it matches plant phenotyping methods植物の表現型データを対象に、クラスタリング手法を現実的な条件でベンチマークするシミュレーション枠組みとR診断ツールを開発しており、表現型解析手法が研究の中心である。
abstractWe developed a flexible and empirically calibrated simulation framework, using phenotypic data from West African fonio (Digitaria exilis), to benchmark the performance of eleven clustering algorithms under both idealized and realistic scenarios.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete R simulation/clustering/evaluation script on Zenodo (DOI 10.5281/zenodo.15877863), a paper-specific, publicly actionable code asset. The empirical fonio trait data belong to a prior cited study (Bio et al.), not this paper, and supporting files/DOCode · publicthe complete R script used to simulate phenotypic datasets, apply clustering algorithms, and compute evaluation metrics is publicly available on Zenodo: https://doi.org/10.5281/zenodo.15877863Open asset ↗Zenodo · 10.5281/zenodo.15877863lines:107-122Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.
Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。
abstractpredicting the same using key spectral bands and machine learning (ML) techniques
Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built and tested in plants to demonstrate the proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors that can be expressed as a single transcript that are sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterisation method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pigmentation production for remote detection. Applying inducible anthocyanin production coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems.
Why it matches plant phenotyping methods植物の内生アントシアニン生成という可視的な生理状態を、ハイパースペクトル画像と識別アルゴリズムで非破壊検出する方法が研究の主要な技術的貢献であるため。
abstractUsing hyperspectral imaging and a discriminative target characterisation method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands.
Amylose content (AC) is a key determinant of the processing quality and genetic attributes of broomcorn millet ( Panicum miliaceum L. ). In this study, 134 broomcorn millet accessions from both domestic and international sources were systematically evaluated over two consecutive years (2022–2023) to characterize spectral differences among various processing forms (grain, millet, and flour) and grain colors, and to explore their relationships with AC. High-precision hyperspectral prediction models were developed and validated. The results showed that white-grained accessions exhibited the highest reflectance in the visible (400–780 nm) and near-infrared (1100–2450 nm) regions, whereas black-grained accessions showed the strongest absorption in the near-infrared region. Correlation analysis revealed a significant positive association between thousand-grain weight and AC (r = 0.211), while the length-to-width ratio was strongly and negatively correlated with yield (P < 0.01), suggesting that rounder grains are more conducive to high-yield breeding. Among the modeling approaches, the modified chlorophyll absorption ratio index (MCARI) combined with Savitzky–Golay (SG) preprocessing achieved the highest prediction accuracy for millet (validation set R 2 = 0.858, RPD = 2.0). The full-spectrum partial least squares (Full-PLS) model demonstrated the best overall predictive performance, with validation R 2 values of 0.615, 0.897, and 0.923 for grain, millet, and flour, respectively. This study provides an efficient and non-destructive approach for quality assessment and precision breeding in broomcorn millet, with potential applications in crop phenomics and digital agriculture. • The first three-element morphological spectral library of broomcorn millet was created. • The synergistic enhancement mechanism of round grains was discovered. • A Full-R-PLS universal model was constructed, with an R 2 of 0.923 in the flour validation set. • The MCARI-SG optimization algorithm was developed, achieving precise field prediction.
Why it matches plant phenotyping methodsヒパースペクトル計測と予測モデルを用いて穀粒・雑穀のアミロース含量を非破壊推定する手法を開発・検証しており、植物器官形質の取得方法が研究の中心である。
abstractHigh-precision hyperspectral prediction models were developed and validated.
Foxtail millet is a vital grain whose amino acid content affects nutritional quality. Traditional detection methods are destructive, time-consuming, and inefficient. This work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet. To address these limitations, this study developed a rapid, non-destructive approach for quantifying eight essential amino acids-lysine, phenylalanine, methionine, threonine, isoleucine, leucine, valine, and histidine-in foxtail millet (variety: Changnong No. 47) using near-infrared hyperspectral imaging. A total of 217 samples were collected and used for model development. The spectral data were preprocessed using Savitzky-Golay, adaptive iteratively reweighted penalized least squares, and standard normal variate. The key wavelengths were extracted using the competitive adaptive reweighted sampling algorithm, and four regression models-Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)-were constructed. The results showed that the key wavelengths selected by CARS account for only 2.03-4.73% of the full spectrum. BiLSTM was most suitable for modeling lysine (R 2 = 0.5862, RMSE = 0.0081, RPD = 1.6417). CNN demonstrated the best performance for phenylalanine, methionine, isoleucine, and leucine. SVR was most effective for predicting threonine (R 2 = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains.
Why it matches plant phenotyping methodsフォックステイルミレット粒のアミノ酸含量という植物器官の品質形質を、近赤外ハイパースペクトル画像と回帰モデルで非破壊推定する手法の開発が中心である。
abstractThis work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet.
MaizeMilletRiceSeed / grainMorphology / geometry measurementSegmentationRoot system architecture
Radicle length is a critical indicator of seed vigor, germination capacity, and seedling growth potential. However, existing measurement methods face challenges in automation, efficiency, and generalizability, often requiring manual intervention or re-annotation for different seed types. To address these limitations, this paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops. The method leverages the Segment Anything Model (SAM) as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids, which are then fed into SAM for precise segmentation of the seed coat and radicle. The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length. Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944, demonstrating excellent segmentation performance. Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error (MAE) was 0.273 mm and the coefficient of determination (R 2 ) was 0.982, confirming the method's high precision for rice. On the Otherseed dataset, the predicted radicle lengths for maize ( Zea mays ), pearl millet ( Pennisetum glaucum ), and rye ( Secale cereale ) are consistent with the observed radicle length distributions, demonstrating strong cross-species performance. These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice, with validated applicability to other crop species.
Why it matches plant phenotyping methodsイネを中心に複数作物の幼根長を画像から自動抽出・推定する手法を開発し、セグメンテーション性能と測定精度を検証しているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes an automated method, LenRuler, with a primary focus on rice seeds and validation in multiple crops.
Reproduction assets foundThe authors explicitly state that the LenRuler code and software are publicly available on GitHub, providing the paper's radicle-length phenotyping analysis pipeline (SAM-based segmentation, YOLO detection, Gaussian classification).Code · publicThe code and software are available on GitHub at https://github.com/cccccabbage/LenRuler .Open asset ↗cccccabbage/LenRulerlines:351-417Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published23 Aug 2025Journal of Computational Methods in Sciences and EngineeringCited by 0 · OpenAlex ↗
In order to improve the accuracy and efficiency of ideal strain design in cereal grains, an intelligent design system based on gene and phenome clustering of strain height module was constructed, integrating high-throughput SNP data processing, ReliefF gene feature screening, GMM clustering modeling and Bayesian optimization recommender mechanism, to realize the rapid evolution of the target strains in a three-layer heterogeneous structure. Through the validation of phenotype-gene joint tensor modeling and machine learning constructs on 1280 sets of grain samples, the system achieves an R 2 of 0.926, an MSE of 0.022, and a diversity score of 0.85 in plant height prediction, and the optimization module achieves a convergence rate of 96.8% within 150 rounds of iterations, and the recommendation paths have a strong convergence and exploratory ability in the latent space. The analysis concluded that the fine identification and high-dimensional clustering expression of the plant height module significantly enhanced the stability and genetic adaptability of ideal strain combinations, and promoted the modeling expression and intelligent practice of precision breeding in cereals.
Why it matches plant phenotyping methods遺伝子・表現型テンソルモデリングと機械学習により草丈を推定する計算手法および設計システムが研究の中心であり、表現型推定を検証している。
abstractan intelligent design system based on gene and phenome clustering of strain height module was constructed
Amidst health-conscious consumption trends, functional foods rich in gamma-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, naturally abundant in these compounds, plays vital roles in nervous system regulation, cellular metabolism, and fetal development. In this work, we introduced hyperspectral imaging for detecting these nutrients in millet, collecting spectral data from 190 samples across 19 varieties. Four "coarse-fine" wavelength screening strategies were innovatively proposed: interval spectral variables initially identified wavelengths with strong explanatory power, followed by model cluster analysis for precise optimization. A stacked BiLSTM-Adaboost model was built for efficient prediction. Results revealed significant variations in GABA and vitamin B9 content among varieties, offering crucial insights for breeding and quality assessment. The GABA prediction model achieved R2=0.5421, RMSE=13.9349, RPD=1.4778; the vitamin B9 model attained R2=0.8932, RMSE=4.6676, RPD=3.0600. This integration of technology and methodological innovation provides a novel pathway for rapid, non-destructive millet nutrient analysis, facilitating intelligent quality control, industry advancement, and functional food development.
Why it matches plant phenotyping methodsコムギではなくフォックステイルミレットの種子中GABA・ビタミンB9という植物器官の形質を、ハイパースペクトル画像と予測モデルで非破壊推定する方法の開発・評価が研究の中心である。
abstractwe introduced hyperspectral imaging for detecting these nutrients in millet, collecting spectral data from 190 samples across 19 varieties.
