Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.
Why it matches plant phenotyping methodsFWRRを用いて穀類の収量安定性を推定・予測する統計的手法を検討し、環境数やデータ構成による予測性能を評価しているため、収量形質の計算的フェノタイピング手法が中心です。
abstractMethods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability.
Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R2 = 0.90) and weedy yield (MAE = 258, R2 = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からキャノピー形質を高スループットに抽出し、機械学習による予測性能も評価することが研究の中心であるため、植物フェノタイピング手法の実質的応用・検証に該当する。
abstractThis study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Abstract Ground cover is a key trait in organic cereal production, contributing to weed suppression, soil protection, and yield stability. Assessing canopy development manually is labor-intensive, whereas unmanned aerial vehicle (UAV) phenotyping offers a high-throughput and objective alternative. In this study, we evaluated the potential of UAV-derived ground cover measurements combined with genomic prediction to support breeding for organic spring oat ( Avena sativa L.) and wheat ( Triticum aestivum L.). A diverse panel of 461 oat genotypes and 218 spring wheat genotypes were phenotyped using UAVs in May and June. Ground cover exhibited substantial variation in May (oat: mean = 0.43, SD = 0.31; wheat: mean = 0.36, SD = 0.35) and approached to maturity in June for Oat. Narrow-sense heritabilities were intermediate for both species and growth stages (0.30–0.50), indicating potential for breeding for increased ground cover. Genomic prediction models showed higher accuracy for early-season ground cover (May: 0.45) compared with later stages (June: 0.35), consistent with greater phenotypic variation at early growth. Positive correlations were observed between genetic values for early ground cover and grain yield suggesting that selection for early canopy development can be very useful for organic farming. These results demonstrate that UAV-based phenotyping, integrated with genomic prediction, provides an efficient strategy for selecting competitive, high-yielding cultivars in organic spring cereals, particularly through early-season canopy traits.
Why it matches plant phenotyping methodsUAVによる地上被覆の大規模な表現型取得とゲノム予測への統合が研究の中心であり、育種利用を技術的に評価している。
abstractA diverse panel of 461 oat genotypes and 218 spring wheat genotypes were phenotyped using UAVs in May and June.
Accurate and efficient estimations of leaf area index (LAI) are crucial for crop management, including intelligent crop breeding, nutrient management, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral sensors with machine learning models provide high-precision solutions for LAI estimation but are hindered by the challenge of acquiring adequate ground truth data. Transfer learning, one type of deep learning framework, offers a solution by leveraging prior knowledge learned through models that have been pre-trained, thus ensuring robust performance despite limited data availability. This study evaluates the efficacy of the fine-tuning PROSAIL-Informed Deep Neural Network model (PROSAIL-DNN) for estimating LAI of different oat varieties and growth periods using UAV multispectral images. We compared this model with several widely used machine learning algorithms, including partial least squares (PLS), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), and extreme gradient boosting (XGBoost)), and a DNN, using training datasets consisting of 70 %, 60 %, and 50 % of the field data to assess the impact of data volume on model performance. Our results demonstrated that the PROSAIL-DNN model outperformed other algorithms across different growth periods and all growth periods. Specifically, with only 50 % of the training data used, the average R² of the PROSAIL-DNN model was 5.44 %, 28.76 %, 12.98 %, 12.72 %, and 22.90 % higher than those of DNN, Lasso, PLS, SVR, and XGBoost, respectively. The PROSAIL-DNN model also demonstrated higher accuracy in monitoring LAI during different growth periods, especially at early jointing period (P1) (R² = 0.838, RMSE = 0.376, and RPD = 2.483) and at post-heading period (P3) (R² = 0.881, RMSE = 0.298, and RPD = 2.896). Our findings underscore the potential of combining PROSAIL with deep transfer learning to accurately and robustly estimate oat LAI at various growth periods using UAV multispectral images with limited field data. This approach provides a solid foundation for applying transfer learning in crop monitoring and can be adapted for other crop variables in future studies.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL-DNNを用いて、オート麦の葉面積指数（LAI）を推定する画像解析・機械学習手法が研究の中心であり、複数モデルとの技術比較とデータ量による性能評価も実施している。
abstractThis study evaluates the efficacy of the fine-tuning PROSAIL-Informed Deep Neural Network model (PROSAIL-DNN) for estimating LAI of different oat varieties and growth periods using UAV multispectral images.
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy (R2 = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から飼料作物の成熟度という植物状態を推定し、ニューラルネットワークと可視化プラットフォームまで構築しているため、表現型取得・推定手法が中心である。
abstractachieved a high predictive accuracy (R2 = 0.985) for estimating the continuous maturity index
Abiotic stresses, such as heat and drought, often reduce crop yields by harming plant health. Plants have evolved complex signaling networks to mitigate environmental impacts, making monitoring in-situ biosignals a promising tool for assessing plant health in real time. In this study, needle-like sensors were used to measure electrical potential changes in oat and canola plants under heat and drought stress conditions. Signals were recorded over a 30-min period and segmented into time intervals of 1-, 5-, 10-, 20-, and 30-min. Machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machines, were applied to classify stress conditions and estimate biomass based on 14 extracted bioelectrical features, such as signal amplitude and entropy. Results showed that heat stress primarily altered signal patterns, whereas drought stress affected the signal intensity, possibly due to a reduction in the flow rate of charged ions. Random Forest classifier successfully identified over 85 % of stressed crops within 30 min of signal recording. These signals also explained 58–95 % of the variation in plant aboveground and root biomass, depending on stress intensity and crop genotype. This study demonstrates the potential of using bioelectrical sensing as a rapid and efficient tool for stress detection and biomass estimation. Future research should explore the ability to use biosensors to capture genetic variability to mitigate abiotic stresses and combine this with remote sensing and other emerging precision agriculture technologies.
Why it matches plant phenotyping methods植物の生体電気信号を測定し、機械学習でストレス状態を分類するとともにバイオマスを推定するセンシング手法が研究の中心であり、植物表現型の取得・推定方法を実質的に評価している。
abstractneedle-like sensors were used to measure electrical potential changes in oat and canola plants under heat and drought stress conditions.
The objective of this study is to use synchrotron-based X-ray fluorescence imaging (XFI) and bulk analysis to investigate elements (Mn, Fe, Cu, Zn, P, S, K, Ca) distributions and relative concentrations in four cool-season oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit) obtained from the same growing location, soil conditions and harvest time at the University of Saskatchewan. XFI at the Canadian Light Source's BioXAS-Imaging beamline (5 μm resolution, 15 keV) revealed that P, K, Mn, and Zn were concentrated in the aleurone layer, scutellum, and embryo, while Ca was only localized in the aleurone layer and scutellum in the four oat varieties. Notably, S and Cu were distributed in all parts of the seed across four varieties, but the intensity was low in the endosperm. Bulk analysis results show that there were significant differences in the relative concentrations of K, Fe and Zn among four oat varieties harvested for three consecutive years (2018, 2019, 2020) at the completely mature stage. CDC Nasser oat had the lowest K and Zn, while CDC Haymaker had the highest Fe among the oat varieties. These findings highlight the impact of variety on nutritional quality and could help inform future biofortification strategies to enhance the micronutrient content for human and animal diets. This work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI. Unlike rice, oats showed minimal mineral accumulation in the hull, ensuring nutritional retention post-milling. Overall, this study shows XFI's potential as a non-destructive tool for cereal grain analysis and supports breeding nutrient-dense oat varieties to address global micronutrient deficiencies.
Why it matches plant phenotyping methodsオーツ種子の元素分布・濃度という植物器官形質を高解像度X線蛍光イメージングで取得し、非破壊測定法としての有用性を示すことが中心的です。
abstractThis work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI.
Natural biocomposites such as wood and plant cell walls exhibit prominent mechanical properties largely attributed to the nanoscale organization of fibrous components, such as cellulose, which often adopt chiral arrangements. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization provides insights into material properties as well as opportunities for designing bioinspired materials with tunable mechanical and functional properties that extend far beyond natural biocomposite materials.
