Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.
Why it matches plant phenotyping methods植物画像から形態形質を自動抽出するツールの開発と、手動測定との性能検証が中心であるため。
abstractHere, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images.
Reproduction assets foundThe paper's own phenotyping tool BioIMA (source code, documentation, example datasets, and user manual) is publicly available on the authors' GitHub repository, directly supporting the paper's image-based trait extraction and validation analyses.Code · publicis powered by embedded models
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currently including SAM (Kirillov et al., 2023) and mobile SAM (Zhang et al., 2023),
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which are executed locally through ONNX Runtime for efficient inference without
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internet connectivity. Source code, documentation, example datasets, and a user manual
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are publicly available on GitHub (https://github.com/jingwanglab/BioIMA).101
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
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https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.
Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育（フェノロジー）段階を自動推定する手法が研究の中心であり、植物状態の取得・分類に直接関わる。
abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.
Accurate estimation of the photorespiratory CO 2 compensation point (Γ*) is essential for describing the balance between Rubisco carboxylation and oxygenation and for parameterising biochemical models of photosynthesis. Γ* and the rate of CO 2 release in the light (D L ) are commonly estimated using the Laisk method, based on measurements of net CO 2 assimilation rate (A net ) at low chloroplastic CO 2 concentrations (c c ), under several sub-saturating irradiance levels. However, many widely used temperature dependence relationships for Γ* (Γ*(T)) were derived using conventional linear implementations of the Laisk method, despite the intrinsically nonlinear behaviour of the A net -c c response predicted by the photosynthetic theory. Here, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels. Gas exchange of sunflower leaves was measured across a wide temperature range from 3.9°C to 42.0°C. The conventional linear implementation generated highly dispersed pairwise intersections and unstable estimates of both Γ* and D L , including some physiologically unrealistic negative D L values at low temperatures. In contrast, the mechanistically constrained Laisk-FvCB framework produced physiologically meaningful temperature responses and substantially reduced methodological artefacts associated with linear extrapolation. Using this framework, we derived a revised in vivo Γ*(T) relationship described by an Arrhenius-type function with Γ*(25) = 43.4 μmol mol -1 and an apparent activation energy of 27.7 kJ mol -1 , such that Γ*(T) = 43.4 exp[11.176 ((T - 25)/(T + 273.15))], where T is leaf temperature in °C. Comparison with other widely used Γ*(T) formulations showed substantial divergence at temperature extremes, often exceeding the variability expected from realistic interspecific differences in Rubisco specificity among C 3 species.
Why it matches plant phenotyping methods植物葉のガス交換からΓ*と光呼吸CO₂放出速度を推定する改良Laisk-FvCB手法を提示し、従来法との比較で推定の安定性と方法論的アーティファクトを検証しているため、植物表現型測定法が中心である。
abstractHere, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels.
This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.
Why it matches plant phenotyping methodsUAV画像と深層学習により作物の生育・フェノロジー段階を自動推定する方法が研究の中心であり、植物状態の抽出性能も評価している。
abstractThis study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, ( i ) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; ( ii ) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify the three insect classes visiting the most sunflower (non- Bombus bees, bumble bees, lepidopterans); ( iii ) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; ( iv ) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of ±10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.
Why it matches plant phenotyping methods植物遺伝型の花への訪花昆虫誘引性を推定する画像取得・深層学習パイプラインを開発し、訪花頻度の予測性能も検証しており、表現型取得法が中心である。
abstractwe present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field
The co-evolutionary arms race between crops and their parasites requires continuous identification of new resistance mechanisms. Broomrape (Orobanche cumana), a root parasitic plant, poses a severe threat to sunflower (Helianthus annuus) production, yet the genetic architecture underlying host resistance remains poorly understood. To address this, we established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population. Combining this phenotypic resource with a dual genome-wide association study (GWAS) strategy based on both single nucleotide polymorphisms (SNPs) and k-mers, we highlight the genetic basis of broomrape resistance at unprecedented resolution. Our analyses revealed quantitative trait loci (QTLs) and identified novel candidate genes, including putative leucine-rich repeat receptor kinases potentially involved in parasite recognition and defense activation. Importantly, the k-mer approach circumvented reference genome bias and uncovered key genomic introgressions from wild Helianthus relatives that contribute substantially to resistance. These findings demonstrate the utility of integrating high-resolution phenotyping with advanced association mapping to dissect complex host-parasite interactions. Moreover, they emphasize the enduring value of wild germplasm as a reservoir of adaptive variation, providing crop breeders with crucial tools to counter the rapid evolutionary dynamics of parasitic plants.
Why it matches plant phenotyping methods根部の寄生程度を定量する高スループット表現型解析プラットフォームの確立が明示され、遺伝解析の基盤として方法が実質的に扱われている。
abstractwe established a high-throughput phenotyping platform to quantify root infestation across a diverse sunflower association mapping (SAM) population.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the paper-specific raw phenotyping images on Zenodo, the k-mer genotype data on the sunflower genome database, and the authors' analysis code on the Hübner lab GitHub repository, all with public URLs.Dataset · publicAll phenotypes raw images for Gadot and Yavor are available through the Zenodo repository ( https://doi.org/10.5281/zenodo.18961268 ).Open asset ↗Zenodo · 10.5281/zenodo.18961268lines:238-238Code · publicCode is accessible through the Hübner lab github: https://github.com/hubner-lab/Sunflower-Broomrape-paper .Open asset ↗Hübner lab github · hubner-lab/Sunflower-Broomrape-paperlines:238-238Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research.
Why it matches plant phenotyping methodsヒマワリ頭花の発達・不稔種子数という植物形質を、画像取得とYOLOによる推定パイプラインで定量化する手法を開発・評価しており、方法が研究の中心である。
abstractwe developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads.
Reproduction assets foundThe authors state the source code is available on GitHub and the CVAT-annotated dataset is available via a public share link; the GitHub repository URL is explicitly provided and matches an allowed URL. The dataset link itself is not given, so only the code/checkpoint repository qualifies as an actionable public asset.Code · publicThe developed system is available as a Telegram bot [ 19 ] and the source code is available on GitHub [ 20 ]. The CVAT annotated dataset is available via a public share link.Open asset ↗lines:84-103Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Accurate prediction of sunflower oil yield is essential for crop management and breeding decisions, particularly under water-limited environments. In this study, 100 pure oilseed sunflower lines were evaluated under normal and drought stress conditions across two consecutive growing seasons (2014–2016), and all morphological and physiological variables were measured directly in the field. Input variables were selected based on their previously demonstrated influence on grain yield and oil-related traits, while the complete two-year dataset was used for model training, validation, and testing following data augmentation, standardization, and 7-fold cross-validation. The predictive performance of multiple linear regression (MLR) and deep learning regression (DLR) models was compared using different combinations of input variables. The DLR model consistently demonstrated superior performance, achieving higher accuracy and lower prediction errors across all scenarios. The best results were obtained under drought stress with 11 input variables, where the DLR model achieved (R2 = 0.98, RMSE = 0.4, MSE = 0.16, MAE = 0.20 (train), R2 = 0.96, RMSE = 0.55, MSE = 0.31, MAE = 0.34 (test)), along with markedly lower RMSE and MAE values than MLR. Even when fewer variables were used, the DLR model maintained strong predictive ability, highlighting its capacity to learn complex nonlinear relationships and generalize from limited data. Overall, the findings underscore the potential of DLR as a robust predictive tool for estimating oil yield under contrasting environmental conditions and provide practical implications for genotype selection, harvest planning, and sunflower breeding strategies.
Why it matches plant phenotyping methods深層学習回帰によるヒマワリ油収量という植物形質の推定手法を開発し、複数モデルの性能比較と交差検証を行っており、予測・検証が研究の中心である。
abstractThe predictive performance of multiple linear regression (MLR) and deep learning regression (DLR) models was compared using different combinations of input variables.
Introduction Accurate identification of crop diseases is crucial for ensuring crop quality and yield. However, existing deep learning models for crop disease identification lack robustness in complex field environments and suffer from large model parameter sizes, which makes them difficult to deploy on resource-constrained devices. This gap between laboratory models and practical field applications necessitates the development of a lightweight and robust identification model. Methods To address these challenges, this paper proposes a lightweight YOLO-CGA model for sunflower disease identification and deploys it on a Raspberry Pi for field application. The model incorporates three key improvements based on YOLOv8n-cls: (1) A CBAM_ADown module is designed, which integrates attention mechanisms with asymmetric downsampling to enhance feature extraction and noise suppression in complex image backgrounds; (2) The C2f module of YOLOv8n-cls is replaced with the C3Ghost module, which utilizes ghost convolution to reduce parameter count while preserving fine-grained features; (3) An AFC_SPPF module is constructed, which aggregates multi-scale disease features through a multi-branch adaptive fusion structure to improve recognition performance for diverse lesions. Results Experimental results on three major datasets show that the proposed YOLO-CGA model achieves high identification accuracy: 98.48% on the BARI-Sunflower dataset, 98.32% on the Cotton Disease Dataset, and 91.11% on the FGVC8 dataset. Meanwhile, the model maintains a lightweight property with only 0.92M parameters, which is significantly fewer than that of other comparative models. Discussion The deployment of the YOLO-CGA model on the Raspberry Pi end device effectively bridges the gap between laboratory models and field applications, fulfilling the demand for real-time and on-site crop disease identification. The integration of attention mechanisms, ghost convolution, and multi-scale feature fusion enables the model to balance accuracy, robustness, and lightweight performance, making it suitable for resource-limited field scenarios.
Why it matches plant phenotyping methodsヒマワリ病害を画像から識別する軽量モデルを開発・評価し、Raspberry Piへ実装しており、植物の病害状態を推定する方法が研究の中心である。
abstractExperimental results on three major datasets show that the proposed YOLO-CGA model achieves high identification accuracy
The technological advancements in recent decades have facilitated the widespread adoption of unmanned aerial systems (UAS) and remote sensing methodologies in agricultural practices. These innovative approaches enable precise crop monitoring, early stress detection, and yield optimization through the analysis of vegetation indices derived from aerial imagery. This study investigates the application of UAS-acquired multispectral data and vegetation indices for crop health assessment, with particular emphasis on barley (Hordeum vulgare L.) and sunflower (Helianthus annuus L.). Geographic Information Systems (GIS) serve as a critical platform for integrating, processing, and visualizing remote sensing data, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), and Normalized Difference Red Index (NDRI). Our research methodology employed a DJI Mavic 3M UAS equipped with multispectral sensors to conduct aerial surveys of agricultural plots in the Sofia region of Bulgaria. The acquired data were processed to generate index maps that facilitate quantitative assessment of crop physiological status.The implemented workflow demonstrates an efficient technology for rapid identification of agronomic issues and supports data-driven decision making. The results highlight the potential of UAS-GIS integration for precision agriculture applications, particularly in monitoring cereal and oilseed crops under temperate climatic conditions. This approach provides agricultural stakeholders with timely, spatially explicit information for crop management while establishing a framework for future research in precision farming technologies.
Why it matches plant phenotyping methodsUASマルチスペクトル画像と植生指数により作物の生理状態・健全性を定量評価する取得・解析ワークフローが研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractThis study investigates the application of UAS-acquired multispectral data and vegetation indices for crop health assessment
Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.
Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。
abstractpredicting the same using key spectral bands and machine learning (ML) techniques
Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R2 = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement.
Why it matches plant phenotyping methods作物キャノピーのLAIという明示的な植物形質を、放射伝達モデルと機械学習で衛星データから推定し、不確実性定量化とサイト間検証まで行う手法中心の研究。
abstractWe present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification
Accurate monitoring of sunflower heads is critical for yield prediction, yet traditional methods are labor-intensive. This study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge. The proposed method used a Dual-Branch YOLOv10n model, leveraging multi-modal data for precise detection of sunflower heads at various growth stages. Feature indices were designed and a two-step clustering technique was applied to extract sunflower head point clouds, from which geometric parameters such as diameter and volume are computed. The detection model achieved high accuracy (precision: 0.9, recall: 0.894, mAP@50: 0.932) across growth stages. A strong correlation (R² = 0.80) was found between diameter measurements from point cloud and ground-truth data, while volume showed good alignment with biomass (R² = 0.61). This method offers an innovative, efficient solution for field-scale crop monitoring and yield estimation, advancing agricultural practices.
Why it matches plant phenotyping methodsUAV画像・深層学習・点群解析を統合し、ヒマワリ頭部の検出から直径・体積という植物器官形質を抽出・検証する方法が研究の中心であるため。
abstractThis study proposed a novel framework integrating UAV remote sensing, deep learning, and point cloud analysis to address this challenge.
The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure.
Why it matches plant phenotyping methodsヒマワリ花頭の形態形質を、画像セグメンテーションと3D点群で非接触抽出する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractThe diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters.
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
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Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Background/aim Sunflower ( Helianthus annuus ) is a crop of high economic and nutritional importance that continues to suffer significant yield losses due to foliar diseases. Traditional image-based and laboratory detection techniques remain limited by subjectivity, cost, and scalability. Transfer learning (TL) has recently emerged as an effective approach to overcoming these challenges involving the reuse of pretrained deep models for plant pathology tasks. Presented here is a systematic examination of recent TL-based studies on sunflower disease classification to identify prevailing trends, research gaps, and future opportunities. Materials and methods A structured Scopus query was employed to retrieve peer-reviewed articles published between 2021 and 2025. Strict inclusion and exclusion criteria ensured technical relevance to TL-based sunflower disease detection. Subsequently, 30 studies meeting the criteria were critically reviewed and analyzed in terms of model architecture, dataset characteristics, preprocessing strategies, and reported evaluation metrics. The comparative assessment focused on convolutional neural networks (CNNs), transformer-based architectures, and hybrid models. Results The analysis revealed a dominant reliance on pretrained CNNs such as ResNet, VGG, Inception, and EfficientNet. Several studies employed lightweight or federated learning variants to enhance deployment feasibility under field conditions. Among the commonly observed challenges were limited dataset diversity, class imbalance, and insufficient explainability. A key word cooccurrence analysis indicated an evolving research focus, transitioning from basic deep learning implementation to explainable and privacy-preserving frameworks optimized for edge devices. Conclusion The review revealed substantial progress in TL applications for the diagnosis of sunflower disease but underscored the need for larger, standardized datasets and cross-regional validation. Future studies should prioritize interpretable, adaptive architectures that can function in real-world agricultural environments. The insights drawn from this synthesis extend beyond sunflower pathology, offering a foundation for scalable, domain-transferable TL solutions in broader plant disease detection contexts.
Why it matches plant phenotyping methodsヒマワリ病害を画像から分類する転移学習手法を体系的に比較・評価したレビューであり、植物病害状態の表現型取得手法が中心です。
abstractPresented here is a systematic examination of recent TL-based studies on sunflower disease classification to identify prevailing trends, research gaps, and future opportunities.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Summary The ongoing evolutionary arms race between crop plants and their parasites necessitates a constant exploration of new genetic resistance. Broomrape ( Orobanche cumana ), a devastating parasitic plant, presents a formidable challenge to sunflower production, yet the genetic mechanisms underlying host resistance are still largely unknown. To address this gap, we developed a high-throughput phenotyping platform to quantify root infestation in a highly diverse sunflower association mapping (SAM) population. Using a dual GWAS approach with both SNPs and k-mers, we were able to pinpoint the genetic basis of resistance. Our findings validate previously identified QTLs with greater resolution and reveal several novel candidate genes conferring resistance, including putative leucine-rich repeat receptor kinases. Critically, the k-mer mapping approach circumvented reference genome bias, highlighting key introgressions from wild Helianthus species that have contributed to broomrape resistance. This research provides a powerful methodology for gene discovery and demonstrates that wild relatives remain a vital source of genetic material, offering breeders a significant advantage in the ongoing battle against rapidly evolving parasites.
Why it matches plant phenotyping methodsヒマワリの根への寄生侵入を定量するハイスループット表現型計測プラットフォームの開発と適用が研究の中心であり、植物病害状態の測定法に該当する。
abstractwe developed a high-throughput phenotyping platform to quantify root infestation
To address the challenges of complex structures, large organ-scale variations, and dense internal organization in plant 3D point clouds, This paper proposes Multi-PointNet++ algorithm based on multi-scale local interaction to enhance the accuracy and detail extraction capability of plant point cloud segmentation. We first construct the EMA-Shuffle attention module during feature extraction, combining Enhanced Multiscale Attention (EMA) with Shuffle Attention to enable efficient local feature interaction through multi-scale fusion. At the same time, the OREPA reparameterized convolution module enhances feature extraction and model performance while keeping the structure lightweight. For feature fusion, we design a pyramid fusion module based on SFPN that integrates triple sampling strategies and bidirectional feature propagation to effectively align geometric information across scales. Our method improves mIoU by 7.1% and 5.3%, and mAcc by 6.7% and 13.7% on the public PLANesT-3D and self-built sunflower datasets, respectively, significantly outperforming other models. This approach offers robust 3D semantic support for high-throughput plant phenotype extraction.
Why it matches plant phenotyping methods植物3D点群からの器官・構造抽出を目的とする新規セグメンテーション手法を開発し、公開データセットと自作データセットで性能検証しているため、植物表現型取得法が中心である。
abstractThis paper proposes Multi-PointNet++ algorithm based on multi-scale local interaction to enhance the accuracy and detail extraction capability of plant point cloud segmentation.
Accurate segmentation of leaf regions plays a vital role in plant phenotyping and agricultural analysis. This paper presents AKDUNet, a lightweight UNet-based architecture that integrates attention gates and knowledge distillation to improve segmentation performance while minimizing computational complexity. The architecture replaces traditional skip connections with attention gates to focus on salient spatial features and employs a two-stage training pipeline, where a compact student model learns from a deeper teacher model using a tailored distillation loss function. AKDUNet is evaluated on two benchmark datasets (CWFID and Sunflower) and outperforms a range of state-of-the-art models, including UNet++, Inception UNet, VGG-based UNets, SDUNet, INSCA UNet, and SegFormer. Ablation studies confirm the advantages of attention modules, and qualitative analyses using Grad-CAM visualizations reveal the model's ability to effectively focus on crucial leaf structures. The results demonstrate that AKDUNet is not only computationally efficient but also highly accurate, making it suitable for real-time deployment in resource-constrained agricultural environments.
Why it matches plant phenotyping methods植物の葉領域を抽出する画像セグメンテーション手法の開発とベンチマーク評価が中心であり、葉面積などの表現型取得に直接利用できる。
abstractAccurate segmentation of leaf regions plays a vital role in plant phenotyping and agricultural analysis.
Reproduction assets foundThe paper evaluates AKDUNet leaf segmentation on the public CWFID dataset (and a Sunflower dataset), and the acknowledgments explicitly state the datasets are publicly available at the authors' cited repository URL. No author analysis code or trained model checkpoints are released.Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
The tilt angle of sunflower flower heads is an important phenotypic characteristic that influences their growth and development, as well as the efficiency of mechanised harvesting in precision agriculture. Addressing the issues of low accuracy, high cost, and the risk of plant damage associated with traditional manual measurement methods, this study proposes a non-contact measurement method combining deep learning and geometric analysis to achieve precise measurement of sunflower flower head tilt angles. The specific method involves optimising the lightweight YOLO11-seg model to enhance instance segmentation performance for sunflower flower heads and stems (compared to the initial YOLO11 model, recall rate improved by 3.7%, mAP50 improved by 1.8%, a reduction of 0.29M parameters, and a decrease in computational load of 0.5 GFLOPs), and extracting the surface contour of the flower head and the centreline contour of the stem based on the mask map output by the model. After achieving precise region segmentation through image processing, the geometric analysis module performs elliptical fitting on the flower head contour to obtain the main axis direction, performs curve fitting on the stem contour, and selects the tangent direction at the intersection point of the flower head. The angle between the two is calculated as the tilt angle of the flower head. In the measurement experiment, 220 images were used for testing, with manual protractor measurement results as the reference. The algorithm achieved a measurement accuracy of RMSE = 2.93°, MAE = 2.43°, and R 2 = 0.94. The results indicate that this method significantly improves measurement efficiency and operational convenience while maintaining accuracy. The system does not require contact with the plant, demonstrating good accuracy, adaptability, and practicality. The tilt angle information obtained is of great significance for path planning of harvesting robots, adjustment of gripping postures, and positioning control of end-effectors, and can serve as a key perception module in the automation process of sunflower flower head placement and drying operations in precision agriculture.