Published1 Jun 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Pearl millet, known for its nutritional excellence and climatic resilience, is becoming important in addressing food and nutritional security Current work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains. The model is quick, economic and non-destructive alternative to traditional methods, useful in advancing the single plant progenies for improving nutrient content in segregating generations. Spectra were acquired from 403 varied genotypes, and mathematical optimizations using derivatives were performed to enhance the models. The optimal configurations were "2,36,6,2" (order of derivatives, gap, first smoothing and second smoothing, respectively) for amylose, "2,32,6,2" for starch, "2,32,8,2" for oil and protein, and "3,36,6,2" for phytic acid. The models were refined using modified partial least squares (MPLS) regression on spectra processed to eliminate variations with standard normal variate (SNV) and detrending (DT) techniques. The adjusted MPLS models exhibited impressive coefficients of determination of 0.985, 0.984, 0.986, 0.969 and 0.993 for amylose, protein, oil, starch and phytic acid, respectively. The SEP(C) values for amylose (0.347), starch (0.732), protein (0.313), phytic acid (0.014), and oil (0.162) suggest variable levels of predictive precision. Validation with independent samples showed superior predictive performance with coefficients of determination values ranging from 0.878 for phytic acid to 0.976 for protein, minimal bias, high ratios of prediction to deviation (2.93–5.81), and no significant differences between the predicted and reference values (p > 0.05). These advanced Near-Infrared Spectroscopy models allow quick and cost-effective nutritional assessment of pearl millet germplasm and breeding lines, supporting biofortification initiatives and enhancing nutritional security.
Why it matches plant phenotyping methodsNIRSによる穀粒栄養成分の推定モデルを開発し、独立試料で予測性能を検証しており、遺伝資源・育種系統の形質取得が研究の中心である。
abstractCurrent work introduces Near Infrared Spectroscopy models to estimate nutrients in pearl millet grains.
Digital tools and non-destructive monitoring techniques are crucial for real-time evaluations of crop output and health in sustainable agriculture, particularly for precise above-ground biomass (AGB) computation in pearl millet ( Pennisetum glaucum ). This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. Smartphone-based RGB imaging was used for data collection, and Shapley additive explanations (SHAP) methodology evaluated predictor importance. The SHAP analysis identified Normalized Green-Red Difference Index (NGRDI) and plant height as the most influential features for AGB estimation. XGBoost achieved the highest accuracy (R 2 = 0.98, RMSE = 0.26) with a comprehensive feature set, while CNN-based models also showed strong predictive ability. Random Forest Regression performed best with the two most important features, whereas Support Vector Regression was the least effective. These findings demonstrate the effectiveness of CNNs and shallow machine learning for non-invasive AGB estimation using cost-effective RGB imagery, supporting automated biomass prediction and real-time plant growth monitoring. This approach can aid small-scale carbon inventories in smallholder agricultural systems, contributing to climate-resilient strategies.
Why it matches plant phenotyping methodsスマートフォンRGB画像から植物の地上部バイオマスを推定する画像・機械学習手法が研究の中心であり、特徴量比較と精度評価も実施しているため、植物フェノタイピング手法として含める。
abstractSmartphone-based RGB imaging was used for data collection
Accurate, high-throughput canopy phenotyping using UAV-based multispectral remote sensing is critically important for optimizing the management and breeding of foxtail millet in rainfed regions. This study integrated multi-temporal field measurements of leaf water content, SPAD-derived chlorophyll, and leaf area index (LAI) with UAV imagery (red, green, red-edge, near-infrared bands) across two sites and two consecutive years (2023 and 2024) in Shanxi Province, China. Various modeling approaches, including Random Forest, Gradient Boosting, and regularized regressions (e.g., Ridge, Lasso), were evaluated for cross-regional and cross-year extrapolation. Results showed that single-site modeling achieved coefficients of determination (R²) up to 0.95, with mean relative errors of 10%–15% in independent validations. When models were transferred between sites, R² generally remained between 0.50 and 0.70, although SPAD estimates exhibited larger deviations under high-nitrogen conditions. Even under severe drought in 2024, cross-year predictions still attained R² values near 0.60. Among these methods, tree-based models demonstrated strong capability for capturing nonlinear canopy trait dynamics, whereas regularized regressions offered simplicity and interpretability. Incorporating multi-site and multi-year data further enhanced model robustness, increasing R² above 0.80 and markedly reducing average prediction errors. These findings demonstrate that rigorous radiometric calibration and appropriate vegetation index selection enable reliable UAV-based phenotyping for foxtail millet under diverse environments and time frames. The proposed approach thus provides strong technical support for precision management and cultivar selection in semi-arid foxtail millet production systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から葉水分、クロロフィル、LAIを推定するモデルを開発・比較し、地域間・年次間で検証しており、植物形質取得手法が研究の中心である。
abstractAccurate, high-throughput canopy phenotyping using UAV-based multispectral remote sensing is critically important for optimizing the management and breeding of foxtail millet in rainfed regions.
In drier zones pearl millet faces moderate to severe drought stress at seedling stage leading to mortality and poor plant stand. An effective and rapid drought screening protocol as well as tolerant genotypes are required to address the issue. Hydroponics provide an uniform stress environment which we utilized in the study to create dehydration stress in pearl millet seedlings through root dehydration. Stress duration for 7 days @ 6 h/day differentiated the tolerant and susceptible genotypes. Pearl millet minicore collection of 207 genotypes were divided in to four groups based on seedlings traits measured under stress. Phenotypic response of seedlings to dehydration stress measured through drought score showed moderate correlation with quantum yield and fresh shoot weight. In all, 32 genotypes showed tolerant reaction to seedling drought stress. Both tolerant and susceptible genotypes identified under hydroponics performed at par under soil-pot conditions. Genotypes IP 3642, IP 20995, IP 10665, IP 1556, IP 2322, IP 8562, IP 18579, IP 8472, IP 5711, IP 277, IP 11268 showed better seedling growth parameters under rapid dehydration through hydroponics as well in progressive soil drying under pot conditions.
Why it matches plant phenotyping methodsハイドロポニクスによる幼苗脱水ストレスの迅速な耐 drought スクリーニングプロトコルを構築し、土壌ポット条件でも検証しており、表現型取得法が研究の中心である。
abstractAn effective and rapid drought screening protocol as well as tolerant genotypes are required to address the issue.
Plant diseases lead to productivity loss, although they may be controlled with ongoing observation. Examiners have struggled long with the challenge of accurately detecting plant diseases in grain crops, such as wheat, rice, maize, millet, and ragi. Traditional manual monitoring methods can be time-consuming and prone to errors. To address this issue, this study explores the application of deep learning techniques for automated and accurate disease identification. The proposed method begins with image pre-processing using a Hybrid Gaussian-Wiener filter to reduce noise in leaf images. Deep learning models are then implemented the Moore-Penrose pseudo-inverse Weighted Deep Ensemble Neural Networks (DENN) for common bacterial and fungal diseases, and the Squeeze-and-Excitation Vision Transformer (SEViT) for diseases caused by specific fungi. To improve efficiency and accuracy, transfer learning is employed. The resulting system achieves an impressive classification accuracy of 99.56%. This research demonstrates the potential of deep learning for plant disease detection, which could significantly benefit farmers and plant pathologists by enabling early disease identification and intervention.
Why it matches plant phenotyping methods葉画像から植物病害を自動分類する深層学習手法の開発・適用が研究の中心であり、植物の病害状態を直接推定しているため含める。
abstractthis study explores the application of deep learning techniques for automated and accurate disease identification
Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.
Why it matches plant phenotyping methodsフォックステイルミレット種子のCT画像から内部器官を分割し、形態形質を抽出する画像解析手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractThis paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds
BACKGROUND: graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation. RESULTS: This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH. CONCLUSION: The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.
Why it matches plant phenotyping methodsフォックステールミレットの生育形質を画像から抽出する高スループット画像システムと画像処理手法を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality.
Reproduction assets foundThe paper's authors explicitly state that the source code of their foxtail millet image processing program (used to extract phenotypic i-traits such as PH, CHA, SPA, compactness indices, and colour pixel features) is publicly available on GitHub. The MDSi database URL is a general transcriptomic resource, not a paper-Code · publicmation about plant health and physiological responses.
The program was created using the OpenCV library within the Microsoft Visual Studio (C++) environment. The extracted phenotypic data were converted to CSV file format for further analysis and storage. The source code of the program is available for download and reference at https://github.com/ScreenPlant/Foxtail-Millet-Image-Processing .