Why it matches plant phenotyping methods植物細胞壁のセルロース微繊維配向を定量的に再構成・測定する3D SED法が研究の中心であり、植物構造形質の取得手法に該当する。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
The objective of this study is to use synchrotron-based X-ray fluorescence imaging (XFI) and bulk analysis to investigate elements (Mn, Fe, Cu, Zn, P, S, K, Ca) distributions and relative concentrations in four cool-season oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit) obtained from the same growing location, soil conditions and harvest time at the University of Saskatchewan. XFI at the Canadian Light Source's BioXAS-Imaging beamline (5 μm resolution, 15 keV) revealed that P, K, Mn, and Zn were concentrated in the aleurone layer, scutellum, and embryo, while Ca was only localized in the aleurone layer and scutellum in the four oat varieties. Notably, S and Cu were distributed in all parts of the seed across four varieties, but the intensity was low in the endosperm. Bulk analysis results show that there were significant differences in the relative concentrations of K, Fe and Zn among four oat varieties harvested for three consecutive years (2018, 2019, 2020) at the completely mature stage. CDC Nasser oat had the lowest K and Zn, while CDC Haymaker had the highest Fe among the oat varieties. These findings highlight the impact of variety on nutritional quality and could help inform future biofortification strategies to enhance the micronutrient content for human and animal diets. This work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI. Unlike rice, oats showed minimal mineral accumulation in the hull, ensuring nutritional retention post-milling. Overall, this study shows XFI's potential as a non-destructive tool for cereal grain analysis and supports breeding nutrient-dense oat varieties to address global micronutrient deficiencies.
Why it matches plant phenotyping methodsX線蛍光イメージングを用いてオート麦種子の元素分布・相対濃度を高解像度かつ非破壊で取得し、その方法の有用性を主要な成果として示しているため、植物器官の化学的形質測定法として採用。
abstractThis work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI.
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:135-161Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
1 Abstract Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have been largely quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Here, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under two distinct shape categories, and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including arabidopsis ( Arabidopsis thaliana) , brachypodium ( Brachypodium distachyon ), medicago ( Medicago truncatula ), oat ( Avena sativa ), rice ( Oryza sativa ), teff ( Eragostis tef ) and tomato ( Solanum lycopersicum ). The application of pyRootHair enables users to rapidly screen large numbers of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variaton on plant performance.
Why it matches plant phenotyping methods根毛形態という植物形質を画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・検証しており、表現型取得手法が研究の中心である。
abstractHere, we present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from images of plant roots grown on agar plates.
The modelling and prediction of important agronomic traits using remotely sensed data is an evolving science and an attractive concept for plant breeders, as manual crop phenotyping is both expensive and time consuming. Major limiting factors in creating robust prediction models include the appropriate integration of data across different years and sites, and the availability of sufficient genetic and phenotypic diversity. Variable weather patterns, especially at higher latitudes, add to the complexity of this integration. This study introduces a novel approach by using photothermal time units to align spectral data from unmanned aerial system images of spring, winter, and facultative oat (Avena sativa) trials conducted over different years at a trial site at Aberystwyth, on the western Atlantic seaboard of the UK. The resulting regression and classification models for various agronomic traits are of significant interest to oat breeding programmes. The potential applications of these findings include optimising breeding strategies, improving crop yield predictions, and enhancing the efficiency of resource allocation in breeding programmes.
Why it matches plant phenotyping methodsUASマルチスペクトル画像とフォトサーマル時間単位を統合し、オート育種試験の農業形質を予測する手法が研究の中心である。
abstractThis study introduces a novel approach by using photothermal time units to align spectral data from unmanned aerial system images
Reproduction assets foundThe paper's Data Availability Statement points to a public deposit of the study's UAS spectral and ground-truth oat trial data at the Aberystwyth Data Repository (DOI 10.20391/ec0863ab-3b5c-434b-837e-74bae4400387). No author analysis code or trained models are explicitly deposited; the supplementary materials contain只有Dataset · publicData Availability Statement: Data are available from the Aberystwyth Data Repository:
https://doi.org/10.20391/ec0863ab-3b5c-434b-837e-74bae4400387.Open asset ↗Aberystwyth Data Repository · 10.20391/ec0863ab-3b5c-434b-837e-74bae4400387pdf-page:19 lines:1-56Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Natural biocomposites such as wood and plant cell walls exhibit remarkable mechanical properties largely attributed to their nanoscale chiral organization of fibrous components, such as cellulose. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization opens new understanding of materials properties as well as opportunities for the design of bio-inspired materials with tunable mechanical and functional properties.
Why it matches plant phenotyping methods植物細胞壁中のセルロース fibril の3D配向を定量マッピングする画像計測・再構成法が研究の中心であり、植物構造形質の取得手法を開発している。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
Reproduction assets foundThe article's Data and Code Availability statement declares that the SED datasets (diffraction data from oat husk and birch wood) and the authors' custom Python analysis script are publicly available on Zenodo (DOI: 10.5281/zenodo.15647651). This is a paper-specific, public, actionable asset directly reproducing the 3DDataset · publicData and Code Availability
SED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code · publicSED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R2 = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R2 = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R2 = 0.86), AWP with red-edge reflectance (R2 = 0.71), turnips with NIR, GNDVI, and CHM (R2 = 0.95), and mixed species with NIR and blue band reflectance (R2 = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.
Why it matches plant phenotyping methodsUAV搭載マルチスペクトル画像と植生指数・キャノピー高モデルを用いて、被覆作物のバイオマスという植物形質を推定し、モデル性能を比較・検証しているため、フェノタイピング手法が中心である。
abstractThis study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass
Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R2 values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R2 values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R2 values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からオートの収量を推定するアンサンブル解析手法を構築・比較しており、植物形質の取得・推定が研究の中心である。
abstractAn ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield.
Plant domestication is thought to create trade-offs between high yield and stress tolerance, raising concerns about yield stability in future climates. Previous studies have found limited direct evidence for such trade-offs, often focusing on weakened defenses associated with higher growth rates. However, trade-offs can also occur when traits (such as yield in agriculture) optimized for favorable conditions perform less efficiently in stressful conditions. Deciphering the mechanisms driving these trade-offs is crucial for maintaining yield in changing environments. We examine leaf growth, a key trait influencing carbon assimilation, in eight species of grasses. We use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics, finding that domesticated plants generally have longer leaves, larger division zones and higher cell production rates. We found no clear evidence of trade-off between domestication and drought response in final leaf length. However, a trade-off is observed in development as wild species exhibited a smaller decrease in elongation zone size under drought than their domesticated counterparts. These nuanced trade-offs associated with domestication highlight the importance of examining physiological traits and mechanisms in greater detail, possibly informing breeding strategies to enhance crop resilience in the face of climate change. Highlight This study uses a high throughput pipeline to characterize leaf elongation responding to drought stress across eight species including barley, wheat, oat and wild relatives.
Why it matches plant phenotyping methods葉の顕微鏡画像から細胞寸法・位置を自動抽出する機械学習パイプラインが、葉の成長特性評価の中心的手法として用いられているため。
abstractWe use a machine learning pipeline to automatically extract cell dimensions and positions from leaf microscope images to study cell kinematics
Cover crops (CC) immobilize mineral soil N in their biomass, preventing N losses during crop rotation intervals. As the CC biomass is incorporated into the soil and decomposes, N is released for the following main crop. The efficiency of CC N uptake and release depends on CC quantity and quality, which can be enhanced in mixtures. Traditional N uptake measurements are labour-intensive and limited in capturing spatial variability. We calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures. This innovative approach combined vegetation indices, textural features, and a photogrammetry-derived canopy surface model to predict CC traits. Linear models were trained for biomass, N uptake, and C:N predictions, while a K-Nearest-Neighbour model was trained for N concentration. When evaluated on the test set, the calibrated remote sensing models accurately predicted CC aboveground biomass (R 2 : 0.71, RMSE: 287.1 kg/ha, NRMSE: 11.74 %), N concentration (R 2 : 0.80, RMSE: 1.77 gN /kg, NRMSE: 6.96 %), N uptake (R 2 : 0.56, RMSE: 9.38 kgN /ha, NRMSE: 15.08 %), and C:N ratio (R 2 : 0.62, RMSE: 1.86, NRMSE: 10.98 %). The field experiment included monocultures, bi-, and tri-species mixtures of common vetch ( Vicia sativa ), black oat ( Avena strigosa ), and fodder radish ( Raphanus sativus ). N uptake was similar between treatments, yet the CC species differed in strategies, producing high biomass with low N concentration or vice versa. This study provides a basis for spatially predicting key CC traits using UAV optical data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、テクスチャ特徴、フォトグラメトリ由来モデルを用いて、植物のバイオマス、窒素濃度、窒素吸収量、C:N比を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractWe calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures.