Why it matches plant phenotyping methodsヒマワリ花盤の傾斜角という植物形質を、画像セグメンテーションと幾何解析で非接触測定する手法を開発し、手動測定を基準に精度検証しているため、方法が研究の中心である。
abstractthis study proposes a non-contact measurement method combining deep learning and geometric analysis to achieve precise measurement of sunflower flower head tilt angles.
CottonSunflowerLeafPhysiological trait estimationWater status / transpiration
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including the Scholander pressure bomb. By automating more intimate interactions with foliage in vivo, it would be possible to inject chemical and biological probes that reveal more phenotypes—such as water stress in response to varying environmental conditions and visible gene expression to measure the success of gene engineering applications. To address this, we developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes. This allows for nondestructive, in situ, multispecies applications. We used two probes: Agrobacterium tumefaciens carrying the RUBY gene as a reporter system for plant transformation and nanoparticle hydrogels for measuring leaf water potential (ψ). Our hourglass-shaped design enabled the gripper to exert higher forces with reduced radial expansion compared with conventional designs, achieving an injection success rate above 91%. Studies on sunflower ( Helianthus annuus L.) and cotton ( Gossypium hirsutum L.) showed that our method achieved an average 12-fold increase in infiltration areas, with substantially less leaf damage—3.6% in sunflower and none in cotton—compared with the needle-free syringe method. Enabling long periods of successful in vivo phenotyping on both species after precise and safe foliar delivery underscores the potential of the leaf gripper for robotic plant bioengineering.
Why it matches plant phenotyping methods植物葉への非破壊プローブ導入と光学的・生理的表現型取得を可能にするソフトロボティクス手法の開発が中心であり、単なる生物学的処理実験ではない。
abstractwe developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes.
ABSTRACT The flowering date of sunflowers is a crucial trait that significantly influences crop management practices and product placement. Traditional ground methods for data collection are labor-intensive and subjective, requiring field scientists to manually estimate and record data in the field. This trait can be measured by counting the number of days from planting until 50% of plants in each research plot have reached flowering at R5 developmental growth stage. However, this method is time-consuming and may overlook valuable information related to flowering rates and duration. Flowering time of sunflower also can be approximated by counting the number of heads (flowers) across multiple dates. We propose a method for rapidly counting sunflower heads to model flower counts over time and estimate flowering time using RGB images acquired by Unmanned Aerial Vehicles (UAVs). The method developed employs a deep learning model trained to detect sunflower heads from UAV imagery and modeling these counts over time using a logistic function to estimate the 50% flowering date. The experimental results obtained from this method enabled estimation of the flowering date with a high correlation to ground measurements ( r > 0.91). Significantly, this approach not only reduces labor but also improves the precision of data collection. Moreover, an increase of 6% in heritability across trials, compared to traditional methods, suggests that our approach contributes to a deeper genetic understanding of flowering dynamics. This includes enhanced insights into the timing and rates of flowering, essential for optimizing breeding strategies and understanding genetic responses to environmental conditions. This innovative approach offers a promising avenue for enhancing the efficiency and accuracy of sunflower phenotyping.
Why it matches plant phenotyping methodsUAV画像からヒマワリ頭花を深層学習で検出し、開花時期という植物形質を推定する手法が研究の中心であり、地上測定との相関による検証も行っている。
abstractWe propose a method for rapidly counting sunflower heads to model flower counts over time and estimate flowering time using RGB images acquired by Unmanned Aerial Vehicles (UAVs).
In sunflower breeding, plant phenotyping of white rot (WR) resistance requires a significant amount of resources. Thus the present study aimed to evaluate the possibility to make a more efficient phenotyping of WR resistance in sunflower hybrids per unit of allocated resources. The Degree of Genetic Determination (DGD) estimated from 37 commercial hybrids evaluated by their relative incubation period (RIP) and relative daily lesion growth (RDLG) for 3 years (y) in field experiments designed with 3 replications (r) and 12 plants/plot (pl/p), i.e. 108 plants/hybrid (pl/h), was compared with DGDs estimated using < 108 pl/h. When using fewer resources, DGD values were estimated with less precision in all year-replication-plant/plot combinations. The bias between the DGD averages estimated and the benchmarked DGD values of 0.78 (RIP) and 0.63 (RDLG) and, consequently, the inaccuracies of such estimations increased gradually. The 3y-2r-6pl/p combination was the level of allocated resources showing a still acceptable relative genotypic variability detected for RIP, given that a 100% probability of the DGD estimated was higher than the proposed threshold (DGD = 0.60) value, although the probability for RDLG was quite lower. That combination also resulted in a not-to-be overlooked gain in relative genotypic variability per unit of allocated resources in relation to that with 108 pl/h. So, the cost associated with resources, such as land, seeds, time, and personnel, allocated to assess WR resistance could be reduced without significantly altering the accuracy and precision of the DGD values estimated respect to the benchmarked ones.
Why it matches plant phenotyping methodsヒマワリの菌核病抵抗性という植物病害形質について、圃場フェノタイピングの資源配分・サンプリング設計を比較し、推定精度と再現性を評価しているため、測定方法の技術的検証が中心である。
abstractThe Degree of Genetic Determination (DGD) estimated from 37 commercial hybrids evaluated by their relative incubation period (RIP) and relative daily lesion growth (RDLG) for 3 years (y) in field experiments designed with 3 replications (r) and 12 plants/plot (pl/p)
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Stomatal conductance (g s ) quantifies the rate of exchange of carbon dioxide for photosynthesis and water vapor for transpiration between plant leaves and the atmosphere. g s is usually measured by handheld devices like porometers , and readings are manually taken in the field, which is time-consuming and labor-intensive. In this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling. The experiment was conducted in a research field equipped with an HTP platform in 2020 and 2021 involving maize, sorghum, soybean, sunflower , and winter wheat . Weather variables including dew point temperature, wind speed , air temperature, solar radiation, and relative humidity were collected by an onsite weather station . Plot-level canopy temperature, soil temperature , and seven vegetation indices were acquired using a thermal infrared camera, a multispectral camera, and a visible near-infrared spectrometer integrated on the HTP platform. Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for g s , and model performance was evaluated by Coefficient of Determination (R 2 ) and Root Mean Squared Error (RMSE). The result showed that RFR and SVR outperformed PLSR in g s modeling. The RFR model achieved R 2 of 0.63 and RMSE of 0.16 mol m −2 ·s −1 with the combination of phenotyping data and weather data. It outperformed the model using only the weather data (R 2 =0.35 and RMSE=0.21 mol m −2 ·s −1 ), or the model using only the phenotyping data (R 2 =0.46 and RMSE=0.19 mol m −2 ·s −1 ). This result suggested that high-throughput plant phenotyping data effectively complement weather data in estimating g s rapidly and non-destructively through ML. With the wide adoption of HTP technologies in aerial and ground-based platforms, this research provides a practical framework to estimate g s at large scale for crop breeding and irrigation management .
Why it matches plant phenotyping methodsHTPセンサーデータと機械学習を用いて、植物の生理形質である気孔コンダクタンスを大規模・非破壊推定する方法が研究の中心であり、モデル性能も評価している。
abstractIn this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling.
Accurate and automated detection of sunflower plants, along with assessments of their growth stages and health conditions, is crucial for enabling precision agriculture and improving crop management. In this work, we present a drone-based dataset of annotated sunflower images, derived from high-resolution videos captured at two distinct locations in Bangladesh. The original dataset comprises 1649 images extracted from drone footage of the BARI Surjomukhi-3 variety under various orientations, health conditions, and weather scenarios. After meticulous annotation using the Roboflow platform and augmentation with seven distinct techniques, the dataset expanded to 4286 images in Pascal VOC format. Detailed metadata-including geospatial coordinates, timestamped acquisition conditions, and camera settings-accompanies the dataset to support reproducibility and model generalization. By offering a comprehensive suite of annotated and augmented images, this dataset provides a valuable resource for developing and refining computer vision models geared toward sunflower detection, maturity evaluation, and yield prediction, ultimately advancing sustainable farming practices and decision-making tools in agricultural research.
Why it matches plant phenotyping methodsヒマワリ画像を注釈付きデータセットとして構築し、成長段階・健康状態・成熟度などの植物状態推定を支援することが中心であり、再利用可能な画像ベース表現型データセットに該当する。
abstractwe present a drone-based dataset of annotated sunflower images
Reproduction assets foundThis Data in Brief article describes a drone-based annotated sunflower image dataset from Bangladesh, publicly deposited on Mendeley Data (DOI 10.17632/txct4k36ct.1) with a companion Roboflow Universe project for annotation conversion. Both are paper-specific, public, and directly actionable.Dataset · publicrsingdi, and Amjhupi, Meherpur
Country: Bangladesh
Latitude and longitude:
Nagoriakandi, Narsingdi: Latitude 23.906801° N, Longitude 90.710563° E
Amjhupi, Meherpur: Latitude 23.744897° N, Longitude 88.69174° E
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/txct4k36ct.1
Direct URL to data: https://data.mendeley.com/datasets/txct4k36ct/1
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Value of the Data
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Drone-captured, high-resolution images meticulously annotated for sunflower detection, growth stage, and health conditions enable the development of precise computer vision models [ 1 ].
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Unlike conventional drone images taken from overhead perspectives, the dataset includes images captured at lowOpen asset ↗Mendeley Data · 10.17632/txct4k36ct.1lines:1-51Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
We present a method for real-time terahertz imaging that employs a hydrogen cyanide (HCN) laser as a terahertz source at 0.89 THz and an AlGaN/GaN high-electron-mobility transistor (HEMT) terahertz detector as a camera. We developed an HCN laser and constructed a transmission imaging system based on it. This combination utilizes a high-power HCN laser with a highly sensitive terahertz detector, enabling practical applications of real-time terahertz imaging. A resolution test plane was produced to determine that the system could achieve a lateral resolution of 2 mm, and real-time terahertz imaging was carried out on Siemens star, pistachios, and sunflower seeds. The results demonstrate that the hidden structures inside nuts can be observed by terahertz imaging. Through our analysis of terahertz images of both sunflower seeds and pine nuts, we successfully assessed their fullness and demonstrated the capability to distinguish between full and unfilled nuts. These findings validate the potential of this technique for future applications in nut detection. We discuss the limitations of the current setup, potential improvements, and possible applications, and we outline the introduction of aspherical lenses and terahertz transmission tomography.
Why it matches plant phenotyping methodsテラヘルツ画像システムの構築・性能評価が中心で、種子内部構造と充実度を非破壊測定する植物フェノタイピング手法に該当する。
abstractWe present a method for real-time terahertz imaging that employs a hydrogen cyanide (HCN) laser as a terahertz source at 0.89 THz and an AlGaN/GaN high-electron-mobility transistor (HEMT) terahertz detector as a camera.
To develop new sunflower varieties, it is important to accurately assess phenotypic traits that influence yield, disease resistance, and stress tolerance. Automation allows for systematic data collection and more informed decision-making in the breeding process. The goal of the work was to enhance the efficiency of selecting and predicting the development of sunflower genotypes through the development of new methods, hardware, and software tools for quantitative phenotypic characterisation of seeds. The methods for determining phenotypic characteristics of sunflower seeds, including geometric dimensions, mass, colour, rheological properties, and seed surface properties, have been presented and improved. A module for determining the morphological properties of seeds (geometric dimensions, mass, surface colour, etc.) was developed. This module was configured for high precision in the individual measurement of the geometric dimensions of sunflower seeds, with the determination of their shape and colour. It ensured low labour intensity and high technological efficiency in the implementation of the phenotyping procedure (determination, identification, and separation) of seeds. The methodology for analysing the rheological properties of seeds was refined, and a method for their automatic determination, along with a corresponding module, was substantiated. The proposed module maintains the accuracy of individual measurement of the rheological properties of seeds, consistent with modern measurement tools, while ensuring low labour intensity and high technological efficiency. Additionally, the module significantly reduced the influence of the human factor on the accuracy of measuring the rheological properties of seeds. The proposed module for determining seed surface properties ensured the accuracy of individual measurements of the coefficients of static and sliding friction of seeds, aligning with modern measurement tools, while also ensuring low labour intensity and high technological efficiency. This also significantly minimised the influence of the human factor on the accuracy of these measurements. The use of automated devices in practical conditions can help optimise the selection of seeds with the best characteristics
Why it matches plant phenotyping methodsヒマワリ種子の形態・質量・色・レオロジー・表面特性を定量化する自動装置、ハードウェア、ソフトウェアを開発・改良しており、植物表現型取得法が研究の中心である。
abstractThe goal of the work was to enhance the efficiency of selecting and predicting the development of sunflower genotypes through the development of new methods, hardware, and software tools for quantitative phenotypic characterisation of seeds.
The rapid advancement in smart agriculture has introduced significant challenges, including data scarcity, complex and diverse disease features, and substantial background interference in agricultural scenarios. To address these challenges, a disease detection method based on few-shot learning and diffusion generative models is proposed. By integrating the high-quality feature generation capabilities of diffusion models with the feature extraction advantages of few-shot learning, an end-to-end framework for disease detection has been constructed. The experimental results demonstrate that the proposed method achieves outstanding performance in disease detection tasks. Across comprehensive experiments, the model achieved scores of 0.94, 0.92, 0.93, and 0.92 in precision, recall, accuracy, and mean average precision (mAP@75), respectively, significantly outperforming other comparative models. Furthermore, the incorporation of attention mechanisms effectively enhanced the quality of disease feature representations and improved the model's ability to capture fine-grained features.
Why it matches plant phenotyping methodsヒマワリの病害状態を検出・分類する新規Few-shot学習／拡散モデル手法が研究の中心であり、植物病害表現型の計算的取得に該当する。
titleA Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification.
Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these challenges. To address this, we propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet. LeafNet draws inspiration from the block-wise VGG19 architecture but incorporates several optimizations, including a reduced number of parameters, smaller input size, and faster inference time while maintaining competitive accuracy. The proposed LeafNet leverages small, uniform convolutional filters to capture fine-grained details of plant disease features, with an increasing number of channels to enhance feature extraction. Additionally, it integrates channel attention mechanisms to prioritize disease-related features effectively. We evaluated the proposed method on four datasets: the benchmark plant village (PV), the data repository of leaf images (DRLIs), the newly curated plant composite (PC) dataset, and the BARI Sunflower (BARI-Sun) dataset, which includes diverse and challenging real-world images. The results show that the proposed performs comparably to state-of-the-art methods in terms of accuracy, false positive rate (FPR), model size, and runtime, highlighting its potential for real-world applications.
Why it matches plant phenotyping methods植物病害の画像ベース診断を目的とする軽量CNNを開発し、複数データセットで精度・誤検出率・モデルサイズ・推論時間を評価しており、病害状態の表現型推定手法が中心である。
abstractwe propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet.
Reproduction assets foundThe paper's authors publicly released their LeafNet analysis code on GitHub, and the plant leaf image datasets used for their phenotyping experiments (PV, DRLI, BARI-Sun) are openly available. The PC dataset is a composite of PV and DRLI and is not independently deposited.Code · publicTo promote
reproducibility and facilitate further research, the source code is publicly available at:
(https://github.com/sanaparez/LeafNet)Open asset ↗sanaparez/LeafNetpdf-page:3 lines:1-54Dataset · publicThe datasets utilized in this study are openly available at PV Dataset
(https://github.com/spMohanty/PlantVillage-Dataset)Open asset ↗spMohanty/PlantVillage-Datasetpdf-page:15 lines:1-59Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Powering and communicating with wearable devices on bio-interfaces is challenging due to strict weight, size, and resource constraints. This study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health. It features foldable solar panels along with all essential flexible electronic components, resulting in a compact system that is lightweight enough for small plants. To tackle the low-energy density of solar power, we developed an ultralow-energy light communication mechanism inspired by fireflies. Together with unmanned aerial vehicles and deep learning algorithms, this approach enables efficient data retrieval from multiple devices across large agricultural fields. With its simple deployment, it shows great potential as a low-cost plant phenotyping tool. We believe our energy and communication solution for wearable devices can be extended to similar resource-limited and challenging scenarios, leading to exciting applications.
Why it matches plant phenotyping methods植物の樹液流を長期モニタリングするウェアラブルセンシング装置と通信・取得システムを開発しており、植物生理状態の計測およびフェノタイピング基盤が研究の中心である。
abstractThis study presents a sunflower-like plant-wearable sensing device that harnesses solar energy, achieving complete energy self-sustainability for long-term monitoring of plant sap flow, a crucial indicator of plant health.
Crop yield prediction is a complex task with nonlinear relationships due to its dependence on multiple factors such as polygenic traits, environmental effects, genetics and environment interactions, etc. These cases make conventional statistical techniques unable to explain the nonlinear and complex relationship between performance and its components. This research was conducted to estimate sunflower seed yield using multiple linear regression (MLR) and convolutional neural network (CNN). It also investigated the effect of different input variables on deep learning modeling of sunflower seed yield under normal and drought stress (DS) conditions. The 100 pure lines of oil seed sunflower were investigated during two crop years in the field under normal and DS conditions in terms of seed yield and morphological traits. The CNN model was implemented using different combinations of input variables. In this regard, all studied parameters were first used as input variables, and then stepwise regression was performed for both conditions. In this step, the input variables for yield modeling with the CNN model consisted of parameters included in the regression model and those common in normal and DS conditions. The CNN model with two input variables (head diameter [HD] and number of leaves [NL]), which were common in the regression model for both conditions, achieved higher accuracy and performance in predicting sunflower yield under normal conditions (R² =0.921, MAE=5.425, and RMSE=6.462). Nonetheless, in DS conditions, the CNN model with seven input variables (i.e., leaf width [LW], NL, plant height [PH], days to flowering [DF], stem diameter [SD], petiole length [PL], and HD) demonstrated higher accuracy and performance in predicting sunflower yield (R² =0.915, MAE=3.632, and RMSE=4.330). The CNN model outperformed the MLR model in both conditions in terms of accuracy and performance. Sensitivity analysis identified LW, NL, and length of leaf [LL] traits as important and influential traits for yield prediction under normal and DS conditions. The CNN model was successful in reducing the number of variables needed to model sunflower seed yield. With the important parameters identified, sunflower yield can be predicted with higher accuracy, lower cost, and in a shorter time, even if other parameters are not available, in both normal and drought-prone conditions. The CNN model can potentially be used as a promising tool for predicting sunflower yield in yield increase programs under different growing conditions.
Why it matches plant phenotyping methodsCNNとMLRによるヒマワリ種子収量という植物形質の推定を比較・評価し、入力形質の感度分析と精度検証を行っており、計算的な形質推定手法が中心である。
abstractThis research was conducted to estimate sunflower seed yield using multiple linear regression (MLR) and convolutional neural network (CNN).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Sunflower (Helianthus annuus) is a widely cultivated crop that exhibits a trait known as capitulum (or head) inclination at maturity. This trait is influenced by various structural factors, including head weight, stem traits, and plant height. A sunflower head should be at an angle at which the head faces the ground to avoid damage from the sun and birds. While this desired inclination range is known, current methods, including visual estimation and a model of measuring inclined length of the stem, fail to provide precise measurements of angle. This study introduces novel approaches to mathematically measure the head inclination angle. The research, which was conducted over the 2022 and 2023 growing seasons, involved an aluminum rod equipped with a ruler and a digital protractor to measure various height and angle components. Using the data collected, three methods were applied for measuring inclination: a previously published model as a control, a trigonometry‐based approach using angle and height measurements, and other model‐based approaches. A linear model resulted in a formula to calculate the head angle of any plant based solely on two height measurements, the highest point of the plant at both bloom (R5) and maturity (R9). Calculations of heritability and correlation suggest this method has created a precise alternative to existing estimation methods. The resulting formula has the potential to be paired with measurements from high‐throughput phenotyping methods, such as those facilitated with drones and ground robots, to fully automate the process of collecting head inclination data.