Analysis of i-traits throughout the entire growth periodOpen asset ↗ScreenPlant/Foxtail-Millet-Image-Processinglines:50-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
As a gluten-free cereal with high nutritional properties, pearl millet [Pennisetum glaucum (L.) R. Br.] has been increasingly regarded as an alternative dryland resilient food crop with enriched grain nutritional value. This paper explores the potential of single-kernel near-infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non-destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains. Samples harvested from two consecutive years (2021 and 2022) were evaluated under dryland and irrigated conditions in Kansas State University, Agricultural Research Center, Hays (ARCH), KS and were analyzed using SKNIR and conventional laboratory methods. Model calibrations were developed using partial least squares regression. Results showed satisfactory performance of models with standard errors cross-validation of 1.04%, 0.17%, 0.39%, 0.21%, and 0.16%, respectively, for protein, moisture, fat, fiber, and ash content. The findings suggest that SKNIR can be a potential tool for high-throughput pearl millet composition screening efficiently, which will assist breeders and grain processors to optimize grain properties and enhance the grain quality and products.
Why it matches plant phenotyping methods単粒NIR分光とPLS回帰による穀粒成分の非破壊・高スループット推定モデルを開発・検証しており、植物器官（穀粒）の形質取得が中心である。
abstractThis paper explores the potential of single-kernel near-infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non-destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains.
Early and accurate plant disease detection is crucial for sustainable agriculture. This research proposes an Ensemble Residual Sequence Net (ERSN) architecture, combining LSTM and ResNet-50, for classifying plant diseases in pearl millet. Targeting diseases like downy mildew, ergot, blast, rust, and smut, the model achieved high accuracy (94.7%), precision, recall, F I-score, specificity, and sensitivity. This innovative approach demonstrates the potential of deep learning in enhancing plant disease management and safeguarding crop yields.
Why it matches plant phenotyping methods画像に基づき植物病害を分類・検出する深層学習手法の開発と性能評価が主題であり、植物の病害状態を推定するフェノタイピング手法に該当する。
abstractThis research proposes an Ensemble Residual Sequence Net (ERSN) architecture, combining LSTM and ResNet-50, for classifying plant diseases in pearl millet.
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
Why it matches plant phenotyping methodsミレットにおけるフェノミクス手法とゲノミクスの統合を扱うレビューであり、高スループット表現型解析プラットフォームと形態・生理・性能データを中心的に論じている。
abstractThis review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures.
ABSTRACT Background Recent developments in hybridization chain reaction (HCR) have enabled robust simultaneous localization of multiple mRNA transcripts using fluorescence in situ hybridization (FISH). Once multiple split initiator oligonucleotide probes bind their target mRNA, HCR uses DNA base-pairing of fluorophore-labeled hairpin sets to self-assemble into large polymers, amplifying the fluorescence signal and reducing non-specific background. Few studies have applied HCR in plants, despite its demonstrated utility in whole mount animal tissues and cell culture. Our aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy. Results Application of HCR to 10 µm paraffin sections of 17-day-old Setaria viridis (green millet) inflorescences using confocal microscopy revealed that the transcripts of the transcription factor KNOTTED 1 ( KN1 ) were localized to developing floret meristem and vascular tissue while SHATTERING 1 ( SH1 ) and MYB26 transcripts were co-localized to the breakpoint below the floral structures (the abscission zone). We also used methacrylate de-embedment with 1.5 µm and 0.5 µm sections of 3-day-old Arabidopsis thaliana seedlings to show tissue specific CHLOROPHYLL BINDING FACTOR a/b ( CAB1 ) mRNA highly expressed in photosynthetic tissues and ELONGATION FACTOR 1 ALPHA ( EF1 α ) highly expressed in meristematic tissues of the shoot apex. The housekeeping gene ACTIN7 ( ACT7 ) mRNA was more uniformly distributed with reduced signals using lattice structured-illumination microscopy. HCR using 1.5 µm methacrylate sections was followed by backscattered imaging and scanning electron microscopy thus demonstrating the feasibility of correlating fluorescent localization with ultrastructure. Conclusion HCR was successfully adapted for use with both paraffin and methacrylate de-embedment on diverse plant tissues in two model organisms, allowing for concurrent cellular and subcellular localization of multiple mRNAs, antibodies and other affinity probe classes. The mild hybridization conditions used in HCR made it highly amenable to observe immunofluorescence in the same section. De-embedded semi-thin methacrylate sections with HCR were compatible with correlative electron microscopy approaches. Our protocol provides numerous practical tips for successful HCR and affinity probe labeling in electron microscopy-compatible, sectioned plant material.
Why it matches plant phenotyping methods植物組織で複数mRNAを局在化するHCR法を最適化し、異なる切片材料・モデル植物・顕微鏡法で実証した方法開発研究である。
abstractOur aim was to optimize this technique for sectioned plant tissues embedded with paraffin and methacrylate resins, and to test its utility in combination with immunolocalization and subsequent correlation with cell ultrastructure using scanning electron microscopy.
ABSTRACT: Foxtail millet (Setaria italica L.) is a cereal crop with potential as a functional food due to its high nutritional value and its wide adaptability to unfavorable environmental conditions. Seed-related traits of foxtail millet are difficult to observe due to its small size. Therefore, the development of an efficient and accurate method for characterizing foxtail millet seeds using digital imaging technology is essential. This study aimed to characterize the seed morphology for variability assessment of Indonesian local foxtail millet genotypes and to develop a model to estimate the 100-seed weight using ImageJ. A total of 28 Indonesian local foxtail millet genotypes were used in this study for seed morphology characterization and the development of the 100-seed weight estimation model. Foxtail millet genotypes from different regions in Indonesia exhibited diverse seed morphologies. The 100-seed weight estimation model (y = 0.123x - 0.0821 (R² = 0.9223)) demonstrated a highly significant positive correlation (r = 0.96, p < 0.01) between the predicted and actual 100-seed weights. The correlation coefficient from model validation was 0.8731 (p < 0.01), indicating that the obtained model could estimate the 100-seed weight of foxtail millet seeds in future studies.
Why it matches plant phenotyping methodsデジタル画像を用いた種子形態の取得と、ImageJによる100粒重推定モデルの開発・検証が研究の中心であり、植物形質の測定手法に該当する。
abstractTherefore, the development of an efficient and accurate method for characterizing foxtail millet seeds using digital imaging technology is essential.
Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm −2 ) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination ( R 2 ) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm −2 ) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm −2 ) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.
Why it matches plant phenotyping methodsPolSARセンサーデータと機械学習により、作物のバイオマスおよび草丈を推定し、回帰手法の性能を比較・評価しているため、表現型取得手法が中心である。
abstractThe potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated.
Reproduction assets foundThe paper's Data Availability Statement deposits two paper-specific public assets: the ground-truth field campaign dataset (GT Points, Zenodo 10.5281/zenodo.10403380) and the authors' RFR/MLR prediction code (Zenodo 10.5281/zenodo.10403352). Generic ESA software (PolSARpro, SNAP) is excluded as a general library.Dataset · publicEarth and Space Science
THULASIRAMAN ET AL.
10.1029/2022EA002799
17 of 20
Appendix A: Supplementary Data
Supplementary data to this article, ground truth points collected during 12 August 2019, campaign is provided in
.xlsx format (https://doi.org/10.5281/zenodo.10403380).Appendix B: RFR and MLR Algorithm
The algorithm applied for Random Forest and MLR is displayed below in Figure B1.
Data Availability Statement
The fully polarimetric RADARSAT-2 data was purchased from MDA corporation. The field data collected
during the study can be accessed from Supporting Information section (Appendix A) (htOpen asset ↗Zenodo · 10.5281/zenodo.10403380pdf-raw-page:17 lines:1-77Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Millets, comprising a diverse group of small‐seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high‐throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi‐omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate‐smart, nutrient‐rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
Why it matches plant phenotyping methodsミレットのフェノミクスをゲノミクスと統合して複雑形質を解明するレビューであり、高スループット表現型取得とその応用が中心的に扱われている。
abstractThis review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 13 Sept 2026
Abstract Plant growth and resilience is greater than the sum of its component traits, with important traits influencing one another. This interdependency makes it challenging to identify the genetic determinants of key agronomic traits, such as water use efficiency. Measuring traits such as plant height and size is possible via image analysis software, such as PlantCV, but these traits are often highly correlated. Furthermore, plant size is estimated using a 2D projection of a 3D object, which is more difficult for plants with complex body plans. To address these problems, we developed a generalizable, biologically-informed model describing the temporal coordination of semi-sequential phytomeric growth in the model grass Setaria . Our approach integrates time dependence with water usage, the growth of phytomers, and the emergence of side shoots or tillers, improving our ability to estimate both phytomer-level phenotypes and water usage traits in high-throughput. We developed new PlantCV methods to inform the Phytomeric Growth Model, and estimated parameter traits using a high-throughput phenotyping dataset of a recombinant inbred line (RIL) panel in well-watered and drought conditions. Model parameter estimates identified additional quantitative trait loci (QTL) for new traits compared to the directly measured PlantCV-derived estimates, and predicted the relationships underlying QTL control of composite traits through co-localization of model parameters to loci controlling size and water use metrics. The Phytomeric Growth Model estimates improved our ability to estimate the growth of tillers and obtain a model-based estimate of marginal water use efficiency (defined here as the ratio of biomass gained per gram of water transpired), both identified as highly influential on plant drought responses through random forest classification. Our approach demonstrates the value of integrating mechanistic modeling with high-throughput imaging to extract new information at scale.