Reproduction assets foundThe paper's Data availability statement explicitly states the authors' R code for image processing, model training, and figure production is publicly available on the authors' WUR GitLab repository (uav4covercroptraits). No phenotype dataset or image deposit is stated separately.Code · publictal for the
UAV data acquisition.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2024.100608.
Data availability
The R code generated during this study to process the images, train
the models and produce the figures, is publicly available at https://git.wur.nl/dall002/uav4covercroptraits.References
[1] C. Aita, S.J. Giacomini, Crop residue decomposition and nitrogen release in singleOpen asset ↗git.wur.nl/dall002/uav4covercroptraitspdf-raw-page:10 lines:1-89Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いてオート麦の飼料バイオマスを推定する手法が研究の中心であり、植物形質の取得・予測ワークフローを実質的に評価している。
abstractThis research focused on estimating and predicting forage oat biomass using UAV and agronomic variables.
Contemporary crop research programs involve the evaluation of numerous micro-plots spread across extensive experimental fields. As a result, there is a growing need to depart from labor-intensive manual measurements when assessing phenological data. The growing significance of high throughput phenotyping platforms (HTTP), including unmanned aerial vehicles (UAVs), has rendered these technologies essential in crop research. The overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions. To pinpoint DH and DM prediction, a total of nine UAV surveys were conducted throughout the entire crop cycle. Prediction models for DH and DM using VIs were successfully developed for various crop species, explaining 65 % of the variance in bread wheat and 75 % in oats. The highest percentages of variance explained were achieved when models were developed separately for the two irrigation conditions (well-irrigated and rainfed). However, the percentage of variance explained by these models decreased when applied to barley (R²<0.5 for DH). Notably, including final plant height as a predictor increased the percentage of variance explained by the models only for irrigated bread wheat. Furthermore, the utilization of multi-temporal equations, which amalgamated data from diverse UAV surveys, notably enhanced the percentage of variance explained by the model (+160.71 % improvement in DH predictions), particularly those tailored to each specific crop species and irrigation condition. The investigation additionally established a thorough protocol for modeling the phenological aspects of cereal crops utilizing data acquired from UAVs, thereby enhancing the accessibility of this technology for measurements of phenology in large crop research programs. • Low-cost UAVs enable adequate phenology estimates using VIs in wheat and oats. • VIs near maturity and plant height are key to phenology prediction. • Improved models for heading date combine multiple UAV surveys during grain filling. • Single flight near maturity suffices for accurate prediction of days to maturity. • Phenology prediction models for barley were less robust.
Why it matches plant phenotyping methodsUAVのRGB画像から植生指数を抽出し、出穂日・成熟日という植物表現型を予測する手法を開発・検証しており、測定ワークフローが研究の中心です。
abstractThe overall objective of this study is to explore and validate the use of HTTP methodologies, specifically the potential of vegetation indices (VIs) derived from conventional RGB images, to forecast the date of heading (DH) and maturity (DM) for various cereal crops under different irrigation conditions.
Mechanised non-contact, non-destructive imaging methodologies have revolutionised plant phenotyping, increasing throughput well beyond what was possible using traditional manual methods. Quantifying the variation in post-harvest material such as seeds and fruits, usually the economically important part of the crop, can be critical for commercial quality assessment as well as breeding and research. Therefore, reliable methods that gather metrics of interest, quickly and efficiently, are of widespread interest across sectors. This study focuses on evaluating the phenotyping capabilities of the Cgrain Value™, a novel grain imaging machine designed for quality and purity assessment and used primarily in commercial cereal production and processing. The performance of the Cgrain Value™ in its generation of high-throughput quantitative phenotypic data is compared with a well-established machine, MARVIN, assessing repeatability and reproducibility across a range of metrics. The findings highlight the potential of the Cgrain Value™, and some shortcomings, to provide detailed three-dimensional size, shape, and colour information rapidly, offering insights into oat grain morphology that could enhance genome-wide association studies and inform the breeding efforts in oat improvement programmes.
Why it matches plant phenotyping methodsオート麦種子の形態形質を取得する画像計測機器を対象に、既存機器との反復性・再現性を比較評価しており、フェノタイピング手法の技術検証が中心である。
abstractThis study focuses on evaluating the phenotyping capabilities of the Cgrain Value™, a novel grain imaging machine designed for quality and purity assessment
Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories.
Why it matches plant phenotyping methodsSAR植生指数を用いて作物の生育特性・フェノロジーを測定し、NDVI等との比較、安定性評価、分類性能検証を行うことが中心であり、作物状態のセンサー計測法の検証に該当する。
abstractWe evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season.
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our correlation results ranged from r 2 = 0.81 for protein in wheat and oats to r 2 = 0.99 for polyphenol content in lettuce in both the Reflectometer and laboratory spectrophotometer assessment, suggesting the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Repeatability evaluation demonstrated good reproducibility of the Reflectometer to assess crop phytochemical content. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物の植物化学成分量を測定する低コスト反射計を、実験室用分光光度計と比較して精度・再現性検証しており、植物形質の取得手法の技術的検証が中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all Bionutrient Institute data (reflectometer/spectrometer phytochemical measurements used in this study) are publicly available in the authors' GitLab repository, and the authors' data-processing pipeline code is also publicly hosted on GitLab.Dataset · publicAll data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset . The data used in this manuscript covers samples submitted up to 7/31/2022.Open asset ↗our-sci/bionutrient-institute/datasetlines:156-212Code · publicAn automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.Open asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:132-143Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Accurate and timely monitoring of biomass in breeding nurseries is essential for evaluating plant performance and selecting superior genotypes. Traditional methods for phenotyping above-ground biomass in field conditions requires significant time, cost, and labor. Unmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost. While Vegetation Indices (VIs) extracted from remote sensing imagery have been widely employed for biomass estimation, they mainly capture spectral information and disregard the 3D canopy structure and spatial pixel relationships. Addressing these limitations, this study, conducted in 2020 and 2021, aimed to explore the potential of integrating UAV multispectral imagery-derived canopy spectral, structural, and textural features with machine learning algorithms for accurate oat biomass estimation. Six oat genotypes planted at two seeding rates were evaluated in two South Dakota locations at multiple growth stages. Plot-level canopy spectral, structural, and textural features were extracted from the multispectral imagery and used as input variables for three machine learning models: Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The results showed that (1) in addition to canopy spectral features, canopy structural and textural features are also important indicators for oat biomass estimation; (2) combining spectral, structural, and textural features significantly improved biomass estimation accuracy over using a single feature type; (3) machine learning algorithms showed good predictive ability with slightly better estimation accuracy shown by RFR (R 2 = 0.926 and relative root mean square error (RMSE%) = 15.97%). This study demonstrated the benefits of UAV imagery-based multi-feature fusion using machine learning for above-ground biomass estimation in oat breeding nurseries, holding promise for enhancing the efficiency of oat breeding through UAV-based phenotyping and crop management practices.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からスペクトル・構造・テクスチャ特徴を抽出し、機械学習でオートの地上部バイオマスを推定するフェノタイピング手法を中心に、特徴融合とモデル性能を評価している。
abstractUnmanned Aerial Vehicles (UAVs) offer a rapid and non-destructive approach for phenotyping multiple field plots at a low cost.
Abstract Crop diversification is a potential strategy to increase the stability and productivity of crops, while reducing pathogen pressures and pesticide requirements. Crop variety mixtures provide some of these diversification benefits and their cultivation is fully compatible with current mechanized agronomic practices. However, the development of optimal variety mixtures is a long, labour‐intense process requiring extensive field trials. High throughput field phenotyping (HTFP) methods provide promising applications in field testing because they allow for precise, repeatable, and rapid measurements of crop properties. Here, we evaluated the use of HTFP for developing high‐performing oat (Avena sativa) variety mixtures by testing its suitability to predict diversity yield benefits from repeated canopy measurements across the growing season. Analyzing 26 mixtures of five varieties, we found significant overyielding at harvest, that is, mixtures were on average more productive than expected based on component pure stands. This grain yield overyielding was well predicted from deviations between mixture and pure stand canopy cover estimations, derived from HTFP mid‐way through the growing season. This shows that (i) positive interactions between oat varieties occur already at an early stage, (ii) such interactions lead to increased potential for light interception, (iii) HTFP offers rapid, scalable methods to screen for performant variety mixtures.