Why it matches plant phenotyping methodsヒマワリ頭花傾斜角という植物形質を対象に、既存法との比較を含む数学的測定法と計算式を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces novel approaches to mathematically measure the head inclination angle.
The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality.
Why it matches plant phenotyping methodsヒマワリ種子の正常・損傷・変形状態をテラヘルツ画像から非破壊分類するモデルを開発・検証しており、植物器官の状態取得が研究の中心である。
abstractThis paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds.
Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Tₛ). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Tₛ, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Tₛ, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Tₛ became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R²), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.
Why it matches plant phenotyping methods作物のLAIという植物形質を赤縁クロロフィル指数から推定するモデルを開発し、複数作物・生育段階で検証している。ET推定への応用も含むが、LAI取得・推定手法が中心的な技術貢献である。
abstractIn this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ) considering the hysteresis between them.
In this research, plant pathogens are considered as big data because of the numerical counts for high intensity pixels in the images. The research presents an automated approach for early detection of plant diseases using image processing techniques. By analyzing the color features of leaf areas, the k-means algorithm for color segmentation and the Gray-Level Co-Occurrence Matrix (GLCM) are used for disease classification. A novelty of this research is that it illustrates four categories of plants to analyze and compare: (1.) Grain, represented by Rice Plant Leaf Data; (2.) Fruit, represented by banana plant leaf data, (3.) Flower, represented by sunflower plant leaf data; and (4.) Vegetable, represented by potato plant leaf data. Six stages of image processing are applied to real data for diseases of leaf smut for rice, black sigatoka for banana, leaf scars for sunflower, and late blight for potato. Finally, a comparison of the image processing for each of the four plant types, conclusions, and future research directions are presented.
Why it matches plant phenotyping methods葉画像から植物病害を自動検出・分類する画像処理手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として含める。
abstractThe research presents an automated approach for early detection of plant diseases using image processing techniques.
Key message This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O 2 - , OH - ), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.
Why it matches plant phenotyping methodsヒマワリ感染個体の状態を対象に、Vis-NIRハイパースペクトル画像と機械学習で感染・非感染を判別する非破壊的な表現型取得法を開発しており、方法が中心的である。
abstractIn this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed.
Phenological observations are expensive and demanding in terms of manpower to monitor the vegetation stages. Therefore, satellite products have opened new possibilities for easier and more widespread data collection. Nowadays biomass estimations extensively rely on these tools, given their extensive spatial and temporal coverage, which are defined by indicators such as vegetation indices, which describe the biomass growth, canopy structure, vegetation health and even water management etc. However, the detection of flowering stages through remote sensing is less explored, with fewer established methods available.This study investigates temporal phenological changes during the blooming period of the most widely cultivated oilseed crops in Hungary in 2021, specifically the sunflower (Helianthus annuus L.) and the winter-cultivated oilseed rape (Brassica napus L.). The objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products. The investigation is conducted across seven distinct regions, using honey bees as bioindicators of the fields.Methodologies outlined in prior scientific literature, focusing on the analysis of anthesis timing and duration in major nectar-producing crops utilizing Sentinel-1 SAR and Sentinel-2 optical products, serve as the basis for this research. Within each radius study areas, the parcel-averaged and smoothed daily time series were acquired. The estimation of the blooming phases was achieved through parcel smoothing methods using daily non-parametric local regression (loess) approach which showed better performance compared to the Savitzky-Golay (SG) algorithm. In our flowering detection analysis, we also examined the differences in the ascending and descending orbits and their combined results. In the case of oil seed rape, the NDVI index reached its maximum after flowering, while for sunflower it varied. Additionally, we investigated the outcomes of all polarization and method combinations within each crop type.Our study enables the comprehension of temporal flowering patterns in bee pasture crops through the integration of SAR and optical measurements. Additionally, it supports the utilization of beehive scales to provide field-based reference data for estimating anthesis.The research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project. Project No. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme.
Why it matches plant phenotyping methods衛星SAR・光学データから作物の開花時期・期間を推定する手法が中心で、loessとSavitzky–Golay法の性能比較も行っているため、植物フェノタイピング研究に該当する。
abstractThe objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products.
This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool.
Why it matches plant phenotyping methods植物の水ストレス状態をレーザースペックルのAVD指標で測定し、品種間差を評価するセンサー型フェノタイピング手法の適用が中心である。
abstractThis study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool.
Accurate determination of crops growth period plays an important role in field management and agricultural decision-making. The current work mostly extracts the crop normalized difference vegetation index curve from multi-temporal data and identifies the crop phenology based on its trend or special nodes. However, these time-series-based identification methods are difficult to be applied to practically crop monitoring tasks. In this paper, the unmanned aerial vehicle remote sensing platform is used to collect the multi-spectral images of the experimental field and identify the sunflower growth period based on the different population features during its different growth periods. According to the actual field management needs, this study obtains the plot-level sunflower growth period result by analyzing statistically the distribution area of different sunflower periods in a field plot. This study uses the data of 2018 in the study area to build the model and test its performance on the data of 2019. Through comparative experiments, PSPNet can achieve a good balance between accuracy and efficiency in this study. Further, given to time-series relationship between the adjacent growth periods classification, this paper proposes an improved loss function to weight different types of misclassification to optimize model performance. The results show that improved PSPNet with proposed weighted loss function achieves the optimal recognition accuracy of 89.01%, which provides a solution for sunflower growth period recognition based on the single-phase data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と深層学習により、ヒマワリの生育期という植物状態を plot レベルで推定し、モデル比較・改良損失関数・異年次性能評価を行っており、表現型取得・推定手法が中心である。
abstractthe unmanned aerial vehicle remote sensing platform is used to collect the multi-spectral images of the experimental field and identify the sunflower growth period
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
Why it matches plant phenotyping methodsヒマワリ画像から生育・老化に関わるフェノロジー段階を深層学習で推定する手法を開発・比較し、データベース化と性能評価も行っており、フェノタイピング手法が研究の中心です。
abstractwe evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field.
Sunflower is an annual species of the Asteraceae family, and it occupies a relevant position in the world market business as one of the most important oilseed crops. Given the current geopolitical situation and climate change, the agri-food supply chain of sunflower is in crisis. In this context, precision agriculture, especially remote sensing, can address demands for more production and greater sustainability. The aim of the present systematic review is to evaluate the available scientific literature on precision agriculture applied to sunflower crop, specifically the use of hyperspectral data to calculate vegetation indices or create crop growth models. The systematic review follows specific guidelines and a well-described review protocol. A total of 104 studies were included in the review, starting from raw search in different data sources (Scopus, Web of Science, Springer Link, and Science Direct) and following with the application of inclusion criteria. Results focused on the following main topics: crop management (i.e., management zones, yield prediction, vegetation indices correlations), sunflower crop growth monitoring (i.e., identify different growth stages and vegetation parameters), weed management, and industrial applications. The role of hyperspectral sensors has been thoroughly investigated to help choose ideal wavelengths related to vegetation indices. Future research should prioritise water stress management, time-saving evaluation of new sunflower hybrids, and crop growth models.
Why it matches plant phenotyping methodsヒマワリに対するハイパースペクトル計測、植生指数、成長モニタリングを扱う系統的レビューであり、植物形質・生育状態の取得手法が中心です。
titleA systematic review on precision agriculture applied to sunflowers, the role of hyperspectral imaging
Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.
Why it matches plant phenotyping methods全身部位のハイスループット画像化と新規フェノタイピング手法が明示され、低温応答の形態評価における手法が中心的に記述されている。
abstractWe performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots.
Reproduction assets foundThe paper's plant-phenotyping measurements (morphological, chlorophyll/anthocyanin/flavonoid, root traits, yield and seed composition across 12 experiments) were deposited on Recherche Data Gouv under doi:10.57745/4HNS1J, with a specific sub-dataset (persistentId doi:10.57745/4HNS1J.2598) referenced in Table 1. No codeDataset · publicby the French National Association for Research and
Technology (ANRT).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available
in Recherche Data Gouv at https://entrepot.recherche.data.gouv.fr/,
reference number https://doi.org/10.57745/4HNS1J.REFERENCES
Abbass, K., Qasim, M.Z., Song, H., Murshed, M., Mahmood, H. & Younis, I.Open asset ↗Recherche Data Gouvpdf-raw-page:16 lines:1-76Dataset · publicter
dynamics,
chlorophyll
content,
anthocyanin
content,
flavonoid
content,
nitrogen
balance,
fatty
acids
of
seeds
Tables
S2
and
S3
21TE01‐02
22EX01‐02
Early
and
late
Field
2
(n
=
22)
Vigour,
total
leaf
area
and
plant‐height
dynamics,
flowering
date,
yield,
yield
components
Table
S4
Note:
Data
were
submitted
to
the
public
portal
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/4HNS1J.2598 | LECONTE ET AL.
13653040,
2025,
4,
Downloaded
from
https://onlinelibrary.wiley.com/doi/10.1111/pce.14941
by
Mount
Vernon
Nazarene
University,
Wiley
Online
Library
on
[30/12/2025].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
OnliOpen asset ↗doi:10.57745/4HNS1J.2598pdf-raw-page:3 lines:1-265Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published24 May 20242024 5th International Conference for Emerging Technology (INCET)Cited by 8 · OpenAlex ↗
This research presents a new use of Convolutional Neural Networks (CNN) integrated with Federated Learning (FL) for detecting and classifying illnesses on sunflower leaves. The centre of this investigation is a systematic analysis of findings obtained from local data., which are transformed into global knowledge with the help of sophisticated federated averaging approaches. The findings show the efficiency of our model., which is outstanding. The macro averages for accuracy., recall and F1-scores also reveal an upward trend of continuous improvement starting kr _1 (82.95) to up between 83.74 as at kr_3 met kp_ 4atkr(translation truncated). Weighted and micro averages also demonstrate a rising tendency., reflecting that the model becomes more accurate in terms of its ability to manage class imbalances. Specifically., the micro averages also improve similarly., starting at 83.05 and ending at 94.88 for kr_5. Still., the weighted ones change from just two decimal points below (from an average of close to 17 times ten minus six). That difference changes drastically because now we are going from having about. The research finds that federated averaging helps transform local data into global data. It demonstrates how the individual client can contribute individually to the learning of a model. Still., it also shows that in federated learning., collaborative and distributed nature has enhanced performance over time. In conclusion., the study demonstrates a convincing case for utilizing FL and CNN in rural situations., particularly for recognizing illnesses afflicting sunflower leaves. The experimental and statistical data reveal the efficiency of this method., which may lead to its broad utilization in plant pathology for better precision agriculture.
Why it matches plant phenotyping methodsヒマワリ葉の病害状態を画像からCNN・連合学習で検出・分類する手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractThis research presents a new use of Convolutional Neural Networks (CNN) integrated with Federated Learning (FL) for detecting and classifying illnesses on sunflower leaves.
SunflowerMultispectral / hyperspectralSeed / grainClassificationPhysiological trait estimationWater status / transpiration
Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.
Why it matches plant phenotyping methodsヒパースペクトル画像とケモメトリクスを用いて、ヒマワリ種子の活力と水分含量という植物形質を非破壊推定する手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstractBy employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content.
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.
Why it matches plant phenotyping methods植物フェノタイピングにおける機械学習応用を体系的に概観するレビューであり、表現型推定・取得手法が中心です。
abstractTherefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers.
Diseases in agricultural plants are one of the most important problems of agricultural production. These diseases cause decreases in production and this poses a serious problem for food safety. One of the agricultural products is sunflower. Helianthus annuus, generally known as sunflower, is an agricultural plant with high economic value grown due to its drought-resistant and oil seeds. In this study, it is aimed to classify the diseases seen in sunflower leaves and flowers by applying deep learning models. First of all, it was classified with ResNet101 and ResNext101, which are pre-trained CNN models, and then it was classified by adding squeeze and excitation blocks to these networks and the results were compared. In the study, a data set containing gray mold, downy mildew, and leaf scars diseases affecting the sunflower crop was used. In our study, original Resnet101, SE-Resnet101, ResNext101, and SE-ResNext101 deep-learning models were used to classify sunflower diseases. For the original images, the classification accuracy of 91.48% with Resnet101, 92.55% with SE-Resnet101, 92.55% with ResNext101, and 94.68% with SE-ResNext101 was achieved. The same models were also suitable for augmented images and classification accuracies of Resnet101 99.20%, SE-Resnet101 99.47%, ResNext101 98.94%, and SE-ResNext101 99.84% were achieved. The study revealed a comparative analysis of deep learning models for the classification of some diseases in the Sunflower plant. In the analysis, it was seen that SE blocks increased the classification performance for this dataset. Application of these models to real-world agricultural scenarios holds promise for early disease detection and response and may help reduce potential crop losses.
Why it matches plant phenotyping methodsヒマワイラ葉・花の画像から病害状態を分類する深層学習手法を比較評価しており、植物病害表現型の取得・推定が研究の中心である。
abstractIn this study, it is aimed to classify the diseases seen in sunflower leaves and flowers by applying deep learning models.
MaizeSunflowerField / plotLaboratory / benchtopStem / branchPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
There is currently a need for inexpensive, continuous, non-destructive water potential measurements at high temporal resolution ( Helianthus annuus ) (petioles and stems) and a monocotyledon ( Zea mays ) species (stems) for 1 week during dehydration and re-watering treatments under laboratory conditions. We also demonstrated the ability of the device to record branch and trunk diameter variation of a woody dicotyledon ( Rhus typhina ) in the field. Under laboratory conditions, we compared our device (hereafter 'contact' dendrometer) with modified versions of another open-source dendrometer (the 'optical' dendrometer). Overall, contact and optical dendrometers were well aligned with one another, with Pearson correlation coefficients ranging from 0.77 to 0.97. Both dendrometer devices were well aligned with direct measurements of xylem water potential, with calibration curves exhibiting significant non-linearity, especially at water potentials near the point of incipient plasmolysis, with pseudo R 2 values (Efron) ranging from 0.89 to 0.99. Overall, both dendrometers were comparable and provided sufficient resolution to detect subtle differences in stem water potential (ca. 50 kPa) resulting from light-induced changes in transpiration, vapour pressure deficit and drying/wetting soils. All hardware designs, alternative configurations, software and build instructions for the contact dendrometers are provided.
Why it matches plant phenotyping methods安価なオープンソース樹幹径計を開発し、水ポテンシャルと茎径成長を高時間・空間分解能で測定する手法を比較検証しており、植物表現型取得が研究の中心である。
titleDevelopment and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all sensor software, 3D prints, photos, and schematics in a public GitHub repository, and the dendrometer measurement data are provided as a CSV in the supplementary information (no allowed URL for the SI itself). The GitHub repository is a paper-specific,公开,可Code · publicins, CO 80526, USA.
Data Availability
All data used in this study are available for download from the supplemental information as a CSV file (sup_data_all_dendro.csv). All software needed for operating sensors, 3D prints, photos, and schematics have been included in the SI materials, and can also be freely accessed via github ( https://github.com/sean-gl/dendrometer_water_potential_device ).
Sources of Funding
J.J.S. was supported by an NSF Postdoctoral Research Fellowship in Biology, Grant No. IOS-1907338.
Contributions by the Authors
All authors contributed meaningfully to the manuscript. S.M.G., J.J.S., B.A., S.K.P. and J.M. designed the experiment and collected the data. S.M.G. wrote theOpen asset ↗https://github.com/sean-gl/dendrometer_water_potential_devicelines:224-283Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 7 Sept 2026
MaizeSoybeanSugar beetSunflowerAerial / UAVField / plotLiDAR / point cloudRootWhole plant / canopy / plot / fieldAnnotation / quality control
Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.
Why it matches plant phenotyping methodsUAS搭載LiDARの運用パラメータ最適化と多時期点群からの作物キャノピー高さ推定を検証する、植物フェノタイピング手法の開発・ベンチマーク研究である。
abstractThis article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis.
Excessive yield losses are due to delayed identification of leaf diseases, resulting in lower farmer revenue. To address this, early and precise detection methods are crucial. Sunflower, a vital commercial crop in India for edible oil production, faces issues like leaf blight, downy mildew, powdery mildew, and leaf spot. Our research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques. We compiled sunflower leaf images from village databases and farm visits. Initially, color images were converted to grayscale, and the noise was reduced using Gaussian filtering during image pre-processing. Disease-affected areas were segmented using edge-based approaches. Hybrid feature extraction was then employed to encompass color and texture-related attributes. The K-Nearest Neighbors (KNN) algorithm facilitated disease classification. The proposed algorithm’s performance was benchmarked against SVM, RF and DT classifiers. Remarkably, our algorithm achieved exceptional accuracy: 98% for leaf blight, 97.3% for downy mildew, 95% for powdery mildew, and 96.5% for leaf spot detection. These results underscore the algorithm’s effectiveness in combating plant diseases and enhancing agricultural productivity.
Why it matches plant phenotyping methodsヒマワリ葉の画像から病変部位を抽出し、特徴量抽出と分類を行う病害状態の画像ベース表現型解析手法が研究の中心であり、複数分類器との性能比較も実施している。
abstractOur research aims to create an algorithm that effectively identifies these diseases using hybrid feature extraction techniques.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Leaf senescence is a complex mechanism governed by multiple genetic and environmental variables that affect crop yield. It is the last stage of leaf development and is characterized by an active decline in the photosynthetic rate, nutrient recycling, and cell death. Leaf senescence begins in the lower leaves, and photoassimilates are translocated to the younger tissues. During early anthesis, leaf senescence becomes crucial for grain filling, because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain the required yields. Furthermore, genotypes with early senescence were correlated with poor yield and low seed quality. Like all the crops growing in the field, studying phenology and its correlation with the senescence process is a laborious task where most of the parameters depend on highly trained people who conduct sampling and measurements. Several high-throughput phenotyping techniques have been developed in recent years. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies.
Why it matches plant phenotyping methodsヒマワリ画像から生育・フェノロジー段階を推定する深層学習手法を比較評価し、Sunphenoモデルを構築・適用しており、表現型取得手法が研究の中心である。
abstractIn this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field.
Early plant disease diagnosis is necessary for several purposes, including reducing yield losses, monitoring and predicting infections, detecting host resistance, and studying basic host–pathogen biological processes. However, early detection has been limited by a trained workforce and the ability to identify the problem early in the growing season. Artificial intelligence/machine learning algorithms can help fill this gap. Automatic leaf detection using machine learning is proposed in this study. The presented approach consists of three stages: pre‐processing, feature extraction, and classification. Initially, the input image is transformed into the red, green, and blue formatand the noise in the green band is removed using a median filter. Then important features of the green band are extracted. After feature extraction, the extracted features are fed to the optimized artificial neural network classifier to classify an image as normal or diseased. To improve artificial neural network (ANN) performance, the ANN parameters are chosen optimally using the adaptive sunflower optimization (ASFO) algorithm. Then, the infected region is separated using a level set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity; the proposed method reached the maximum accuracy of 97.94% for plant disease prediction.
Why it matches plant phenotyping methodsイネ葉の病斑・感染領域を画像から抽出し、ANNで健全／罹病を分類する手法の開発・評価が中心で、植物病害状態の表現型計測に該当する。
abstractAutomatic leaf detection using machine learning is proposed in this study.