Why it matches plant phenotyping methods高スループット画像解析と生物学的成長モデルを統合し、フィトマー成長や水利用効率などの植物表現型を抽出する手法の開発が研究の中心である。
abstractWe developed new PlantCV methods to inform the Phytomeric Growth Model
Abstract Crop yield prediction before harvest is a key issue in managing agricultural policies and making the best decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their agricultural targets. However, crop yield estimates rely on labor-intensive surveys in Ethiopia. To solve this, we used Sentinel-2, crop canopy analyzer, and ground-truthing data to estimate grain yield (GY) and aboveground biomass (AGB) of two major crops, teff and finger millet, in 2020 and 2021 in Ethiopia’s Aba Gerima catchment. We performed a supervised classification of October Sentinel-2 images at the tillering stage. Among vegetation indices and leaf area index (LAI) used to predict teff and finger millet GY and AGB, the enhanced vegetation index (EVI) and normalized-difference VI (NDVI) provided the best fit to the data. NDVI and EVI most influenced teff AGB ( R 2 = 0.87; RMSE = 0.50 ton/ha) and GY ( R 2 = 0.84; RMSE = 0.14 ton/ha), and NDVI most influenced finger millet AGB ( R 2 = 0.87; RMSE = 0.98 ton/ha) and GY ( R 2 = 0.87; RMSE = 0.22 ton/ha). We found a close association between GY and AGB and the satellite EVI and NDVI. This demonstrates that satellite images can be employed in yield prediction studies. Our results show that satellite and crop canopy analyzer-based monitoring can facilitate the management of teff and finger millet to achieve high yields and more sustainable food production and environmental quality in the area. The results could be reproducible under similar study catchment conditions and boost crop yield. Extrapolation of the models to other areas requires local validation. To improve crop monitoring for farmers and reduce expenses, we suggest integrating time series Sentinel-2 images along with LAI obtained from crop canopy analyzers collected during the cropping season.
Why it matches plant phenotyping methods衛星画像と作物キャノピー解析器を用いて、作物の収量および地上部バイオマスを推定する測定・推定手法が研究の中心であり、地上実測による評価も行っているため。
abstractUsing remote sensing techniques in yield estimation studies
Chloroplasts can change their intracellular position responding to environmental conditions. In addition to the well-known photorelocation movements, i.e., accumulation and avoidance movements in response to light, mesophyll chloroplasts in C4 plants change their intracellular positioning from the cell periphery to the bundle sheath cell side (aggregative movement) in response to environmental stresses such as drought, salinity, and hyperosmosis, under light. Previous studies on the aggregative movement have been limited in two-dimensional (2D) information on the transverse sections of leaves, because aggregated mesophyll chloroplasts gather inside the leaf tissues, which need to be cut for observation. However, the 2D analysis on the cross sections is difficult to investigate accurately the aggregated chloroplasts overlapped each other in the depth direction. Therefore, there are few studies examining the anatomical features of the aggregated chloroplasts at the cellular level. Here in this study, we established the workflow for three-dimensional (3D) observation using a confocal laser scanning microscope (CLSM), which can investigate a thick section as a stack of optical sections, followed by 3D reconstruction of mesophyll cells and chloroplasts. Using this method, we visualized the 3D representations of mesophyll cells of green foxtail (Setaria viridis), which is a model of C4 plant, and investigated the chloroplasts individually and quantified their structures or intracellular positions before and after the aggregative movement. The 3D data of individual chloroplasts in a whole cell revealed that the aggregated positioning is independent with chloroplast volume or surface area, and that chloroplasts did not change their shape before and after the movement.
Why it matches plant phenotyping methods植物細胞・葉緑体の3D画像取得と再構成ワークフローを確立し、葉緑体の構造および細胞内位置を定量化しており、表現型取得法が研究の中心です。
abstractHere in this study, we established the workflow for three-dimensional (3D) observation using a confocal laser scanning microscope (CLSM), which can investigate a thick section as a stack of optical sections, followed by 3D reconstruction of mesophyll cells and chloroplasts.
Scattered trees in farmer fields, also known as agroforestry parkland, are integrated part of West African smallholder agricultural landscapes. While they are used for centuries by farmers, they are now recognised by the scientific and politic communities as a mean to face climate changes (Skole et al., 2021). Fractional cover (FCover) is an important biophysical parameter allowing to monitor the crop growth. Satellite images has been proven very efficient for crop FCover estimation in various ecosystems (Gräzing et al 2021). However, in agroforestry parklands, the presence of trees inside the fields induced a huge variability that can be hardly captured by traditional approach relying on satellite images and ground information. We propose an original empirical framework relying on the combining use of UAV-based FCover and Sentinel-2 data to estimate the pearl millet FCover at landscape scale in an agroforestry parkland of Senegal. 6 UAV images were acquired during the 2021 cropping season and the millet FCover has been derived from a threshold of UAV images for 95 subplots on a 3-m grid and used as targeted variable. 4 vegetation indices and 8 texture featured calculated from S2 data were used as models’ predictors. 3 machine learning regression algorithms (RF, GBM and SVM) and a multiple linear regression (MLR) model were calibrated over the 3-m grid using a cross-validation approach and different scenarii of modelling were tested: (1) fit the four models date by date dataset, (2) fit the four models on all dates dataset with and without date information as predictor, (3) single models vs a meta-model resulting from the stacking of the different models. Our results evidenced that for each model tested the accuracy is dependent to the millet growth stage, the vegetative period being overall the one allowing to reach the higher accuracy. MLR is not adapted to estimate millet FCover (R² between 0.07 and 0.13) while the machine learning models gave overall good results, RF being the better one (R² between 0.45 and 0.69).We have shown that the use of date information as predictor allowed to improve the FCover estimation (R² increases up to 24%) however, the use of a meta-model didn’t significantly improve the accuracy suggesting that RF, GBM and SVM are robust enough for millet FCover estimation in such kind of landscape.While the original workflow we proposed in this study need to be confirmed by adding data from the 2022 cropping season, the results obtained show promising opportunities for improving the crop monitoring in heterogeneous landscapes. The next step will be to better understand the influence of trees on the millet FCover, at the field scale and at the landscape scale.
Why it matches plant phenotyping methodsUAV画像からキビのFCoverを抽出し、Sentinel-2と機械学習で推定するワークフローの開発・精度評価が研究の中心であり、植物キャノピー形質を直接扱っている。
abstractWe propose an original empirical framework relying on the combining use of UAV-based FCover and Sentinel-2 data to estimate the pearl millet FCover at landscape scale
As one of the ancient cultivated crops in China, millet has the characteristics of high nutritional value, drought resistance and barrenness. It also plays an important role in ensuring the supply of food in our country. At present, most of the millet breeding work uses manual extraction of phenotypic information, which is laborintensive and inefficient. Therefore, the development of an automated, efficient and accurate millet phenotype detection method has practical significance for the extraction of the millet genome. In this study, a combination of sparse reconstruction based on Structure from Motion (SfM) and Patch-based Multi-View Stereo (PMVS) was used to select three different varieties of millet. A total of 81 samples of 9 samples in each period were reconstructed to obtain a 3D model of millet. The combination of conditional filtering and statistical filtering is used to remove the noise points generated during the photographing process, and finally the obtained point cloud data is used to measure the agronomic traits of millet such as plant height and leaf area. The results show that the interval angle of 5° is the best reconstruction angle of millet. The coefficient of determination R2 of point cloud measurement results and manual measurement data regression analysis is higher than 0.94, indicating that the method used for 3D reconstruction has high applicability to different millet in different periods and high-throughput measurement of millet by the method in this paper is feasible. This study provides a theoretical basis for a millet phenotypic information measurement device
Why it matches plant phenotyping methodsSfM/PMVSによる3D再構成、点群ノイズ除去、農業形質測定を中心に開発・検証した植物フェノタイピング手法研究である。
abstractthe development of an automated, efficient and accurate millet phenotype detection method has practical significance
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Irrigation of crops accounts for a significant portion of fresh water consumption. In order to utilize this resource more efficiently, it is necessary to engineer crops that can more efficiently use water. Water use efficiency, defined as the ratio of plant growth to water used, is a complex property of plants affected by many different factors. Despite this complexity, genetic variability has been able to be identified in a number of different crops. The C4 model species Setaria viridis remains under-studied in this regard and consequently we sought to identify promising genetic loci contributing to variation in water use efficiency. In order to accomplish this goal we leveraged the high-throughput phenotyping platform at the Donald Danforth Plant Science center to grow S. viridis in well-watered and water-limited conditions. This automated system enables strict control of watering regimes as well as measures of plant traits extracted from photographs using computer vision. Combining these two sets of data allows for direct measurement of whole-plant water-use efficiency on a daily basis which was used as a response variable in a genome wide association study. Significant associations were found for water-use efficiency and related traits. These loci were then prioritized further by pooling information across each day of an experiment and across multiple experiments to zero in on the most likely locations of genes responsible for driving water-use efficiency in S. viridis.