Why it matches plant phenotyping methodsHTFPによるキャノピー被覆率の反復測定と収量予測を、品種混合のスクリーニング手法として中心的に評価しているため。
abstractHigh throughput field phenotyping (HTFP) methods provide promising applications in field testing because they allow for precise, repeatable, and rapid measurements of crop properties.
High throughput phenotyping has wide applications to evaluate genetic traits, plant growth and development in different biotic and abiotic stress environments, as well as under different agriculture management strategies. Additionally, understanding product efficacy and identifying the mode of action prior to field testing would improve product pipeline development for agriculture manufacturers and distributors. WinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness. We have evaluated pesticide product efficacy in the presence and absence of adjuvants. Adjuvants are materials added to a pesticide to enhance performance by improving absorption, spreading, sticking, and penetration properties of the pesticide’s active ingredient(s). We have measured a statistically significant increase in pesticide efficacy with the addition of an adjuvant in multiple, independent case studies showing the increased plant health benefit. In one study, we observed a 31% decrease in diseased oat plant tissue [when defined as pixels with Normalized Difference Vegetation Index (NDVI) value <0.3] when an adjuvant was added to a commercial fungicide compared to the untreated control and a 23% improvement when compared to the fungicide alone. Multispectral imaging has also allowed us to build interactive, 3-D models that demonstrate product coverage and penetration. Combining quantitative measurements with interactive, illustrative models, we can more effectively communicate product efficacy results to retail owners and growers. Future directions include evaluating biological product efficacy in which more nuanced plant responses are observed in biotic and abiotic stress environments.
Why it matches plant phenotyping methodsマルチスペクトル画像を用いて植物の健康状態・病害組織を定量化し、農薬・アジュバント効果評価へ実質的に適用しているため、表現型取得法の応用研究として含める。
abstractWinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness.
High throughput phenotyping has wide applications to evaluate genetic traits, plant growth and development in different biotic and abiotic stress environments, as well as under different agriculture management strategies. Additionally, understanding product efficacy and identifying the mode of action prior to field testing would improve product pipeline development for agriculture manufacturers and distributors. WinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness. We have evaluated pesticide product efficacy in the presence and absence of adjuvants. Adjuvants are materials added to a pesticide to enhance performance by improving absorption, spreading, sticking, and penetration properties of the pesticide's active ingredient(s). We have measured a statistically significant increase in pesticide efficacy with the addition of an adjuvant in multiple, independent case studies showing the increased plant health benefit. In one study, we observed a 31% decrease in diseased oat plant tissue [when defined as pixels with Normalized Difference Vegetation Index (NDVI) value <0.3] when an adjuvant was added to a commercial fungicide compared to the untreated control and a 23% improvement when compared to the fungicide alone. Multispectral imaging has also allowed us to build interactive, 3-D models that demonstrate product coverage and penetration. Combining quantitative measurements with interactive, illustrative models, we can more effectively communicate product efficacy results to retail owners and growers. Future directions include evaluating biological product efficacy in which more nuanced plant responses are observed in biotic and abiotic stress environments.
Why it matches plant phenotyping methodsマルチスペクトル画像とNDVIによる植物の健康状態・病害組織の定量化を、農薬・アジュバント効果評価の中心的な手法として適用しているため。
abstractWinField United is using multispectral imaging in different stress conditions within a controlled environment setting to evaluate product effectiveness.
Cover crops and precision fertilization are two core strategies to advance sustainable agriculture. Based on a review of proven achievements in remote sensing of vegetation, a novel approach is proposed to use remote-sensing of cover crops to map soil nutrient availability and to produce prescription maps for precision basal fertilization prior to sowing the following cash crop. The first goal of this manuscript is to introduce the concept of using remote-sensing of cover crops as 'reflectors' or 'bio-indicators' of soil nutrient availability. This concept has two components: 1. mapping nitrogen availability using remote-sensing of cover crops; 2. using remotely-detected visual symptoms of cover crops' nutrient deficiencies to guide sampling schemes. The second goal was to describe two case studies that initially evaluated the feasibility of this concept in a 20 ha field. In the first case study, cover crops mixtures containing legumes and cereals were sown during two seasons in soils with different nitrogen levels. Cereals dominated the mixture when soil nitrogen levels were low, while legumes dominated when levels were high. Plant height and texture analysis derived from UAV-RGB-images were used to measure differences between the dominant species as an indicator of soil nitrogen availability. In the second case study, in an oat cover crop, three different appearances of visual symptoms (phenotypes) were observed throughout the field, and laboratory analysis showed they significantly differed in their nutrient levels. Spectral vegetation indices and plant height derived from UAV-RGB-images were analyzed by a multi-stage classification procedure to differentiate between the phenotypes. The classified product was interpreted and interpolated to generate a high-resolution map showing nutrient uptake for the whole field. The suggested concept essentially elevates the services cover crops can provide to benefit sustainable agriculture if incorporated with remote-sensing. The potentials, limitations and open questions concerning the suggested concept are discussed.
Why it matches plant phenotyping methods被覆作物の表現型（草丈、テクスチャ、栄養欠乏症状）をUAV画像・スペクトル指標から抽出し、分類・マッピングするリモートセンシング手法が研究の中心であるため。
abstractPlant height and texture analysis derived from UAV-RGB-images were used to measure differences between the dominant species as an indicator of soil nitrogen availability.
Five winter oat (Avena sativa L.) varieties were set in a small-plot field experiment to examine the abiotic stress considering silicone and sulphur foliar fertilization treatments under temperate and dry climatic conditions in Hungary. Numerous in situ and laboratory measurements were performed to describe the crop's condition at various phenological stages. Drones with multispectral, thermal and LiDAR payloads monitored the field both with high temporal and spatial resolution. A high level of GIS data assimilation was performed in order to handle the different spatial-related parameters in one interface. It is a multi-purpose experiment, and for all of them it is an important criterion whether the study was carried out in a truly homogeneous area. Practically, it means that we ignore the patterns of the crop or the soil. If this is not the case, the various parameters measured should be evaluated accordingly. Hence, our study's main goal here is to reveal the soil and crop heterogeneity level. For this, all the measured parameters are involved in the multi-parameter analysis by which the heterogeneity level of the site can be assessed.Practically, by this, we can answer the main question: is the field suitable to carry out analysis such as abiotic stress studies or yield prediction modelling on it or shall we handle certain parts differently?Based on the example of our experiment we design a workflow by which the heterogeneity level of a small-plot field can be assessed and provide a solution for how to handle it in order not to involve data which may mislead analysis.
Why it matches plant phenotyping methods小区圃場の土壌・作物不均一性を評価するため、UAVのマルチスペクトル、熱、LiDARデータと多パラメータ解析を統合した再利用可能な評価ワークフローを設計しており、作物状態の取得・解析が中心である。
abstractDrones with multispectral, thermal and LiDAR payloads monitored the field both with high temporal and spatial resolution.
Cultivar identification plays an important role in ensuring the quality of oat production and the interests of producers. However, the traditional methods for discrimination of oat cultivars are generally destructive, time-consuming and complex. In this study, the feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis. The principal component analysis (PCA), linear discrimination analysis (LDA) and support vector machines (SVM) were applied to classify seeds of 16 oat cultivars according to their morphological features, spectral traits or a combination thereof. The results demonstrate that clear differences among cultivars of oat seeds could be easily visualized using the multispectral imaging technique and an excellent discrimination could be achieved by combining data of the morphological and spectral features. The average classification accuracy of the testing sets was 89.69% for LDA, and 92.71% for SVM model. Therefore, the potential of a new method for rapid and nondestructive identification of oat cultivars was provided by multispectral imaging combined with multivariate analysis.
Why it matches plant phenotyping methodsオート種子の形態・スペクトル特徴をマルチスペクトル画像と多変量解析で取得・分類する手法が研究の中心であり、植物生殖資材の表現型・品種識別に直接関係するため。
abstractthe feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis.