Plant diseases, such as fungi and parasites, disrupt or alter the plant’s essential functions. In order to prevent potential economic losses from these plant diseases, early diagnosis of these diseases is crucial. The use of Deep Learning models for the detection and classification of plant diseases has been found to dramatically increase the speed of diagnosis while at the same time minimizing the amount of error involved. Taking advantage of a variety of tools and techniques, including transfer learning and ensemble learning, we have experimented with different deep-learning models and pre-existing architectures to find out which combination would best fit the data we gathered from the Sun Flower Fruits and Leaves dataset. Moreover, our best model has the ability to classify and detect sunflower disease with an accuracy of 97.91%, which is a considerable improvement over state-of-the-art models currently used for the classification and detection of sunflower disease.
Why it matches plant phenotyping methodsヒマワリ葉・果実画像から病害状態を分類・検出する深層学習手法を比較・評価しており、植物病害表現型の取得・推定が中心である。
abstractwe have experimented with different deep-learning models and pre-existing architectures to find out which combination would best fit the data we gathered from the Sun Flower Fruits and Leaves dataset.
Reproduction assets foundThe paper's sunflower disease image dataset is explicitly declared publicly available on Mendeley Data with a direct URL in the Data Availability statement; no author code or models are shared.Dataset · publicompliance with Ethical Standards
Conficts of interest The authors declare that they have no confict of interest.
Funding No funding was received to assist with the preparation of this manuscript.
Data Availability The paper uses the publicly available dataset for Sun Flower
Fruits and Leaves. The dataset is openly avvailable at https://data.mendeley.com/datasets/b83hmrzth8/1
Human Participants This article does not contain any studies involving human
participants performed by any of the authors.
References
1. Abbas, A., Jain, S., Gour, M., Vankudothu, S.: Tomato plant disease detection using
transfer learning with c-gan synthetic images. Computers and Electronics in Agriculture
187, 106279 (Open asset ↗b83hmrzth8/1pdf-raw-page:24 lines:1-40Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Sunflower ( Helianthus annuus L. ) is the second most important oil seed crop in Europe. The seeds are used as confection seeds and, more importantly, to generate an edible vegetable oil, which in normal varieties is rich in the polyunsaturated fatty acid linoleic acid. Linoleic acid is biosynthesized from oleic acid through activity of the oleate desaturase FATTY ACID DESATURASE 2 (FAD2), which in seeds is encoded by FAD2-1 , a gene that's present in single copy in sunflowers. Defective FAD2-1 expression enriches oleic acid, yielding the high oleic (HO) acid trait, which is of great interest in oil seed crops, since HO oil bears benefits for both food and non-food applications. Chemical mutagenesis has previously been used to generate sunflower mutants with reduced FAD2-1 expression and here it was aimed to produce further genetic material in which FAD2-1 activity is lost and the HO trait is stably expressed. For this purpose, a sunflower mutant population was created using gamma irradiation and screened for fad2-1 mutants with a newly developed HPLC-based fatty-acid profiling system that's suitable for high-throughput analyses. With this approach fad2-1 knock-out mutants could be isolated, which stably hyper-accumulate oleic acid in concentrations of 85-90% of the total fatty acid pool. The genetic nature of these new sunflower lines was characterized and will facilitate marker development, for the rapid introgression of the trait into elite sunflower breeding material.
Why it matches plant phenotyping methods育種対象となる種子脂肪酸組成を高スループットに抽出する新規HPLCプロファイリング法を開発し、変異体スクリーニングに実質的に用いているため、手法が中心的です。
abstractscreened for fad2-1 mutants with a newly developed HPLC-based fatty-acid profiling system that's suitable for high-throughput analyses
South Africa is dependent on dryland farming, causing crops to be sown only once the rainy season begins. With climate change altering weather patterns, unpredictable rainy seasons may lead to cash crops such as maize and soybean being planted later, which makes them susceptible to extreme temperatures and drought. Farmers often switch to planting sunflower ( Helianthus anuus L.) under these conditions because of its shorter rotation time and hardier nature. For this reason, sunflower tends to be planted outside of its optimal planting window. To understand the effects of different planting dates on sunflower growth and development, a planting date trial was established in Potchefstroom under the Phenospex FieldScan system. Low-and high-density monthly plantings were sown from October to March and scanned three times a day (RGB, NIR, Laser). Values are extracted to calculate plant growth and health indices over the season for each planting date. The aim of this project is to develop a Shiny application that can analyse the Phenospex data outside of the platform. Phenospex software is expensive and impractical to use in the South African context with unstable infrastructure because it requires the sever to be on in one location for analysis in a different location. The Shiny app will allow Phenospex data to be analysed offline, offering more flexibility in an African setting.
Why it matches plant phenotyping methodsPhenospexのRGB・NIR・レーザーデータから植物の成長・健全性指標を抽出し、オフライン解析するShinyアプリの開発が中心であり、植物フェノタイピング手法・ソフトウェアに該当する。
abstractThe aim of this project is to develop a Shiny application that can analyse the Phenospex data outside of the platform.
Abstract Plant phenotyping is the science to quantify the quality, photosynthesis, development, growth, and biomass productivity of different crop plants. In the past, plant phenotyping employed methods such as grid count and regression models. However, the grid count method proved to be labor‐intensive and time‐consuming, while the regression model lacked accuracy in calculating leaf area. To address these challenges, a portable automatic platform was developed for precise ground‐based imaging of field plots. This platform consisted of a frame, an RGB camera, a stepper motor, a control board, and a battery. The RGB camera captured images, which were then processed using MATLAB software. Statistical analysis was performed to compare the results obtained from the grid count, regression model, and image processing techniques. The correlation coefficient (r) between the image processing technique and the regression model for sunflower was found to be 0.98 and 0.97, respectively, whereas for kidney bean it was 0.99 and 0.96, respectively. The minimum and maximum values for leaf area density (LAD) of all selected sunflower leaves were determined to be 0.132 and 0.714 m²/m³, respectively. For kidney bean leaves, the minimum and maximum mean LAD values were found to be 0.081 and 0.239 m²/m³, respectively. Ergonomic aspects of the developed automatic system were studied. The developed system had lower physiological parameters, such as working heart rate of 99 beats/min, work pulse of 18 beats/min, oxygen consumption of 786 mL/min, and energy consumption of 11.5 kJ/min compared to the grid count method. Thus, developed automatic ground‐based imaging system would significantly reduce physiological workload and associated hazards. Therefore, the developed method proved satisfactory in comparison to other techniques, offering a quick, efficient, and user‐friendly approach for determining plant phenotypes.
Why it matches plant phenotyping methods圃場作物の葉面積密度などの表現型を画像から推定する自動撮像プラットフォームを開発し、既存手法と比較検証しているため、フェノタイピング手法が中心である。
abstracta portable automatic platform was developed for precise ground‐based imaging of field plots
In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.
Why it matches plant phenotyping methods植物葉の画像から病変領域を抽出し、深層学習で病害を分類する手法が研究の中心であり、植物の病害状態を直接推定するため対象範囲に含める。
abstractIn this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower.
Crop modeling is an effective tool for simulating crop growth under various agricultural water and salinity management practices. However, most crop models fail to describe the root dynamics in response to soil stresses adequately. To address this issue, field experiments were conducted by planting sunflowers in saline soils. Three machine learning (ML) models of random forest (RF), gaussian process regression (GPR), and extreme gradient boosting (XGBoost) were initially introduced for predicting root length density (RLD). Then, by coupling with a crop model SWAP, the soil salt content (SSC), soil water content (SWC), and crop growth indicators of leaf area index (LAI) and dry matter (DM) were simulated. Results show that RF and XGBoost models could predict RLD more accurately than the GPR model, with root mean square error (RMSE) lower than 0.473 cm cm-3. Compared to using a typical cubic polynomial function (CPF) of RLD in the SWAP model, similar SWC and SSC simulation results were obtained based on the ML models. However, for the crop growth simulation, the performances of ML models were significantly better than the CPF. Especially for LAI simulation in the high salinity fields, the relative root mean square error (RRMSE) in the RF model was 0.222–0.282 lower than in the CPF. Moreover, compared to the XGBoost model of RLD, more accurate and stable simulation results of SWC, SSC, and LAI were obtained based on the RF model. These results illustrate that ML models, especially the RF model, can be used to quantify RLD dynamics and improve crop modeling performances.
Why it matches plant phenotyping methods機械学習により植物の根長密度（RLD）動態を定量化・予測し、精度比較と作物モデルへの適用を行っており、植物形質推定手法が研究の中心である。
abstractThree machine learning (ML) models of random forest (RF), gaussian process regression (GPR), and extreme gradient boosting (XGBoost) were initially introduced for predicting root length density (RLD).
The automatic measurement of developmental and physiological responses of sunflowers to water stress represents an applied challenge for a better knowledge of the varieties available to growers, but also a fundamental one for identifying the biological, genetic and molecular bases of plant response to their environment.On INRAE Toulouse's Heliaphen high-throughput phenotyping platform, we set up two experiments, each with 8 varieties (2*96 plants), and acquired images of plants subjected or not to water stress, using a light barrier on a daily basis. At the same time, we manually measured the leaf surfaces of these plants every other day for the duration of the stress, which lasted around ten days. The images were analyzed to extract morphological characteristics of the segmented plants and different models were evaluated to estimate total plant leaf areas using these data.A linear model with a posteriori smoothing was used to estimate total leaf area with a relative squared error of 11% and an efficiency of 93%. Leaf areas estimated conventionally or with the developed model were used to calculate the leaf expansion and transpiration responses (LER and TR) used in the SUNFLO crop model for 8 sunflower varieties studied. Correlation coefficients of 0.61 and 0.81 for LER and TR respectively validate the use of image-based leaf area estimation. However, the estimated values for LER are lower than for the manual method on Heliaphen, but closer overall to the manual method on greenhouse-grown plants, potentially suggesting an overestimation of stress sensitivity.It can be concluded that the LE and TR parameter estimates can be used for simulations. The low cost of this method (compared with manual measurements), the possibility of parallelizing and repeating measurements on the Heliaphen platform, and of benefiting from the Heliaphen platform's data management, are major improvements for valorizing the SUNFLO model and characterizing the drought sensitivity of cultivated varieties.
Why it matches plant phenotyping methodsヒマワリの葉面積を画像から推定する手法を開発し、手動測定と比較検証しており、画像ベース表現型取得が研究の中心である。
abstractThe images were analyzed to extract morphological characteristics of the segmented plants and different models were evaluated to estimate total plant leaf areas using these data.
Root length density (RLD) is an indispensable input for driving almost all agro-hydrological models, but it is difficult to measure and has strong plasticity to soil environments, thus it is a challenge to characterize the dynamics of RLD if crops suffering adverse soil stress at field scale. Soil salinity is a major abiotic stress that restricts crop shoot growth and yield formation, but it may stimulate root growth of salt-tolerant crops. In this study, based on 256 datasets of actual root length density (ARLD) for sunflower grown under saline conditions and influencing factors as days after sowing (DAS), root depth (RD), soil salt content (SSC), soil water content (SWC), and leaf area index (LAI), we (1) clarified the limitations of cubic polynomial, exponential, and power elementary functions of root depth (RD) for ARLD prediction; and (2) established and compared three novel machine learning models (MLMs) including gaussian process regression (GPR), multivariate adaptive regression spline (MARS), and random forest (RF) with eight combinations of inputs (COIs). Results show the distribution of the sunflower’s ARLD was significantly different in fields with different salinity levels, the peak value of ARLD in the low-salt field was smaller than 1.2 cm·cm⁻³, but it could be over 3.0 cm·cm⁻³ in the high-salt field. Except at the early growth stage (DAS = 28–30), all the elementary functions failed to fit the ARLD accurately with the RMSE ranging from 0.39 to 0.97 cm·cm⁻³ and R² lower than 0.38. The higher prediction accuracies for ARLD were obtained in MLMs, especially with the COI of DAS + RD + SSC + LAI. Moreover, the accuracies of RF and GPR models (RMSE ranging from 0.36 to 0.37 cm·cm⁻³ and R² greater than 0.73 in the test) were higher than the MARS. The spatiotemporal distribution of simulated ARLD in the GPR model was relatively smooth, while it presented certain discontinuity in the RF model. In general, the RLD plasticity of sunflower in saline soil should not be ignored, the MLMs models, such as the GPR and RF models, are more applicable for RLD prediction than the elementary function models.
Why it matches plant phenotyping methodsヒマワリの根長密度という植物形質を対象に、複数の機械学習モデルを開発・比較し、予測精度を検証しているため、計算による表現型推定手法が研究の中心です。
abstractestablished and compared three novel machine learning models (MLMs) including gaussian process regression (GPR), multivariate adaptive regression spline (MARS), and random forest (RF)
In agricultural systems, rapid information from data collection and processing is an important factor for stakeholders and researchers to correctly account for the spatial and temporal variability of crop and soil factors. The aim of the present study was to investigate soil-plant-water systems and interactions using manual and remote sensing techniques in a small agricultural catchment. Four land use types of forest, grassland, vineyard, and cropland (sunflower) were investigated in different slope positions. At the same time, three different tillage practices were applied in the vineyard between the rows: grassed (NT), cover cropped (CC), and tilled (T) inter rows. We evaluated NDVI measurements from three different sources (PlantPen - PP, Meter Group - MG, Sentinel-2 - S2) representing different scales (leaves, 0.33m2, and 100m2). We also compared ground and satellite measurements of varying vegetation indices.Spectral reflectance sensors were used on the slopes of grassland, cropland, and three vineyard sites. The Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI) sensors were used to measure leaf reflectance. A hemispherical sensor set was used for each measurement. Hand-held instruments were used to measure the topsoil soil water content (SWC) and temperature, leaf NDVI and chlorophyll concentrations, and Leaf Area Index (LAI) every two weeks. Satellite data, such as NDVI, green (GCI) and red edge (RECI) chlorophyll indices, and soil-adjusted vegetation index (SAVI), were obtained from the Sentinel-2 database on days when both ground and satellite overpass occurred within 24 hours.Land use types and slope position have a strong influence on vegetation growth. The highest overall NDVI and leaf chlorophyll values were observed in vineyard and forest samples, and the lowest in grassland. SWC and temperature were the lowest in the forest and vineyards. SWCs were significantly different for T and CC samples (p 0.05). For the other three land use types, there were no significant differences in values between slope positions. Chlorophyll data showed a very strong correlation between Sentinel-2 retrieved data and hand-held measurements, with r=0.84 for grassland (GCI), r=0.83 for NT (GCI), and r=0.87 for T (RECI). Strong correlations were found between the different sources of NDVI for the grassland samples (e.g. r=0.97, p
Why it matches plant phenotyping methods葉から圃場まで異なるセンサー・リモートセンシング手法によるNDVI等の植生指標を比較し、地上測定と衛星推定値の相関を評価しているため、植物状態の取得手法の検証が中心です。
abstractThe aim of the present study was to investigate soil-plant-water systems and interactions using manual and remote sensing techniques in a small agricultural catchment.
Sunflower broomrape (Orobanche cumana) is a root parasitic weed that severely limits sunflower yield in large areas of Europe and Asia. Early detection of the parasite can facilitate site-specific control of this weed. However, most of its life-cycle takes place in the soil sub-surface and by the time that O. cumana shoots emerge, the damage to the crop is irreversible. The main aim of this study was to evaluate the potential use of hyperspectral imaging for the early detection of parasitism by monitoring changes in spectra obtained from the host plants. A field experiment was conducted on infested and non-infested sunflower plants, imaged by a ground-based hyperspectral camera at two early parasitism stages that are relevant for herbicide application. A logistic regression model was used to classify infected and non-infected plants, 31 and 38 days after sunflower planting, with 76 and 89% accuracy, respectively. A partial dataset, containing only 10 spectral bands of the hyperspectral dataset, gave 69 and 82% accuracy, indicating the potential of multi-spectral sensors for the detection task. Sampling pixels from specific sunflower leaf segments improved the classification compared to non-specific sampling. This study thus contributes to establishing a basis for future development of site-specific weed management of O. cumana and of other broomrape species.
Why it matches plant phenotyping methodsヒマワリの寄生状態をハイパースペクトル画像から推定する手法を開発・評価しており、分類精度や波長選択、画素サンプリングの比較が中心である。
abstractThe main aim of this study was to evaluate the potential use of hyperspectral imaging for the early detection of parasitism by monitoring changes in spectra obtained from the host plants.
Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10-20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection.
Why it matches plant phenotyping methods植物の形態をフォトグラメトリで3次元モデル化し、ドリフト散布液の植物捕捉効率を推定・検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractWe also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants.
Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と放射伝達モデルを用いて作物のLAI・群落クロロフィル含量を推定し、照明条件やLUT分布の影響を比較・検証しており、形質取得手法が研究の中心である。
titleMapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables
Abstract The Indian economy is directly dependent on agricultural production. As the world grows and moves rapidly, identifying crop leaf diseases plays an important role in agriculture. Most of them are related to automation, and you also need to maintain the crop leaf with a little automation, and each crop has a specific need. To survive, it must be met, so companies need to develop systems that can communicate with their users. Currently, precision agriculture is being introduced to increase yields using the latest cutting-edge agriculture technology. An automatic disease detection system used to make instant and accurate decisions about plant diseases for farmers. This speeds up the diagnostic process. But in recent episodes, the show seemed a bit out of focus. One of these methods does not work well with the most important method, system diagnostics. In this paper, we propose an IoT based disease detection technique for crop using hybrid soft computing techniques (CDD-HSC). First, we segment the disease area from test leaf image using improved sunflower optimization (ISO) algorithm which is an important aspect for disease classification. Second, we introduce a multi-swarm snake optimization (MSSO) algorithm for optimal feature selection among multiple features in feature extraction stage. Then, we illustrates coach-learning induced capsule neural network (CL-CNN) for diseases classification in crop leaf with multi-classes. IoT concept used to transfer classification results to the corresponding former through mobile for immediate prevention in crop leaf diseases detecting, which limits the unwanted human delay. Finally, the performance of proposed CDD-HSC technique can analyze with different datasets and the results should sows the effectiveness of proposed method over existing methods in terms of accuracy, precision, F-measure and precision.
Why it matches plant phenotyping methods作物葉画像から病斑領域を抽出し、特徴選択と分類を行う疾病状態の画像ベース計測手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractwe propose an IoT based disease detection technique for crop using hybrid soft computing techniques (CDD-HSC).
SunflowerField / plotStem / branchPhysiological trait estimationWater status / transpiration
The detection of water changes in plant stems by non-destructive online methods has become a hot spot in studying the physiological activity of plant water. In this paper, the ultrasonic radio-frequency echo (RFID) technique was used to detect water changes in stems. An algorithm (improved hybrid differential Akaike's Information Criterion (AIC)) was proposed to automatically compute the position of the primary ultrasonic echo of stems, which is the key parameter of water changes in stems. This method overcame the inaccurate location of the primary echo, which was caused by the anisotropic ultrasound propagation and heterogeneous stems. First of all, the improved algorithm was analyzed and its accuracy was verified by a set of simulated signals. Then, a set of cutting samples from stems were taken for ultrasonic detection in the process of water absorption. The correlation between the moisture content of stems and ultrasonic velocities was computed with the algorithm. It was found that the average correlation coefficient of the two parameters reached about 0.98. Finally, living sunflowers with different soil moistures were subjected to ultrasonic detection from 9:00 to 18:00 in situ. The results showed that the soil moisture and the primary ultrasonic echo position had a positive correlation, especially from 12:00 to 18:00; the average coefficient was 0.92. Meanwhile, our results showed that the ultrasonic detection of sunflower stems with different soil moistures was significantly distinct. Therefore, the improved AIC algorithm provided a method to effectively compute the primary echo position of limbs to help detect water changes in stems in situ.
Why it matches plant phenotyping methods茎の水分状態という植物生理形質を超音波で非破壊測定し、一次エコー位置を抽出する改良AICアルゴリズムを開発・検証しているため、フェノタイピング手法が中心である。
abstractAn algorithm (improved hybrid differential Akaike's Information Criterion (AIC)) was proposed to automatically compute the position of the primary ultrasonic echo of stems, which is the key parameter of water changes in stems.