Why it matches plant phenotyping methods植物形質を画像から抽出する高スループット表現型解析プラットフォームを用い、水利用効率を日次で測定するワークフローが研究の中心的手法であるため。
abstractwe leveraged the high-throughput phenotyping platform at the Donald Danforth Plant Science center
Pores in the leaf epidermis called stomata allow plants to take up carbon dioxide for photosynthesis, but are also pathways for water vapor loss. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which can be applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models and their ability to accurately detect epidermal structures is constrained by the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool trained and successfully applied to Zea mays, to analyze images from a closely related grass called Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel amounts of diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R 2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, and a roadmap for the translation of a machine learning tool to analyze stomatal patterning in diverse datasets of new plant species.
Why it matches plant phenotyping methodsSetariaの葉画像から気孔を自動検出する機械学習ツールの再訓練・適応と、専門家比較による精度検証が研究の中心であるため、植物フェノタイピング手法として含める。
abstractNew image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning
Abstract Background: A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. Results: We conducted experiments using a dataset of Setaria viridis. Experiments show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method. The best mean absolute error between the hand-labeled and estimated tiller numbers by the proposed method is 0.57. Conclusions: We realized applying DNN methods to tiller number estimation methods by using pretext tasks. The proposed method outperformed the conventional approach.
Why it matches plant phenotyping methods深層ニューラルネットワークを用いて植物の分げつ数を自動推定する手法を開発・比較しており、植物表現型の取得が研究の中心である。
abstractan automated approach is necessary for high-throughput phenotyping
Reproduction assets foundThe paper's tiller estimation experiments use the Setaria viridis image dataset (25,570 images, 576 with hand-labeled tiller numbers), which the authors explicitly state is publicly available in the figshare repository. No author analysis code or trained models are reported as deposited.Dataset · publicThe dataset analyzed during the current study are available in the figshare repository,
https://figshare.com/articles/dataset/DDPSC_Phenotyping_Manuscript_1_Files/1272859 [11].Open asset ↗figshare · 1272859pdf-page:12 lines:1-70Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Why it matches plant phenotyping methods穀粒という植物器官のタンパク質含量をNIRセンサーと機械学習で推定する校正モデルを開発・評価しており、形質取得・抽出法が研究の中心である。
abstractWe explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra.
Reproduction assets foundThe authors explicitly state that the custom CNN analysis code for this paper's NIR protein-prediction models is publicly available on GitHub at the authors' repository URL, which matches an allowed URL. Supplementary tables are only referenced via a placeholder (www.mdpi.com/xxx/s1) and are not actionable; the Video SCode · publicced by the quality
and size of the datasets used for training the model. To minimize the “over-fitting” error,
the large dataset was used and split carefully to include the different multi-cereal species
in both the calibration and validation dataset (as described in section 2.5.1). The code is
available on the GitHub platform (https://github.com/adamavip/nirs-protein-prediction)
and its particular parts can be now utilized to enhance and develop other pipelines and
products.
For our dataset, the algorithms built using ML-based methods (particularly, the
stacked ensemble model via Hone Create and custom-designed CNN; section 3.4)
achieved the better comparative metrics for both spectra typesOpen asset ↗adamavip/nirs-protein-predictionpdf-raw-page:14 lines:1-54Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Why it matches plant phenotyping methods植物の気孔画像を対象とする機械学習ツールの種間適用性を検証し、新種に対応する再訓練と専門家比較による精度評価を行っており、表現型取得・抽出手法が研究の中心です。
abstractHere we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis.
Abstract Proso millet (Panicum miliaceum L.), one of the major cultivated millets, serves as a complement to major cereal crops due to its drought tolerance and low input demands. Timing of heading is one of the key agronomic traits associated with its adaptation to a target environment and a major focus in breeding. Conventionally, heading percentage of a plot (genotype) was rated visually by breeders in field. Despite many successful studies reported in automatic head detections in other small grain species, little progress had been made to estimate heading percentage especially when multiple tillers exist. This study aimed to develop a method for automatic proso millet panicle detection and, more importantly, heading percentage estimation using regular red‐green‐blue images collected by an unmanned aerial vehicle. Aerial images of two dates were collected at heading stage in 2020 in Scottsbluff, NE. Faster regions with convolutional neural network models were trained to detect and count proso millet panicles in each plot. Then, using a sigmoid model, the number of detected panicles was converted to heading percentage without having the information of stand count and the number of tillers. Overall, the system achieved the highest coefficient of determination of 0.728 for proso millet heading percentage estimation, and an accuracy of 92.4% in determining whether a plot reached a certain threshold of heading (50% in this study). The methods developed in this study on heading percentage estimation can directly aid in decision making in proso millet breeding and can be ultimately incorporated into an automated proso millet high‐throughput phenotyping pipeline.
Why it matches plant phenotyping methodsUAV画像と深層学習により、プロソミレットの穂検出から出穂率を推定する手法を開発しており、植物形質取得が研究の中心です。
abstractThis study aimed to develop a method for automatic proso millet panicle detection and, more importantly, heading percentage estimation using regular red‐green‐blue images collected by an unmanned aerial vehicle.
Elephant grass is a promising plant for economic and sustainable energy production.However, adapted cultivars and efficient strategies for selecting genotypes aimed at energy biomass production is essential.Remote sensing techniques provide spatiotemporal information from plants in an agile, non-destructive and non-invasive way.The present study aimed to use remote sensors onboard an unmanned aerial vehicle (UAV) to monitor elephant grass genotypes and assist in plant phenotyping for energy biomass production.The experimental plots were imaged in the visible and near infrared bands.Imaging was carried out in 66 experimental plots in the José Henrique Bruschi Experimental Field (CEJHB), located in Coronel Pacheco, MG, Brazil.The experiment was arranged in a randomized block design with three replications, and 22 elephant grass genotypes were evaluated.The aggregated index iMAPNDRE was strongly correlated with the dry matter production observed in the field, therefore a method with potential application for estimating the biomass of elephant grass genotypes.Thus, sensors aboard UAV platforms can assist breeders to select the best elephant grass genotypes for energy production.
Why it matches plant phenotyping methodsUAVの可視・近赤外画像と集約指標を用いて、エレファントグラス遺伝子型の乾物生産量を推定する植物フェノタイピング手法を実質的に適用・評価している。
abstractThe present study aimed to use remote sensors onboard an unmanned aerial vehicle (UAV) to monitor elephant grass genotypes and assist in plant phenotyping for energy biomass production.
Stomata, microscopic pores on leaf surfaces, regulate the uptake of carbon dioxide and the simultaneous loss of water vapor by leaves. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which in turn have been applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models, and their ability to accurately detect epidermal structures is constrained to morphologies found within the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool, which was previously trained and successfully applied to Zea mays, to analyze images from a closely related grass, Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, while also providing a roadmap for translation of a machine learning to analyze stomatal patterning in new plant species.
Why it matches plant phenotyping methods植物の気孔形質を画像から自動抽出する機械学習ツールの種間適用性を評価し、Setaria向けに再訓練・検証した研究であり、フェノタイピング手法が中心である。
abstractNew image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning
Pearl millet can be viably used for food diversification due to its balanced nutritional composition. Nutritional parameters are conventionally assessed using labour and time-intensive strenuous conventional methods for germplasm screening. Near-infrared reflectance spectroscopy (NIRS) uses near-infrared sections of the electromagnetic spectrum for precise and speedy determination of biochemical parameters for large germplasm. MPLS (Modified Partial Least Squares) regression based NIRS prediction models were developed to assess starch, resistant starch, amylose, protein, oil, total dietary fibre, phenolics, total soluble sugars, phytic acid for high throughput screening of pearl millet germplasm. Mathematical treatments executed by permutation and combinations for calibrating the model, where 2nd, 3rd, and 4th derivatives produced the best results. Treatments “4,5,4,1” was finalized for protein, oil, resistant starch, total dietary fibre, “3,4,4,1” for phenolics, “2,8,4,1” for amylose, “2,4,4,1” for phytic acid, “4,7,4,1” for total soluble sugars and “2,8,4,1” for starch. Treatments with the highest 1-Variance ratio, RSQᵢₙₜₑᵣₙₐₗ (coefficient of determination) values, lowest SEC(V) (standard error of cross-validation), SEP(C) (standard error of performance) were identified for subsequent validation. External validation determined the prediction accuracy based on RSQₑₓₜₑᵣₙₐₗ, RPD (residual prediction deviation), SD (standard deviation), p-value ≥ 0.05 and low SEP(C).