Early-vigour is a plant trait phenotypically characterised by a rapid expansion of leaf area in early stages of crop growth, before canopy closure. Although early-vigour is already used as a selection criteria in breeding programmes for grain cereals, its genetic variability and potential benefits for oat cover crops is unknown. In this study, we screened 231 oat lines from a commercial forage breeding programme using high-throughput field phenotyping to quantify canopy development rates through aerial photographs. Results showed a wide genetic variability for early-vigour in oats, with canopy cover differences of up to ∼20% during the approximate 30 days period from emergence to full canopy cover. Two oat genotypes with contrasting canopy cover development rates (opposite quartile ranges of the population) were subsequently selected for a detailed investigation of underlying mechanisms explaining early-vigour during two field trials. For these genotypes, the size of individual leaves was found to be the main factor driving differences, with 50% larger leaf area before the sixth leaf from the base of the main tiller in the high early-vigour genotype. A process-based biophysical model (APSIM-NextGen oats) was then parameterised to quantify variability on potential early-vigour benefits across four representative agricultural target environments. This was done by simulating the two selected oat genotypes across 30-years of historical weather data from each of the four distinct climatic zones, considering two contrasting soil types and three possible cover crop sowing dates. Results showed early-vigour to provide overall positive ecosystems services with pooled medians of 7–18% increase in above ground biomass and 5–13% increase in nitrogen uptake which caused a consequent 4–9% reduction in nitrogen leaching losses depending on the location/soil/management combination. Such decline in relative plant-trait effects across different components of the production system (above-ground biomass, above-ground N and N leaching reduction) was also accompanied by increasing variability in responses, with pooled coefficients of variation around 31%, 69% and 80% respectively. Similarly, our results also highlight the relative dilution of trait effects across scales. For instance, a 50–70% difference in basal leaf size at the plant-organ scale caused a 6–10% potential reduction of N leaching at the agricultural system scale. These results highlight the importance of multi-metric evaluations of spatial and temporal variability in trait effects to better inform breeding and selection programmes. Finally, our practical implementation of previously conceptualised methods illustrates the increased depth of understanding about plant-trait benefits when combining interdisciplinary approaches such as high throughput phenotyping, classic crop physiology field experimentation and biophysical modelling. The principles of this approach can be extended to assess the relative value of other traits across different species, managements and environments.
Why it matches plant phenotyping methods高スループット圃場フェノタイピングを用いて航空写真からキャノピー発達率を定量化することが研究の中心的手法であり、早期生育旺盛性という植物形質を評価している。
abstractwe screened 231 oat lines from a commercial forage breeding programme using high-throughput field phenotyping to quantify canopy development rates through aerial photographs.
There is limited information regarding the morphometric relationships of panicle traits in oat (Avena sativa) and their contribution to phenology and growth, physiology, and pathology traits important for yield. To model panicle growth and development and identify genomic regions associated with corresponding traits, 10 diverse spring oat mapping populations (n = 2,993) were evaluated in the field and 9 genotyped via genotyping-by-sequencing. Representative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables. Spatial modeling and days to heading were used to account for environmental and phenological variances, respectively. Panicle variables were intercorrelated, providing reproducible archetypal and growth models. Notably, adult plant resistance for oat crown rust was most prominent for taller, stiff stalked plants having a more open panicle structure. Within and among family variance for panicle traits reflected the moderate-to-high heritability and mutual genome-wide associations (hotspots) with numerous high-effect loci. Candidate genes and potential breeding applications are discussed. This work adds to the growing genetic resources for oat and provides a unique perspective on the genetic basis of panicle architecture in cereal crops.
Why it matches plant phenotyping methodsオート麦の穂をスキャンし、手動・自動画像解析で60以上の形態形質を抽出して、再現可能な成長・形態モデルを構築している。遺伝解析を含むが、画像ベースの穂形態計測と解析ワークフローが主要な貢献である。
abstractRepresentative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables.
Reproduction assets foundThe paper's Data Availability statement deposits the paper-specific phenotype dataset (Supplementary Dataset 1), panicle image dataset (Supplementary Dataset 2), and R analysis code on Zenodo under DOI 10.5281/zenodo.7011302, which is an allowed URL. This directly reproduces the paper's panicle phenotyping measurementsCode · publicSupplementary Dataset 1 (phenotypes), Supplementary Dataset 2 (panicle images), R code, and corresponding metadata are available online at https://zenodo.org/ , registered under: https://doi.org/10.5281/zenodo.7011302 .Open asset ↗zenodo.org · 10.5281/zenodo.7011302lines:747-785Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.
Why it matches plant phenotyping methods風洞実験と有限要素シミュレーションを統合し、植物茎の曲げ挙動・強度という表現型を予測・説明する手法が研究の中心である。
abstractWe ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions.
Reproduction assets foundThe paper's wind tunnel plant phenotyping assets are publicly available: raw wind tunnel videos of the cereal plants (DRUM repository), the authors' video-analysis scripts (GitHub), and the multi-scale finite-element model code (Dryad). Supplementary material with sample video and analysis details is on Figshare.Code · publiche scripts used and location of the data analysed from the wind tunnel experiment. Multi-scale material model codes in Python, Abaqus model file and python script for automatized simulations at different wind speed levels pertaining to multi-scale finite-element model simulations are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.612jm644j [ 53 ].
Supplementary material is available online [ 54 ].
Authors' contributionsOpen asset ↗Dryad Digital Repository · 10.5061/dryad.612jm644jlines:229-239Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 8 Sept 2026
Canopy covers can be measured using destructive (visual) and non-destructive methods (spectral indices, photogrammetry, visual assessment, and quantum sensor). The precision of crop cover estimation, however, is dependent on the selection of appropriate methods. Studies were conducted at the Indian Grassland and Fodder Research Institute, Jhansi to compare the forage crops canopy cover estimated using photogrammetry software (Canopeo and SamplePoint) and visual assessments. Assessments were performed in three summer crops (corn, cowpea, and sorghum), two winter crops (Egyptian clover, and oats), and bare ground condition. For each plot, three nadir images (directly above the canopy) were captured using digital cameras from a height of 1.5 m above the soil surface between 10 AM to 2 PM on bright sunny days. The results indicated that the relationships between visual assessment and Canopeo (regression coefficient, (R 2 = 0.96), visual assessment and SamplePoint (0.96), and Canopeo and SamplePoint (0.98) were linear when data were pooled across all the crops. SamplePoint and Canopeo is further, appropriate for cowpea (Pearson coefficient ( R = 0.99 and 0.94), oats (0.92 and 0.97), and sorghum (0.46 and 0.51), respectively. SamplePoint and Canopeo are not suitable for berseem (-0.15) and corn (-0.61), respectively, due to dead residues after the first harvest in berseem and taller corn might have influenced the image quality. Therefore, the stage of the crop, the height of the crop, and dead residues around the plants can greatly influence the estimation of crop cover. In conclusion, the results indicated that this photogrammetry software can be used for non-destructive crop canopy measurement with the above-mentioned precautions in the forage crops tested. •Forage canopy cover is estimated generally by visual scoring, and the outcome varies widely from person to person.•Photogrammetry methods (Canopeo and SamplePoint) were positiviely correlated with visual scoring for cowpea, oats, and sorghum.•However, Canopeo and SamplePoint may not suitable for taller crops like corn and ratoon crops like berseem.
Why it matches plant phenotyping methods飼料作物のキャノピー被覆率という植物形質を対象に、CanopeoとSamplePointの画像解析法を比較・検証しており、フェノタイピング手法が研究の中心です。
abstractThe precision of crop cover estimation, however, is dependent on the selection of appropriate methods.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.
Why it matches plant phenotyping methodsX線CTと自動解析アルゴリズムを開発し、穀類の穂・粒形質を高速かつ非破壊で抽出する手法が研究の中心で、精度・効率も検証している。
abstractwe developed a high-throughput and non-destructive method to analyse cereal spikes.
The application of herbicides in agriculture has significantly increased in recent decades. While many herbicides improve the efficiency and efficacy of weed control, their excessive use at the wrong growth stage can cause crop foliar damage, higher input cost and negative environmental footprints. There are limited techniques to accurately monitor herbicide effects. Visual ratings are highly subjective and require extensive training or experience. High-throughput digital imaging is a promising tool to measure plant herbicide interaction in field crops. In this study, proximal and aerial based advanced sensors have been utilized to evaluate different herbicide modes-of-action in two model species, tame oat [ Avena sativa ; model for wild oat ( Avena fatua )] and oriental mustard [ Brassica juncea ; model for wild mustard ( Sinapis arvensis )]. The experimental trials were performed at three agro-climatic locations in Canada (Lethbridge (AB), Saskatoon (SK), and Lacombe (AB)). The proximal and UAV multispectral imagery data were collected for baseline (before treatment) and 1, 3, 7, 10, 14 and 21 days after treatments (DAT), alongside visual ratings. The Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index, Chlorophyll Vegetation Index, and Optimized Soil Adjusted Vegetation Index were used to assess variation of different DAT pigment content (photosynthetic rate) and chlorosis (damage %) in plot vegetation. The variation in obtained temporal indices (NDVI) suggest that the developed technology has potential to replace visual ratings (R 2 ≈0.65-0.94) and can be used as a rapid screening tool for herbicide activity. Therefore, remote sensing tools could improve the precision and consistency of future herbicide assessments.