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.
Why it matches plant phenotyping methods花の検出と時系列表現型の抽出を行う深層学習手法を開発し、ベンチマークデータセットと評価も提示しており、植物フェノタイピング手法が中心である。
abstractwe introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis.
Reproduction assets foundThe paper publicly releases the FlowerPheno benchmark dataset (17,022 multiview RGB image sequences of sunflower, canna, and coleus with ground-truth flower bounding boxes) and the FlowerPhenoNet source code, both with explicit availability statements and URLs.Dataset · publicThe dataset can be freely downloaded
from https://plantvision.unl.edu/dataset, accessed on 15 February 2021.Open asset ↗plantvision.unl.edupdf-page:4 lines:1-41Code · publicThe source code is available at
https://github.com/localchocotaco/FlowerPhenoNet, accessed on 27 November 2022.Open asset ↗github.com/localchocotaco/FlowerPhenoNetpdf-page:18 lines:1-58Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet which uses deep neural networks for detecting flowers from multiview image sequences for high throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.
Why it matches plant phenotyping methods花の検出から開花時期・花数・成長軌跡などの植物表現型を抽出する手法を開発し、ベンチマークデータセットと性能評価も提示しており、フェノタイピング手法が研究の中心である。
abstractwe introduce a novel method called FlowerPhenoNet which uses deep neural networks for detecting flowers from multiview image sequences for high throughput temporal plant phenotyping analysis.
Sunflower ( Helianthus annuus L.) is today the third leading oilseed crop in the world and seed yield is a valuable trait for breeders and researchers. The sunflower capitulum is composed of 700 to 3000 individual flowers on a flattered receptacle. Most reproductive stages (R5 to R6) have at least two disc flower phenophase's coexisting in the same receptacle (E1 to E4). Today, researchers in agroecology and breeders manually quantify the number of disc flowers that achieve the anthesis at different developmental stages of the receptacle. The presented method applies a bioinformatic tool to estimate: (1) the number of disc flowers of each phenophase that are constituting the sunflower´s capitula at different reproductive stages and, (2) the number of developing seeds of each sunflower capitulum. The ImageJ software was used as an image-analysis tool on sunflower capitula photographs. A use case and method validation for each presented protocol is provided. This method will contribute to correlation analysis in agroecological studies and also would be useful for the early prediction of seed yield in breeding programs.•This is a simple method for the estimation of the number of disc flowers at each phenophase in the sunflower receptacle.•It is based on integrating the knowledge of sunflower reproductive development with an open-source image analysis platform applied in single workflows.•This is a precise, non-destructive, rapid, and low-cost method; thus, it has the potential to be adopted as a phenotyping tool for sunflower breeding and research in agroecology.
Why it matches plant phenotyping methodsヒマワリ頭花の小花フェノフェーズと発達種子数をImageJ画像解析で推定する方法を開発・検証しており、植物表現型取得が中心である。
abstractThe presented method applies a bioinformatic tool to estimate: (1) the number of disc flowers of each phenophase that are constituting the sunflower´s capitula at different reproductive stages and, (2) the number of developing seeds of each sunflower capitulum.
Acquiring the crop plant count is critical for enhancing field decision-making at the seedling stage. Remote sensing using unmanned aerial vehicles (UAVs) provide an accurate and efficient way to estimate plant count. However, there is a lack of a fast and robust method for counting plants in crops with equal spacing and overlapping. Moreover, previous studies only focused on the plant count of a single crop type. Therefore, this study developed a method to fast and non-destructively count plant numbers using high-resolution UAV images. A computer vision-based peak detection algorithm was applied to locate the crop rows and plant seedlings. To test the method’s robustness, it was used to estimate the plant count of two different crop types (maize and sunflower), in three different regions, at two different growth stages, and on images with various resolutions. Maize and sunflower were chosen to represent equidistant crops with distinct leaf shapes and morphological characteristics. For the maize dataset (with different regions and growth stages), the proposed method attained R² of 0.76 and relative root mean square error (RRMSE) of 4.44%. For the sunflower dataset, the method resulted in R² and RRMSE of 0.89 and 4.29%, respectively. These results showed that the proposed method outperformed the watershed method (maize: R² of 0.48, sunflower: R² of 0.82) and better estimated the plant numbers of high-overlap plants at the seedling stage. Meanwhile, the method achieved higher accuracy than watershed method during the seedling stage (2–4 leaves) of maize in both study sites, with R² up to 0.78 and 0.91, respectively, and RRMSE of 2.69% and 4.17%, respectively. The RMSE of plant count increased significantly when the image resolution was lower than 1.16 cm and 3.84 cm for maize and sunflower, respectively. Overall, the proposed method can accurately count the plant numbers for in-field crops based on UAV remote sensing images.
Why it matches plant phenotyping methodsUAV RGB画像から作物個体数を抽出するコンピュータビジョン手法の開発・比較・頑健性検証が中心であり、植物形態・生育状態の測定に該当する。
abstractTherefore, this study developed a method to fast and non-destructively count plant numbers using high-resolution UAV images.
Sunflower is one of the most valuable oilseeds in the world due to its high-quality oil and wide adaptation to climatic and soil conditions. Salinity is one of the most harmful environmental stresses and severely reduces the yield of crops. In the present study, the effectiveness of multiple regression techniques, convolutional neural network (CNN), and artificial neural network (ANN) are investigated using regression results as input variables in the estimation of sunflower grain yield under normal and salinity conditions, separately. Then the most important parameters identified in two conditions (head diameter, plant height, and weight of five seeds) were used in the CNN model to predict grain yield for the time when we do not know the growth conditions of the plant. The fitted model had R²=0.914, MAPE=4.95, MAE=0.163, and RMSE=1.699. The results showed that the CNN model provides the best estimation for sunflower grain yield compared to the ANN and multiple regression models. Sensitivity analysis showed that head diameter was the most effective trait on sunflower seed yield. Yield estimation with head diameter, identified as the most influential parameter, with the CNN model produced acceptable performance and accuracy.
Why it matches plant phenotyping methodsCNN・ANN・回帰モデルによるヒマワリの穀粒収量推定と性能比較が研究の中心であり、植物形質から収量という植物状態を推定する計算的フェノタイピング手法に該当する。
abstractthe effectiveness of multiple regression techniques, convolutional neural network (CNN), and artificial neural network (ANN) are investigated
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
Stomata and leaf veins play an essential role in transpiration and the movement of water throughout leaves. These traits are thus thought to play a key role in the adaptation of plants to drought and a better understanding of the genetic basis of their variation and coordination could inform efforts to improve drought tolerance. Here, we explore patterns of variation and covariation in leaf anatomical traits and analyze their genetic architecture via genome-wide association (GWA) analyses in cultivated sunflower (Helianthus annuus L.). Traits related to stomatal density and morphology as well as lower order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach. Leaf, stomatal, and vein traits exhibited numerous significant correlations that generally followed expectations based on functional relationships. Correlated suites of traits could further be separated along three major principal component (PC) axes that were heavily influenced by variation in traits related to gas exchange, leaf hydraulics, and leaf construction. While there was limited evidence of colocalization when individual traits were subjected to GWA analyses, major multivariate PC axes that were most strongly influenced by several traits related to gas exchange or leaf construction did exhibit significant genomic associations. These results provide insight into the genetic basis of leaf trait covariation and showcase potential targets for future efforts aimed at modifying leaf anatomical traits in sunflower. Significance StatementUsing traditional and automated/high-throughput (using a novel deep learning approach) phenotyping methods we studied leaf anatomical variation in sunflower. Genome-wide association (GWA) analyses identified numerous genomic regions underlying individual trait variation and regions underlying major multivariate axes of phenotypic variation. These results illustrate the value of employing a multivariate approach to GWA analyses and shed light on the extent to which leaf trait (co-)variation can be genetically decoupled to explore novel phenotypic space.
Why it matches plant phenotyping methods葉の解剖形質を画像から取得し、特に小脈密度を新規深層学習手法で推定しており、植物形質抽出法の適用が明示された研究である。
abstractTraits related to stomatal density and morphology as well as lower order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach.
Sunflowers are agricultural seed crops that can be used for essential edible oils and ornamental purposes. This cash crop is primarily cultivated in North and South America. Sunflower crops are prone to various diseases, insects, and nematodes, resulting in a wide range of production losses. Digital image processing and computer vision approaches have been widely utilized to categorize and detect plant diseases including leaves, fruits, and flowers over the last few decades. Early diagnosis of infections in sunflowers helps to prevent them from spreading throughout the farm and reducing financial losses to the farmers. This article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases. The dataset contains healthy and affected sunflower leaves and flowers with downy mildew, gray mold, and leaf scars. The images were captured manually between 25 th to 29 th November 2021 from the demonstration farm of Bangladesh Agricultural Research Institute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1.
Why it matches plant phenotyping methodsヒマワリ葉・花の画像データセットを提供し、病害状態の画像ベース推定・分類を可能にすることが中心であるため、植物フェノタイピング用データセットとして含める。
abstractThis article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases.
Reproduction assets foundThe paper's own sunflower disease image dataset (467 original + 1668 augmented images) is publicly hosted on Mendeley Data with an explicit direct link and DOI, directly reproducing the paper's phenotyping measurements.Dataset · publicnstitute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1 .
Keywords: Agriculture, Sunflower dataset, Computer vision, Deep learning
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 2022 Jan 30; Revised 2022 Mar 5; Accepted 2022 Mar 7; Collection date 2022 Jun.
Specification Table
Subject
CompuOpen asset ↗lines:1-55Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Energy and moisture contents are important food chemical attributes. In the current study, a nondestructive Terahertz (THz) time-domain imaging system was first time used for evaluating the energy and moisture distributions of sunflower seed kernels inside shells. For this task, a dual autoencoders (AE)-generative adversarial nets (GAN) spectral dehulling semi-supervised model was developed. The model could automatically learn the kernel information from the latent representations of the spectra of the intact seeds through adversarial learning to achieve feature disentanglement. Results indicated that the generated kernel images had similar features to the original kernel images and high-quality chemical distribution maps for energy and moisture contents of sunflower seed kernels inside shells were successfully obtained. As the current method took the advantage of the characteristics of THz imaging and selected a suitable deep learning algorithm, it has the potential to generalize for imaging other chemical substances of other dry shelled seeds or biological samples (moisture content and thickness below 15% and 5 mm, respectively).
Why it matches plant phenotyping methodsヒマワリ種子内部のエネルギー・水分分布を取得するTHzイメージングとAE-GAN解析モデルの開発が研究の中心であり、植物器官の化学的状態を直接推定する手法である。
abstracta nondestructive Terahertz (THz) time-domain imaging system was first time used for evaluating the energy and moisture distributions of sunflower seed kernels inside shells
Abstract. Due to food security and agricultural land management, it is crucial for decision makers and farmers to predict crop yields. In remote sensing based agricultural studies, spectral resolutions of satellite images, as well as temporal and spatial resolution, are important. In this study, we investigated whether there is a relationship between the Normalized Different Vegetation Index (NDVI) and Normalized Different Vegetation Index Red-edge (NDVIred) indices derived from the Sentinel-2 satellite. In addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation. In this context, yield data of 48 sunflower parcels were obtained in 2018. The obtained results showed that both NDVI and NDVIred can be used to estimate the yield of sunflowers. The best results were obtained from the combination of the NDVI and the CNN technique with the RMSE equal to 20,874 Kg/da on 30 June 2018. Concerning the results, although there is not much superiority between the two indices, the best results were generally obtained from CNN as the method.
Why it matches plant phenotyping methodsSentinel-2画像からNDVI等を抽出し、CNN・ANN・回帰によってヒマワリの圃場収量を推定・比較しており、植物形質の取得・推定手法が中心である。
abstractIn addition, the efficiency of linear regression, Convolutional Neural Network (CNN), and Artificial Neural Network (ANN) techniques are examined with the use of indices in yield estimation.
Sunflower lodging is a common agricultural disorder taking place in the middle and late sunflower growth periods. This disorder reduces the sunflower seed yield, damages the seed quality, and hence usually causes great losses in both crop quantity and quality. Sunflower lodging is mainly caused by extreme and destructive weather events, which have been recently occurring more frequently. This is why it is highly crucial to develop methods for fast and accurate identification of sunflower lodging. In this work, an efficient method for sunflower lodging identification is proposed based on image fusion and deep semantic segmentation of remote sensing images obtained from an unmanned aerial vehicle (UAV). First, the resolution of low-resolution multispectral images was enhanced through matching their features with those of high-resolution visible-range images. Then, for effective lodging assessment, high-quality multispectral images with rich spectral information and high spatial resolution were obtained through fusing the visible-range images and the enhanced multispectral ones. Subsequently, in order to refine the identification outcomes, a variant of the segmentation network (SegNet) deep architecture was developed for semantic segmentation. This variant has skip connections, separable convolution, and a conditional random field. Experimental evaluation shows that the fusion-based approaches clearly outperform the no-fusion ones in terms of the lodging identification accuracy for all compared architectures including support vector machine (SVM), fully convolutional network (FCN), SegNet, and the proposed SegNet variant. Meanwhile, the deep semantic segmentation methods consistently outperform the classical SVM one with hand-crafted features. As well, the improved SegNet method outperformed all of the compared methods and achieved the best accuracies of 84.4% and 89.8% without and with image fusion, respectively, on one test. The corresponding accuracies on another test set were 76.6% and 83.3%, respectively. Moreover, the proposed method can also identify the sunflower lodging and non-lodging patterns and separate them from the background. These capabilities are highly beneficial for lodging hazard assessment and sunflower harvest survey. Overall, the proposed method effectively exploited UAV remote sensing image data with fusion and deep semantic segmentation modules in order to provide a useful reference for sunflower lodging assessment and mapping.
Why it matches plant phenotyping methodsUAV画像融合と深層セマンティックセグメンテーションにより、ヒマワリの倒伏状態を直接推定する手法を開発・評価しており、植物状態の取得方法が研究の中心である。
abstractIn this work, an efficient method for sunflower lodging identification is proposed based on image fusion and deep semantic segmentation of remote sensing images obtained from an unmanned aerial vehicle (UAV).
In precision agriculture and plant breeding, the amount of data tends to increase. This massive data is becoming more and more complex, leading to difficulties in managing and analysing it. Optical instruments such as NIR Spectroscopy or hyperspectral imaging are gradually expanding directly in the field, increasing the amount of spectral database. Using these tools allows access to non-destructive and rapid measurements to classify new varieties according to breeding objectives. Processing this massive amount of spectral data is challenging. In a context of genotype discrimination, we propose to apply a method called parSketch-PLSDA to analyse such a massive amount of spectral data. ParSketch-PLSDA is a combination of an indexing strategy (parSketch) and the reference method (PLSDA) for predicting classes from multivariate data. For this purpose, a spectral database was formed by collecting 1,300,000 spectra generated from hyperspectral images of leaves of four different sunflower genotypes. ParSketch-PLSDA is compared to a PLSDA. Both methods use the same set of calibration and test. The prediction model obtained by PLSDA has a classification error close to 23% on average across all genotypes. ParSketch-PLSDA method outperforms PLSDA by greatly improving prediction qualities by 10%. Indeed, the model built with ParSketch-PLSDA has the ability to take into account non-linearities among data sets. These results are encouraging and allow us to anticipate the future bottleneck related to the generation of a large amount of data from phenotyping.
Why it matches plant phenotyping methods葉のハイパースペクトル画像から大量のスペクトルデータを取得し、遺伝子型識別のための新しい解析手法parSketch-PLSDAを提案・比較評価しており、フェノタイピングデータの取得・解析ワークフローが中心である。
abstractwe propose to apply a method called parSketch-PLSDA to analyse such a massive amount of spectral data
Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.
Why it matches plant phenotyping methods植物フェノタイピング用のマルチモーダル・時系列データセットを開発し、複数のコセグメンテーション手法をベンチマークする研究であり、画像からの植物抽出・表現型解析手法が中心です。
abstractThis paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions.
Reproduction assets foundThe paper's CosegPP plant image dataset (Buckwheat/Sunflower, multi-modal, with ground-truth masks) is publicly deposited on Zenodo per the Data Availability statement. The GitHub repos mentioned (MIG, Subdiscover, DeepCO3) are cited third-party prior-work code, not authors' paper-specific analysis code.Dataset · publicData Availability: All relevant data underlying this study are available at https://doi.org/10.5281/zenodo.5117176 .Open asset ↗zenodo · 10.5281/zenodo.5117176lines:138-150Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting. However the huge computational complexity and massive storage requirements make it a great challenge to deploy them in real-time applications, especially on devices with limited resources. In this study, a rapid and highly efficient lightweight CNN based on visual attention, namely SeedSortNet, is proposed for seed sorting. First, a dual-branch lightweight feature extraction module Shield-block is elaborately designed by performing identity mapping, spatial transformation at higher dimensions and different receptive field modeling, and thus it can alleviate information loss and effectively characterize the multi-scale feature while utilizing fewer parameters and lower computational complexity. In the down-sampling layer, the traditional MaxPool is replaced as MaxBlurPool to improve the shift-invariant of the network. Also, an extremely lightweight sub-feature space attention module (SFSAM) is presented to selectively emphasize fine-grained features and suppress the interference of complex backgrounds. Experimental results show that SeedSortNet achieves the accuracy rates of 97.33% and 99.56% on the maize seed dataset and sunflower seed dataset, respectively, and outperforms the mainstream lightweight networks (MobileNetv2, ShuffleNetv2, etc.) at similar computational costs, with only 0.400M parameters (vs. 4.06M, 5.40M).
Why it matches plant phenotyping methods種子画像から種子の外観・純度に関わる状態を分類する軽量CNNを開発しており、画像取得・特徴抽出手法が研究の中心である。
abstractSeed sorting based on machine vision provides an effective solution to this problem.
Reproduction assets foundThe paper's Data Availability statement provides public access to both datasets and the authors' analysis code: the haploid/diploid maize seed dataset (from Altuntaş et al. 2019) hosted at rovile.org, and the SeedSortNet code plus the authors' sunflower seed dataset on GitHub.Dataset · publicThe maize seed dataset comes from Altuntaş et al. (2019): https://doi.org/10.1016/j.compag.2019.104874 and is available at: http://www.rovile.org/datasets/haploid-and-diploid-maize-seeds-dataset/ .Open asset ↗lines:571-586Code · publicThe seedsortnet code and sunflower seed dataset are available at GitHub: https://github.com/Huanyu2019/Seedsortnet .Open asset ↗Huanyu2019/Seedsortnetlines:571-586Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Background The parasitic plant Orobanche cumana is one of the most important threats to sunflower crops in Europe. Resistant sunflower varieties have been developed, but new O. cumana races have evolved and have overcome introgressed resistance genes, leading to the recurrent need for new resistance methods. Screening for resistance requires the phenotyping of thousands of sunflower plants to various O. cumana races. Most phenotyping experiments have been performed in fields at the later stage of the interaction, requiring time and space. A rapid phenotyping screening method under controlled conditions would need less space and would allow screening for resistance of many sunflower genotypes. Our study proposes a phenotyping tool for the sunflower/ O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction. Results We optimized the phenotyping of sunflower/ O. cumana interactions by using rhizotrons (transparent Plexiglas boxes) in a growth chamber to control culture conditions and Orobanche inoculum. We used a Raspberry Pi computer with a picamera for acquiring images of inoculated sunflower roots 3 weeks post inoculation. We set up a macro using ImageJ free software for the automatic counting of the number of tubercles. This phenotyping tool was named RhizOSun. We evaluated five sunflower genotypes inoculated with two O. cumana races and showed that automatic counting of the number of tubercles using RhizOSun was highly correlated with manual time-consuming counting and could be efficiently used for screening sunflower genotypes at the tubercle stage. Conclusion This method is rapid, accurate and low-cost. It allows rapid imaging of numerous rhizotrons over time, and it enables image tracking of all the data with time kinetics. This paves the way toward automatization of phenotyping in rhizotrons that could be used for other root phenotyping, such as symbiotic nodules on legumes.
Why it matches plant phenotyping methods根部画像からOrobancheの結節数を自動抽出するRhizOSunを開発・評価しており、植物表現型取得法が研究の中心です。
abstractOur study proposes a phenotyping tool for the sunflower/ O. cumana interaction under controlled conditions through image analysis for broomrape tubercle analysis at early stages of the interaction.
Progresses in agronomy rely on accurate measurement of the experimentations conducted to improve the yield component. Measurement of the plant density is required for a number of applications since it drives part of the crop fate. The standard manual measurements in the field could be efficiently replaced by high-throughput techniques based on high-spatial resolution images taken from UAVs. This study compares several automated detection of individual plants in the images from which the plant density can be estimated. It is based on a large dataset of high resolution Red/Green/Blue (RGB) images acquired from Unmanned Aerial Vehicules (UAVs) during several years and experiments over maize, sugar beet and sunflower crops at early stages. A total of 16247 plants have been labelled interactively on the images. Performances of handcrafted method (HC) were compared to those of deep learning (DL). The HC method consists in segmenting the image into green and background pixels, identifying rows, then objects corresponding to plants thanks to knowledge of the sowing pattern as prior information. The DL method is based on the Faster Region with Convolutional Neural Network (Faster RCNN) model trained over 2/3 of the images selected to represent a good balance between plant development stage and sessions. One model is trained for each crop. Results show that simple DL methods generally outperforms simple HC, particularly for maize and sunflower crops. A significant level of variability of plant detection performances is observed between the several experiments. This was explained by the variability of image acquisition conditions including illumination, plant development stage, background complexity and weed infestation. The image quality determines part of the performances for HC methods which makes the segmentation step more difficult. Performances of DL methods are limited mainly by the presence of weeds. A hybrid method (HY) was proposed to eliminate weeds between the rows using the rules developed for the HC method. HY improves slightly DL performances in the case of high weed infestation. When few images corresponding to the conditions of the testing dataset were complementing the training dataset for DL, a drastic increase of performances for all the crops is observed, with relative RMSE below 5% for the estimation of the plant density.
Why it matches plant phenotyping methodsUAV画像から個体を検出・計数し、作物密度を推定する画像解析手法を比較・開発しており、植物フェノタイピング手法が研究の中心である。
abstractThis study compares several automated detection of individual plants in the images from which the plant density can be estimated.
Reproduction assets foundThe paper's authors explicitly state that the deep-learning model architecture and data augmentation details are given in their public code repository on GitHub, which is an authors' public URL implementing the paper's plant detection/counting analysis.Code · public258 architectural details are given in the code (https://github.com/EtienneDavid/plants-counting-detection)Open asset ↗EtienneDavid/plants-counting-detectionpdf-page:9 lines:1-52Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Sunflower seeds (technically achenes) are characterized by a wide spectrum of sizes, shapes, and colors. These traits are genetically correlated with the branching plant architecture loci, which were introgressed into restorer lines to facilitate efficient hybrid production. To break this genetic correlation between branching and seed traits, high resolution mapping of the genes that regulate seed traits is necessary. Recent progress in genomics permits acquisition of comprehensive genotyping data for a large diversity panel, yet a major constraint for exploring the genetic basis of important phenotypes across large diversity panels is the ability to screen and characterize them efficiently. Here, we implement a cost-effective image analysis pipeline to phenotype seed characteristics in a large sunflower diversity panel comprised of 287 individuals that represents most of the genetic variation in cultivated sunflower. A genome-wide association analysis was performed for seed-coat size and shape traits and significant signals were identified around genes regulating phytohormone activity. In addition, significant seed-coat color QTLs were identified and candidate genes that effect pigmentation were detected including a phytomelanin regulating gene on chromosome 17. Finally, QTLs associated with the seed-coat striped pattern were identified and phytohormone regulating candidate genes were detected. The implementation of image analysis phenotyping for GWAS allowed efficient screening of a large diversity panel and identification of valuable genetic factors effecting seed characteristics at the finest resolution to date.
Why it matches plant phenotyping methodsヒマワリ種子の形態・色・模様を抽出する画像解析パイプラインを実装し、大規模パネルの表現型取得を効率化することが中心的な方法論的貢献である。
abstractHere, we implement a cost-effective image analysis pipeline to phenotype seed characteristics in a large sunflower diversity panel comprised of 287 individuals
With the development of digital image technology, we can easily obtain a large number of crop growth images. Through effective analysis of the image, the growth information of crops can be obtained, which can better direct agricultural production. The efficiency of traditional seedling growth monitoring is low, especially in large-scale farmland, which takes a lot of time. Artificial method timely restricts scientific decision-making of cultivation crops. The progress of machine vision and image processing technology provides a new way for harmlessly monitoring of crop seedling growth .The results of image analysis can help agricultural producers to understand the growth of crop seedlings quickly and accurately, so as to take effective management as soon as possible. In this paper, the images of sunflower seedling collected in farmland environment are taken as the research object. The main research content is to segment green crops from soil background. Segmentation method of sunflower seedling image based on color features and Ostu threshold segmentation is proposed. The method is simple in calculation, and can adapt to the segmentation of farmland environment images, which lays the foundation for crop recognition process. Based on the image recognition results, the algorithm locates the seedlings. Through the rapid identification of sunflower seedlings, it is possible to fill the gaps with seedlings where the seedlings are less distributed. On the contrary, if the seedlings are too dense, the number of seedlings needs to be reduced. The algorithm provides a basis for precise management. The results show that the algorithm with extra green feature can quickly and effectively identify sunflower seedlings from background, and locate the seedlings based on the image recognition results. This algorithm is not sensitive to soil moisture and light conditions, and is less affected by crop residual coverage, so it can adapt to different soil environment which realize the non-destructive monitoring of sunflower seedlings.
Why it matches plant phenotyping methods画像処理による作物苗の土壌からの分離、認識、位置推定を中心的に開発しており、苗の分布・生育状態の非破壊モニタリングに用いる方法であるため。
abstractSegmentation method of sunflower seedling image based on color features and Ostu threshold segmentation is proposed.
Resistance of sunflower to basal stalk rot (BSR) caused by the fungus Sclerotinia sclerotiorum is quantitative, controlled by multiple genes contributing small effects. Consequently, artificial inoculation procedures allowing sufficient throughput and resolution of resistance are needed to identify highly resistant sunflower germplasm resources and to map loci contributing to resistance. The objective of this study was to develop a greenhouse-based method for evaluating sunflower quantitative resistance to BSR that would be simple, space- and time-efficient, high throughput, high resolution, and correlated with field observations. Experiments were conducted with 5-week-old sunflower plants and Sclerotinia -infested millet seed as inoculum to assess the impact of pot size and temperature and to determine the most favorable inoculum rate and placement. Subsequently, an additional experiment was performed to assess the correlation of the greenhouse inoculation procedure with field results by using a panel of 32 sunflower genotypes with known field response to BSR previously determined in multiyear, multilocation artificially inoculated trials. Experimental observations indicated that the newly developed greenhouse inoculation procedure provided improved resolution to identify highly resistant genotypes and was strongly correlated with field observations. This method will be useful for screening of sunflower experimental and breeding materials, disease phenotyping of genetic mapping populations, and evaluation of resistance to different pathogen isolates.
Why it matches plant phenotyping methodsヒマワリの病害抵抗性という植物状態を評価する温室接種・評価法を開発し、圃場結果との相関で検証した研究であり、表現型取得法が中心である。
abstractThe objective of this study was to develop a greenhouse-based method for evaluating sunflower quantitative resistance to BSR that would be simple, space- and time-efficient, high throughput, high resolution, and correlated with field observations.
As an esthetic trait, ray floret color has a high importance in the development of new sunflower genotypes and their market value. Standard methodology for the evaluation of sunflower ray florets is based on International Union for the Protection of New Varieties of Plants (UPOV) guidelines for sunflower. The major deficiency of this methodology is the necessity of high expertise from evaluators and its high subjectivity. To test the hypothesis that humans cannot distinguish colors equally, six commercial sunflower genotypes were evaluated by 100 agriculture experts, using UPOV guidelines. Moreover, the paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis but still leaves the final decision to the evaluator. For this purpose, we created a new Flower Color Image Analysis ( FloCIA ) software for sunflower ray floret digital image segmentation and automatic classification into one of the categories given by the UPOV guidelines. To assess the benefits and relevance of this method, accuracy of the newly developed software was studied by comparing 153 digital photographs of F 2 genotypes with expert evaluator answers which were used as the ground truth. The FloCIA enabled visualizations of segmentation of ray floret images of sunflower genotypes used in the study, as well as two dominant color clusters, percentages of pixels belonging to each UPOV color category with graphical representation in the CIE (International Commission on Illumination) L ∗ a ∗ b ∗ (or simply Lab) color space in relation to the mean vectors of the UPOV category. Precision (repeatability) of ray flower color determination was greater between dUPOV based expert color evaluation and software evaluation than between two UPOV based evaluations performed by the same expert. The accuracy of FloCIA software used for unsupervised (automatic) classification was 91.50% on the image dataset containing 153 photographs of F 2 genotypes. In this case, the software and the experts had classified 140 out of 153 of images in the same color categories. This visual presentation can serve as a guideline for evaluators to determine the dominant color and to conclude if more than one significant color exists in the examined genotype.
Why it matches plant phenotyping methodsヒマワリの花弁色という植物形質を対象に、画像分割・自動分類ソフトウェアFloCIAを開発し、専門家評価との比較で精度を検証しているため、方法が研究の中心である。
abstractthe paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis
Reproduction assets foundThe paper states that the data and code (FloCIA software analysis) supporting the study are publicly available on Zenodo, with the URL given in the Methods and as a footnote. This is a paper-specific, publicly actionable asset covering the sunflower ray floret image dataset and analysis code.Code · publicThe data and code that support the findings of this study are publicly available on https://zenodo.org/record/4068475#.X31utGgza71 .Open asset ↗zenodo · 4068475lines:329-339Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
We analyze the multilayer structure of sunflower leaves from Terahertz data measured in the time-domain at a ps scale. Thin film reverse engineering techniques are applied to the Fourier amplitude of the reflected and transmitted signals in the frequency range f < 1.5 Terahertz (THz). Validation is first performed with success on etalon samples. The optimal structure of the leaf is found to be a 8-layer stack, in good agreement with microscopy investigations. Results may open the door to a complementary classification of leaves.
Why it matches plant phenotyping methodsテラヘルツ時域計測と薄膜逆解析によりヒマワリ葉の多層構造を推定する手法を開発・検証しており、葉の形態状態の取得が研究の中心である。
abstractWe analyze the multilayer structure of sunflower leaves from Terahertz data measured in the time-domain at a ps scale.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absorption properties of sunflower leaves, related to their chemical composition or internal structure. A laser diode system at two wavelengths 660 nm and 785 nm combined with polarization has been set up to differentiate four sunflower genotypes. REP-ASCA was used as a method to analyze parameters extracted from speckle patterns by reducing sources of measurement error. First findings have shown that measurement errors are mostly due to unwilling residual specular reflections. Moreover, results outlined that the genotype significantly impacts measurements. The variables involved in genotype dissociation are mainly related to scattering properties within the leaf. Moreover, an example of genotype classification using REP-ASCA outcomes is given and classify genotypes with an average error of about 20%. These encouraging results indicate that a laser speckle system is a promising tool to compare sunflower genotypes. Furthermore, an autonomous low-cost sensor based on this approach could be used directly in the field.
Why it matches plant phenotyping methodsヒマワリ葉の表現型を非破壊測定するレーザースペックル光学センサーを開発・評価しており、測定誤差や遺伝子型識別性能も検証しているため、方法が中心的です。
abstractwe study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping
Background and aims Synchrotron- and laboratory-based micro-X-ray fluorescence (µ-XRF) is a powerful technique to quantify the distribution of elements in physically large intact samples, including live plants, at room temperature and atmospheric pressure. However, analysis of light elements with atomic number (Z) less than that of phosphorus is challenging due to the need for a vacuum, which of course is not compatible with live plant material, or the availability of a helium environment. Method A new laboratory µ-XRF instrument was used to examine the effects of silicon (Si) on the manganese (Mn) status of soybean (Glycine max) and sunflower (Helianthus annuus) grown at elevated Mn in solution. The use of a helium environment allowed for highly sensitive detection of both Si and Mn to determine their distribution. Key results The µ-XRF analysis revealed that when Si was added to the nutrient solution, the Si also accumulated in the base of the trichomes, being co-located with the Mn and reducing the darkening of the trichomes. The addition of Si did not reduce the concentrations of Mn in accumulations despite seeming to reduce its adverse effects. Conclusions The ability to gain information on the dynamics of the metallome or ionome within living plants or excised hydrated tissues can offer valuable insights into their ecophysiology, and laboratory µ-XRF is likely to become available to more plant scientists for use in their research.
Why it matches plant phenotyping methods生体植物中の元素分布を取得する実験室µ-XRFの技術適用・適応が中心で、SiとMnの植物体内分布という生理状態を測定している。
abstractA new laboratory µ-XRF instrument was used to examine the effects of silicon (Si) on the manganese (Mn) status of soybean (Glycine max) and sunflower (Helianthus annuus) grown at elevated Mn in solution.
SunflowerTobaccoLeafStomata / guard-cell complexPhysiological trait estimationStomatal traitsWater status / transpiration
O_LIMinimum conductance (gw,min) in leaves is important for water relations in land plants. Yet, its regulation is unclear due to measurement constraints. C_LIO_LICuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci) of amphi-stomatous tobacco and sunflower. We estimated gcw in a series of light and dark experiments, and partitioned gw,min into cuticle and stomatal components. Some leaves were detached to simulate severe drought through desiccation conditions where gw,min is generally determined. C_LIO_LIBetween light and dark experiments each gcw was in close agreement, and successfully corrected the discrepancies of calculations from direct measurements. In the dark, either stomatal or cuticle conductance dominated the gw,min, suggesting either of them can control the minimum water loss. In the detached leaves, gcw could not be estimated likely due to unsaturation in the leaf airspace, and gw,min was progressively underestimated. C_LIO_LIBesides cuticle, leaf water status is a potential pitfall of the standard gas exchange model. Our technique is useful to study the minimal gas exchange as well as to refine the model. C_LI
Why it matches plant phenotyping methods葉の最小コンダクタンスを分解・推定する測定技術を開発し、光・暗条件で検証しており、植物の生理形質取得が中心である。
abstractCuticle conductance to water vapor (gcw) was estimated from the difference between calculated and direct measurement of CO2 concentration in the leaf airspace (Ci)
O_LICuticle conductance (gcw) can bias calculations of intercellular CO2 concentration inside the leaf (Ci) when stomatal conductance (gsw) is small. C_LIO_LIWe examined how the light induction of photosynthesis impacts calculations by directly measuring Ci along with standard gas exchange in sunflower and tobacco leaves. C_LIO_LIWhen photosynthesis was induced from dark to saturating light (1200 mol m-2 s-1 PAR) the calculated Ci was significantly larger than measured Ci and the difference decreased as gsw increased. This difference could lead to over-estimation of rubisco deactivation by limited CO2 supply during early induction of photosynthesis. However, only small differences in Ci were observed during the induction from shade (50 mol m-2 s-1 PAR) because gsw was sufficiently large. The induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements. These gcw estimates succeeded in correcting the calculation, suggesting that the cuticle was the major source of error. C_LIO_LIDespite a technical restriction to amphi-stomatous leaves, the presented technique has a potential to provide insights into the cuticle conductance on intact stomatous leaf surfaces. C_LI
Why it matches plant phenotyping methods光合成誘導中の直接Ci測定とガス交換を組み合わせ、葉面のクチクラコンダクタンスを推定・補正する技術が研究の中心であり、植物の生理形質を取得する方法として適格です。
abstractThe induction from dark also allowed robust estimations of gcw when combined with direct Ci measurements.
With the increase of the world’s population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower parcels were identified in Zile district of Tokat province, Turkey which has dense sunflower production. In this study, ten Vegetation Indices (VIs) were performed by using multi-temporal Sentinel-2 data obtained during the growth stages of sunflower plant and yield estimation was obtained. As a result, the indices obtained on 30 June, at the stage of inflorescence emergence, provided coefficient of determination (R2) higher than 0.67 and The Root Mean Square Error (RMSE) lower than 13 kg/da. Among the VIs, the best forecast obtained by NDVI (R2 = 0.74 and RMSE = 10.80 kg/da) approximately three months before the harvest of sunflower.
Why it matches plant phenotyping methodsSentinel-2画像と植生指数を用いてヒマワリの生育段階と収量を推定し、精度指標で評価しており、植物形質取得手法の適用・検証が中心です。
abstractThe study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant.
SunflowerThermalLeafRootPhysiological trait estimationGrowth / development / phenologyStomatal traitsPlant / canopy temperatureWater status / transpiration
Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.
Why it matches plant phenotyping methods熱画像を用いて葉温から蒸散・気孔開度を直接定量する大規模スクリーニング法の開発と実証が中心であり、単なる生物学的測定ではない。
abstractThe screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
SunflowerThermalLeafRootStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsPlant / canopy temperatureWater status / transpiration
Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.
Why it matches plant phenotyping methods熱画像と統計解析を用いて葉温から蒸散・気孔開度を直接定量する大規模スクリーニング法が、研究の中心的な技術貢献として提示されている。
abstractThe screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging.
Abstract Background and Aims X-ray fluorescence microscopy (XFM) is a powerful technique to elucidate the distribution of elements within plants. However, accumulated radiation exposure during analysis can lead to structural damage and experimental artefacts including elemental redistribution. To date, acceptable dose limits have not been systematically established for hydrated plant specimens. Methods Here we systematically explore acceptable dose rate limits for investigating fresh sunflower (Helianthus annuus) leaf and root samples and investigate the time–dose damage in leaves attached to live plants. Key Results We find that dose limits in fresh roots and leaves are comparatively low (4.1 kGy), based on localized disintegration of structures and element-specific redistribution. In contrast, frozen-hydrated samples did not incur any apparent damage even at doses as high as 587 kGy. Furthermore, we find that for living plants subjected to XFM measurement in vivo and grown for a further 9 d before being reimaged with XFM, the leaves display elemental redistribution at doses as low as 0.9 kGy and they continue to develop bleaching and necrosis in the days after exposure. Conclusions The suggested radiation dose limits for studies using XFM to examine plants are important for the increasing number of plant scientists undertaking multidimensional measurements such as tomography and repeated imaging using XFM.
Why it matches plant phenotyping methods植物試料のX線蛍光顕微鏡（XFM）について、測定による損傷や元素再分布を系統的に評価し、許容線量限界を検証・設定している。植物の元素分布・構造状態を取得する測定法の技術的妥当性が中心である。
abstractThe suggested radiation dose limits for studies using XFM to examine plants are important for the increasing number of plant scientists undertaking multidimensional measurements such as tomography and repeated imaging using XFM.
Abstract. Information on several crop bio-physical parameters is important as inputs for crop growth modelling, leaf stress analysis, crop health study and productivity point of view. Conventionally, biophysical parameters are measured in laboratory methods which are time consuming, laborious and destructive in nature. With the advent of remote sensing technology, the limitations of conventional methods can be overcome. Moreover, due to its narrow absorption bands at different wavelength, use of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters. In the present study, field as well as laboratory based spectro-radiometer observations were carried out at Agronomy Department of VisvaBharati University, West Bengal, on Sunflower crop at its peak vegetation stage towards retrieving different bio-physical parameters, specifically leaf area index (LAI), chlorophyll content index (CCI), fluorescence etc. Different foliar boron (no boron, 0.15% and 0.20%) and irrigation (4–6 irrigations) treatments, i.e. total nine treatments with three replications, were applied on sunflower crop during different phenological stages to achievemaximum ranges of the bio-physical parameters. The LAI, CCI and fluorescence parameters were collected using canopy analyzer,chlorophyll content meter and portable gas exchange system, respectively. In each of the treatments, total four hyperspectral measurements were collected, which were further corrected for noise and smoothened using Savitzky-Golay filtering. Total thirty-four narrow band indices were computed based on the hyperspectral data, and the regression analysis was carried out among the indices and bio-physical parameters. The regression parameters were further deployed on the hyperspectral indices to retrieve the bio-physical parameters. The Gitelson & Merzylak-1 (GM-1) and Carter Indices-1 (CI-1) were found to the best indices for retrieving the LAI and CCI, respectively with correlation correlation (r) values of 0.87 and 0.80. On the other hand, Normalized Phaenophytinization Index (NPQI) and GM-1 were found to best for retrieving the Fv/Fm (dark) and Fvˈ/Fmˈ (light) with correlation(r)values of 0.92 and 0.76, respectively. Hence, the hyperspectral remote sensing be successfully utilized for retrieving several bio-physical parameters both at field (canopy level) and laboratory (leaf level) conditions.
Why it matches plant phenotyping methodsヒマワリのLAI、クロロフィル、蛍光などの植物形質を、フィールドハイパースペクトル計測とスペクトル指標・回帰により推定する手法が研究の中心であり、技術的な検索・検証を含むため採用。
abstractuse of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.
Why it matches plant phenotyping methods低コストSfMおよびRGB-Dによる植物3D再構成手法を開発・比較し、草丈、葉面積指数、乾物バイオマスを実測値で検証しており、表現型取得手法が研究の中心である。
abstractThis study examined the suitability of two low-cost systems for plant reconstruction.
Effective control of the parasitic weed sunflower broomrape ( Orobanche cumana Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively. The main challenge in developing such control practices for O. cumana lies in the fact that most of its life-cycle occurs in the soil sub-surface and by the time shoots emerge and become observable, the damage to the crop is irreversible. This paper approaches early O. cumana detection by hypothesizing that its parasitism already impacts the host plant morphology at the sub-soil surface developmental stage. To validate this hypothesis, O. cumana- infested sunflower and non-infested control plants were grown in pots and imaged weekly over 45-day period. Three-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level. Among the parameters estimated, height and first internode length were the earliest definitive indicators of infection. Furthermore, the detection timing of both parameters was early enough for herbicide post-emergence application. Considering the fact that 3-D morphological modeling is nondestructive, is based on commercially available RGB sensors and can be used under natural illumination; this approach holds potential contribution for site specific pre-emergence managements of parasitic weeds and as a phenotyping tool in O. cumana resistant sunflower breeding projects.
Why it matches plant phenotyping methods3D画像ベースの植物形態再構成と器官レベル形質推定を開発・検証し、寄生感染の早期検出に利用しているため、植物フェノタイピング手法が中心です。
abstractThree-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level.
This study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup in the spectral domain of 400–1000 nm for sunflower leaves in real-world. Dataset 1 includes hypercubes of sunflower leaves in two varieties with different life growth stages, while Dataset 2 is comprised of healthy and contaminated sunflower leaves suffering from powdery mildew (PM) and/or septoria leaf spot (SLS). Cross polarised (R⊥), parallel polarised (R||) reflectance signals, RBS(R|| + R⊥) and RSS (R||-R⊥) spectra were obtained and used to develop partial least squares-discriminant analysis (PLS-DA) models. Surface information played an important role in separating two varieties of leaves due to the fact that the best model performance was achieved by using RSS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of RBS mean spectra outperformed other models. The best classification model for disease detection was achieved by using pixel R⊥ spectra with the correct classification rate (CCR) of 0.963 for both cross validation and prediction, meaning that subsurface spectral features were the most important to detect infected leaves. The resulting classification maps were also displayed to visualize the distribution of the infected regions on the leaf samples. The overall results obtained in this research showed that the developed polarized-HSI system coupled with multivariate analysis has considerable promise in agricultural real-world applications.
Why it matches plant phenotyping methods偏光ハイパースペクトル画像システムを開発し、葉の品種・生育段階・病害状態を画像スペクトルから分類・可視化しており、植物表現型取得手法が研究の中心である。
abstractThis study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup
CORE IDEAS: Confection sunflower yield was related to active optical sensor readings. Oilseed sunflower yield was not related to active optical sensor readings. Oilseed sunflower yield was related to crop height measurements. Active‐optical (AO) sensors have been used in several crops as a yield‐prediction tool for N management, but not in sunflower (Helianthus annuus). The need for in‐season N and its rate can be determined through using a yield and AO relationship. By comparing predicted yield from an area of sufficient N to another area of the field, the yield difference multiplied by the N required for the additional yield results in the fertilizer N rate. This study was conducted to determine what parameters, including plant height and plant stand, would be most useful in relating AO sensor readings to sunflower yield. The experiments were conducted in 2015 and 2016 on a total of 20 locations in North Dakota. The experimental design was a split‐plot randomized complete block, with six N rates as the main plot treatments and P rate (four in 2015 and two in 2016) as split plot treatment. Since P had no influence on yield, it was ignored in this study. The AO sensors were the GreenSeeker and Holland Crop Circle. The AO sensors were used when the sunflower was at the V6 and V12 growth stages. Manual height and an acoustic height sensor were used on most sites at the time of AO sensor readings. Sensor readings with and without consideration of crop height were subjected to regression analysis with crop yield. The AO sensor readings were related to confection sunflower yield, but not oilseed sunflower yield. Oilseed sunflower yield was most related to crop height.
Why it matches plant phenotyping methodsヒマワリの収量予測における能動光学センサーと草丈測定の有用性を回帰分析で比較・検証しており、植物形質（収量・草丈）の取得および予測手法が研究の中心である。
abstractThis study was conducted to determine what parameters, including plant height and plant stand, would be most useful in relating AO sensor readings to sunflower yield.
The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R2) of 0.82 and 0.81 indicate that there is a strong relationship between the maize height and SAR parameters including VV + VH and VV, respectively. The maize CC is well correlated with VV parameter (R2 = 0.73), but it is observed that at the later growing stage the correlation became weaker. This means that the sensitivity decreases with increasing vegetation cover growth. Compared to maize, the sensitivity of SAR parameters to wheat variables is often good at the early stage. However, the highest correlation with wheat height represented by Alpha (α) decomposition parameter (R2 = 0.67). The sunflower height has an insignificant correlation with the majority of SAR parameters and only VH polarization shows low sensitivity (R2 = 0.31). The sunflower CC shows relatively higher correlation with VV polarization (R2 = 0.46) at the early stage while no considerable correlation is observed at the later stage. It is found that Sentinel-1 has a high potential for estimation of crop height and CC of the maize as a broad-leaf crop. The same is not true for sunflower as another broad-leaf crop.
Why it matches plant phenotyping methodsSentinel-1 SARパラメータによる作物の草丈・キャノピー被覆率推定への感度を比較評価しており、植物形質の取得・検証が研究の中心である。
abstractThe aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Heliaphen is an outdoor platform designed for high-throughput phenotyping. It allows the automated management of drought scenarios and monitoring of plants throughout their lifecycles. A robot moving between plants growing in 15-L pots monitors the plant water status and phenotypes the leaf or whole-plant morphology. From these measurements we can compute more complex traits, such as leaf expansion (LE) or transpiration rate (TR) in response to water deficit. Here, we illustrate the capabilities of the platform with two practical cases in sunflower (Helianthus annuus): a genetic and genomic study of the response of yield-related traits to drought, and a modeling study using measured parameters as inputs for a crop simulation. For the genetic study, classical measurements of thousand-kernel weight (TKW) were performed on a biparental population under automatically managed drought stress and control conditions. These data were used for an association study, which identified five genetic markers of the TKW drought response. A complementary transcriptomic analysis identified candidate genes associated with these markers that were differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used a crop simulation model to predict the impact on crop yield of two traits measured on the platform (LE and TR) for a large number of environments. We conducted simulations in 42 contrasting locations across Europe using 21 years of climate data. We defined the pattern of abiotic stresses occurring at the continental scale and identified ideotypes (i.e., genotypes with specific trait values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can assist the identification of the genetic architecture controlling complex response traits and facilitate the estimation of ecophysiological model parameters to define ideotypes adapted to different environmental conditions.
Why it matches plant phenotyping methods植物の形態・水分状態・蒸散などを自動取得する屋外高スループット表現型解析プラットフォームが研究の中心であり、遺伝解析と作物モデルへの応用も示している。
abstractHeliaphen is an outdoor platform designed for high-throughput phenotyping.
In this paper, we propose a method of automated data processing allowing to detect weeds and assess crop sprouts quality and quantity based on RGB images obtained by unmanned aerial vehicles (UAVs). The process consists of four main stages: 1) vegetation map generation with the use of modified Triangular Greenness Index (TGI); the index is defined as the area of a triangle formed by 3 points on a spectral curve with wavelengths of 480, 550 and 670 nm and estimates leaf chlorophyll content based on RGB images; 2) determination of the position of crop rows and spaces between rows based on the vegetation map; 3) detection of weeds and generation of an appropriate weed map; 4) division of crop rows into non-intersecting fragments and calculating vegetation density in each (the ratio of vegetation area to the total fragment area). By changing the empirically defined parameters of map thresholds of fragment density, one can obtain a map that describes quality of crop sprouts. Unlike existing methods, the proposed scheme does not require presence of infrared data and can be applied to usual RGB images with the use of wide-spread types of UAVs. The method was tested on RGB images of flax and sunflower sprouts collected with SONY ILCE6000 camera in June, 2017 in Altai Territory. The images were taken at the height of 150 m, spatial resolution was 1.5 cm/pixel. The size of each image was 6000x4000 pixels. Test results confirmed high efficiency of the proposed method.
Why it matches plant phenotyping methodsRGB画像から作物の発芽個体群の品質・量を評価する画像解析手法を提案・検証しており、植物状態の抽出が中心的です。雑草検出も含みますが、作物の植生密度による品質評価が明示されています。
abstractwe propose a method of automated data processing allowing to detect weeds and assess crop sprouts quality and quantity based on RGB images obtained by unmanned aerial vehicles (UAVs).
Siderophores are natural metal chelating agents that strongly control the biogeochemical metal cycles such as Fe in the environment. This article describes a new methodology to detect and quantify at the micromolar concentration the spatial distribution at millimeter scale of siderophores within the root's system. The "universal" CAS assay originally designed for bacterial siderophores detection and later designed for fungus was adapted here for diffusive equilibrium in thin film gel techniques (DET). The method was calibrated against the marketed desferrioxamine mesylate (DFOM) siderophore and applied with experiments performed with sunflower ( Helianthus annuus) and wheat ( Triticum aestivum) cultivated on free iron agar medium plates. We present here the first results with 2D images of the siderophores distribution in the vicinity of the root system of plants. With this technique we detected (i) the production of siderophores on bacteria inoculated ( Pseudomonas fluorescens) environments and (ii) hotspots of natural iron binding ligands production up to 50 μM in the wheat rhizosphere. The lower detection limit in our experiment was 2.5 μmol/L. This new technique offers a unique opportunity to investigate the siderophore production in two dimensions in a wide range of applications from laboratory experiments to natural systems very likely using an in situ and nondestructive tool.
Why it matches plant phenotyping methods根系周辺の植物由来シデロフォア分布を2D画像として取得・定量する測定法を開発し、校正および植物での適用を行っており、フェノタイプ取得法が中心である。
abstractThis article describes a new methodology to detect and quantify at the micromolar concentration the spatial distribution at millimeter scale of siderophores within the root's system.
This article presents experimental data describing the physiology and morphology of sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France). Here, we provide data including specific leaf area, osmotic potential and adjustment, carbon isotope discrimination, leaf transpiration, plant architecture: plant height, leaf number, stem diameter. We also provide leaf areas of individual organs through time and growth rate during the stress period, environmental data such as temperatures, wind and radiation during the experiment. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the physiological basis of heterosis. It is available on the following repository: https://doi.org/10.25794/phenotype/er6lPW7V.
Why it matches plant phenotyping methods植物の形態・生理形質を高スループット表現型解析プラットフォームで取得した再利用可能なデータセットであり、表現型データ資源の提供が中心です。
abstractDrought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France).
Reproduction assets foundThe article is a Data in Brief paper whose entire content is the paper's own eco-physiological phenotyping dataset (24 sunflower genotypes, water deficit, Heliaphen platform). The authors explicitly deposit the data publicly in the SUNRISE Phenotype Archive with DOI 10.25794/phenotype/er6lPW7V, described as csv/xls/pdfDataset · publicData accessibility Data are with this article and also publicly available in the SUNRISE
Archive depository with following DOI: 10.25794/phenotype/er6lPW7VOpen asset ↗10.25794/phenotype/er6lPW7Vpdf-raw-page:3 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published1 Oct 20182018 25th IEEE International Conference on Image Processing (ICIP)Cited by 14 · OpenAlex ↗
This article presents a 3D approach in plant growth monitoring and deals with the tracking of leaves of sunflower plants. Our aim is to compute time-series of individual leaf area, under water stress and control conditions. These data will then be used by biologists to study the drought resistance of various sunflower species. Our method to track the leaves in 3D has been evaluated on a set of 132 point clouds obtained via classical structure-from-motion techniques and multi-view stereo software. These 3D acquisitions have been performed on 12 sunflower plants (6 water-stressed, 6 well-watered) during a period of one month (11 measurement dates per sunflower plant). This method gives promising results for both conditions (water-stressed and well-watered), for different species and is able to follow the growth of the plants, as well as to detect new leaf emergence and leaf decay.
Why it matches plant phenotyping methods3D画像から個葉を追跡し、葉面積や葉の出現・枯死を時系列推定する手法が研究の中心であり、植物表現型計測手法の評価も実施している。
abstractOur aim is to compute time-series of individual leaf area
Sunflower floral dimensions are essential for assessing pollinators attraction and estimating seed yields. Dimensions measured manually at present are subjective and time-intensive, therefore, an image processing method was developed as an alternative, which was objective, non-destructive, produces various outputs, and rapid. An ImageJ user-coded plugin with a field image acquisition method was developed to measure the dimensions of individual sunflower components, such as head, disc, and ray florets. Two measurement methods, direct (using the thresholded binary image) and wrapping-polygon (using a polygonal enclosure) were tested. The ‘pixel-march’ method made multiple radial dimension measurements (diameter) on the ray florets binary image in a single computation. The effect of multiple measurements (2, 4, 8, 16, 32, 64, 128, and 180 along 0–180° angles) was studied to determine an effective number of measurements, and user-friendly sunflower dimension prediction models from ImageJ’s standard output parameters were developed. Results indicated that (i) a minimum of 32 measurements for the sunflower head and ray florets dimensions, but only eight measurements for the sunflower disc, were necessary; (ii) wrapping-polygon method was efficient compared to direct; (iii) equivalent diameter (ED) and fitted ellipse minor axis (MinA) were well correlated (r≥0.88) to the accurate mean 180 measurements (D180) for all sunflower components; (iv) linear models for predicting D180 using ED and MinA performed better (R2>0.99) for head and disc than for ray florets (R2>0.76); (v) user-friendly linear models using the mean of two manual measurements of the head (D2h) for predicting D180 and area were good only for the head (R2>0.92), and not suitable for disc (R2≤0.62) and ray florets (R2≤0.43); and (vi) the developed image processing method results were accurate, quick (≈11 s in Windows 10, Intel Core i5, and 8 GB RAM laptop), and have the potential to be adapted to other species.
Why it matches plant phenotyping methodsヒマワリ花器官の寸法という植物形態形質を画像処理で取得・推定する手法を開発し、測定法の比較と精度検証を行っており、フェノタイピング手法が中心である。
abstractan image processing method was developed as an alternative, which was objective, non-destructive, produces various outputs, and rapid.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 10 Sept 2026
Abstract Heliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.
Why it matches plant phenotyping methods自動化された屋外ハイスループット表現型解析プラットフォームの設計・能力実証が中心で、植物形態、水状態、葉展開、蒸散を継続的に取得する方法を示している。
abstractHeliaphen is an outdoor pot platform designed for high-throughput phenotyping.
Salicylic acid (SA) is an important phytohormone. It plays an essential role in regulating many physiological processes of plants. Most of the conventional methods for SA detection are based on in vitro processes. More attention should be paid to develop in vivo methods for SA detection. In this work, Pt nanoflowers and GO were simultaneously electrodeposited and reduced on a Pt wire microelectrode in one step. The Pt nanoflowers/ERGO modified Pt microsensor demonstrated high sensitivity and selectivity for SA. SA could be detected from 100 pM to 1 μM with a detection limit of 48.11 pM. Then this microsensor was used to detect SA in the stem of sunflower seedlings under different salt stresses in vivo . The result showed that with the increasing concentration of salt, SA levels decreased. Our result was also confirmed by UPLC-MS and gene expression analysis. To the best of our knowledge, this is the first report of in vivo detection of SA in plants using the Pt nanoflowers/ERGO modified Pt microelectrode. It is foreseeable that our strategy could pave the way for the in vivo detection of phytohormones in plants.
Why it matches plant phenotyping methods植物体内の植物ホルモン濃度という生理状態を測定するマイクロセンサーを開発・性能評価し、ヒマワリで実証しており、測定法が研究の中心である。
abstractMore attention should be paid to develop in vivo methods for SA detection.
This study was performed in order to evaluate efficiency of near-infrared reflectance spectroscopy (NIRS) for the determination of fatty acid composition ratio of sunflower seeds and to compare performance of calibration methods. Calibration equations were developed using modified partial least squares (MPLS) and partial least squares (PLS) regression methods. Ninety-three sunflower seed varieties were from test field of East Mediterranean Agricultural Research Institute. In order to determine the reference fatty acid values needed to construct calibration in NIRS analysis, sunflower seed samples were analyzed by gas chromatography method. Coefficients of determination (R 2 ) in calibration were developed using MPLS and PLS as follows: for palmitic acid 0.706-0.664, for stearic acid 0.615-0.654, for oleic acid 0.996-0.994, for linoleic acid 0.995-0.994, for arachidic acid 0.768-0.643, for linolenic acid 0.818-0.763, for behenic acid 0.891-0.776, for eicosapentaenoic 0.933-0.892, for unsaturated fatty acid 0.837-0.890 and for saturated fatty acid 0.837-0.890 respectively. The results showed that NIRS was a reliable technique that can be used as a tool for rapid pre-screening of fatty acid composition of sunflower seeds.
Why it matches plant phenotyping methodsヒマワリ種子の脂肪酸組成という種子形質を対象に、NIRSの校正式と回帰法を開発・比較し、迅速スクリーニング手法として評価しているため、測定法が中心である。
abstractThis study was performed in order to evaluate efficiency of near-infrared reflectance spectroscopy (NIRS) for the determination of fatty acid composition ratio of sunflower seeds and to compare performance of calibration methods.
A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues.An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for.The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30m) and hectometric (330m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France.Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE=0.05–0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE=0.11).
Why it matches plant phenotyping methods衛星観測を融合して植物キャノピーのFAPARという生理・生物物理形質を推定するDHFアルゴリズムを開発し、衛星データと地上測定で技術検証しており、フェノタイピング手法が中心である。
abstractAn algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step.
Vulnerability curves (VCs) are a useful tool to investigate the susceptibility of plants to drought-induced hydraulic failure, and several experimental techniques have been used for their measurement. The validity of the bench dehydration method coupled to hydraulic measurements, considered as a 'golden standard', has been recently questioned calling for its validation with non-destructive methods. We compared the VCs of a herbaceous crop plant (Helianthus annuus) obtained during whole-plant dehydration followed by i) hydraulic flow measurements in stem segments (classical destructive method) or by ii) in vivo micro-CT observations of stem xylem conduits in intact plants. The interpolated P 50 values (xylem water potential inducing 50% loss of hydraulic conductance) were -1.74 MPa and -0.87 MPa for the hydraulic and the micro-CT VC, respectively. Interpolated P 20 values were similar, while P 50 and P 80 were significantly different, as evidenced by non-overlapping 95% confidence intervals. Our results did not support the tension-cutting artefact, as no overestimation of vulnerability was observed when comparing the hydraulic VC to that obtained with in vivo imaging. After one scan, 25% of plants showed signs of x-ray induced damage, while three successive scans caused the formation of a circular brownish scar in all tested plants. Our results support the validity of hydraulic measurements of samples excised under tension provided standard sampling and handling protocols are followed, but also show that caution is needed when investigating vital plant processes with x-ray imaging.
Why it matches plant phenotyping methods植物の水理機能低下・木部塞栓を測定するmicro-CT法と従来の破壊的水力測定を比較検証しており、表現型取得法の妥当性評価が研究の中心です。
abstractWe compared the VCs of a herbaceous crop plant (Helianthus annuus) obtained during whole-plant dehydration followed by i) hydraulic flow measurements in stem segments (classical destructive method) or by ii) in vivo micro-CT observations of stem xylem conduits in intact plants.
Summary Hyperspectral sensing can detect slight changes in plant physiology, and may offer a faster and nondestructive alternative for water status monitoring. This premise was tested in the current study using a narrow‐band ‘water balance index’ ( WABI ), which is based on independent changes in leaf water content (1500 nm) and the efficiency of the nonphotochemical quenching ( NPQ ) photo‐protective mechanism (531 nm). The hydraulic, photo‐protective and spectral behaviors of five important crops – grapevine, corn, tomato, pea and sunflower – were evaluated under water deficit conditions in order to associate the differences in stress physiology with WABI suitability. Rapid alterations in both leaf water content and NPQ were observed in grapevine, pea and sunflower, and were effectively captured by WABI . Apart from water status monitoring, the index was also successful in scheduling the irrigation of a vineyard, despite phenological and environmental variability. Conversely, corn and tomato displayed a relatively strict stomatal regime and/or mild NPQ responses and were, thus, unsuitable for WABI ‐based monitoring. WABI shows great potential for irrigation scheduling of various crops, and has a clear advantage over spectral models that focus on either of the abovementioned physiological mechanisms.
Why it matches plant phenotyping methodsWABIというスペクトル指標を用いた植物水分状態・生理状態の非破壊推定法を複数作物で評価し、灌漑スケジューリングへの適用性も検証しており、表現型取得法が中心である。
abstractHyperspectral sensing can detect slight changes in plant physiology, and may offer a faster and nondestructive alternative for water status monitoring.
Broomrape ( Orobanche and Phelipanche spp.) parasitism is a severe problem in many crops worldwide, including in the Mediterranean basin. Most of the damage occurs during the sub-soil developmental stage of the parasite, by the time the parasite emerges from the ground, damage to the crop has already been done. One feasible method for sensing early, below-ground parasitism is through physiological measurements, which provide preliminary indications of slight changes in plant vitality and productivity. However, a complete physiological field survey is slow, costly and requires skilled manpower. In recent decades, visible to-shortwave infrared (VIS-SWIR) hyperspectral tools have exhibited great potential for faster, cheaper, simpler and non-destructive tracking of physiological changes. The advantage of VIS-SWIR is even greater when narrow-band signatures are analyzed with an advanced statistical technique, like a partial least squares regression (PLS-R). The technique can pinpoint the most physiologically sensitive wavebands across an entire spectrum, even in the presence of high levels of noise and collinearity. The current study evaluated a method for early detection of Orobanche cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R. Seeds of susceptible and resistant O. cumana sunflower varieties were planted in infested (15 mg kg -1 seeds) and non-infested soil. The plants were examined weekly to detect any physiological or structural changes; the examinations were accompanied by hyperspectral readings. During the early stage of the parasitism, significant differences between infected and non-infected sunflower plants were found in the reflectance of near and shortwave infrared areas. Physiological measurements revealed no differences between treatments until O. cumana inflorescences emerged. However, levels of several macro- and microelements tended to decrease during the early stage of O. cumana parasitism. Analysis of leaf cross-sections revealed differences in range and in mesophyll structure as a result of different levels of nutrients in sunflower plants, manifesting the presence of O. cumana infections. The findings of an advanced PLS-R analysis emphasized the correlation between specific reflectance changes in the SWIR range and levels of various nutrients in sunflower plants. This work demonstrates potential for the early detection of O. cumana parasitism on sunflower roots using hyperspectral tools.
Why it matches plant phenotyping methodsヒマワリの地下寄生を植物の反射スペクトルと生理指標から早期検出する方法を評価しており、病害・寄生状態の表現型取得が研究の中心です。
abstractThe current study evaluated a method for early detection of Orobanche cumana parasitism in sunflower that combines plant physiology, hyperspectral readings and PLS-R.
SunflowerChlorophyll fluorescenceThermalLeafStress / disease detectionPhotosynthesis / fluorescencePlant / canopy temperature
Although the impact of Orobanche cumana Wallr. on sunflower ( Helianthus annuus L.) becomes evident with emergence of broomrape shoots aboveground, infection occurs early after sowing, the host physiology being altered during underground parasite stages. Genetic resistance is the most effective control method and one of the main goals of sunflower breeding programmes. Blue-green fluorescence (BGF) and thermal imaging allow non-destructive monitoring of plant diseases, since they are sensitive to physiological disorders in plants. We analyzed the BGF emission by leaves of healthy sunflower plantlets, and we implemented BGF and thermal imaging in the detection of the infection by O. cumana during underground parasite development. Increases in BGF emission were observed in leaf pairs of healthy sunflowers during their development. Lower BGF was consistently detected in parasitized plants throughout leaf expansion and low pigment concentration was detected at final time, supporting the interpretation of a decrease in secondary metabolites upon infection. Parasite-induced stomatal closure and transpiration reduction were suggested by warmer leaves of inoculated sunflowers throughout the experiment. BGF imaging and thermography could be implemented for fast screening of sunflower breeding material. Both techniques are valuable approaches to assess the processes by which O. cumana alters physiology (secondary metabolism and photosynthesis) of sunflower.
Why it matches plant phenotyping methodsBGF画像と熱画像を用いて感染植物の生理状態を非破壊的に検出し、育種材料のスクリーニングへ応用する方法が研究の中心である。
abstractBlue-green fluorescence (BGF) and thermal imaging allow non-destructive monitoring of plant diseases, since they are sensitive to physiological disorders in plants.
Vegetation biochemical and biophysical variables are important for many ecological, agronomic, and meteorological applications. Among the main variables, water and chlorophyll are essentials due to directly affect the plant photosynthetic capacity and crop productivity. The objective of this study was develop and validate models capable of estimating water and chlorophyll status in sunflower leaves under progressive water stress, based on the visible/near-infrared region (Vis/NIR) spectral reflectance and chemometric technique. The water and chlorophyll models were adjusted considering the spectral reflectance from the 500–1039 nm wavelengths by using partial least squares regressions (PLSR). In the external validation, high determination coefficient (0.8386 and 0.8097) and low mean bias error (−0.40 dry basis and 0.09 mg g−1) values for water and chlorophyll, respectively, indicating that their predictive capabilities and accuracies of the models were satisfactory. Results showed that spectrometry has potential to be applied as an alternative method in quantifying water and chlorophyll status in sunflower leaves in a non-destructive, quick, and consistent way.
Why it matches plant phenotyping methodsヒマワリ葉の水分・クロロフィル状態をVis/NIR分光で非破壊推定するモデルを開発・外部検証しており、植物形質取得法が中心的です。
abstractThe objective of this study was develop and validate models capable of estimating water and chlorophyll status in sunflower leaves under progressive water stress, based on the visible/near-infrared region (Vis/NIR) spectral reflectance and chemometric technique.
Sunflower downy mildew is a disease of high global economic impact as well as a causal agent that is extremely difficult to eradicate. During the past decades, several approaches for the determination of Plasmopara halstedii (Ph) races have been used worldwide and are discussed in this review. Procedures of isolation, cultivation and maintenance of Ph isolates, as well as the screening of sunflower for resistance, are also critically reviewed. The predominant, globally used resistance screening protocol is a ‘whole seedling immersion’ inoculation. ‘Soil drench’ inoculation allows more precise control of the number of Ph zoosporangia applied to a single sunflower seedling. A detached leaf assay has been described, but it has been used mainly for Ph subcultivation and fungicide tests. For race determination, a differential set consisting of nine sunflower genotypes has been used since 1988, coupled with a numerical triplet code for virulence phenotyping of Ph. The increasing variability in global Ph populations has demonstrated the inadequacy of the current set of differentials, and several researchers have proposed additional public lines as new differentials. Furthermore, bulk isolates may show different results in repeated tests, as Ph may contain genetically distinct zoospores within a single zoosporangium. For precise race determination, single zoosporangia or single zoospore isolates are advisable. However, due to low success of isolation, approximately 1–2%, this method cannot be applied in routine Ph race screening. Methods surveyed in this review have a broad spectrum of applications, including taxonomic studies.
Why it matches plant phenotyping methodsヒマワリの病害抵抗性スクリーニング手法と評価プロトコルを中心に批判的レビューしており、植物の病害状態・抵抗性を取得する方法論が主要内容である。
abstractProcedures of isolation, cultivation and maintenance of Ph isolates, as well as the screening of sunflower for resistance, are also critically reviewed.
Abstract Secondary metabolites serve multiple functions in plants, and play a key role in many ecological processes. Accordingly, the quantification of such compounds is central to addressing many questions in plant science. Alongside precision analytical methods like gas and liquid chromatography-mass spectrometry, there exists a substantial niche for inexpensive and rapid spectrophotometric approaches if their usefulness in a system can be demonstrated. This study seeks to examine the utility of two commonly used colorimetric methods – the Folin-Ciocalteu assay and the aluminum complexation assay – for quantifying variation in leaf phenolic and flavonoid content among members of the genus Helianthus , the sunflowers. Among species known a priori to vary substantially in both the diversity and relative concentrations of secondary metabolites, both assays detect substantial variation among species. Moreover, total phenolic content as assessed by the Folin-Ciocalteu assay correlates positively with concentrations of multiple individual phenolic compounds as quantified by high performance liquid chromatography-mass spectrometry, indicating that the Folin-Ciocalteu describes variation in sunflower phenolic content. Additionally, the diversity of flavonoids known from Helianthus include a number of those known to be sensitive to the aluminum complexation assay, indicating that this assay may also be a useful descriptor of relative variation in sunflower flavonoid content. In total, both the Folin-Ciocalteu and aluminum complexation assays appear to capture useful, if coarse, variation in secondary metabolites among Helianthus species, and seem useful as rapid low-cost methods for exploratory research, preliminary analyses, and potentially useful for high-throughput phenotyping within wild or cultivated sunflower with proper calibration.
Why it matches plant phenotyping methods野生ヒマワリの葉のフェノール類・フラボノイド量を測定する比色法の有用性と妥当性を検証しており、植物形質の定量法として中心的に扱っている。
abstractThis study seeks to examine the utility of two commonly used colorimetric methods – the Folin-Ciocalteu assay and the aluminum complexation assay – for quantifying variation in leaf phenolic and flavonoid content among members of the genus Helianthus
Abstract. Accurate full 3D reconstruction of plants is a crucial step in phenotyping the canopy geometry of living plants to measure features, such as plant height, plant volume, leaf count, leaf size, and internode distance. In this work, an in-field, full 3D reconstruction system for plant phenotyping is described. The system utilized simultaneous, multi-view, high-resolution color digital imagery for true 3D reconstruction of the crop. The cameras were mounted on an arc-shaped superstructure and organized into 16 stereo pairs in four separate arcs. In this study, a comparison of structure-from-motion (SfM) and stereo vision (using stereo cameras) techniques in terms of full 3D reconstruction and measurement of plant features is introduced. System performance was verified in outdoor, on-farm experiments conducted at multiple time points in the growth cycle of sunflower. Results show, that both SfM and stereovision techniques can yield satisfactory 3D reconstruction models and are suited for high-throughput phenotyping without destroying any leaves or stems of the plant. By taking into account small details in the plant and edge preservation for leaves, the custom stereovision algorithms for this system could outperform the SfM technique and provided superior 3D reconstruction results.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出を目的とする圃場フェノタイピングシステムを開発し、SfMとステレオビジョンを比較・検証しているため、方法が中心的である。
abstractIn this work, an in-field, full 3D reconstruction system for plant phenotyping is described.
The effects of herbicides from three mode-of-action groups - inhibitors of protoporphyrinogen oxidase (carfentrazone-ethyl), inhibitors of carotenoid biosynthesis (mesotrione, clomazone, and diflufenican), and inhibitors of acetolactate synthase (amidosulfuron) - were studied in sunflower plants (Helianthus annuus). Raman spectroscopy, chlorophyll fluorescence (ChlF) imaging, and UV screening of ChlF were combined to evaluate changes in pigment composition, photosystem II (PSII) photochemistry, and non-photochemical quenching in plant leaves 6d after herbicide application. The Raman signals of phenolic compounds, carotenoids, and chlorophyll were evaluated and differences in their intensity ratios were observed. Strongly augmented relative content of phenolic compounds was observed in the case of amidosulfuron-treated plants, with a simultaneous decrease in the chlorophyll/carotenoid intensity ratio. The results were confirmed by in vivo measurement of flavonols using UV screening of ChlF. Herbicides from the group of carotenoid biosynthesis inhibitors significantly decreased both the maximum quantum efficiency of PSII and non-photochemical quenching as determined by ChlF. Resonance Raman imaging (mapping) data with high resolution (150,000-200,000 spectra) are presented, showing the distribution of carotenoids in H. annuus leaves treated by two of the herbicides acting as inhibitors of carotenoid biosynthesis (clomazone or diflufenican). Clear signs were observed that the treatment induced carotenoid depletion within sunflower leaves. The depletion spatial pattern registered differed depending on the type of herbicide applied.
Why it matches plant phenotyping methodsラマン分光、クロロフィル蛍光イメージング、UVスクリーニングを組み合わせ、葉の色素分布とPSII生理状態を高解像度で取得・評価する方法適用が中心的に記述されている。
abstractRaman spectroscopy, chlorophyll fluorescence (ChlF) imaging, and UV screening of ChlF were combined to evaluate changes in pigment composition, photosystem II (PSII) photochemistry, and non-photochemical quenching in plant leaves 6d after herbicide application.
Broomrape, caused by the root holoparasite Orobanche cumana, is the main biotic constraint to sunflower oil production worldwide. By the time broomrape emerges, most of the metabolic imbalance has been produced by O. cumana to sunflower plants. UV-induced multicolor fluorescence imaging (MCFI) provides information on the fluorescence emitted by chlorophyll (Chl) a of plants in the spectral bands with peaks near 680 nm (red, F680) and 740 nm (far-red, F740). In this work MCFI was extensively applied to sunflowers, either healthy or parasitized plants, for the first time. The distribution of red and far-red fluorescence was analyzed in healthy sunflower grown in pots under greenhouse conditions. Fluorescence patterns were analyzed across the leaf surface and throughout the plant by comparing the first four leaf pairs (LPs) between the second and fifth week of growth. Similar fluorescence patterns, with a delay of 3 or 4 days between them, were obtained for LPs of healthy sunflower, showing that red and far-red fluorescence varied with the developmental stage of the leaf. The use of F680 and F740 as indicators of sunflower infection by O. cumana during underground development stages of the parasite was also evaluated under similar experimental conditions. Early increases in F680 and F740 as well as decreases in F680/F740 were detected upon infection by O. cumana. Significant differences between inoculated and control plants depended on the LP that was considered at any time. Measurements of Chl contents and final total Chl content supported the results of MCFI, but they were less sensitive in differentiating healthy from inoculated plants. Sunflower infection was confirmed by the presence of broomrape nodules in the roots at the end of the experiment. The potential of MCFI in the red and far-red region for an early detection of O. cumana infection in sunflower was revealed. This technique might have a particular interest for early phenotyping in sunflower breeding programs. To our knowledge, this is the first work where the effect of a parasitic plant in its host is analyzed by means of fluorescence imaging in the red and far-red spectral regions.
Why it matches plant phenotyping methods蛍光イメージングを用いて感染植物の生理状態を定量し、Orobanche感染の早期検出能を評価しており、表現型取得法が研究の中心である。
abstractUV-induced multicolor fluorescence imaging (MCFI) provides information on the fluorescence emitted by chlorophyll (Chl) a of plants in the spectral bands with peaks near 680 nm (red, F680) and 740 nm (far-red, F740).
The objective of this study was to improve the understanding of radar signals for sunflower and to assess the potential of microwave and optical satellite data to monitor crop parameters (leaf area index (LAI) or crop height (CH)) from sowing to harvest by determining the best suitable antenna configurations (i.e. frequency, polarization, and incidence angle). These parameters have been targeted since they are considered key parameters to derive some important agronomical or physical indicators of the crop (i.e. grain yield, leaf nitrogen concentration, radiation interception). This study is based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO laboratory in 2010 (MCM’10) for an agricultural region located in southwestern France. From sunflower emergence to harvest, satellite images were regularly acquired by TerraSAR-X, Radarsat-2, Alos, Formosat-2, and Spot-4/5, quasi-synchronously with in situ measurements (combining soil and vegetation observations). The time series of synthetic aperture radar (SAR) images demonstrate a wide range of configurations, with different frequencies, polarization states (XHH, CHH/VV/VH/HV, and LHH), and incidence angles (from 24 to 53°). The first results have shown that the angular radar sensitivity is greater in the C-band than in the X-band for the co-polarized signals (HH or VV) and for low normalized difference vegetation index (NDVI 0.82, relative root mean square error or rRMSE 0.75, rRMSE < 15%). Further analyses are needed to confirm the promising results observed in the L-band. Finally, a modified version of the water cloud model (WCM) allows analysis of the signal components from the soil and/or the different vegetation layers.
Why it matches plant phenotyping methods衛星光学・SARデータからヒマワリのLAIと作物高を推定し、周波数・偏波・入射角の比較や水雲モデルによる解析を行う測定手法が研究の中心である。
abstractto assess the potential of microwave and optical satellite data to monitor crop parameters (leaf area index (LAI) or crop height (CH)) from sowing to harvest by determining the best suitable antenna configurations
Detection of nitric oxide (NO) in plant cells is mostly undertaken using diaminofluorescein (DAF) dyes. Serious drawbacks and limitations have been identified in methods using DAF as a probe for NO detection. The present work reporting an alternative fluorescent probe for NO detection is thus proposed for varied applications in plant systems for physiological investigations. This method involves a simple, two-step synthesis, characterization, and application of MNIP-Cu {Copper derivative of [4-methoxy-2-(1H-napthol[2,3-d]imidazol-2-yl)phenol]} for specific and rapid binding with NO, leading to its detection in plant cells by epifluorescence microscopy and confocal laser scanning microscopy (CLSM). Using sunflower (Helianthus annuus L.) whole seedlings, hypocotyl segments, stigmas from capitulum, protoplasts, and isolated oil bodies, present investigations demonstrate the versatile nature of MNIP-Cu in applications for NO localization studies. MNIP-Cu can detect NO in vivo without any time lag (ex. 330-385 nm; em. 420-500 nm). It exhibits fluorescence both under anoxic and oxygen-rich conditions. This probe is specific to NO, which enhances its fluorescence due to MNIP-Cu complexing with NO and treatment with PTIO leads to quenching of fluorescence. It is relatively nontoxic when used at a concentration of up to 50 μM.
Why it matches plant phenotyping methods植物細胞内の一酸化窒素を可視化・検出する蛍光プローブを開発し、複数の植物試料で適用・検証しており、表現型（生理状態）取得法が研究の中心である。
abstractThe present work reporting an alternative fluorescent probe for NO detection is thus proposed for varied applications in plant systems for physiological investigations.