Why it matches plant phenotyping methodsNIRS予測モデルを開発し、交差検証・外部検証まで行っており、パールミレット遺伝資源の種子栄養形質を高スループットに取得する方法が研究の中心である。
abstractMPLS (Modified Partial Least Squares) regression based NIRS prediction models were developed to assess starch, resistant starch, amylose, protein, oil, total dietary fibre, phenolics, total soluble sugars, phytic acid for high throughput screening of pearl millet germplasm.
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
Why it matches plant phenotyping methods画像とIoTデータから作物病害を推定する低コストの収集・分類フレームワークを開発し、モデル性能と特徴抽出を評価しており、植物病態の表現型取得が中心である。
abstractTherefore, there is a requirement for automating plant disease detection and classification.
Reproduction assets foundThe authors explicitly state that the pearl millet blast/rust image dataset collected as part of this research is publicly available on Kaggle, making it a paper-specific, publicly actionable phenotype image dataset. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicData prepared as a part of this research is available at https://www.kaggle.com/kalpitgupta/blast-and-rust-compressed . The researchers who wish to use the dataset available at the above link must cite this article.Open asset ↗Kaggle · kalpitgupta/blast-and-rust-compressedlines:417-422Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.
Why it matches plant phenotyping methods宇宙borne・UAVスペクトルデータと回帰手法を比較し、LAI、葉クロロフィル濃度、群落含水量という植物形質の推定精度を評価しており、センシングによる表現型取得が研究の中心です。
abstractThis study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Why it matches plant phenotyping methods種子の発芽状態を画像と機械学習で自動抽出し、手動計数と性能比較しているため、植物表現型取得法が中心です。
abstractWe here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.
Reproduction assets foundThe paper's authors publicly released the labeled germination image dataset (~24,000 annotated images of 2449 seeds) on Mendeley Data and their machine learning analysis code on GitHub, both explicitly stated in the Availability of data and materials section.Dataset · publicThe generated and labeled training data is freely available on Mendeley Data: http://dx.doi.org/10.17632/4wkt6thgp6.2 .Open asset ↗Mendeley Data · 10.17632/4wkt6thgp6.2lines:164-248Code · publicThe code for our proposed machine learning–based model can be found on GitHub: https://github.com/grimmlab/GerminationPrediction .Open asset ↗GitHub · grimmlab/GerminationPredictionlines:164-248Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
Abstract An infra red thermographic analysis of well watered control and well watered heat stressed pearl millet ( Pennisetum glaucum ) was conducted at ICAR – Central Research Institute for Dryland Agriculture as a part of high resolution phenomics studies to identify the individual quantitative physiological parameters by plant phenotyping that form the basis for more complex abiotic stress tolerant traits. It was seen that the temperature gradient increased gradually from ground level to the top in the control non heat stressed plant. In contrast, it was seen that in the heat stressed plant the temperature increased up to the middle of the plant and then started to decrease at the top of the plant in comparison with the non heat stressed control plant. Our results indicate that the lowering of temperatures in the top of the heat stressed plant may be a mechanism by which the heat stressed plant acclimates to stress by regulating its transpiration thereby bringing in a cooling effect to counter stress.
Why it matches plant phenotyping methods赤外線サーモグラフィーによる植物温度・温度勾配の取得を、植物フェノタイピングおよび生理形質の定量化として明示的に扱っており、手法の適用が中心です。
abstractto identify the individual quantitative physiological parameters by plant phenotyping
BACKGROUND AND OBJECTIVES: The aim of this study was to construct near‐infrared reflectance spectroscopy (NIRS) models to test the contents of apparent amylose, total starch, and crude protein for both foxtail millet grain and dehusked millet grain. The construction of models is based on chemical values and optimized spectral data of 111 core foxtail millet germplasm materials after analyzation of partial least square (PLS) method with cross‐validation. FINDINGS: Model calibration showed that there was better fitting degree of dehusked millet grain than that of foxtail millet. The coefficient determination for calibration (Rc²) of former is >0.843, while that of latter is >0.823. The coefficient determination of prediction (Rp²) of each component ranged from 0.794 to 0.888 for foxtail millet grain and 0.833 to 0.970 for dehusked millet grain. CONCLUSIONS: The constructed NIRS models of both samples can be used to rapidly test foxtail millet quality, and the nondestructive nature of foxtail millet grain‐based test (which maintains seed vigor) better meets the requirements of the breeding, although the accuracy needs to be further improved. SIGNIFICANCE AND NOVELTY: This study developed a rapid method to detect the main nutritional quality for foxtail millet using two different samples, which can be used in germplasm resource innovation and contribute to the acceleration of cereal breeding.
Why it matches plant phenotyping methodsフォックステールミレット種子の栄養形質をNIRSで非破壊・迅速推定するモデルを開発し、交差検証と予測性能評価を行っているため、植物形質取得法が中心です。
abstractThe aim of this study was to construct near‐infrared reflectance spectroscopy (NIRS) models to test the contents of apparent amylose, total starch, and crude protein for both foxtail millet grain and dehusked millet grain.
Genetic selection for whole-plant water use efficiency (yield per transpiration; WUE plant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUE plant such as intrinsic water use efficiency (WUE i ; CO 2 assimilation rate per stomatal conductance). Measuring WUE i by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUE i over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant . However, a genetic link between δ 13 C leaf and WUE plant in a C 4 species has not been well established. Therefore, to determine if there is a genetic relationship in a C 4 plant between δ 13 C leaf and WUE plant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUE plant in a recombinant inbred line (RIL) population created between the C 4 grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for δ 13 C leaf were found and co-localized with transpiration, biomass accumulation, and WUE plant . Additionally, WUE plant for each of the δ 13 C leaf QTL allele classes was negatively correlated with δ 13 C leaf , as would be predicted when WUE i influences WUE plant . These results demonstrate that δ 13 C leaf is genetically linked to WUE plant , likely to be through their relationship with WUE i , and can be used as a high-throughput proxy to screen for WUE plant in these C 4 species.
Why it matches plant phenotyping methods葉の炭素安定同位体比を全植物体の水利用効率の高スループット代理指標として検証し、WUEとの遺伝的関連を評価しており、表現型取得・スクリーニング法が中心的です。
abstractleaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant
Reproduction assets foundThe paper's QTL analysis was performed with the authors' custom Python and R scripts, publicly deposited on GitHub (foxy_qtl_pipeline). No public phenotype dataset URL is stated in the supplied blocks.Code · publicin Feldman et al. (2017); Feldman et al. (2018) were
308
used in this study, and here the methods used are repeated. QTL mapping was
309
performed on day 27 within each treatment group using functions in the R/qtl and
310
funqtl packages (Kwak et al., 2016). The functions were called by a set of custom
311
Python and R scripts (https://github.com/maxjfeldman/foxy_qtl_pipeline). Two
312
complimentary analysis methods were utilized. First, a single QTL model genome
313
scan using Haley-Knott regression was performed to identify QTL exhibiting LOD
314
score peaks greater than a permutation-based significance threshold (α = 0.05, n =
315
1000). Second, a stepwise forward/backward selection proceOpen asset ↗maxjfeldman/foxy_qtl_pipelinepdf-raw-page:11 lines:1-77Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Soil salinity is a global environmental challenge for crop production. Understanding the uptake and transport properties of salt in plants is crucial to evaluate their potential for growth in high salinity soils and as a basis for engineering varieties with increased salt tolerance. Positron emission tomography (PET), traditionally used in medical and animal imaging applications for assessing and quantifying the dynamic bio-distribution of molecular species, has the potential to provide useful measurements of salt transport dynamics in an intact plant. Here we report on the feasibility of studying the dynamic transport of 22 Na in millet using PET. Twenty-four green foxtail (Setaria viridis L. Beauv.) plants, 12 of each of two different accessions, were incubated in a growth solution containing 22 Na + ions and imaged at 5 time points over a 2-week period using a high-resolution small animal PET scanner. The reconstructed PET images showed clear evidence of sodium transport throughout the whole plant over time. Quantitative region-of-interest analysis of the PET data confirmed a strong correlation between total 22 Na activity in the plants and time. Our results showed consistent salt transport dynamics within plants of the same variety and important differences between the accessions. These differences were corroborated by independent measurement of Na + content and expression of the NHX transcript, a gene implicated in sodium transport. Our results demonstrate that PET can be used to quantitatively evaluate the transport of sodium in plants over time and, potentially, to discern differing salt-tolerance properties between plant varieties. In this paper, we also address the practical radiation safety aspects of working with 22 Na in the context of plant imaging and describe a robust pipeline for handling and incubating plants. We conclude that PET is a promising and practical candidate technology to complement more traditional salt analysis methods and provide insights into systems-level salt transport mechanisms in intact plants.
Why it matches plant phenotyping methods植物体内のナトリウム輸送という生理形質を、PET画像と定量ROI解析で時系列測定する方法の実現可能性・定量性を検証しており、フェノタイピング手法が中心である。
abstractHere we report on the feasibility of studying the dynamic transport of 22 Na in millet using PET.
Association mapping studies have enabled researchers to identify candidate loci for many important environmental resistance factors, including agronomically relevant resistance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately and consistently measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response to treatment directly from images. Using two synthetically generated image datasets, we first show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We then demonstrate an example application of an interspecific cross of the model C4 grass Setaria. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling association mapping studies without the need for engineering complex image processing pipelines.
Why it matches plant phenotyping methods画像から処理応答を自動検出・定量する新規フェノタイピング手法を提案し、合成データで検証した方法開発研究。
abstractIn this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response to treatment directly from images.
Reproduction assets foundThe paper provides two paper-specific public assets: the LSP-Lab implementation of the Latent Space Phenotyping method on GitHub, and a figshare deposit containing the full datasets and utility scripts needed to reproduce the paper's results and figures. Setaria and sorghum image datasets are third-party (Baxter group)Dataset · publicFunding
This research was funded by a Canada First Research Excellence Fund grant from the
Natural Sciences and Engineering Research Council of Canada.
Data Availability
Full datasets and utility scripts needed for reproducing the results and figures presented in
Section 3 can be found at https://figshare.com/s/f710381c04c01e2ba319. The data
for the Setaria RIL experiment and the sorghum experiment are available from the sources
referenced by the authors of these datasets [8, 34].
References
[1] Virtual laboratory. http://www.algorithmicbotany.org/virtual_laboratory/.Accessed: 2017-08-01.
[2] Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg. LatenOpen asset ↗figshare · f710381c04c01e2ba319pdf-raw-page:17 lines:1-44Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016–2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R² validation (R²val) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R²val = 0.53, rRMSEP = 13.9%), maize (R²val = 0.53, rRMSEP = 18.7%) and finger millet (R²val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546–910 nm (lablab), 750–794 nm (maize) and 686–814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.
Why it matches plant phenotyping methodsテラストリアル hyperspectral imaging とランダムフォレストにより作物バイオマスを推定し、複数年・栽培条件でモデル性能を検証しており、植物形質の取得・推定手法が研究の中心である。
abstractThe aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply.
A set of 42 millet (panicum miliaceum L.) samples was investigated for its protein content using standard Kjeldahl analysis and near-infrared spectroscopy. The performance of three handheld spectrometers was compared to a benchtop instrument. The used spectrometers operate in different regions of the NIR, which gives interesting insights into the applicability of each region. Additionally, semi-automated, consumer-oriented multivariate data analysis was compared to sophisticated data evaluation. The performance of the near-infrared instruments was compared using important statistical parameters of the established cross- and test set validated partial least squares regression (PLS-R) models. Milled and intact samples were analysed, in order to further evaluate the importance of homogeneity. The results showed that the benchtop spectrometer is capable of accurately analysing protein content of millet grains, with root mean square error (RMSEP) values for milled and intact grains of approximately 0.5%. Two PLS-R models of handheld instruments also yielded good results for milled grains with RMSEP values of about 0.6%. The semi-automated multivariate data analysis showed some drawbacks compared to standard data processing software. For intact grains, however, similar results could be achieved.
Why it matches plant phenotyping methodsハンドヘルドNIRによる millet 粒のタンパク質含量という種子形質の測定法を、複数機器・解析手法・試料状態で比較検証しており、測定法が中心です。
abstractThe performance of three handheld spectrometers was compared to a benchtop instrument.
MilletFlowerClassificationTrackingGrowth / development / phenology
In many plant species, the time of day at which flowers open to permit pollination is tightly regulated. Proper time of flower opening, or Time of Day of Anther Appearance ( TAA ), may coordinate flowering opening with pollinator activity or may shift temperature sensitive developmental processes to cooler times of the day. The genetic mechanisms that regulate the timing of this process in cereal crops are unknown. To address this knowledge gap, it is necessary to establish a monocot model system that exhibits variation in TAA. Here, we examine the suitability of Setaria viridis , the model for C4 photosynthesis, for such a role. We developed an imaging system to monitor the temporal regulation of growth, flower opening time, and other physiological characteristics in Setaria. This system enabled us to compare Setaria varieties Ames 32254, Ames 32276, and PI 669942 variation in growth and daily flower opening time. We observed that TAA occurs primarily at night in these three Setaria accessions. However, significant variation between the accessions was observed for both the ratio of flowers that open in the day vs. night and the specific time of day where the rate is maximal. Characterizing this physiological variation is a requisite step toward uncovering the molecular mechanisms regulating TAA. Leveraging the regulation of TAA could provide researchers with a genetic tool to improve crop productivity in new environments.
Why it matches plant phenotyping methodsSetariaの花開時刻や成長を時系列画像とニューラルネットワーク・運動解析で取得するイメージングシステムを開発しており、植物形質の取得手法が研究の中心です。
titleNeural Net Classification Combined With Movement Analysis to Evaluate Setaria viridis as a Model System for Time of Day of Anther Appearance.
Reproduction assets foundThe paper's phenotyping analysis code (camera optimization loop, day-time classification function and trained model, bristle density and growth scripts) is publicly available in the authors' DohertyLab GitHub repository, and a sample A10 day/night image dataset is deposited on Harvard Dataverse. Supplementary material,Code · publiclines never intersect, then the row of the first white pixel not classified as a bristle from the top of the image was used. The bristle density was then calculated for each image in a set. All day time images after panicle emergence were averaged to determine the final bristle density for that panicle. Scripts are available at https://github.com/DohertyLab/Setaria-Flower-Opening-Time .
Quantification of growth
ImageJ Fiji ( https://imagej.net/Fiji/Downloads ) was used to analyze the daily growth pattern of seedlings from 3 to 10 days after emergence. Dawn and dusk images at 8 a.m. and 8 p.m. were imported as an image sequence. In three replicates of each variety the second and/or third leavOpen asset ↗DohertyLab/Setaria-Flower-Opening-Timelines:43-49Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
To assist with efforts to engineer a C4 photosynthetic pathway into rice, forward-genetic approaches are being used to identify the genes modulating key C4 traits. Currently, a major challenge is how to screen for a variety of different traits in a high-throughput manner. Here we describe a method for identifying C4 mutant plants with increased CO2 compensation points. This is used as a signature for decreased photosynthetic efficiency associated with a loss of C4 function. By exposing plants to a CO2 concentration close to the CO2 compensation point of a wild-type plant, individuals can be identified from measurements of chlorophyll a fluorescence. We use this method to screen a mutant population of the C4 monocot Setaria viridis (L.)P.Beauv. generated using N-nitroso-N-methylurea (NMU). Mutants were identified at a frequency of 1 per 157 lines screened. Forty-six candidate lines were identified and one line with a heritable homozygous phenotype selected for further characterisation. The CO2 compensation point of this mutant was increased to a value similar to that of C3 rice. Photosynthesis and growth was significantly reduced under ambient conditions. These data indicate that the screen was capable of identifying mutants with decreased photosynthetic efficiency. Characterisation and next-generation sequencing of all the mutants identified in this screen may lead to the discovery of novel genes underpinning C4 photosynthesis. These can be used to engineer a C4 photosynthetic pathway into rice.
Why it matches plant phenotyping methodsC4変異体を高スループットに同定するためのクロロフィル蛍光測定法を開発・適用し、スクリーニング性能も評価しているため、植物フェノタイピング手法が中心である。
abstractHere we describe a method for identifying C4 mutant plants with increased CO2 compensation points.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
MilletWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyWater status / transpiration
Plant growth and water use are interrelated processes influenced by genetically controlled morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use are correlated strongly in this system; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use, and WUE all were heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions can be utilized to predictably modulate trait values given a specified precipitation regime in the model C4 genus Setaria.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームで植物サイズと水利用を日次定量し、モデル化によってWUE関連形質を抽出しており、表現型取得・解析が研究の中心的手法である。
abstractthis study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × Setaria viridis recombinant inbred line population at daily intervals
MaizeMilletRootClassificationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Recent progress in root phenotyping has focused mainly on increasing throughput for genetic studies, while identifying root developmental patterns has been comparatively underexplored. We introduce a new phenotyping pipeline for producing high-quality spatiotemporal root system development data and identifying developmental patterns within these data. The SmartRoot image-analysis system and temporal and spatial statistical models were applied to two cereals, pearl millet ( Pennisetum glaucum ) and maize ( Zea mays ). Semi-Markov switching linear models were used to cluster lateral roots based on their growth rate profiles. These models revealed three types of lateral roots with similar characteristics in both species. The first type corresponds to fast and accelerating roots, the second to rapidly arrested roots, and the third to an intermediate type where roots cease elongation after a few days. These types of lateral roots were retrieved in different proportions in a maize mutant affected in auxin signaling, while the first most vigorous type was absent in maize plants exposed to severe shading. Moreover, the classification of growth rate profiles was mirrored by a ranking of anatomical traits in pearl millet. Potential dependencies in the succession of lateral root types along the primary root were then analyzed using variable-order Markov chains. The lateral root type was not influenced by the shootward neighbor root type or by the distance from this root. This random branching pattern of primary roots was remarkably conserved, despite the high variability of root systems in both species. Our phenotyping pipeline opens the door to exploring the genetic variability of lateral root developmental patterns.
Why it matches plant phenotyping methods根系の時空間データを取得・解析し、側根の成長パターンを分類する新しいフェノタイピングパイプラインが研究の中心であるため。
abstractWe introduce a new phenotyping pipeline for producing high-quality spatiotemporal root system development data and identifying developmental patterns within these data.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Field‐based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype‐to‐phenotype relationships in next‐generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field‐grown grass crop. Plant area index (PAI) estimated from below‐canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = .79). Twenty‐seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty‐one were found in four clusters of colocalized QTL. Analysis of image‐based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high‐throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.
Why it matches plant phenotyping methods圃場の低コスト半球画像と光減衰モデルによるPAI推定を、破壊的収穫によるバイオマス測定と比較検証し、QTL検出への有用性を評価しており、画像表現型取得法が中心である。
abstractThe use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
MilletWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Plant growth and water use are interrelated processes influenced by the genetic control of both plant morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvements of WUE using traditional methods have proven difficult due to low throughput and environmental heterogeneity encountered in field settings. To overcome these limitations the study presented here utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica x Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use in this system are strongly correlated; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use and WUE were all heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions of the model C4 species Setaria can be utilized to predictably modulate trait values given a specified precipitation regime.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームを用いて植物サイズと水利用を日次定量し、WUE関連の再利用可能な表現型形質をモデル化しているため、表現型取得・解析が研究の主要部分です。
abstractthe study presented here utilizes a high-throughput phenotyping platform to quantify plant size and water use
Above-ground biomass production is a key target for studies of crop abiotic stress tolerance, disease resistance and yield improvement. However, biomass is slow and laborious to evaluate in the field using traditional destructive methods. High-throughput phenotyping (HTP) is widely promoted as a potential solution that can rapidly and non-destructively assess plant traits by exploiting advances in sensor and computing technology. A key potential application of HTP is for quantitative genetics studies that identify loci where allelic variation is associated with variation in crop production. And, the value of performing such studies in the field, where environmental conditions match that of production farming, is recognized. To date, HTP of biomass productivity in field trials has largely focused on expensive and complex methods, which – even if successful – will limit their use to a subset of wealthy research institutions and companies with extensive research infrastructure and highly-trained personnel. Even with investment in ground vehicles, aerial vehicles and gantry systems ranging from thousands to millions of dollars, there are very few examples where Quantitative trait loci (QTLs) detected by HTP of biomass production in a field-grown crop are shown to match QTLs detected by direct measures of biomass traits by destructive harvest techniques. Until such proof of concept for HTP proxies is generated it is unlikely to replace existing technology and be widely adopted. Therefore, there is a need for methods that can be used to assess crop performance by small teams with limited training and at field sites that are remote or have limited infrastructure. Here we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population. This provides a case study of a HTP technology that can deliver results for QTL mapping without high costs or complexity.
Why it matches plant phenotyping methods低コストの半球画像化と光減衰モデリングを用いて、圃場作物のバイオマス表現型を取得し、破壊的測定およびQTLとの一致性で検証した研究であり、フェノタイピング手法が中心です。
abstractHere we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population.
We present a novel absorption correction approach for elemental distribution images obtained with a laboratory confocal micro X-ray fluorescence spectrometer. The procedure is suited especially for biological samples, as a constant dark matrix with a varying minor or trace element distribution is assumed. The constant absorption in the sample is extracted from depth dependent measurements. By using the concept of an effective excitation energy, depth-dependent, and element-specific excitation energy values are calculated. For each voxel of a full 3D measurement, a correction is performed taking into account the actual number of voxels in the excitation and detection path. As proof of concept, the embryonic region of pearl millet seeds is investigated. Data are measured from the top and bottom side, resulting in a good agreement after the application of the absorption correction procedure. The distribution of elemental micronutrients is compared in seeds of two pearl millet genotypes. The corrected images illustrate different localization patterns of the micronutrient elements in pearl millet seed tissues.
Why it matches plant phenotyping methods3D元素分布画像の吸収補正手法を開発し、パールミレット種子で補正性能と遺伝子型間の微量元素局在を検証しており、植物試料の形質取得技術が中心である。
abstractWe present a novel absorption correction approach for elemental distribution images obtained with a laboratory confocal micro X-ray fluorescence spectrometer.
Background and aims Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut ( Vigna subterranea ) and proso millet ( Panicum miliaceum ) have been used as an example and the new opportunities presented by this approach have been analysed. Methods Three-dimensional plant reconstruction, based on stereo cameras, combined with ray tracing was implemented to explore the light environment within the Bambara groundnut-proso millet intercropping system and associated monocrops. Gas exchange data were used to predict the total carbon gain of each component crop. Key results The shading influence of the tall proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area. Conclusions Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multi-species cropping systems, enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops, and enables the prediction of new productive combinations of previously untested crops.
Why it matches plant phenotyping methodsステレオカメラによる3次元植物再構成とレイトレーシングを開発・適用し、作物キャノピーの光環境や生産性を定量化する手法が研究の中心である。
abstractHere, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment.
Pearl millet is an important staple food for farming communities across semi-arid tropical systems of South Asia and Sub-Saharan Africa where production suffers uncertain precipitation. This work is undertaken under the premise that maximizing grain yield under water-limited conditions depends on both maximizing water use and ensuring water availability for the grain filling period. Here we discuss the phenotyping methods targeting the variability in plant water use strategies which determine the crop production success in water-limited environments. A fine-mapping population of pearl millet, segregating within the previously identified drought tolerance quantitative trait locus (QTL) on chromosome 2 (LG02), was tested across different experimental environments (pot culture, high-throughput phenotyping platform (LeasyScan), Lysimeter, and Field). Recombinants were then analyzed for traits at different levels of plant organization, ranging from water-use traits (transpiration rate, leaf area, plant organ dry weights, etc.) to crop production and agronomic traits (grain yield, tiller number, harvest index, etc.) The linkages between traits across the experimental systems were analyzed, using principal component analysis (PCA) and QTL co-localization approach. The functional relevance of the phenotyping systems was traced by PCA analysis. Furthermore, we found four regions within the LG02-QTL underlying substantial co-mapping of water-use related and agronomic traits. These regions were identified across the experimental systems and justified linkages between water- use traits were phenotyped at lower level of plant organization to the agronomic traits assessed in the field. Therefore, the phenotyping systems at ICRISAT are validated and well set to accelerate crop breeding for drought adaptations.
Why it matches plant phenotyping methods複数の植物表現型取得系（LeasyScan、ライシメータ、圃場等）を用いて水利用形質を評価し、表現型システム自体の機能的妥当性を検証しているため、方法の検証・応用が中心である。
abstractHere we discuss the phenotyping methods targeting the variability in plant water use strategies which determine the crop production success in water-limited environments.
Water limitations decrease stomatal conductance (g(s)) and, in turn, photosynthetic rate (A(net)), resulting in decreased crop productivity. The current techniques for evaluating these physiological responses are limited to leaf-level measures acquired by measuring leaf-level gas exchange. In this regard, proximal sensing techniques can be a useful tool in studying plant biology as they can be used to acquire plant-level measures in a high-throughput manner. However, to confidently utilize the proximal sensing technique for high-throughput physiological monitoring, it is important to assess the relationship between plant physiological parameters and the sensor data. Therefore, in this study, the application of rapid sensing techniques based on thermal imaging and visual-near infrared spectroscopy for assessing water-use efficiency (WUE) in foxtail millet (Setaria italica (L.) P. Beauv) was evaluated. The visible-near infrared spectral reflectance (350-2500 nm) and thermal (7.5-14 µm) data were collected at regular intervals from well-watered and drought-stressed plants in combination with other leaf physiological parameters (transpiration rate-E, A(net), g(s), leaf carbon isotopic signature-δ(13)C(leaf), WUE). Partial least squares regression (PLSR) analysis was used to predict leaf physiological measures based on the spectral data. The PLSR modeling on the hyperspectral data yielded accurate and precise estimates of leaf E, gs, δ(13)C(leaf), and WUE with coefficient of determination in a range of 0.85-0.91. Additionally, significant differences in average leaf temperatures (~1°C) measured with a thermal camera were observed between well-watered plants and drought-stressed plants. In summary, the visible-near infrared reflectance data, and thermal images can be used as a potential rapid technique for evaluating plant physiological responses such as WUE.
Why it matches plant phenotyping methods可視近赤外分光・熱画像とPLSRにより、植物の水利用効率や生理形質を高スループット推定するセンシング手法を評価しており、表現型取得法が研究の中心である。
abstractThe PLSR modeling on the hyperspectral data yielded accurate and precise estimates of leaf E, gs, δ(13)C(leaf), and WUE