Why it matches plant phenotyping methods近接・UAVマルチスペクトル画像と植生指数を用いて除草剤による作物の損傷・クロロシスを定量化し、目視評価との一致度を検証しているため、表現型取得手法が研究の中心である。
abstractHigh-throughput digital imaging is a promising tool to measure plant herbicide interaction in field crops.
Infection of seeds of cereal plants with fusarium affects their optical luminescent properties. The spectral characteristics of excitation (absorption) in the range of 180-700 nm of healthy and infected seeds of wheat, barley and oats were measured. The greatest difference in the excitation spectra of healthy and infected seeds was observed in the short-wave range of 220-450 nm. At the same time, the excitation characteristics of infected seeds were higher than those of healthy ones, and the integral parameter Η in the entire range was 10-56% higher. A new maximum appeared at the wavelength of 232 nm and the maximum value increased by 362 nm. The spectral characteristics were measured when excited by radiation at wavelengths of 232, 362, 424, 485, 528 nm and the luminescence fluxes were calculated. It is established that the photoluminescence fluxes Φ in the short-wave ranges of 290-380 nm increase by 1.58-3.14 times and 390-550 nm-by 1.44-2.54 times. The fluxes in longer wavelength ranges do not change systematically and less significantly: for wheat, they decrease by 12% and increase by 19%, for barley, they decrease by 10% and increase by 33%. The flux decreases by 43-71% for oats. Based on the results obtained for cereal seeds, it is possible to further develop a method for detecting fusarium infection with absolute measurements of photoluminescence fluxes in the range of 290-380 nm, or when measuring photoluminescence ratios: for wheat seeds when excited with wavelengths of 424 nm and 232 nm (Φ424/Φ232); for barley seeds-when excited with wavelengths of 485 nm and 232 nm (Φ485/Φ232) and for oat seeds-when excited with wavelengths of 424 nm and 362 nm (Φ424/Φ362).
Why it matches plant phenotyping methods穀物種子のフザリウム感染状態を蛍光スペクトル・フォトルミネッセンスで検出する測定法を検討し、判別指標と今後の検出法を提示しており、植物状態の取得法が中心である。
titleDetection of Fusarium infected seeds of cereal plants by the fluorescence method.
Infrared thermography has shown after roots of grasses freeze, ice spreads into the crown and then acropetally into leaves initially through vascular bundles. Leaves freeze singly with the oldest leaves freezing first and the youngest freezing later. Visualising the vascular system in its native 3-dimensional state will help in the understanding of this freezing process. A 2 cm section of the crown that had been infiltrated with aniline blue was embedded in paraffin and sectioned with a microtome. A photograph of the surface of the tissue in the paraffin block was taken after the microtome blade removed each 20 μm section. Two hundred to 300 images were imported into Adobe After Effects and a 3D volume of the region infiltrated by aniline blue dye was constructed. The reconstruction revealed that roots fed into what is functionally a region inside the crown that could act as a reservoir from which all the leaves are able to draw water. When a single root was fed dye solution, the entire region filled with dye and the vascular bundles of every leaf took up the dye; this indicated that the vascular system of roots was not paired with individual leaves. Fluorescence microscopy suggested the edge of the reservoir might be composed of phenolic compounds. When plants were frozen, the edges of the reservoir became leaky and dye solution spread into the mesophyll outside the reservoir. The significance of this change with regard to freezing tolerance is not known at this time. Thermal cameras that allow visualisation of water freezing in plants have shown that in crops like wheat, oats and barley, ice forms first at the bottom of the plant and then moves upwards into leaves through water conducting channels. Leaves freeze one at a time with the oldest leaves freezing first and then younger ones further up the stem freeze later. To better understand why plants freeze like this, we reconstructed a 3-dimensional view of the water conducting channels. After placing the roots of a wheat plant in a blue dye and allowing it to pull the dye upwards into leaves, we took a part of the stem just above the roots and embedded it in paraffin. We used a microtome to slice a thin layer of the paraffin containing the plant and then photographed the surface after each layer was removed. After taking about 300 images, we used Adobe After Effects software to re-construct the plant with the water conducting system in three dimensions. The 3D reconstruction showed that roots fed into a roughly spherical area at the bottom of the stem that could act as a kind of tank or reservoir from which the leaves pull up water. When we put just one root in dye, the entire reservoir filled up and the water conducting channels in every leaf took up the dye. This indicates that the water channels in roots were not directly connected to specific leaves as we had thought. When plants were frozen, the dye leaked out of the reservoir and spread into cells outside. Research is continuing to understand the significance of this change during freezing. It is possible that information about this effect can be used to help breeders develop more winter-hardy crop plants.
Why it matches plant phenotyping methods植物体内の血管系を連続画像から3次元再構成し、凍結時の漏出状態を可視化する画像計測法が研究の中心であり、植物の構造・生理状態を定量的に観察している。
abstractVisualising the vascular system in its native 3-dimensional state will help in the understanding of this freezing process.
All plant breeding programs are dependent on plant phenotypic and genotypic data, but the development of phenotyping technology has been slow relative to that of genotyping. Crown rust ( Puccinia coronata f. sp. avenae Erikss.) is the most important disease of cultivated oat ( Avena sativa L.), making the development of disease-resistant oat cultivars an important breeding objective. Visual observation is the most common scoring method, but it can be laborious and subjective. We visually scored a diverse collection of 256 oat lines at a total of 27 time points in three disease nursery environments. Multispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations. The photos were analyzed, and subsets of the spectral properties of each plot were measured. Random forest modeling was used to model the relationship between the spectral properties of the plots and visually observed disease severity. The ability of the photo data and the random forest model to estimate visually observed disease severity was evaluated using three different cross-validation analyses. We specifically addressed the issue of assessing phenotyping accuracy across and within time points. The accuracy of the photo estimates was greatest for adult plants shortly before they began to senesce. Accuracy outside of that time frame was generally low but statistically significant. Unmanned aerial vehicle-mounted sensors could increase disease scoring efficiency, but additional investigation into the spectral signature of disease severity at all plant growth stages may be necessary to automate accurate full-season measurements.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とランダムフォレストにより、オートの病害重症度を推定・検証する手法が研究の中心である。
abstractMultispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations.
The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.
Why it matches plant phenotyping methods航空ハイパースペクトル画像から収量・バイオマスを推定するAutoML解析ワークフローを構築・評価しており、植物形質推定手法が中心である。
abstractThis research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass
Abstract Background Micro-CT (X-ray computed tomographic) allows for 3D visualization of an entire structure, both internally and externally. The different materials are identified based on the differences in their ability to attenuate X-ray, with the differences being converted into a range of grey values. Low-density plant tissues with limited differences in grey values create a challenge in differentiating the cellular structures. In addition, internal movements due to autolysis, degradation, and shrinkage during dehydration of the tissues during scanning give rise to blurry images. Results In this study, oats and wheat were scanned using micro-CT to optimize the use of micro-CT in low-density plants. With the assistance of chemical fixing, phosphotunstate and a chemical drying agent, we were able to visualize microstructures of cereal stems. These preparation steps allow us to create 3D micrographs of low-density stem nodes suggesting key structural differences that are correlated with lodging resistance. Conclusion Micro-CT is a valuable tool to create 3D structural images of low-density material. Multiple steps to prepare the samples to stop autolysis, increase the contrast during scanning and eliminate internal movement are described in this paper. This process allowed for visualization of the stem nodal region suggesting morphology related to lodging resistance.
Why it matches plant phenotyping methods低密度植物茎の内部構造を取得するため、マイクロCT撮像と試料調製を最適化・開発しており、植物形態の3D表現型取得が中心です。
abstractIn this study, oats and wheat were scanned using micro-CT to optimize the use of micro-CT in low-density plants.
Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford ( r = 0.2-0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R 2 = 0.20-0.25) and higher error (RMSE = 700-800 kg/ha) than training models (R 2 = 0.50-0.60; RMSE = 500-690 kg/ha). In South Shore, validation models were only able to explain approx. 15-20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習によるオーツの地上部バイオマス推定を主目的とし、育種圃場でのモデル構築・検証を中心に扱うため、植物表現型計測手法として適格。
abstractThe main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass.
The quantification of β -glucan in oats is of immense importance for plant breeders and food scientists to develop plant varieties and food products with a high quantity of β -glucan. However, the chemical analysis of β -glucan is time consuming, destructive, and laborious. In this study, near-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β -glucan content in oats. The interval Partial Least Square (iPLS) along with correlation matrix plots were employed to analyze the NIR spectrum from 700-1300 nm, 1300-1900 nm, and 1900-2500 nm for the selection of important wavelengths for the prediction of β -glucan. The NIR spectral data were pre-treated using Savitzky Golay smoothening and normalization before employing partial least square regression (PLSR) analysis. The PLSR models were established based on the selection of wavelengths from PLS loading plots that present a high correlation with β -glucan content. It was observed that wavelength region 700-1300 nm is sufficient for the satisfactory prediction of β -glucan of hulled and naked oats with R 2 c of 0.789 and 0.677, respectively, and RMSE < 0.229.
Why it matches plant phenotyping methodsオーツ種子のβ-グルカン含量をNIR分光とケモメトリクスで非破壊推定する手法の開発・検証が研究の中心であり、植物形質の測定法に該当する。
abstractnear-infrared (NIR) spectroscopy in conjunction with Chemometrics was employed for rapid and non-destructive prediction of β -glucan content in oats.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.
Why it matches plant phenotyping methods種子の品種分類を目的としたハイパースペクトル画像と深層転移学習の手法開発・評価が研究の中心であり、育種・遺伝資源スクリーニングに再利用可能な表現型取得ワークフローを扱うため。
abstractThis study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning.
OatRootSeed / grainMorphology / geometry measurementGrowth / development / phenologyRoot system architecture
Abstract Seed vigor is crucial for crop early establishment in the field and is particularly important for forage crop production. Oat (Avena sativa L.) is a nutritious food crop and also a valuable forage crop. However, little is known about the genetics of seed vigor in oats. To investigate seed vigor-related traits and their genetic architecture in oats, we developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE). Root number, root surface area, and shoot length were measured in two replicates. Variables such as growth rate were derived. Using a genome-wide association (GWA) approach, we identified 34 and 16 unique loci associated with root traits and shoot traits, respectively, which corresponded to 41 and 16 unique SNPs at a false discovery rate < 0.1. Nine root-associated loci were organized into four sets of homeologous regions, while nine shoot-associated loci were organized into three sets of homeologous regions. The context sequences of five trait-associated markers matched to the sequences of rice, Brachypodium and maize (E-value < 10−10), including three markers matched to known gene models with potential involvement in seed vigor. These were a glucuronosyltransferase, a mitochondrial carrier protein domain containing protein, and an iron-sulfur cluster protein. This study presents the first GWA study on oat seed vigor and data of this study can provide guidelines and foundation for further investigations.
Why it matches plant phenotyping methods画像ベースの表現型取得パイプラインを開発し、根・シュート形質を抽出して大規模適用しており、フェノタイピング手法が中心的です。
abstractwe developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicPhenotypic data collected in the study have been uploaded to T3/Oat: https://triticeaetoolbox.org/oat/ .Open asset ↗T3/Oatlines:89-100Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Recently, phenotyping has become one of the main bottlenecks in plant breeding and fundamental plant science. This is particularly true for plant disease assessment, which has to deal with time-consuming evaluations and the subjectivity of visual assessments. In this work, we have developed an open source Robust, User-friendy Script Tool (RUST) for semi-automated evaluation of leaf rust diseases. RUST runs under the free Fiji imaging software (developed from ImageJ), which is a well-recognized software among the scientific community. The script enables the evaluation of leaf rust diseases using a color transformation tool and provides three different automation modes. The script opens images sequentially and records infection frequency (pustules per area) (semi-)automatically for high-throughput analysis. Furthermore, it can manage several scanned leaf segments in the same image, consecutively selecting the desired segments. The script has been validated with nearly 900 samples from 80 oat genotypes ranging from resistant to susceptible and from very light to heavily infected leaves showing a high accuracy with a Lin's concordance correlation coefficient of 0.99. The analysis show a high repeatability as indicated by the low variation coefficients obtained when repeating the measurement of the same samples. The script also has optional steps for calibration and training to ensure accuracy, even in low-resolution images. This script can evaluate efficiently hundreds of leaves facilitating the screening of novel sources of resistance to this important cereal disease.
Why it matches plant phenotyping methods葉のさび病感染頻度を画像から半自動測定するソフトウェアを開発し、多数サンプルで精度と再現性を検証しており、植物表現型取得法が中心である。
abstractIn this work, we have developed an open source Robust, User-friendy Script Tool (RUST) for semi-automated evaluation of leaf rust diseases.
Abstract An essential prerequisite for the efficient biomechanical tailoring of crops is to accurately relate mechanical behavior to compositional and morphological properties across different length scales. In this article, we develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure. We first discuss the experimental multiscale characterization based on microimaging (micro-CT, light microscopy, transmission electron microscopy) and chemical analysis, with a particular focus on oat stems. We then derive in detail a general micromechanics-based model of macroscale stiffness and strength. We specify our model for oats and validate it against a series of bending experiments that we conducted with oat stem samples. In the context of biomechanical tailoring, we demonstrate that our model can predict the effects of genetic modifications of microscale composition and morphology on macroscale mechanical properties of thale cress that is available in the literature.
Why it matches plant phenotyping methods作物茎の微細構造を画像化・解析し、マクロな力学特性を予測するマルチスケール手法を開発し、オーツ麦の曲げ実験で検証しているため、植物形質取得・推定法が研究の中心である。
abstractwe develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure.
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization , where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.
Why it matches plant phenotyping methods合成画像で学習したインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発・検証し、複数作物への適用性能も評価しているため、方法が中心的である。
abstractan instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset.
The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming.
Why it matches plant phenotyping methodsUAV RGB画像と写真測量により作物の草高・バイオマスを推定し、地上測定との比較、モデル検証、交差検証、予測マップ作成まで行っており、植物形質取得手法が研究の中心である。
abstractA recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
BACKGROUND: Violent movement of crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly detrimental to cool-season cereals such as oat, barley, and wheat; contributing to yield and economic losses. Phenotyping the movement of cereal crops in real-time could aid in the breeding and selecting of lodging resistant cereals. Since no methods exist to quantify dynamic, real time plant responses in an agricultural setting, we devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system. RESULTS: We present both the image analysis method for identifying predefined regions of a 2D field design as they appear on 360° field of view video, as well as a signal processing pipeline to quantify movement from time varying color signals from plot canopies within these predefined field regions. We detected significant differences in the natural frequency and amplitude of plant movement from video of 16 cereal cultivars planted in a randomized complete block design on five different windy days. Natural frequencies quantified by this method averaged 1.37 Hz, while over 2.5-fold differences in amplitude within similar frequency ranges were detected across the 16 cereal cultivars. CONCLUSIONS: This method is sensitive enough to systematically differentiate small frequency and amplitude differences in cultivar movement, and shows promise for investigating the physiological basis for differences in cereal movement and lodging resistance. The relative accuracy of the plot demarcation protocol suggests it could be used for other high-throughput phenotyping applications that require both high image resolution and a large field of view.
Why it matches plant phenotyping methods360度動画と信号処理により作物群落の動的な動き（周波数・振幅）を定量する表現型計測法を開発・実証しており、方法が研究の中心である。
abstractwe devised a video analysis protocol to quantify mean frequency and amplitude of plant movement for a 360° field of view camera system
Introduction Oats (Avena sativa L.) are a whole grain cereal recognised for their health benefits and which are cultivated largely in temperate regions providing both a source of food for humans and animals, as well as being used in cosmetics and as a potential treatment for a number of diseases. Oats are known as being a cereal source high in dietary fibre (e.g. β-glucans), as well as being high in antioxidants, minerals and vitamins. Recently, oats have been gaining increased global attention due to their large number of beneficial health effects. Consumption of oats has been proven to lower blood LDL cholesterol levels and blood pressure, thus reducing the risk of heart disease, as well as reducing blood-sugar and insulin levels. Objectives Oats are seen as a low input cereal. Current agricultural guidelines on nitrogen application are believed to be suboptimal and only consider the effect of nitrogen on grain yield. It is important to understand the role of both variety and of crop management in determining nutritional quality of oats. In this study the response of yield, grain quality and grain metabolites to increasing nitrogen application to levels greater than current guidelines were investigated. Methods Four winter oat varieties (Mascani, Tardis, Balado and Gerald) were grown in a replicated nitrogen response trial consisting of a no added nitrogen control and four added nitrogen treatments between 50 and 200 kg N ha -1 in a randomised split-plot design. Grain yield, milling quality traits, β-glucan, total protein and oil content were assessed. The de-hulled oats (groats) were also subjected to a rapid Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) metabolomic screening approach. Results Application of nitrogen had a significant effect on grain yield but there was no significant difference between the response of the four varieties. Grain quality traits however displayed significant differences both between varieties and nitrogen application level. β-glucan content significantly increased with nitrogen application. The UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples. The method captured a wide range of compounds, inclusive of primary metabolites such as the amino acids, organic acids, vitamins and lipids, as well as a number of key secondary metabolites, including the avenanthramides, caffeic acid, and sinapic acid and its derivatives and was able to identify distinct metabolic phenotypes for the varieties studied. Amino acid metabolism was massively upregulated by nitrogen supplementation as were total protein levels, whilst the levels of organic acids were decreased, likely due to them acting as a carbon skeleton source. Several TCA cycle intermediates were also impacted, potentially indicating increased TCA cycle turn over, thus providing the plant with a source of energy and reductant power to aid elevated nitrogen assimilation. Elevated nitrogen availability was also directed towards the increased production of nitrogen containing phospholipids. A number of both positive and negative impacts on the metabolism of phenolic compounds that have influence upon the health beneficial value of oats and their products were also observed. Conclusions Although the developed method has broad applicability as a rapid screening method or a rapid metabolite profiling method and in this study has provided valuable metabolic insights, it still must be considered that much greater confidence in metabolite identification, as well as quantitative precision, will be gained by the application of higher resolution chromatography methods, although at a large expense to sample throughput. Follow up studies will apply higher resolution GC (gas chromatography) and LC (reversed phase and HILIC) approaches, oats will be also analysed from across multiple growth locations and growth seasons, effectively providing a cross validation for the results obtained within this preliminary study. It will also be fascinating to perform more controlled experiments with sampling of green tissues, as well as oat grains, throughout the plants and grains development, to reveal greater insight of carbon and nitrogen metabolism balance, as well as resource partitioning into lipid and secondary metabolism.
Why it matches plant phenotyping methodsUHPLC-MSによる植物代謝表現型の迅速取得法を開発・反復性評価し、大規模穀類サンプルへの適用可能性と限界も示しているため、代謝測定が単なる生物学的実験の補助ではなく方法論の中心である。
abstractThe UHPLC-MS approach has provided a rapid, sub 15 min per sample, metabolite profiling method that is repeatable and appropriate for the screening of large numbers of cereal samples.
Reproduction assets foundThe paper's UHPLC-MS metabolite profiling data (oat nitrogen supplementation study) is publicly deposited in MetaboLights (MTBLS804), and the authors' ASCA/PLS-S analysis scripts are publicly available on GitHub.Code · publicASCA and PLS-S with RFE were performed within MATLAB 2016a using in-house scripts which are made available freely online at https://github.com/Biospec/cluster-toolbox-v2.0 .Open asset ↗GitHub · Biospec/cluster-toolbox-v2.0lines:106-112Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Trabajo presentado en el II Spanish Symposium on Physiology and Breeding of Cereals (II SEFiMeC), celebrado en Cordoba (Espana) el 6 y 7 de marzo de 2019.-- Organized by excellence network AGL2016-81855-REDT.
Why it matches plant phenotyping methodsオーツ麦のうどんこ病抵抗性を対象に、デジタル画像解析によるハイスループット表現型解析が中心であり、植物病害状態の画像ベース測定に該当する。
titleDigital image analysis for high throughput phenotyping of powdery mildew resistance in oats
Efforts focused on the genetic improvement of seed morphometric and color traits would greatly benefit from efficient and reliable quantitative phenotypic assessment in a nondestructive manner. Although several seed phenotyping systems exist, none of them combine the cost effectiveness, identity preservation, throughput, and accuracy needed for implementation in plant breeding. We integrated an image analysis component into a single‐seed analyzer (SSA) system that also captures near‐infrared reflectance (NIR) and weight data. Through the development and utilization of an open‐source computational image analysis pipeline, image data acquired by two cameras mounted on the SSA machine were automatically processed to derive estimates of length, width, height, volume, and color for 96 individual dehulled groats (seeds) of five oat (Avena sativa L.) genotypes replicated across days. With the exception of color, the four traits were found to be strongly correlated with and have repeatability comparable to manual measurements. The seed color values had moderately strong correlation with those measured by a colorimeter, but further improvements to the SSA system are needed to increase measurement accuracy. These results demonstrate that the SSA system has the potential to provide a low‐cost solution for the rapid, accurate measurement of morphological traits on individual seeds of oat and potentially other crop species, allowing the screening of seeds from numerous genotypes in breeding programs.
Why it matches plant phenotyping methodsオート麦種子の形態・色形質を画像解析とNIR等で自動取得するシステムの開発・検証が研究の中心であり、反復性や手動測定との相関も評価している。
abstractWith the exception of color, the four traits were found to be strongly correlated with and have repeatability comparable to manual measurements.
OatRadishLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
Plant roots growing through soil typically encounter considerable structural heterogeneity, and local variations in soil dry bulk density. The way the in situ architecture of root systems of different species respond to such heterogeneity is poorly understood due to challenges in visualising roots growing in soil. The objective of this study was to visualise and quantify the impact of abrupt changes in soil bulk density on the roots of three cover crop species with contrasting inherent root morphologies, viz. tillage radish (Raphanus sativus), vetch (Vicia sativa) and black oat (Avena strigosa). The species were grown in soil columns containing a two-layer compaction treatment featuring a 1.2 g cm-3 (uncompacted) zone overlaying a 1.4 g cm-3 (compacted) zone. Three-dimensional visualisations of the root architecture were generated via X-ray computed tomography, and an automated root-segmentation imaging algorithm. Three classes of behaviour were manifest as a result of roots encountering the compacted interface, directly related to the species. For radish, there was switch from a single tap-root to multiple perpendicular roots which penetrated the compacted zone, whilst for vetch primary roots were diverted more horizontally with limited lateral growth at less acute angles. Black oat roots penetrated the compacted zone with no apparent deviation. Smaller root volume, surface area and lateral growth were consistently observed in the compacted zone in comparison to the uncompacted zone across all species. The rapid transition in soil bulk density had a large effect on root morphology that differed greatly between species, with major implications for how these cover crops will modify and interact with soil structure.
Why it matches plant phenotyping methodsX線CTと自動根セグメンテーションを用いて根系形態を3次元可視化・定量化しており、植物表現型の取得・抽出が研究の中心である。
abstractThe objective of this study was to visualise and quantify the impact of abrupt changes in soil bulk density on the roots of three cover crop species with contrasting inherent root morphologies
Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
Why it matches plant phenotyping methodsUAV搭載光学・ハイパースペクトルデータから植物形質を推定する回帰モデルを構築し、校正・独立検証まで実施しており、植物形質取得法の適用と技術検証が中心である。
abstractHere we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely.
Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field scale quantification of PSF effects, yet field experiments are warranted to asses actual PSF effects under less controlled conditions. Here we used Unmanned Aerial Vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data was acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE= 5.12 cm, R2= 0.79), chlorophyll content (RMSE= 0.11 g m−2, R2= 0.80), N-content (RMSE= 1.94 g m−2, R2= 0.68), and fresh biomass (RMSE= 0.72 kg m−2, R2= 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
Why it matches plant phenotyping methodsUAV搭載光学・ハイパースペクトルセンサーにより、植物形質を非破壊推定する手法の較正・独立検証と精度評価が研究の中心であるため。
abstractHere we used Unmanned Aerial Vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely.