Pepper / chilliPotatoTomatoLeafClassificationVisualization / data managementDisease symptoms / severity
Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.
Why it matches plant phenotyping methods植物葉の病害状態を画像から推定するCNN手法を提案・評価しており、病害表現型の取得・分類手法が研究の中心である。
abstractThis study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone.
Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
Plant diseases can reduce crop quality and productivity, making early detection an important aspect of modern agriculture. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have shown promising performance in image-based plant disease classification. This study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization. The experiments were conducted using the PlantVillage dataset consisting of 15 classes of healthy and diseased plant leaves.The research process included image preprocessing, data augmentation, transfer learning, model training, performance evaluation, and explainability analysis. The dataset was divided into training and validation sets with a ratio of 80:20. Model performance was evaluated using accuracy, loss, confusion matrix, precision, recall, and f1-score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 92% and a validation loss of 0.19, outperforming EfficientNetB0 which obtained 76% accuracy and 0.82 validation loss. The classification report demonstrated that ResNet50 provided more stable and consistent predictions across most disease classes. Furthermore, Grad-CAM visualization successfully highlighted disease-relevant regions such as lesions, discoloration, and damaged leaf areas, improving the interpretability of the CNN model. The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類・可視化する深層学習手法が研究の中心であり、病徴領域の推定も評価しているため、植物フェノタイピング手法として採用。
abstractThis study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization.
Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella , enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three- color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.
Why it matches plant phenotyping methods植物細胞の形態形成を可視化する三色ライブイメージング法と、植物細胞用に最適化した微小管マーカーの開発が研究の中心である。
abstractDetection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data management
Super-resolution microscopy has transformed our ability to visualize subcellular structures, but its application in plant biology remains challenging due to the optical complexity of plant tissues. Here, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity. This method leverages time-correlated single-photon counting (TCSPC) to separate fluorescence signals based on their lifetimes, enhancing signal specificity and enabling the visualization of elusive subcellular compartments in Arabidopsis thaliana root tips. The protocol covers sample preparation, fluorophore selection, microscope configuration, image acquisition, and data analysis, providing a step-by-step guide to optimize tau-STED imaging for plant cell biology. By addressing the unique challenges of plant tissue imaging, such as autofluorescence, refractive index mismatches, and light scattering, this approach facilitates super-resolution imaging of intracellular structures, including the plant endoplasmic reticulum-Golgi intermediate compartment (ERGIC). This protocol is designed to be accessible to researchers with basic microscopy experience and offers a robust framework for exploring subcellular dynamics in plants with unprecedented detail. Key features • tau-STED integrates STED signals with fluorescence lifetime via phasor analysis at confocal speeds, enabling low-noise super-resolution imaging. • Morphometry analysis workflow at super resolution.
Why it matches plant phenotyping methods植物組織の細胞内構造を超解像で取得・解析する顕微鏡プロトコルであり、植物表現型の画像取得法が中心。超解像下の形態計測ワークフローも含む。
abstractHere, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity.
Growth chamberMultimodalVisualization / data management
Indoor high-throughput plant phenotyping (HTPP) platforms require flexible, interoperable architectures to support reproducible trait acquisition across changing controlled-environment experiments. This paper presents GREENTRIBE, a modular multi-sensor indoor HTPP framework that integrates sensing, robotic coordination, data communication, semantic data management, and crop modelling within a unified phenotyping pipeline. Rather than treating these elements as independent modules, GREENTRIBE connects distributed sensor acquisition, robot-assisted operation, lightweight message exchange, ontology-based data organization, and process-based model assimilation through a layered architecture implemented with CAN, ROS 2, MQTT, OpenSILEX, and STICS. The platform combines a multiscale sensing network with a sensor-independent communication protocol, enabling heterogeneous imaging, environmental, and plant-monitoring devices to be configured within indoor experimental designs. To support traceable and reusable workflows, GREENTRIBE implements an ontology-driven data management layer aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata standards. The architecture further links computer vision and artificial intelligence pipelines with the STICS crop model, allowing multimodal observations to be transformed into biologically interpretable phenotypic information under explicit genotype, environment, and management contexts. Platform validation demonstrated reliable communication, efficient multimodal data handling, and standardized metadata management across the sensing-to-information pipeline. Under the configured acquisition schedule, GREENTRIBE achieved a maximum full data cycle of approximately 200 ms, no measurable losses up to the CAN master, high metadata completeness, and support for 1704 scheduled daily acquisition events from seven devices. Overall, GREENTRIBE provides a modular and interoperable indoor phenotyping framework for reproducible experiments and standardized multimodal phenotypic data generation.
Why it matches plant phenotyping methods植物フェノタイピングのための多センサーHTPP基盤を中心に開発・統合し、通信性能、データ処理、メタデータ管理を検証しているため。
abstractThis paper presents GREENTRIBE, a modular multi-sensor indoor HTPP framework that integrates sensing, robotic coordination, data communication, semantic data management, and crop modelling within a unified phenotyping pipeline.
Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。
abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.
Aerial / UAVWhole plant / canopy / plot / fieldGrowth / time-series analysisTrackingVisualization / data managementGrowth / development / phenology
ABSTRACT Accurate monitoring of plant phenology is essential for agricultural decision‐making, as deciding the right time of fertilizer application, the maturity of the plant, and climate variation. Traditional manual monitoring often fails to capture the temporal variations across large fields. UAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment. In this study, we propose an AI‐driven framework on UAV‐captured Indian mustard plants to predict the phenological progress. We applied deep learning methods, EfficientNet‐B0, and a proposed hybrid model of a Vision Transformer + LSTM to predict phenological growth from UVV‐captured images of the Indian mustard plant. Both models were trained under a supervised regression setup with extensive augmentation and optimization strategies. The results of the study show that the ViT + LSTM outperforms the EfficientNet‐B0 model in terms of prediction accuracy for mustard plant phenology. The R 2 = 0.9974, minimal errors MSE = 0.0111, and RMSE = 0.149 indicate that the ViT + LSTM provides more accurate phenological predictions on temporal and spatial dependencies. Correlation Analysis confirmed a strong linear and monotonic relationship with the ground truth, with r = 0.9989 and ρ = 0.9946. Gram‐CAM visualization showed that the ViT + LSTM captures the meaning area of the plant. These results were further validated using statistical measures such as Pearson's r and Spearman's ρ , which confirmed the reliability and consistency of the model's predictions.
Why it matches plant phenotyping methodsUAV画像から植物の生育フェノロジーを推定する深層学習手法を開発し、複数モデルの精度比較と統計的検証を行っており、フェノタイピング手法が中心である。
abstractUAV‐based imaging combined with deep learning can provide a solution for automated phenological assessment.
Field / plotLiDAR / point cloudRGB / grayscaleRootStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.
Why it matches plant phenotyping methodsスマートフォン画像と軽量深度推定ネットワークにより樹木DBHを推定する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractwe propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization.
ArabidopsisLaboratory / benchtopMicroscopyStem / branchVisualization / data management
Fluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing. Here, we evaluated two canonical Ru(ii) tris-polypyridyl luminophores, Ru1 ([Ru(deeb) 3 ] 2+ ) and Ru2 ([Ru(phen) 3 ] 2+ ), as non-derivatizing stains for fixed Arabidopsis thaliana stem sections. In situ spectral profiling defined practical 405-nm confocal detection windows, and both probes produced reproducible wall-associated photoluminescence enriched in secondary-wall-rich vascular domains, especially xylem vessels and interfascicular fibers. Their anatomical distribution showed qualitative concordance with Wiesner/Mäule lignin histochemistry and condition-validated Safranin O maps, supporting their use as spatial reporters of matrix-associated enrichment within anatomically defined lignified secondary-wall territories. The molecular determinants of this enrichment, including the relative contribution of lignin and other wall polymers, remain to be resolved. Sequential co-staining with Calcofluor White separated broad β-glucan-rich wall architecture from Ru-enriched secondary-wall domains, while spectral-overlap analysis identified far-red Alexa Fluor 647 excitation at 638 nm as the most orthogonal tested third-label configuration. Ligand-comparative DFT descriptors provided structure-property fingerprints summarizing differences in π-surface continuity and electrostatic anisotropy. Overall, these results position canonical Ru(ii) polypyridyl luminophores as confocal-compatible, chemically tractable scaffolds for anatomical imaging of lignified plant-wall territories.
Why it matches plant phenotyping methods植物の木化二次細胞壁を可視化・空間評価する共焦点蛍光染色法の開発と検証が中心であり、単なる生物学的測定ではない。
abstractFluorescent stains for lignified plant walls must operate in chemically heterogeneous, autofluorescent matrices while remaining compatible with confocal multiplexing.
Plant disease detection is critical for sustainable agriculture and food security. While deep learning models achieve high accuracy in leaf disease classification, their black box nature poses limitations for trust and adoption among agricultural practitioners. This study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM). The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy. Grad-CAM visualizations reveal that modern architectures precisely focus on lesion regions, whereas older models occasionally attend to irrelevant features such as leaf veins and edges. Misclassification analysis identifies shadows and natural leaf patterns as primary error sources. This research demonstrates that explainable artificial intelligence is not merely complementary but essential for developing trustworthy agricultural decision support systems.
Why it matches plant phenotyping methods植物葉の病変領域を画像から分類・可視化する手法を比較評価しており、病害状態の表現型抽出が研究の中心です。
abstractThis study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM).
MilletNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Finger millet ( Eleusine coracana (L.) Gaertn.) is a small seeded cereal known for its health benefits and its ability to grow in water-limited and saline environments. Despite its health and adaptation advantages, there is no standardized BBCH scale developed for finger millet, precluding standardization of a defined growth and development scale. The initial step in any breeding program and related research, is the precise, consistent and standardized description of crop growth stages. Therefore, we present a standardized phenological scale to describe and compare different stages of growth and development. In this study, we used four-finger millet accessions from the U.S.D.A. National Plant Germplasm System (NPGS) collection. Using a standardized BBCH chart as the baseline, we describe nine main stages of development to provide a simplified and user-friendly phenological growth staging scale for finger millet. A novel feature of this study was the use of Neural Radiance Fields (NeRF) to generate three-dimensional (3-D) renderings that align growth stages with 3-D imaging, paving the way for future phenological staging studies to incorporate 3-D visualization as a tool for standardization, researcher training, and reduction of observer bias across environments.
Why it matches plant phenotyping methods指のキビの生育段階を標準化する手法を開発し、NeRFによる3D画像化を生育ステージ判定・標準化に組み込んでいるため、植物フェノタイピング手法が中心である。
titlePhenological growth stages of Finger millet (Eleusine coracana (L.)) using 3-D phenotyping with relevance to breeding applications
Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels. Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm 3 , with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study. This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.
Why it matches plant phenotyping methodsトウモロコシ種子内の内胚乳空洞をX線マイクロCTで3次元可視化し、形態・空間配置・体積などの表現型を抽出・定量化することが研究の中心である。
abstractThis study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels.
Published23 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Field / plotNeRF / 3D Gaussian SplattingThermalWhole plant / canopy / plot / field2D/3D reconstructionVisualization / data managementPlant / canopy temperature
Abstract. Urban trees provide critical ecosystem services in dense city environments, yet current workflows for monitoring their thermal behaviour remain confined to 2D desktop-based analysis with no three-dimensional spatial context or field-deployable visualization capability. This paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment. TIR images of a Tilia tomentosa acquired with a FLIR T560 camera are preprocessed with a standardized false-colour palette and fed into the MILo (Mesh-In-the-Loop Gaussian Splatting) framework to reconstruct a thermally attributed 3D mesh. Geometric evaluation against a Z+F IMAGER 5016 TLS reference using the M3C2 algorithm demonstrates that MILo recovers 13.5 times more canopy geometry than traditional multi-view stereo under thermal imagery, with a standard deviation of 4.0 cm. A colourmap inversion procedure recovers per-vertex temperature estimates from the GS-derived mesh colours, yielding a mean absolute difference of 0.7°C against direct T-Cam measurements (thermal camera mounted on the laser scanner), within the combined instrument accuracy of both sensors. The resulting thermal Gaussian Splat was deployed in a custom Android AR application supporting hybrid marker-based and GPS-based spatial anchoring for in-situ visualization. These results demonstrate the technical feasibility of GS-based thermal reconstruction and mobile AR as a medium for communicating three-dimensional canopy thermal information to educators and urban forestry practitioners.
Why it matches plant phenotyping methods都市樹木の葉冠温度と3D形状を取得・可視化する熱画像ベースの再構成パイプラインを開発し、TLSおよび熱カメラとの定量検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment.
ArabidopsisMicroscopyFlowerFruitPanicle / ear / spikeVisualization / data management
Background Precise characterization of gene expression patterns across temporal, cellular, and tissue-specific contexts is fundamental to understanding plant development and function. Recent advances in ClearSee-based tissue clearing have enabled high-resolution visualization of internal structures and fluorescent reporter signals in plant tissues. Although hand sectioning can provide optical access to tissues that are not amenable to whole-mount clearing, its application to submillimeter-scale and fragile Arabidopsis organs and tissues, including developing inflorescence apices, flowers, fruits, and organ boundaries, has remained limited. Consequently, analysis of these tissues has largely depended on specialized microdissection techniques and labor-intensive histological workflows, such as wax- or resin-embedded microtomy, which restrict throughput, accessibility, and routine use. Results We developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues. This method relies only on gentle manual tissue processing under a stereomicroscope and readily available reagents, allowing reproducible preparation of delicate tissues without the need for embedding or specialized equipment. Combined with ClearSee-based clearing and fluorescent reporters, the approach enables high-resolution imaging of internal tissue architecture and gene expression, while preserving tissue integrity and fluorescence signals that are often compromised during conventional embedding and microtomy procedures. Conclusions Our method substantially reduces technical complexity, costs, preparation time, and labor associated with cellular-resolution imaging of small, fragile plant tissues. By providing a simple, scalable, and accessible alternative to conventional histological workflows, this approach facilitates routine analysis of internal developmental processes across diverse plant species.
Why it matches plant phenotyping methods小型・脆弱な植物組織の解剖学的構造と遺伝子発現を細胞解像度で取得する手法を開発・最適化しており、表現型取得法が研究の中心である。
abstractWe developed and optimized a simple hand-sectioning and imaging method that enables routine visualization of anatomical organization and gene expression patterns at cellular resolution in small, fragile Arabidopsis tissues.
Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。
abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding
MaizeMicroscopyCell / cellular structureSeed / grainVisualization / data management
Sexual reproduction in flowering plants relies on double fertilization, a process marked by two fusion events between the male and female gametes that lead to seed formation. Because this process unfolds within the embryo sac embedded deep inside the ovule, direct observation remains technically demanding, especially in maize, where the large size of female reproductive organs presents additional obstacles. The described method enables high-resolution visualization of cellular events unfolding during maize double fertilization. The approach integrates optimized fixation, clearing and confocal imaging of embryo sacs from ears pollinated with fluorescent pollen marker lines. Precise timing of embryo sac fixation is critical, allowing capture of key events such as pollen peri-germ cell membrane break-down or gamete karyogamy. The protocol provides detailed guidance for ovule dissection, fixation, preparation and renewal of the clearing solution and confocal imaging of embryo sacs. This method offers unprecedented access to the cellular events of double fertilization in maize, establishing a robust framework for studying reproductive processes and supporting future discoveries in plant reproduction.
Why it matches plant phenotyping methodsトウモロコシの二重受精過程を高解像度で可視化する固定・透明化・共焦点 imaging プロトコルが研究の中心であり、植物の生殖状態を取得する方法として該当する。
abstractThe described method enables high-resolution visualization of cellular events unfolding during maize double fertilization.
Estimating the nutritional status of rice leaves is crucial for efficient nutrient management and yield enhancement. Traditional wet lab analyses are time-consuming and labor-intensive. This study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves. The proposed framework integrates a differentiable neural search technique using polynomial function approximators and an adaptive activation mechanism, which not only provides improved predictive performance but also deals efficiently with limited training data. The model performance is evaluated across different treatments and crop growth stages using mean absolute error (MAE) and [Formula: see text] values. Experiments were conducted at the Punjab Agricultural University. The results demonstrate that the proposed model achieves MAE values in the range of 0.06-0.11 for SAS-I and 0.06-0.16 for SAS-II across eleven leaf macro/micro nutrients. To further evaluate the reliability of the predicted nutrients beyond the prediction error analysis, uncertainty estimation of nutrients is also performed. Comparative analysis shows that the proposed framework outperforms conventional deep learning baselines and machine learning methods in terms of accuracy and robustness. Furthermore, the t-SNE visualization of learned feature representations effectively clusters similar nutrient values while separating dissimilar ones. The robustness of the proposed framework is further validated through ablation studies, treatment-wise and plot-wise cross-validation, highlighting the contribution of individual components and their performance under varying field conditions. These findings highlight the proposed NAS-based framework for precise and reliable nutrient assessment in precision agriculture.
Why it matches plant phenotyping methods稲葉のマクロ・微量栄養素という植物状態をマルチスペクトル画像から推定する深層学習手法を開発し、比較検証・不確実性評価・アブレーション試験まで行っており、表現型取得・推定法が中心である。
abstractThis study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves.
Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment. This study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction. The platform integrates solar‐powered Raspberry Pi units with a modular software stack, comprising Node‐RED, InfluxDB, Grafana, and Microsoft Azure, for automated data acquisition, transfer, and visualization. In the 2022 growing season, 18 AGIcam systems were deployed in spring and winter wheat ( Triticum aestivum ) breeding trials, maintaining an uptime of over 85% while capturing frequent red‐green‐blue and no‐infrared imagery. Time‐series vegetation indices derived from these images were used to predict yield using random forest and long short‐term memory (LSTM) models. The LSTM approach achieved the highest accuracy approximately one week after heading, with mean prediction errors of 3.41% for spring wheat and 1.62% for winter wheat. These results highlight the potential of IoT‐based platforms such as AGIcam to enable real‐time, scalable, and effective phenotyping solutions for data‐driven crop improvement. The presented work provides open‐source resources for the development and time‐series analysis of IoT data for phenotyping and precision agricultural applications.
Why it matches plant phenotyping methods植物フェノタイピング用のIoTカメラ基盤を開発・実証し、画像由来の時系列形質から収量を予測する方法が研究の中心である。
abstractThis study introduces AGIcam, an open‐source Internet of Things (IoT) camera system for automated and continuous in‐field plant phenotyping and yield prediction.
Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R2 values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms.
Why it matches plant phenotyping methods植物成長のLAI、地上部バイオマス、フェノロジー、収量を予測する仮想センサー・デジタルツインを開発し、精度検証しており、表現型推定手法が研究の中心である。
abstractThis study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China.
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
ArabidopsisMicroscopyCell / cellular structureRootVisualization / data management
Conventional light microscopy is limited in resolution by the diffraction limit of light, restricting the visualization of the nanoscale organization of biomolecules. Expansion microscopy (ExM) has emerged as a powerful technique to overcome this barrier by physically expanding the specimen embedded in a swellable hydrogel without requiring specialized or high-cost imaging hardware. ExM is widely used in animal models, whereas its application to plant tissues has been challenging due to their multicellularity, in which each cell is encompassed by the rigid cell wall, which resists the expansion forces and prevents isotropic swelling. Here, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues. This protocol details critical steps, including immunostaining, anchoring, gelation, denaturation, cell wall digestion, and expansion. Our method achieves an expansion factor of approximately 4.3×, enabling an effective lateral resolution of ~60 nm using a standard confocal microscope. We demonstrate the visualization of microtubules with preserved ultrastructure. This accessible protocol allows plant researchers to perform super-resolution imaging without specialized optical equipment, facilitating detailed structural analysis of plant cells. Key features • Expansion microscopy to break the diffraction barrier by increasing the physical distances between proteins while preserving relative spatial relationships and fluorescence signals. • 4-fold expansion of Arabidopsis root tissues. • 3D super-resolution imaging. • Deep-tissue imaging thanks to optical clearing associated with expansion of hydrogel-embedded specimens.
Why it matches plant phenotyping methods植物組織向けに最適化した超解像イメージングプロトコルを開発し、根の微細構造を定量・可視化する方法が中心である。
abstractHere, we describe a robust and optimized ExM protocol specifically designed for Arabidopsis thaliana root tissues.
MaizeMicroscopyCell / cellular structureLeafVisualization / data management
Maize is a globally important grain crop that is important for food and fuel. Northern corn leaf blight, caused by Exserohilum turcicum , is an important fungal foliar disease of maize that is highly prevalent and causes yield losses globally. Microscopy can be used to visualize plant-fungal interactions on a cellular level, which enables pathology and genetics studies. Host resistance and isolate aggressiveness can be characterized at different stages of disease development, which enables a more detailed understanding of the pathogenesis process and host-pathogen interactions. Our protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope. This protocol uses KOH treatment followed by aniline blue staining, which stains glucans present in plant and fungal cell walls, and samples are visualized using fluorescence microscopy. Quantitative data about fungal structures including the conidia, hyphal structures, and appressoria, the structures formed to push through the plant leaf surface after conidia have germinated, can be obtained from the images generated using this technique. Visualization of these structures can help pathologists understand plant-pathogen interactions for maize and E. turcicum This method has advantages over other methods because the stain is less toxic than other available stains, samples can be processed in a more high-throughput manner than other protocols, and the required supplies are relatively inexpensive.
Why it matches plant phenotyping methodsトウモロコシ葉上の病原体感染構造を蛍光顕微鏡画像から定量する高スループット染色・画像化プロトコルが研究の中心であり、植物病態の表現型取得法に該当する。
abstractOur protocol outlines an efficient, cost-effective method for staining E. turcicum tissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope.
Abstract Rice disease identification is a critical technique for ensuring yield and quality in precision agriculture. However, complex field backgrounds, subtle lesion features, and similar symptomatic manifestations have led to low accuracy and poor robustness in traditional classification methods. To address these issues, this study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions. A high‑quality dataset containing six categories (healthy rice, rice blast, brown spot, bacterial leaf blight, bacterial leaf streak, and bacterial grain rot) was constructed from field images and public datasets, with a total of 5,663 samples. Using ConvNeXt‑Tiny as the backbone, the model integrates a Hybrid Attention Transformer (HAT) to enhance the perception of lesion regions and key channels, and introduces Spatial and Channel Reconstruction Convolution (SCConv) to reduce feature redundancy and strengthen effective information expression. Experiments show that the Improve‑ConvNeXt model achieves 96.27% accuracy on the test set, which is 4.85% higher than that of the original ConvNeXt and significantly outperforms ResNet and DenseNet. The precision, recall, and F1‑score reach 95.84%, 96.11%, and 95.95%, respectively. Confusion matrix and Grad‑CAM visualization prove that the model can accurately focus on lesion areas and effectively distinguish similar diseases. This method provides high precision and strong generalization for rice disease identification in complex field environments, and offers a reliable technical reference for intelligent monitoring and precise management of rice fields.
Why it matches plant phenotyping methodsイネの病徴画像から病害状態を分類する画像・深層学習手法を開発し、データセットと性能比較で技術的に検証しているため、植物フェノタイピング手法が中心です。
abstractthis study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions.
Abstract The detection of plant diseases is essential to the preservation of agricultural productivity and food security, yet the existing technologies are likely to have low interpretability and low generalization in practice. This work suggests a hybrid deep learning model in the form of Data-efficient Image Transformers (DeiT) to detect and classify plant diseases and estimate their severity. The framework takes advantage of DeiT-Base, DeiT-Small, and DeiT-Tiny models to embrace global contextual dependencies of plant leaf images. The proposed hybrid Explainable Artificial Intelligence (XAI) module aims to enhance interpretability by combining Gradient-weighted Class Activation Mapping (Grad-CAM) as a local feature attribution model and Attention Rollout as a global dependency visualization model. In addition, a leaf segmentation method, a HSV-based method, is employed, which isolates disease-relevant regions and minimizes noise to increase the classification accuracy and level of explanation. The damage ratio analysis is combined with attention maps generated by XAI to build a severity estimation module. Experiments on the New Plant Diseases Dataset (Augmented) with large-scale experiments demonstrate that the proposed DeiT-Base model can achieve a maximum accuracy of 99.13, a better result compared to a variety of CNNs, such as ResNet50, DenseNet121, MobileNetV3, EfficientNet, InceptionV3. Also, hybrid XAI framework has better interpretability performance, such as focus score, noise, signal-to-noise ratio (SNR), and entropy, than single explanation procedures. The system proposed is not only capable of improving the accuracy of classification but also has better transparency and meaningful severity estimation which makes it appropriate to the real-world application of precision agriculture.
Why it matches plant phenotyping methods植物葉画像から病害分類と病害重症度を推定する画像解析手法を開発・評価しており、病害状態の表現型取得が中心的な貢献です。
abstractThis work suggests a hybrid deep learning model in the form of Data-efficient Image Transformers (DeiT) to detect and classify plant diseases and estimate their severity.
Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.
Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。
abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was therefore constructed by normalizing all labels to a canonical Crop_Disease format and retaining only those categories for which an unambiguous semantic match existed in both datasets.Open asset ↗lines:335-337Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 13 Sept 2026
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Why it matches plant phenotyping methods植物組織内の構造をFIB-SEMと画像処理で3D再構成・セグメンテーションするワークフローを開発しており、フィリフォーム装置形態という植物器官形質の取得が中心である。
abstractHere, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule.
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureVisualization / data management
While confocal fluorescence microscopy has provided valuable insights into chromatin organization in plant nuclei, its diffraction-limited resolution constrains the investigation of chromatin architecture, motivating the use of super-resolution techniques such as Single-Molecule Localization Microscopy (SMLM). Among these approaches, direct stochastic optical reconstruction microscopy (dSTORM) provides nanoscale resolution in individual cells, enabling precise visualization of chromatin domains, histone modifications, and nuclear organization. While such methods are increasingly applied in mammalian systems, their use in plant biology remains limited, largely due to technical challenges in sample preparation. Here, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana. This protocol starts with seedling fixation to preserve nuclear morphology, followed by gentle tissue chopping and centrifugation to enrich intact nuclei. Isolated nuclei are then fluorophore-labeled in liquid medium and immobilized on low-melting agarose pads, a strategy that enhances stability during prolonged single-molecule imaging sessions. These steps collectively minimize background fluorescence, improve labeling consistency, and increase reproducibility across biological replicates. The resulting preparations provide enhanced clarity for visualizing chromatin modifications and nuclear architecture in plants. By lowering the technical barriers to implement SMLM imaging in Arabidopsis, this protocol provides a versatile means to investigate epigenetic regulation, chromatin organization, and nuclear topological variations at the nanoscale. This work establishes a methodological foundation for applying SMLM to plants, bridging the gap with mammalian cell biology and opening new opportunities to study how nuclear architecture contributes to genome regulation in response to developmental and environmental cues in plant systems.
Why it matches plant phenotyping methods植物核の形態・クロマチン構造を取得するSMLM画像化プロトコルの開発と再現性向上が研究の中心であり、植物フェノタイピング手法として適格です。
abstractHere, we present a streamlined and reproducible workflow for SMLM imaging of nuclei isolated from Arabidopsis thaliana.
This paper presents the development of an IoT-based intelligent pesticide sprinkling system for rice crops using image processing and machine learning techniques. The system aims to overcome the limitations of traditional pesticide spraying methods, which often result in excessive chemical usage, environmental pollution, and health risks to farmers. A camera module (ESP32-CAM) captures real-time images of rice leaves, which are processed using OpenCV and analyzed through a Convolutional Neural Network (CNN) model trained using TensorFlow. The model identifies common rice diseases such as bacterial leaf blight, brown spot, and leaf smut, and determines the infection severity. Based on the detection results, the ESP32 microcontroller activates a relay module that controls a DC pump to spray pesticides only on infected areas. The system also features an IoT-based dashboard for real-time monitoring, visualization, and remote operation. Experimental results demonstrate effective disease classification, with clear visualization using Grad-CAM and probability graphs. The proposed system reduces pesticide usage, minimizes human exposure to harmful chemicals, and enhances crop productivity. It provides a low-cost, efficient, and scalable solution for precision agriculture and smart farming applications.
Why it matches plant phenotyping methods葉画像から病害の種類と感染重症度を推定し、その結果で散布を制御する画像・機械学習システムが研究の中心であり、植物の病害状態を直接評価するため、農業制御用途を含んでも植物フェノタイピングに該当する。
abstractusing image processing and machine learning techniques
Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments.
Why it matches plant phenotyping methods植物葉の病害状態を画像から検出・認識する新規モデルを開発し、実験・アブレーションで性能検証しており、植物フェノタイピング手法が中心である。
abstractthis paper proposes a new detection approach which undergoes three-layer transformations
Multispectral / hyperspectralLeafCalibration / preprocessingSegmentationVisualization / data management
Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it reveals details about plant biochemistry and stress that standard cameras miss. However, processing this data is often challenging. Many labs still rely on loosely organized collections of lab-specific MATLAB or Python scripts, which makes workflows difficult to share and results difficult to reproduce. MVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data. The software handles everything from calibrating raw ENVI files to detecting and clipping individual leaves based on multiple vegetation indices (NDVI, CIRedEdge and GCI). It also includes tools for data augmentation to create training-time variations for machine learning and utilities to visualize spectral profiles. MVOS_HSI can be used as an importable Python library or run directly from the command line. The code and documentation are available on GitHub. By consolidating these common tasks into a single package, MVOS_HSI helps researchers produce consistent and reproducible results in plant phenotyping
Why it matches plant phenotyping methods葉レベルHSIの校正・葉検出・切り出しを含む再現可能な植物表現型解析用ソフトウェアであり、手法が中心。
abstractMVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data.
Reproduction assets foundThis is a software paper describing MVOS_HSI, the authors' open-source Python library for hyperspectral plant-phenotyping preprocessing (calibration, leaf segmentation/clipping, augmentation, spectral plotting). The authors' code is explicitly and publicly available on GitHub at the allowed URL, making it a paper-phenyCode · publicyping.
K eywords Hyperspectral imaging ⋅ \cdot
Plant phenotyping ⋅ \cdot
Data preprocessing ⋅ \cdot
Vegetation indices ⋅ \cdot
Data augmentation ⋅ \cdot
Python
Table 1: Code Metadata for MVOS_HSI
Nr.
Code metadata description
Metadata
C1
Current code version
v0.2.1
C2
Permanent link to code/repository used for this code version
https://github.com/MVOSlab-sdstate/mvos_hsi
C3
Permanent link to Reproducible Capsule
N/A
C4
Legal Code License
MIT License
C5
Code versioning system used
git
C6
Software code languages, tools, and services used
Python 3.x; NumPy, SciPy, Matplotlib
C7
Compilation requirements, operating environments & dependencies
Standard scientific Python environment on Windows, LinOpen asset ↗MVOSlab-sdstate/mvos_hsi · mvos_hsilines:1-122Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published8 Apr 2026International Journal of Engineering and ManufacturingCited by 0 · OpenAlex ↗
Plant diseases have a significant impact on global food security, especially in staple crops like maize (Zea mays). Traditional disease detection systems depend on professional visual inspection, which is labor-intensive, time-consuming, and not scalable for large agricultural areas. Convolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically. A curated dataset of approximately 7,000 high-resolution maize leaf photos was created, representing four classes: healthy, Common Rust (Puccinia sorghi), Northern Leaf Blight (Exserohilum turcicum), and Gray Leaf Spot (Cercospora zeae-maydis). Data were sourced from the Plant Village dataset, real-world field collections from Indian farms, and supplemented synthetically to simulate varied climatic circumstances. Advanced methods including as adaptive learning rate scheduling, gradient clipping, and significant data augmentation were used to train a bespoke CNN model that was improved by transfer learning with ResNet50 and VGG16 backbones. The model attained a test accuracy of 98.2%, beating classic machine learning algorithms like SVM (88.5%) and Random Forest (84.3%). Visualization approaches such as feature maps, Grad-CAM, and LIME improved interpretability and showed the model's capacity to locate disease-relevant features. Web-based user engagement is made possible by deployment-ready implementation, which enables farmers to upload leaf photos for immediate diagnosis. With the potential to cut maize crop losses by 20–30%, this research offers a scalable and affordable alternative to early disease detection in precision agriculture. Future research will investigate autonomous farm management with drone-based real-time surveillance and IoT system integration.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から推定するCNN手法を開発し、データセット、比較評価、精度検証、解釈性分析まで行っており、植物表現型取得が中心である。
abstractConvolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically.
Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.
Why it matches plant phenotyping methods植物表現型抽出を自然言語で自動化するAIシステムの開発であり、ツールとワークフローが研究の中心です。代表的ケーススタディと評価タスクによる検証も行っています。
abstractwe introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction.
Reproduction assets foundThe paper deposits its PhenoAssistant analysis code (with chat logs and generated outputs) on GitHub, and uses public phenotyping datasets: the CVPPP2017 leaf segmentation challenge data (case study 1 training/evaluation) and the CVPPA@ICCV'23 WW2020 winter wheat nutrient-deficiency dataset (case study 3), both on CodaCode · publicThe code for this research, as well as the chat logs and generated outputs of the case studies and evaluations, are available at Github [ https://github.com/vios-s/PhenoAssistant/ ] 78 .Open asset ↗vios-s/PhenoAssistantlines:224-268Dataset · publicThe data used for training and evaluating the computer vision model used in case study 1 are publicly available from the CVPPP2017 Leaf Segmentation Challenge dataset (A1 and A4 subsets) at CodaLab [ https://codalab.lisn.upsaclay.fr/competitions/8970 ].Open asset ↗CodaLab · CVPPP2017lines:224-268Dataset · publicThe winter wheat data used in case study 3 are publicly available from the CVPPA@ICCV'23: image classification of nutrient deficiencies in winter wheat and winter rye dataset (WW2020 subset) at CodaLab [ https://codalab.lisn.upsaclay.fr/competitions/13833 ].Open asset ↗CodaLab · WW2020lines:224-268Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
施設園芸における環境制御では，温度や湿度の空間変動など環境の不均一性を考慮せずに，平均化された指標に基づいた制御が行われており，作物の生育にばらつきが生じる問題があった．これらを解決するためには，主に日照や温度・湿度などの不均一の原因となっている作物群落のキャノピー構造を可視化することが重要である．本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．機器を設置した台車を移動プラットフォームとして，圃場の畝に沿って移動させることで，畝に沿った作物のキャノピー構造を把握する．2つのLiDARの走査面を変えて用いることとし，水平スキャンにより，台車進行方向の作物及び障害物の2Dマッピング，垂直スキャンにより作物の高さ方向のスキャンを時系列的に重ねることで，3Dマッピングを行う．結果として，水平スキャンのデータは，台車の走行制御のための状況把握としては十分な精度で利用可能である．垂直スキャンのデータは，作物の高さ方向の構造を把握できることが確認された．2つのLiDARを搭載したシステムを用いて，圃場で定期的に移動計測を行うことで，作物のキャノピー構造を把握することができ，環境の不均一の要因として利用可能となることが期待される．
Why it matches plant phenotyping methods低コスト2D LiDARによる作物キャノピー構造の可視化手法を開発し、反射の有効性と走査条件を検証しているため、植物表現型取得が研究の中心である。
abstract本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．
Centralizing valuable community data and resources into a user-friendly interface and accessible repository has become essential for agricultural science; embracing Findable Accessible, Interoperable, and Reusable (FAIR) principles is now standard for effective databases. SorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community. The SorghumBase team curates genomic, transcriptomic, variation, and phenotypic information and aggregates community events, providing rich visualizations and bulk data access. The modular framework of the database is built with open-access software to yield a robust, modifiable, and sustainable data infrastructure. Release 9 of SorghumBase includes: (i) 88 sorghum reference genomes and an updated pan-gene index, (ii) over 100 million variants have been mapped onto the 2 genomes, BTx623 and Tx2783, (iii) assignment of 41 million Reference Cluster SNP identifiers (rsIDs) from BTx623 across the pan-genome, (iv) updated gene search homology, gene expression, and germplasm visualizations and features, (v) added and standardized 234 phenotypic data from 40 community-generated GWAS studies and 148 traits from the Sorghum QTL Atlas (Oz Sorghum), (vi) improved news, funding, and a research content management system for community access and interaction, (vii) outreach materials including training documents and videos, and (viii) community engagement initiatives through training and working groups. SorghumBase serves as a hub for sorghum data and stakeholder engagement while promoting community standards to drive research and multi-omics breeding approaches.
Why it matches plant phenotyping methodsソルガムの表現型データを標準化・統合し、可視化とアクセスを提供する研究基盤であり、表現型情報のデータ基盤として中心的です。
abstractSorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community.
Cotton productivity plays a crucial role in the global agricultural economy; however, various leaf diseases significantly threaten crop yield and fiber quality. Early and accurate disease detection is essential for effective crop management, yet traditional inspection methods are time-consuming, labor-intensive, and dependent on expert knowledge, often leading to inconsistent results. Conventional machine learning approaches also face limitations in real-world agricultural environments due to variations in lighting conditions, complex backgrounds, and similarities between disease symptoms. To address these challenges, this research proposes an intelligent framework called Cotton Plant Disease Identification Using ResMobNet with Attention-Guided Localization and Severity Analysis (CPDI-RMN). The proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework. Initially, cotton leaf images are collected from a comprehensive dataset and preprocessed through image resizing, noise removal, contrast enhancement, and Min–Max normalization to improve visual quality and ensure stable model training. Data augmentation techniques such as rotation, flipping, zooming, and brightness adjustment are applied to enhance dataset diversity and improve model robustness against overfitting. For feature enhancement, contour visualization and geometric feature representation are combined with texture analysis using the Gray-Level Co-occurrence Matrix (GLCM) and Laplacian filtering. The core of the framework is the ResMobNet hybrid architecture, which integrates ResNet-50, EfficientNet-B3, and MobileNet-V2 to capture multi-scale spatial and texture features while maintaining computational efficiency. Gradient-Weighted Class Activation Mapping (Grad-CAM) is employed to generate attention maps for disease localization, followed by segmentation to isolate infected regions. Disease severity is then quantified by calculating the percentage of infected leaf area and classifying it into mild, moderate, and severe categories. Experimental results using five-fold cross-validation demonstrate that the CPDI-RMN model achieves 98.85% classification accuracy, outperforming CNN, ANN, ResNet, and MobileNetV2 models. Additionally, the attention-based localization achieves 96.8% Intersection over Union and 98.0% Dice Score, indicating highly accurate disease region detection. Overall, the proposed framework provides a reliable and scalable solution for intelligent cotton disease monitoring and supports precision agriculture through data-driven crop management.
Why it matches plant phenotyping methodsワタ葉画像から病変領域を抽出し、感染面積率に基づいて病害重症度を定量化する画像解析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含める。
abstractThe proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework.
MaizePotatoSoybeanLeafClassificationObject detectionCalibration / preprocessingSegmentationStress / disease detectionVisualization / data management
Background: Plant diseases significantly reduce global crop productivity, creating an urgent demand for intelligent, automated diagnostic systems in agriculture. Traditional manual inspection is labor-intensive, subjective and often ineffective in detecting early or latent symptoms. This study presents a multi-class classification and severity estimation framework for ten plant disease categories: Maize brown spot, maize rust, maize healthy, potato early blight (Alternaria solani), potato late blight (Phytophthora infestans), potato healthy, soybean mosaic virus (SMV), soybean pod mottle virus (SPMV), soybean sudden death syndrome (SDS/SBS) and soybean healthy. The objective is to develop a robust hybrid deep learning model capable of accurate early detection and quantitative severity assessment to support precision agriculture. Methods: A hybrid architecture combining convolutional neural networks (CNN) with LSTM and BiLSTM networks was implemented. The preprocessing pipeline included leaf segmentation, binary masking, defect localization and edge detection to enhance lesion visibility. CNN layers extracted spatial and textural features, while recurrent layers modeled contextual dependencies within feature representations. Performance was evaluated using Precision, Recall, F1-score, defect percentage estimation, convergence analysis and t-SNE visualization. Result: Results demonstrated stable convergence with decreasing loss (0.8-1.2) and improved feature clustering. Defect severity ranged from 0.00% (Soybean healthy) to 87.93% (Maize brown spot). The framework enables early detection (0.29-5% infection), reduces yield loss, minimizes chemical overuse and promotes sustainable smart agriculture systems.
Why it matches plant phenotyping methodsCNN-LSTM/BiLSTMによる葉画像からの病害検出と病徴重症度推定手法の開発が中心であり、植物状態を直接推定している。
abstractThis study presents a multi-class classification and severity estimation framework for ten plant disease categories
MilletPeaField / plotGreenhouseNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstructionVisualization / data managementGrowth / development / phenology
Plant phenotyping in precision agriculture increasingly requires high-fidelity three-dimensional reconstruction and accessible visualization methods. This study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages. We collected multi-view imagery of finger millet, proso millet, mungbean, and field pea under controlled greenhouse conditions, aligning data acquisition with standardized BBCH phenological scales. Camera pose estimation was performed using GLOMAP, followed by reconstruction via both Nerfacto and G-Splat implementations. Quantitative evaluation using PSNR, SSIM, and LPIPS metrics revealed complementary strengths of the two approaches: G-Splat achieved superior structural fidelity, while NeRF provided enhanced perceptual realism. Both reconstruction methods were successfully integrated into an immersive VR greenhouse environment deployed on Meta Quest headsets, maintaining consistently high framerates. This framework establishes a practical foundation for incorporating neural reconstruction and immersive technologies into agricultural phenotyping workflows, supporting both research applications and educational engagement.
Why it matches plant phenotyping methods植物の多視点画像からNeRFと3D Gaussian Splattingで3D形状を再構成し、画質指標で比較評価する統合フェノタイピング基盤の開発・検証が中心である。
abstractThis study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages.
Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from O(n2) to O(log(n)) where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m2 polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、セマンティックセグメンテーション、NDVIを統合した植物レベルの健康状態評価手法が研究の中心であり、手法の構築と検証も行っている。
abstractThis study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level.
Plant diseases significantly threaten global agricultural productivity and food security, causing annual crop yield losses of up to 30%. Early and accurate detection of plant diseases is essential for implementing timely intervention strategies and ensuring sustainable agricultural practices. Recent advances in deep learning have revolutionized automated plant disease detection using leaf images. This study proposes a robust deep learning framework based on ResNet101 integrated with a Feature Pyramid Network (FPN) for multi-class plant disease classification. The model is trained and evaluated using the comprehensive PlantVillage dataset containing 38 distinct plant disease classes across multiple crop species. The Feature Pyramid Network extracts multi-scale features from different layers of the backbone network, enabling the model to capture both fine-grained texture details and high-level semantic information essential for distinguishing visually similar disease symptoms. The proposed architecture employs transfer learning with ImageNet pre-trained weights and is optimized using the Adam optimizer with sparse categorical crossentropy loss. Experimental results demonstrate exceptional performance with macro-averaged precision of 99.45%, recall of 99.54%, and F1-score of 99.50% across 10,861 test samples. Comprehensive evaluation using confusion matrix analysis, Receiver Operating Characteristic (ROC) curves, and feature map visualization confirms the robustness and discriminative capability of the proposed approach. The results indicate that the ResNet101-FPN model provides a reliable, scalable, and deployable solution for automated plant disease diagnosis in precision agriculture systems.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する深層学習フレームワークの開発と評価が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThis study proposes a robust deep learning framework based on ResNet101 integrated with a Feature Pyramid Network (FPN) for multi-class plant disease classification.
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureFlowerRootTissueVisualization / data management
Abstract Cryo‐scanning electron microscopy (CryoSEM) permits the preparation and detailed imaging of bulky samples while keeping them in a hydrated state. For plant biology, cryofractures give information on cell ultrastructure and tissue organisation within a much larger context that is the whole organ or organism. To date, a method to locate fluorescence reporters on the cryofracture has not been reported. Our approach uses a stereofluorescence microscope with an 80 mm working distance and a high‐zoom ratio to image the fracture through a viewing port of the cryopreparation chamber while the sample is still frozen and under vacuum. We have applied this method to look at fluorescent reporters of auxin transport and signalling in plant shoot apices and seedlings, the expression of a poorly characterised gene in the young floral pedicel and nitrogen‐fixing rhizobial bacteria, expressing GFP, in root nodules. This method is applicable to any cryopreserved bulky sample that has a fluorescent output and paves the way for correlative light‐electron microscopy for cryoSEM‐based imaging.
Why it matches plant phenotyping methods植物試料の蛍光レポーターを凍結破断面上で位置特定・画像化する新規CryoFluorSEM法の開発であり、植物の構造・組織状態を取得する方法が中心である。
abstractTo date, a method to locate fluorescence reporters on the cryofracture has not been reported.
Automated phenotyping of wheat growth stages from 3D point clouds is still limited. The study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping. A novel 3D wheat plot detection network—integrating spatial–channel coordinated attention and area attention modules—improves depth-direction feature recognition, and a point-cloud-density-based row segmentation algorithm enables planting-row-scale plot delineation. A supporting software system facilitates 3D visualization and automated extraction of phenotypic parameters. We introduce a dynamic phenotypic index of five temporal metrics (growth stage, slow growth stage, height/area reduction stage, maximum height/area difference stage, and height/area change rate) for growth-stage classification and yield prediction using static and time-series models. Experiments show strong agreement between predicted and measured plot heights (R 2 = 0.937); the detection net achieved AP 3D = 94.15 % and AP BEV = 95.35 % in “easy” mode; and a Bi-LSTM incorporating dynamic traits reached 82.37 % prediction accuracy for leaf area and yield, a 6.14 % improvement over static-trait models. This workflow supports high-throughput 3D phenotyping and reliable yield estimation for precision agriculture. • Developed a novel 3D wheat plot detection net with spatial–channel coordinated attention and area-attention modules, reaching 94.15% AP 3D and 95.35% AP BEV in high-precision mode, outperforming traditional methods. The CFPT 3D module boosts depth-direction feature extraction for dense planting. • Introduced 5 temporal phenotypic metrics (e.g., growth stage transitions, height/area change rates) to capture dynamic growth patterns. • Bi-LSTM models using these traits predicted yield with 82.37% accuracy, 6.14% higher than static-trait models. • Released a PyQt5-based 3D phenotype extraction tool for automated parameter calculation (height, canopy area, LAI) and visualization. • Proposed a density-based row segmentation algorithm enabling accurate row-level phenotyping, validated in single- and multi-row systems.
Why it matches plant phenotyping methods3D画像・点群から小麦区画の形態形質を抽出する手法、検出・行分割アルゴリズム、動的形質指標、ソフトウェアを中心的に開発・検証しているため。
abstractThe study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping.
The integration of hyperspectral imaging (HSI) with machine learning enables non-destructive prediction and visualization of food quality. However, multicollinearity and redundant features in spectral data can reduce model accuracy and increase computational time, emphasizing the need for key wavelength selection. In response, this study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC). A partial least squares regression (PLSR) model using the selected features outperformed recursive feature elimination (RFE) and competitive adaptive reweighted sampling (CARS), achieving a coefficient of determination (R 2 ) of 0.46 and a root mean squared error (RMSE) of 0.70%. The approach was further applied to hyperspectral images to visualize pixelwise DMC distribution, providing spatial insights into fruit composition. Results demonstrate that integrating XAI with evolutionary feature selection offers a noninvasive, transparent, and efficient strategy for assessing and visualizing fruit quality.
Why it matches plant phenotyping methodsリンゴ果実の乾物含量という植物器官形質を、ハイパースペクトル画像とGA・XAIによる波長選択で予測・可視化する手法が研究の中心である。
abstractthis study presents an inventive method combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) to select key wavelengths for predicting apple dry matter content (DMC).
PeachMicroscopyFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traits
Fruit size and shape, which influence horticultural quality, are determined by the number and the size of the cells in the local region. In fruit trees, however, the difficulty of applying molecular genetic approaches has hindered a detailed understanding of the localization and orientation of cell division in developing fruit tissues. In this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops, peach ( Prunus persica ), Japanese apricot ( P. mume ) and the interspecific hybrid Japanese apricot ( P. salicina x P. mume ), providing clear insight into the spatial distribution and orientation of dividing cells. We systematically optimized a 5-ethynyl-2′-deoxyuridine (EdU) labeling protocol for thick ovary tissues by adjusting infiltration conditions and fixation methods. In addition, electron microscopy combined with wide-view tiling visualization was applied to directly identify dividing cells, including those undergoing chromosome segregation and cell plate formation. By combining with machine learning-based detection, we efficiently and objectively identified dividing cells. Using these complementary approaches, we found that cell division activity was broadly distributed throughout pre-anthesis ovaries in all three crops, without pronounced spatial restriction. In contrast, analysis of division orientation revealed region-specific patterns: cells in the outermost exocarp divided predominantly anticlinally, whereas cells in the mesocarp divided largely periclinally, consistent with subsequent ovary (fruit) enlargement. The integrated framework presented here provides a foundation for understanding the spatial and three-dimensional regulation of fruit development and for future studies in fruit morphogenesis and horticulture.
Why it matches plant phenotyping methods植物組織内の細胞分裂という発生状態を可視化・定量する統合フレームワークを開発し、EdU標識、電子顕微鏡、広視野タイリング、機械学習検出を組み合わせて検証・適用しているため、植物フェノタイピング手法が中心である。
abstractIn this study, we established a novel framework to visualize cell division in pre-anthesis ovaries of three drupe crops
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Field / plotAnnotation / quality controlVisualization / data management
BACKGROUND: Accurate acquisition of phenotypic data is critical for cataloguing and utilising genetic variation in cultivated crops, landraces, and their wild relatives. The collection of phenotypic data using handwritten notes often introduces errors which can and should be avoided. Electronic data collection is crucial for ensuring error prevention and data standardisation and thus ensuring high-quality, reliable data. IMPLEMENTATION: This paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research. Building on its predecessor, GridScore, the development of GridScore NEXT was driven by real life, in the field interactions with expert user groups across a number of crops. This iterative design methodology allowed the development and testing of new features. Collaborators from the 'Biodiversity for Opportunities, Livelihoods and Development' (BOLD) project, focusing on crops including rice, grasspea, and alfalfa, along with barley, potato, vegetable and blueberry teams, provided invaluable insights through training sessions and interviews and in the field use of the application. RESULTS: Key improvements to GridScore NEXT include enhanced data collection tools, supporting individual plant phenotyping within plots and enabling new data types such as GPS coordinates and image traits. GridScore NEXT provides customisable user defined validation rules to help prevent errors and incorporates barcode scanning for accurate, efficient data capture. The application offers an increased toolbox of data visualizations over its predecessor including heatmaps and statistical box plots, which aid in identifying potential data issues and understanding trial performance in the field. GridScore NEXT is cross-platform and can operate without an internet connection, making it ideal for field use in remote areas. Its adoption has led to standardisation of methods, significant error reduction, and the timely sharing of data, enabling quicker decision-making in pre-breeding and characterisation experiments. GridScore NEXT is available under an open-source (Apache 2.0) licence and freely available to all with no restrictions. It offers self-hosting options for enhanced data security and privacy. GridScore NEXT shows broad applicability across a diverse range of not only plant phenotyping experiments, but any experiment that requires the collection of accurate data.
Why it matches plant phenotyping methods植物表現型データ収集アプリケーションの開発と検証が論文の中心であり、個体表現型や画像形質を含む圃場データ取得を支援するため、対象範囲に含める。
abstractThis paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research.
Reproduction assets foundThe paper describes GridScore NEXT and its use in BOLD/CPC phenotyping. Authors' public code (GitHub, Zenodo) and public phenotype datasets (BOLD alfalfa, grasspea, rice; CPC characterisation data) are available; blueberry and UKVGB data are request-only.Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicThe CPC datasets used are characterisation datasets which are available from https://germinate.hutton.ac.uk/cpc.Open asset ↗html-lines:528-593Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Continuous, high-frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season-long trait assessment. This study introduces AGIcam, an open-source IoT camera system for automated and continuous in-field plant phenotyping and yield prediction. The platform integrates solar-powered Raspberry Pi units with a modular software stack, comprising Node-RED, InfluxDB, Grafana, and Microsoft Azure, for automated data acquisition, transfer, and visualization. In the 2022 growing season, 18 AGIcam systems were deployed in spring and winter wheat breeding trials, maintaining an uptime of over 85% while capturing frequent RGB and NoIR imagery. Time-series vegetation indices derived from these images were used to predict yield using random forest and Long Short-Term Memory (LSTM) models. The LSTM approach achieved the highest accuracy approximately one week after heading, with mean prediction errors of 3.41% for spring wheat and 1.62% for winter wheat. These results highlight the potential of IoT-based platforms such as AGIcam to enable real-time, scalable, and effective phenotyping solutions for data-driven crop improvement.
Why it matches plant phenotyping methodsAGIcamは圃場での植物表現型取得を目的とするIoTカメラ基盤であり、画像取得、時系列形質抽出、収量予測を技術的に評価しているため、方法・プラットフォームが中心である。
abstractThis study introduces AGIcam, an open-source IoT camera system for automated and continuous in-field plant phenotyping and yield prediction.
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
Background Translation is a fundamental process for every living organism. In plants, the rate of translation is tightly modulated during development and in responses to environmental cues. However, it is challenging to measure the actual translation state of the tissues in vivo. Results Here, we report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC. We combined a method originally developed for the fruitflies with an improved low background split-mVenus BiFC system previously described in plants. We labelled small subunit ribosomal proteins (RPS) and large subunit ribosomal proteins (RPL) of Arabidopsis thaliana with fragments of the mVenus fluorescent protein (FP). We tested the Ribo-BiFC method using transiently expressed recombinant ribosomal proteins in epidermal cells of Nicotiana benthamiana. The BiFC-tagged ribosomal proteins complemented the mVenus molecule and were detected by fluorescence microscopy, potentially visualizing the close proximity of translating assembled 80S ribosomal subunits. Although the resulting signal is less intense than that of known interactors, its detection points to the functionality of the system. Conclusions This Ribo-BiFC approach has further potential for use in stable transgenic lines in enabling the visualisation of translational rate in plant tissues and changing translation dynamics during plant development, under abiotic stress or in different genetic backgrounds.
Why it matches plant phenotyping methods植物組織内の翻訳状態・翻訳速度という生理状態を可視化する新規蛍光イメージング法を導入・検証しており、表現型取得法が研究の中心である。
abstractwe report the introduction of an in vivo translation marker based on bimolecular fluorescence complementation, the Ribo-BiFC.
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
PoplarMicroscopyCell / cellular structureRootVisualization / data management
Abstract Background Cortical microtubules (CMTs), one of the components of cytoskeleton, control the orientation and localization of newly deposited cellulose microfibrils in cell walls, and thereby determine the shape, size, and structure of plant cells. Imaging of CMTs in plant tissues is generally performed using fluorescently labeled specimens under an optical fluorescence or confocal laser scanning microscope. However, optical microscopy has insufficient resolution to visualize individual CMTs, and its observation range is limited to superficial tissue layers that light can penetrate. In contrast, transmission electron microscopy offers high-resolution visualization of CMTs in plant cells but is restricted to slightly oblique ultrathin sections with an approximate thickness of 70–100 nm. Results Herein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM). We successfully observed the arrangement of CMTs in several plant specimens, including young branches of ginkgo ( Ginkgo biloba ), calli from the leaves of hybrid poplar ( Populus sieboldii × P. grandidentata ), and root tips of the adzuki bean ( Vigna angularis ). CMTs were visualized on the protoplasmic fracture face using both cryo-FE-SEM and conventional room-temperature FE-SEM. Conclusions The combination of freeze-fracture techniques with FE-SEM enables the visualization of CMT arrangement in plant tissues at a high resolution and across a broad area without the need for staining or extraction of cellular components. This technique is applicable to various plant tissues and allows for detailed observation of CMTs within these tissues, providing valuable insights into the role of microtubules in the division and differentiation of plant cells.
Why it matches plant phenotyping methods植物組織内の微小管配列を高解像度で可視化するFE-SEMと凍結割断の新規画像取得法を開発しており、植物細胞状態の観察手法が研究の中心です。
abstractHerein, we introduce a technique for visualizing CMTs within unstained plant tissues by combining cryofracture techniques with field emission scanning electron microscopy (FE-SEM).
Moisture plays a critical role in crop growth and development, making accurate, efficient, and non-destructive detection and monitoring of crop water stress essential for advancing crop science research and optimizing production management. Traditional non-destructive methods for monitoring water stress primarily rely on color imaging or partial 2D spectral analysis. However, these methods are limited to two-dimensional features and fail to capture the spatial variability of water stress within the three-dimensional canopy structure of crops. To address this limitation, this study integrates RGB-D cameras and thermal infrared cameras and introduces a method for calculating the 3D spatial distribution characteristics of crop water stress using RGB-D-T fusion analysis. This approach enables high-precision detection and analysis of water stress in strawberry plants. An RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments. Using the YOLOv8-seg deep learning model, semantic segmentation of the crop canopy and the wet reference surface was performed. The segmentation results were fused with 3D point cloud data to generate a 3D dataset incorporating temperature, color, and semantic information. Subsequently, the three-dimensional distribution characteristics and dynamic changes in the canopy water stress index (CWSI) of strawberry plants were analyzed under varying moisture conditions. The results demonstrated that under low moisture gradients (15%–30%), the CWSI value increased significantly and exhibited a concentrated distribution, indicating severe water stress. Conversely, under high moisture gradients (75%–90%), the CWSI value approached zero, reflecting sufficient water supply and complete stress alleviation. Additionally, the study highlighted the variation in the temperature difference between strawberry leaves and the surrounding air, confirming the sensitivity of strawberries to water stress across different reproductive stages. The response to water deficit was most pronounced during the growth phase. By fusing multi-source data, this study achieves 3D visualization and precise quantification of water stress in strawberries, providing innovative insights and technical support for precision irrigation and crop phenotyping research.
Why it matches plant phenotyping methodsRGB-D・熱赤外センサーの融合、3D点群化、深層学習セグメンテーションにより、イチゴの水ストレスを3D定量化する取得・解析手法が研究の中心である。
abstractAn RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments.
Apomixis is an asexual reproductive mechanism that takes place deeply inside the female reproductive organs of the plant, in ovules and seeds. In gametophytic apomixis, an unreduced female gametophyte is produced by a modified meiosis of the megaspore mother cell (dipolspory) or from a somatic initial cell (apospory). The unreduced, nonrecombined egg cell develops subsequently into an embryo by parthenogenesis. The cyto-embryological study of apomixis is challenging because of the inaccessibility of these structures. Consequently, images of apomeiosis and parthenogenesis with high definition are limited to a few species. In this chapter, we show the application of a Feulgen staining protocol combined with confocal microscopy for the study of nonreductional megasporogenesis and autonomous embryo formation in diplosporous apomictic Taraxacum officinale and aposporous apomictic Pilosella piloselloides var. praealta. Using a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells. Furthermore, we highlight the application of this protocol for the study of loss-of-diplospory and loss-of-parthenogenesis mutants in the same species.
Why it matches plant phenotyping methods全載卵巣にFeulgen染色と共焦点顕微鏡を組み合わせ、雌性生殖細胞・胚形成を高解像度で可視化する技術を提示・適用しており、植物の生殖状態を取得する方法が中心である。
abstractUsing a rapid and technically simple method, performed on whole-mount ovaries, we have obtained high-resolution images of the female reproductive cells.
RiceLaboratory / benchtopSeed / grain2D/3D reconstructionVisualization / data management
The distribution of inorganic elements in brown rice has been vigorously investigated for many years using the most advanced instruments of each era. The present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes: 22Na, 45Ca, 54Mn, 55Fe, 60Co, 63Ni, 65 Zn, 90Sr, 203 Hg, and 210 Pb. Autoradiography of tissue sections using the Imaging Plate (IP) fully exploited its advantage of high-throughput imaging, enabling three-dimensional reconstruction that encompassed the entire brown rice grain. Consequently, characteristic distribution patterns of individual elements in the peripheral layer, endosperm, and embryo were identified following radiotracer supplementation to the culture solution. For instance, 63Ni was uniformly distributed within the endosperm during the early stages of development but progressively accumulated in the outer layers and embryo as growth advanced; such a pattern was not observed for 54Mn or 55Fe. To minimize the cost of the experiment, a direct injection method into the node was developed. This approach successfully visualized 203 Hg, demonstrating that its entry into the embryonic tissue is severely restricted irrespective of the developmental stage of the rice grain.
Why it matches plant phenotyping methods褐色米粒を対象に、オートラジオグラフィーとイメージングプレートで元素分布を高スループットに可視化し、三次元再構成する測定手法を中心に扱っているため、植物器官の状態を抽出するフェノタイピング手法として含める。
abstractThe present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes
OliveMicroscopyCell / cellular structureVisualization / data management
The pollen tube is widely recognized as a suitable model for investigating the structure and spatial organization of cell wall components during polarized growth. This chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins, using specific monoclonal antibodies from the JIM series. JIM5 and JIM7 were employed to detect de-esterified and esterified homogalacturonan regions of pectin, respectively, while JIM8 and JIM13 were used to label distinct epitopes of arabinogalactan proteins. The protocol includes pollen germination, paraformaldehyde fixation, enzymatic digestion with cellulysin (for arabinogalactan protein detection only), and sequential antibody incubation, followed by confocal microscopy imaging using FITC filter settings. This approach enables precise visualization of the distribution patterns of pectins and arabinogalactan proteins in the pollen tube wall and provides a reliable framework for further studies on cell wall architecture in plant reproductive tissues.
Why it matches plant phenotyping methods植物花粉管細胞壁の成分分布を共焦点免疫蛍光で可視化するプロトコルが研究の中心であり、植物組織の空間的状態を測定する方法として扱える。
abstractThis chapter describes the application of an established immunofluorescent labeling protocol for the localization of two major cell wall components, pectins and arabinogalactan proteins
Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture.
Why it matches plant phenotyping methodsバジルの水ストレス応答を、RGB・深度・クロロフィル蛍光画像と3D-CNNで非破壊推定するフェノタイピング手法が研究の中心である。
abstractThis study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress.
Aim Phenotypic characters have long been central to species diagnosis and delimitation and remain indispensable even in the age of genomics. However, phenotypic datasets are often complex— spanning dozens of traits of varying types and units, with correlated variables and unbalanced sampling—posing challenges for robust, reproducible analysis. Existing software solutions are fragmented, usually requiring labor-intensive workflows across multiple tools and manual steps, which undermines reproducibility and hinders comparisons across studies. To address these methodological and practical challenges, I introduce Orangutan, an R package designed to provide a flexible, easy-to-implement framework for comparing groups using mensural and meristic data. Innovation Orangutan provides a flexible and efficient framework for analyzing mensural and meristic data, supporting a full suite of statistical and visualization tools optimized for species delimitation and population comparisons. The package streamlines the identification of diagnostic, non-overlapping traits between species, while enabling rigorous assessment of both individual and multivariate trait differences. Core features include optional allometric correction to remove size effects, automated selection of appropriate univariate tests with post hoc comparisons, and integrated multivariate analyses. All outputs, including summary statistics and annotated publication-ready figures, are generated with minimal coding, ensuring accessibility and standardization. Main Conclusions Empirical validation with real-world datasets—including animal and plant species— demonstrates that Orangutan robustly identifies diagnostic traits, reveals both subtle and clear group differences, and achieves high classification accuracy with phenotypic data alone. By automating and unifying key analytical steps, Orangutan promotes reproducibility, transparency, and efficiency in phenotypic research. This package empowers researchers in taxonomy, ecology, and evolutionary biology to adopt quantitative best practices for species delimitation, facilitating comparative studies and advancing methodological standards in morphological data analysis. Orangutan is freely available with comprehensive documentation to support widespread adoption.
Why it matches plant phenotyping methods植物を含む形態形質データの解析・可視化を標準化するRパッケージの開発論文であり、植物種データでの検証も行っているため、表現型解析手法が中心です。
titleOrangutan: an R package for analyzing and visualizing phenotypic data in the context of ecology and systematics
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThe data to reproduce this work and software are freely and publicly available at
https://github.com/metalofis/Orangutan-R.Open asset ↗metalofis/Orangutan-Rpdf-page:15 lines:1-28Dataset · publicThe anole datasets can be downloaded from
https://github.com/metalofis/Orangutan-R/tree/main/example_datasets.Open asset ↗metalofis/Orangutan-R · example_datasetspdf-page:5 lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Code / dataset availability confirmedCrossref · OpenAlex · checked 6 Sept 2026
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
The synergistic development of Internet of Things(IoT), robotics, and Artificial Intelligence (AI) is reshaping the technological paradigms of interdisciplinary laboratories and industrial ecosystems. IoT-enabled vertical farming systems demonstrate significant advantages, achieving yield enhancement while reducing carbon emissions compared to traditional agriculture, thereby providing innovative solutions for sustainable food production. The advancement of robotic technologies further expands the application dimensions of mobile intelligent sensors in vertical farm IoT networks. Based on an autonomous farming system that integrates Unmanned Aerial Vehicle (UAV), sensors, and modular vertical farming units, this study proposes a three dimensional Scene Graph (3DSG)-based hierarchical mapping method for the dynamic monitoring of plant and fruit growth. Through feedback mechanisms, the system optimizes growth conditions by adjusting lighting and nutrient delivery, while the hierarchical mapping architecture reduces detection errors and enables comprehensive 3D visualization. The main contributions of this research include: 1) Pioneering application of 3DSG technology to establish a multi-dimensional spatiotemporal representation model for plant growth processes, supporting interpretable analysis and traceable monitoring; 2) Establishing an uncertainty model through error propagation by systematically analyzing sensor models (covering various common sensor combinations) and integrating these models into 3D object pose estimation algorithms. This highlights the necessity of hierarchical abstraction levels. The system is validated through simulations and real-world experiments, providing a quantitative evaluation of object pose estimation; and 3) An IoT-driven intelligent vertical farming architecture that integrating mobile robotic perception networks and environmental regulation devices, enabling dynamic acquisition and closed-loop control of plant growth parameters. Open-source code is available at https://github.com/allenthreee/scene_graph, video link: https://youtu.be/dhc8RLmX7hc.
Why it matches plant phenotyping methods植物・果実の成長監視と計数を目的に、3Dシーングラフ、SLAM、センサー融合、誤差伝播モデルを開発・検証しており、表現型取得手法が研究の中心である。
titleHierarchical 3-D Scene-Graph-Based Semantic-Metric SLAM for Plant Inspection and Fruit Counting in Intelligent Hydroponics System
Verticillium wilt poses a severe threat to cotton growth and significantly impacts cotton yield. It is of significant importance to detect Verticillium wilt stress in time. In this study, the effects of Verticillium wilt stress on the microstructure and physiological indicators (SOD, POD, CAT, MDA, Chlₐ, Chlb, Chlₐb, Car) of cotton leaves were investigated, and the feasibility of utilizing hyperspectral imaging to estimate physiological indicators of cotton leaves was explored. The results showed that Verticillium wilt stress-induced alterations in cotton leaf cell morphology, leading to the disruption and decomposition of chloroplasts and mitochondria. In addition, compared to healthy leaves, infected leaves exhibited significantly higher activities of SOD and POD, along with increased MDA amounts, while chlorophyll and carotenoid levels were notably reduced. Furthermore, rapid detection models for cotton physiological indicators were constructed, with the Rₚ of the optimal models ranging from 0.809 to 0.975. Based on these models, visual distribution maps of the physiological signatures across cotton leaves were created. These results indicated that the physiological phenotype of cotton leaves could be effectively detected by hyperspectral imaging, which could provide a solid theoretical basis for the rapid detection of Verticillium wilt stress.
Why it matches plant phenotyping methods綿葉の生理指標をハイパースペクトル画像から推定・可視化するモデルを構築しており、植物フェノタイピング手法の開発と応用が中心である。
abstractthe feasibility of utilizing hyperspectral imaging to estimate physiological indicators of cotton leaves was explored
Abstract Plant cell walls are dynamic composites whose architecture determines growth, mechanics, and environmental resilience. Efforts to link pectin structure to function have been limited by the lack of molecular probes with sufficient specificity, a gap that becomes even more pronounced for the intricately branched rhamnogalacturonon-II (RG-II) subclass. Here we report the first fluorescent probes with defined specificity to RG-II, engineered from catalytic site mutants of Bacteroides thetaiotaomicron glycoside hydrolases BT1010 and BT0996. These enzyme-derived probes bind RG-II monomer with high affinity, discriminate against dimeric forms, and localize to cell corners and junctions in Arabidopsis thaliana stems, consistent with RG-II’s unique ability among wall polysaccharides to form borate-mediated, covalent crosslinkages between molecules. Application of these probes revealed spatial partitioning distinct from the homogalacturonan (HG)- and rhamnogalacturonan I (RG-I)-enriched middle lamella, highlighting functional specialization among pectic domains, with RG-II reinforcing cell junctions while HG and RG-I mediate wall flexibility. Our work establishes a generalizable framework for transforming CAZymes into high-precision imaging reagents, enabling molecular-level visualization of structurally complex polysaccharides in the cell wall.
Why it matches plant phenotyping methodsRG-IIを特異的に可視化する蛍光プローブを開発し、植物細胞壁内の空間分布という植物状態を画像で測定する手法を示しているため、フェノタイピング手法が中心的です。
abstractHere we report the first fluorescent probes with defined specificity to RG-II
Reproduction assets foundThe paper deposits its raw microscopy z-stacks and maximum projections on OSF and its custom MATLAB image-analysis code on GitHub, both with explicit availability statements and public URLs.Dataset · publicMicroscopy data that support the findings of this study have been deposited in Open Science Framework. Raw z-stacks, output maximum intensity projections, and annotated figure images in greyscale are available at (https://osf.io/8utvs/overview).Open asset ↗Open Science Frameworklines:319-349Code · publicMATLAB code used for image analysis is available at https://github.com/kristenthorne/GHprobes.git, with usage instructions and example input and output files provided.Open asset ↗GitHub · kristenthorne/GHprobeslines:319-349Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
TomatoMultimodalStereoFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traitsYield / yield components
Introduction The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. Methods To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. The system, implemented on a Jetson TX2 embedded platform, also incorporates ZED stereo vision for 3D localization and a PyQt6-based visualization platform. Results When implemented on Jetson TX2, the system achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, it establishes standardized assessment systems for tomato maturity and yield prediction, and offers integrated modules for disease diagnosis and agricultural large language model consultation. Discussion This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Why it matches plant phenotyping methodsトマトの成熟度・収量予測および病害診断を含む画像・3Dビジョン基盤を開発し、エッジ環境で性能評価しているため、植物表現型取得が中心的な研究である。
abstractan intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm
Reproduction assets foundThe paper's data availability statement explicitly states that the data and code supporting the study are publicly available on GitHub at the authors' repository (GAE-YOLO), which matches an allowed URL. This qualifies as a paper-specific public code asset for the tomato detection/phenotyping analysis.Code · publicThe data and code supporting this study are publicly available at GitHub under the following links: https://github.com/NSSCk/GAE-YOLO .Open asset ↗NSSCk/GAE-YOLOlines:756-834Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Premise Analyzing structural changes along the length of an organ provides insight into its development. However, traditional histological methods are limited by intensive procedures and size restrictions. Micro-computed tomography (microCT) enables non-destructive internal imaging along the length of an organ, but high cost, technical complexity, and limited accessibility hinder widespread application. Here, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software. Methods and results SSV was applied to four fern rhizomes with varied gross morphology and diverse vascular architectures. Specimens were sectioned using a sliding microtome or a handheld blade, and imaged using either a digital camera or smartphone setup. Images were aligned using Fiji and segmented using 3D Slicer. The SSV method enabled continuous visualization of internal stem anatomy over several centimeters and is adaptable to both laboratory and field settings. Conclusions This protocol offers an alternative to microCT for generating 3D anatomical reconstructions, enabling researchers to examine development and structural variation across organs with minimal equipment and software. This accessible protocol reduces technical and financial barriers and is particularly well-suited for comparative studies of vascular tissues, advancing the study of plant anatomy and development.
Why it matches plant phenotyping methods植物器官内部構造を連続撮像・画像処理して3D形態を再構成する低コスト手法の開発と適用が中心であり、植物形態・解剖状態の取得法として収載対象。
abstractHere, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software.
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 6 Sept 2026
The legume-rhizobia symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (PnifH) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) systems. We show that PnifH-driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and PnifH-driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify PnifH-driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobia symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. IMPORTANCEThe legume-rhizobia symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (PnifH) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Why it matches plant phenotyping methods植物根粒の蛍光可視化・定量手法と、深層学習による結節形質の自動画像解析パイプラインを開発・検証しており、植物フェノタイピング手法が中心です。
abstractwe established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Laboratory / benchtopMicroscopyMultimodalCell / cellular structureRootTissueTrackingVisualization / data management
Abstract Root biology is pivotal in addressing global challenges including sustainable agriculture and climate change. However, roots have been relatively understudied among plant organs, partly due to the difficulties in imaging root structures in their natural environment. Here we used microfabricated ecosystems (EcoFABs) to establish growing environments with optical access and employed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution. THG enabled us to observe key plant root structures including the vasculature, Casparian strips, dividing meristematic cells, and root cap cells, as well as subcellular features including nuclear envelopes, nucleoli, starch granules, and putative stress granules. THG from the cell walls of bacteria and fungi also provides label-free contrast for visualizing these microbes in the root rhizosphere. With simultaneously recorded 3PF signal, we demonstrated our ability to investigate root-microbe interactions by achieving single-bacterium tracking and subcellular imaging of fungal spores and hyphae in the rhizosphere.
Why it matches plant phenotyping methodsTHG/3PFによる根の構造を高時空間分解能でラベルフリー取得するイメージング手法を開発・実証しており、植物表現型取得が中心である。
abstractemployed nonlinear multimodal microscopy of third-harmonic generation (THG) and three-photon fluorescence (3PF) to achieve label-free, in situ imaging of live roots and microbes at high spatiotemporal resolution
Brassica vegetablesLettuceRadishMicroscopyMultimodalRootVisualization / data management
Microfibers (MFs), primarily originating from sewage sludge and laundry effluents, are the most prevalent form of microplastics (MPs) in agricultural soils. While their ecological effects have been explored, the visualization, crop-level accumulation, and potential transport mechanisms of MFs within soil-plant systems remain poorly understood. This study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions. Three edible vegetables-lettuce, Chinese cabbage, and cherry radish-were used to evaluate species-specific response patterns. The results revealed clear differences in MF interactions across species: lettuce exhibited strong MF adsorption on root surfaces and subsequent penetration via crack-entry and apoplastic pathways without entering cells. In contrast, Chinese cabbage and cherry radish showed limited MF adsorption and no uptake. These patterns were associated with root permeability and antioxidative capacities, indicating that plant functional traits play a critical role in determining the transport capacity of MPs. Beyond introducing a novel method for MF visualization in complex terrestrial matrices, this study provides new insights into the risks posed by MFs to soil-plant systems. The findings also highlight potential threats to food safety and underscore the need to establish plant-specific thresholds and pollution mitigation strategies to support sustainable agriculture and protect public health.
Why it matches plant phenotyping methods植物体内のマイクロファイバー分布・吸着・蓄積・取り込みを可視化する新規蛍光染色・マルチモーダル顕微鏡ワークフローが研究の中心であり、植物状態の測定法として該当する。
abstractThis study combines 1,3,6,8-pyrene tetrasulfonic acid (PTSA) fluorescent staining with a sequential multimodal microscopy workflow to effectively track the distribution, adsorption, accumulation, and uptake of MFs under realistic soil cultivation conditions.
In perennial fruit crops, climate conditions play a crucial role in influencing phenological stages, fruit quality traits and particularly the occurrence and severity of pests and diseases. The predominant cause of fruit contamination is attributed to fungal and bacterial pathogens, which significantly affect crop health, yield and quality. Mitigating these impacts at an early stage requires advanced methodologies for accurate disease identification and detection. However, a major challenge in precision and smart agriculture lies in the development and availability of reliable image datasets for automated detection, visualization and classification of plant diseases. The growing adoption of the Internet of Things (IoT) has created opportunities for robust systems capable of managing large volumes of agricultural data through efficient processing, storage and transmission. The integration of image processing techniques with intelligent algorithms within IoT frameworks has emerged as a transformative approach, delivering enhanced precision and accuracy in disease monitoring and management across fruit crops. IoT-enabled smart diagnostic systems not only reduce production costs and resource wastage but also revolutionize horticultural practices through automation, leading to improved quality and yield. This review highlights advances in IoT-enabled smart diagnostics for detecting fruit crop diseases under climate conditions. The scope includes examining the interaction between climate variability and disease dynamics, analysing IoT-based frameworks for real-time monitoring and diagnostics and identifying current challenges and future opportunities for sustainable disease management in perennial fruit crops.
Why it matches plant phenotyping methods果樹病害の画像処理・知的アルゴリズム・IoT診断フレームワークを対象とするレビューで、植物病害状態の画像ベース推定手法が中心である。
abstractThe integration of image processing techniques with intelligent algorithms within IoT frameworks has emerged as a transformative approach, delivering enhanced precision and accuracy in disease monitoring and management across fruit crops.
Disentangling pathogen infection signals in plants is critical for understanding the physiological processes that underlie the complex host-pathogen interactions and predicting impending disease outbreak. The rapid progression of rice blast lesions, caused by the filamentous fungus Magnaporthe oryzae, and its imperceptible disease-related symptoms during the asymptomatic stages render real-time detection and visualization challenging. Efforts to reveal pre-visual disease symptoms are of both broad concern and significant interest but remain challenging, as subtle disease signals are often obscured or diluted by other factors at asymptomatic stage. We introduce an imaging spectroscopy-based purification methodology that isolates the disease signals revealed by spectral unmixing on a pixel basis without considering the complex pathogen-induced physiological variations. With multi-temporal proximal hyperspectral imagery, our method captured the transition of disease lesions from asymptomatic to severely symptomatic stages, and successfully distinguished the subtle pathogen-induced signals with few false alarms as early as three days (two days after inoculation, DAI 2) before visual lesions became apparent (DAI 5). The lesion prediction results were confirmed by extensive in vivo visual inspections. Remarkably, we demonstrated that spatially aggregating the isolated disease signals improved the accuracy of pre-visual RB identification to a remarkable level up to 93 % (F1-score = 0.91), enabling unprecedented visualization of potential lesions in a narrow time window of pathogen infection. Although limitations remain regarding model validation and scalability for broader applications, this method represents a significant advancement in early disease forecasting across spectral and spatial domains, and offers new opportunities for high-throughput screening of susceptible varieties in next-generation plant resilience phenotyping.
Why it matches plant phenotyping methodsイネいもち病の病斑を、発病前のハイパースペクトル画像から抽出・予測する画像解析手法を開発し、精度検証している。植物病態の表現型取得が研究の中心である。
abstractWe introduce an imaging spectroscopy-based purification methodology that isolates the disease signals revealed by spectral unmixing on a pixel basis
Field / plotLiDAR / point cloudLeafWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementBiomass / plant weightGrowth / development / phenologyLeaf traits
The acquisition of plant ecological indicators, such as leaf area index and leaf area density values, typically relies on labor-intensive field sampling and measurements, which are often time-consuming and hinder large-scale application. As different plant ecological indicators are closely related to plants’ geometric characteristics, the development of dynamic correlation and prediction methods for relevant indicators has become an important research topic. However, existing 3D plant models are mainly used for visualization purposes, which cannot accurately reflect the plant’s growth process or geometric characteristics. This study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation. With the established plant model, a method for calculating and analyzing ecological indicators, including the leaf area index, leaf area density, aboveground biomass, and aboveground carbon storage, was then proposed. A method for exporting the model-generated data into ENVI-met v.5.0 to simulate the microclimate environment was also established. Then, by taking Daijia Lake Park as an example, this study utilized site planting construction drawings and field survey data to perform parametric modeling of 21,685 on-site trees from 65 species at three different growth stages using Blender v.4.0 and The Grove plugin v.10. The generated plant model’s accuracy was then verified using the 3D IoU ratio between the models and on-site scanned point cloud data. Plant ecological indicators at various stages were then extracted and exported to ENVI-met for microclimate analysis. The workflow integrates the simulation of plant growth dynamics and their interactions with environmental factors. It can also be used for scenario-based predictions in planting design and serves as a basis for urban green space monitoring and management.
Why it matches plant phenotyping methods3D植物モデルを用いて葉面積指数・葉面積密度・地上部バイオマス等の植物形質を抽出するワークフローを開発し、点群データとの3D IoUで精度検証しているため、方法が中心である。
abstractThis study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation.
The increasing global population and the challenges posed by climate change have intensified the demand for sustainable food production. Traditional agricultural practices are often insufficient, leading to significant crop losses due to diseases and pests, despite the widespread use of pesticides and other chemical interventions. This paper introduces a new approach that integrates deep learning techniques, specifically Convolutional Neural Networks (CNNs) with Squeeze and Excitation (SE) networks, to enhance the accuracy of disease detection in fig leaves. By leveraging three pre-trained CNN models—MobileNetV2, InceptionV3, and Xception—this framework addresses data scarcity issues and improves feature representation while minimizing the risk of overfitting. Data augmentation techniques were employed to counteract data imbalance, and visualization tools like Grad-CAM and t-SNE were utilized for model interpretability. The proposed CNN-SE model was trained and evaluated on a fig leaf dataset comprising 1,196 images of healthy and diseased fig leaves, achieving an accuracy of 92.90% with MobileNet-SE, 91.48% with Inception-SE, and 89.62% with Xception-SE. Our model demonstrates superior performance in detecting fig leaf diseases, presenting a robust solution for sustainable agriculture by providing accurate, efficient, and scalable disease management in crops. The code of the proposed framework is available at https://github.com/lafta/SE-block-with-CNN-Models-for-Plant-Disease-Detection.
Why it matches plant phenotyping methodsイチジク葉の画像から健全・罹病状態を推定する深層学習手法を開発・評価しており、植物病害表現型の取得・分類が中心である。
abstractThis paper introduces a new approach that integrates deep learning techniques, specifically Convolutional Neural Networks (CNNs) with Squeeze and Excitation (SE) networks, to enhance the accuracy of disease detection in fig leaves.
Reproduction assets foundThe paper explicitly states that the authors' code for the proposed CNN-SE plant disease detection framework is publicly available on GitHub at the allowed URL. The fig leaf dataset itself is a cited prior dataset ([25]), not a paper-specific deposit.Code · publicThe code of the proposed
framework is available at https://github.com/lafta/SE-block-with-CNN-Models-for-Plant-Disease-Detection.Open asset ↗lafta/SE-block-with-CNN-Models-for-Plant-Disease-Detectionpdf-page:1 lines:1-55Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types – cotton ( Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize ( Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. Significance Repeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton ( Gossypium hirsutum L.) and maize ( Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.
Why it matches plant phenotyping methodsドローン画像から植物表現型を抽出する分布データ解析手法を導入・検証し、遺伝率解析、収量予測、可視化まで行っており、表現型取得・解析法が研究の中心である。
abstractThis study introduces and validates distributional data analysis in two crops and experiment types
MicroscopyCell / cellular structureVisualization / data management
Understanding lipid metabolism in algae is critical to advancing our knowledge on fundamental algal physiology and for harnessing these organisms as platforms for the sustainable production of high-energy lipids. BODIPY is the most prevalently used fluorescent dye for the visualization of lipid droplets (LDs) in algae; however, its limitations warrant exploration of alternatives. Here we evaluate and compare four lipophilic fluorophores (BODIPY, DAF, Cou, DPAS) for their effectiveness in staining LDs in the extremophilic green alga Chlamydomonas priscui. We assess each dyes photophysical properties, synthetic accessibility, LD specificity, cellular toxicity, and suitability for microscopy and flow cytometry. All four dyes successfully stain LDs, but their performance diverges under different experimental conditions. BODIPY permits long-term incubation allowing quantification in time-course studies but exhibits poor LD specificity and high susceptibility to photobleaching. DAF enables polarity-sensitive staining but is highly toxic on prolonged exposure or during cellular stress. Cou and DPAS yield strong LD-specific signals with low cytotoxicity, making them ideal for studies involving environmental stress. However, DPAS requires room-temperature incubation, pointing toward greater potential utility for non-extremophilic algae. These results expand the toolbox for lipid biotechnology research in extremophiles and underscore the importance of tailoring dye selection and experimental conditions to algal physiology.
Why it matches plant phenotyping methods藻類細胞の脂質滴を可視化・定量する蛍光染色法を比較評価し、顕微鏡およびフローサイトメトリーへの適用性、特異性、毒性、光退色を検証しているため、表現型取得法が中心である。
abstractHere we evaluate and compare four lipophilic fluorophores (BODIPY, DAF, Cou, DPAS) for their effectiveness in staining LDs in the extremophilic green alga Chlamydomonas priscui.
Visualization / data managementYield / yield components
Abstract Plant breeders need to evaluate large breeding populations rapidly and accurately to identify and assess genetic variation responsible for many traits, including yield, quality, resistance, and climate resilience. Although advanced molecular tools, including marker‐assisted selection, genomic selection, and gene editing, are being used to accelerate genetic gain in breeding programs, conventional phenotyping is still needed due to the polygenic and environmental interactions related to these traits. Unfortunately, traditional phenotyping at a large scale requires considerable resources and is often subjective, time‐consuming, labor‐intensive, and expensive. To remove the phenotyping bottleneck, the development of efficient and reliable systems for complex trait measurement is needed. Recent advancements in tools and technology are making it easier to collect phenomic data faster at greater resolutions, allowing for the characterization of genotypic lines across the growing season to evaluate performance under different environmental conditions. By combining multiple sources of sensor data, interactions between genotypes and environments (G × E) can be investigated and used to increase the rate of genetic gain and the efficiency of plant breeding programs. However, the hardware and sensors necessary to realize this vision are often cost‐prohibitive for plant breeding programs, and ancillary data management costs can create further barriers to entry. In this review, we outline existing affordable phenomics hardware, sensors, software, and platforms, as well as the challenges that exist to broadly and equitably adopt these tools.
Why it matches plant phenotyping methods植物フェノミクスの低コストなハードウェア、センサー、ソフトウェア、プラットフォームを体系的に扱うレビューであり、フェノタイピング手法が中心です。
abstractIn this review, we outline existing affordable phenomics hardware, sensors, software, and platforms, as well as the challenges that exist to broadly and equitably adopt these tools.
SUMMARY Monitoring endogenous nutrient levels is crucial for maximizing crop yields and optimizing fertilizer use. Here, focusing on phosphorus, an essential nutrient for plant growth, we developed a low-cost and non-invasive biosensor to visualize and predict early stress signaling in plants. By combining plant phosphate (Pi)-deficiency-induced promoter systems with fungal self-sustained bioluminescence systems genetically engineered into tobacco plants, we created sensor plants that emitted more light when experiencing Pi deficiency. This light emission correlated with the expressions of known phosphate-responsive genes and the total phosphorus content in plants, and decreased during Pi recovery conditions, demonstrating the responsiveness and robustness of the sensor plants in reflecting endogenous phosphorus deficiency. The sensor plants responded primarily to Pi deficiency rather than nitrogen or potassium deficiencies and were sensitive to different ranges of external Pi concentrations. Additionally, when grafted onto tomato and chili pepper plants, the sensor plants responded to external phosphorus deficiency, showing promise for monitoring stress signals in different crop species. Using deep-learning-based image analysis techniques, auto-luminescent signals of sensor plants could be detected and used to predict phosphorus deficiency. This study outlines a strategy of creating a self-luminous biosensor to visualize phosphate dynamics in planta and predict nutrient deficiency for sustainable agriculture.
Why it matches plant phenotyping methods植物内リン欠乏状態を自己発光センサーと画像解析で可視化・予測する手法を開発し、応答性・頑健性を検証しているため、植物フェノタイピング手法が中心です。
abstractwe developed a low-cost and non-invasive biosensor to visualize and predict early stress signaling in plants
Rice and corn hold significant importance due to their daily consumption worldwide. Naked-eye observations are not accurate. Therefore, we need an autonomous system that can accurately detect and classify diseases in both plants. We trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy. We added a new dense layer to the known DCNN-based deep learning models and achieved improved accuracy. The best results were observed when our invariants of the InceptionV3, ResNet152, and MobileNetV2 deep learning models were used on corn plant leaves (98.09%, 98.51%, and 89.73%, respectively). These models also performed well on rice plant leaves (98.51%, 93.59%, and 98.57%, respectively). Because InceptionV3 performed well for both plants, we implemented it in NVIDIA Jetson Nano as an end device for the detection and classification of diseases from both plant leaves. Received: 4 January 2025 | Revised: 26 May 2025 | Accepted: 13 June 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset. Author Contribution Statement Zubair Saeed: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – original draft, Project administration. Uzma Nawaz: Validation, Formal analysis, Writing – review & editing. Ali Raza: Conceptualization, Validation, Formal analysis, Writing – review & editing, Visualization. Kamran Javed: Validation, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法を開発・検証し、エッジデバイスにも実装しており、植物状態の取得・推定が研究の中心です。
abstractWe trained and validated publicly available datasets in three deep convolutional neural network (DCNN)-based deep learning models using different learning rates and found that the lowest learning rate was the most effective in achieving the highest accuracy.
Reproduction assets foundThe paper's Data Availability Statement explicitly names two public Kaggle image datasets (rice leaf diseases and corn/maize leaf disease) that were used directly as the phenotyping inputs for this study's disease-classification experiments. No author code, models, or checkpoints are reported as publicly available.Dataset · public.
Ethical Statement
This study does not contain any studies with human or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
WritinOpen asset ↗Kaggle · vbookshelf/rice-leaf-diseasespdf-raw-page:10 lines:1-81Dataset · publicuman or animal
subjects performed by any of the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest to this
work.
Data Availability Statement
The data that support the findings of this study are openly
available in Kaggle at https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases and https://www.kaggle.com/datasets/smaranjit-ghose/corn-or-maize-leaf-disease-dataset.Author Contribution Statement
Zubair Saeed: Conceptualization, Methodology, Software,
Validation, Resources, Data curation, Writing – original draft,
Project administration. Uzma Nawaz: Validation, Formal analysis,
Writing – review & editing. Ali Raza: Conceptualization, Validation,
ForOpen asset ↗Kaggle · smaranjit-ghose/corn-or-maize-leaf-disease-datasetpdf-raw-page:10 lines:1-81Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 15 Sept 2026
Plant reproduction involves dynamic spatiotemporal changes that occur deep within maternal tissues. In ovules of Arabidopsis thaliana (A. thaliana), one of the two synergid cells degenerates at fertilization, while the fertilized egg cell (zygote) undergoes directional elongation followed by asymmetric division to initiate embryonic patterning. However, morphological analysis of these events has been hampered by the limitations of conventional cell wall staining, which fails to label cells lacking complete walls, and by the requirement for transgenic fluorescent reporters to visualize cell outlines. Here, we report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization. This staining method supports high-resolution time-lapse imaging and quantitative analysis of early embryogenesis in living tissues. Importantly, it is applicable not only to the angiosperm A. thaliana but also to the liverwort Marchantia polymorpha (M. polymorpha) and the fern Ceratopteris richardii (C. richardii), enabling the visualization of live reproductive cell structures within maternal tissues and revealing fertilization-associated morphological changes. This simple and robust method thus provides a valuable tool for spatiotemporal and quantitative analyses of reproductive processes across a broad range of plant species, without the need to generate transgenic lines.
Why it matches plant phenotyping methods生きた植物生殖組織の細胞形態を可視化・定量化する蛍光染色法を開発し、複数種で適用・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 report that the membrane-specific fluorescent dye FM4-64 readily permeates ovules, allowing clear visualization of reproductive cell morphology both before and after fertilization.
Effective plant disease detection is vital for sustainable agriculture; however, the computational demands of many deep learning frameworks make them impractical for use in low-resource settings. This study proposes ParaLeafNet, a streamlined Parallel Convolutional Neural Network (CNN) that merges MobileNetV2 and MobileNetV3Small with a Squeeze-and-Excitation (SE) Attention mechanism to improve feature extraction. Tailored for edge applications, ParaLeafNet underwent optimization via TensorFlow Lite and was tested on the PlantVillage dataset, with ablation studies examining the roles of its parallel design and attention system. ParaLeafNet outperformed standard CNN models in plant disease classification, providing both precision and computational efficiency. Visualization techniques confirmed its ability to pinpoint critical disease markers, boosting its utility for real-world scenarios. ParaLeafNet delivers a powerful deep learning solution for real-time plant disease monitoring, fostering sustainable farming practices by enabling farmers to tackle challenges early, curb losses, and advance precision agriculture. Its lightweight architecture ensures compatibility with resource-constrained devices, supporting broader food security goals. Future work will prioritize diverse real-world datasets and enhancements for ultra-low-power systems
Why it matches plant phenotyping methods植物画像から病害状態を推定する軽量CNNを開発・評価しており、病害識別の取得・解析手法が研究の中心である。
abstractThis study proposes ParaLeafNet, a streamlined Parallel Convolutional Neural Network (CNN) that merges MobileNetV2 and MobileNetV3Small with a Squeeze-and-Excitation (SE) Attention mechanism to improve feature extraction.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
The widespread applications of fluorescence imaging in plant science still suffer from challenges including strong auto-fluorescence (chlorophyll) and tissue light scattering, resulting in low signal-to-background ratio (SBR) for in vivo bioimaging. Moreover, the relationship between the transport efficacy of fluorescence probes in plants and their sizes has been rarely investigated. To address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm). As the polyethylene glycol (PEG) chain length increased, the probes' nanosize decreased from 170 to 60 nm. Among them, SYH3 exhibited the fastest entry velocity into Epipremnum Aureum leaf and spread over the leaf veins evenly than the other two probes, of which SYH1 even could hardly entry into the leaf. Meanwhile, SYH3 demonstrated high-contrast imaging of leaf vein with an exceptional signal to background ratio (SBR, ~ 18.6) superior to that of classical NIR-I indocyanine green (ICG) (~ 3.0) and SYH2. This promising imaging ability of leaf veins achieved by size optimization laid the foundation for the early diagnosis of viral infections. In vivo experiments further confirmed that SYH3 effectively accumulated and monitored in the lesion of Tobacco mosaic virus (TMV)-infected Arabidopsis thaliana, which matched well with the green fluorescent protein (GFP)-labeled results. This work represents a significant step forward in plant bioimaging in the cutting-edge NIR-II region.
Why it matches plant phenotyping methods植物体内の葉脈・ウイルス病変を高コントラストに可視化するNIR-II蛍光プローブを開発し、サイズ最適化と既存色素との性能比較を行っているため、植物表現型取得法が中心である。
abstractTo address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
MicroscopyCell / cellular structureMorphology / geometry measurementSegmentationVisualization / data management
ABSTRACT Live‐cell imaging has enabled the visualization of cytoskeletal dynamics with high spatiotemporal resolution, producing vast, and complex datasets. Recent advancements in live‐cell imaging techniques have significantly increased data dimensionality and throughput, challenging conventional qualitative analysis methods. Computational approaches, including machine learning‐based image processing, have emerged as powerful tools for extracting quantitative features from these datasets, facilitating systematic analysis of cytoskeletal organization and dynamics. In this review, we outline image analysis techniques for quantification of cytoskeletal structures, focusing on microscopic image transformation and feature extraction. We discuss classical image‐processing methods, such as filtering and segmentation, as well as recent applications of deep learning in cytoskeletal analysis. Furthermore, we revisit classical studies on cortical microtubule reorganization after plant cytokinesis, and explore how modern computational techniques can provide new insights into traditional concepts.
Why it matches plant phenotyping methods植物細胞骨格の組織化・動態を顕微鏡画像から定量化する画像解析手法を中心に扱うレビューであり、植物の形態・細胞状態の表現型抽出に直接関連する。
abstractIn this review, we outline image analysis techniques for quantification of cytoskeletal structures, focusing on microscopic image transformation and feature extraction.
LeafClassificationStress / disease detectionVisualization / data managementDisease symptoms / severity
In agricultural sector, various Artificial Intelligence (AI) and Machine Learning (ML) techniques have been explored for plant disease detection. Despite the gain in the performance for plant leaf disease detection using Deep Convolutional Neural Networks (DCNNs), their interpretability for higher performance remains a challenge. Explainable AI (XAI) techniques, such as Gradient-weighted Class Activation Mapping (GradCAM) and Layer-wise Relevance Propagation (LRP), enhance model transparency but suffer from limitations in noise sensitivity, clarity, and robustness. In the proposed work, we have explored a novel approach that integrates GradCAM and LRP to improve visual explanations in plant disease classification. The method processes GradCAM outputs to reduce noise, applies element-wise multiplication with LRP-generated heatmaps, and enhances smoothness using Gaussian blur. Evaluations based on Robustness, Complexity, Faithfulness, Localization, and Randomization demonstrate that our approach outperforms standalone GradCAM and LRP, offering clearer and more reliable visualizations.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類し、Grad-CAMとLRPを統合した説明可視化手法を開発・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。
abstractwe have explored a novel approach that integrates GradCAM and LRP to improve visual explanations in plant disease classification.
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published21 May 2025ELCVIA Electronic Letters on Computer Vision and Image AnalysisCited by 2 · OpenAlex ↗
Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious and often inaccurate. Hence the use of machine learning and convolutional neural networks have proven to be of great advantage in terms of accuracy, reliability, ease of implementation etc. This paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss. Further the models have been combined using feature fusion, and their accuracies compared. Finally, a combination of ResNet and AlexNet has been proposed with an impressive accuracy of 99.97%. Further, Grad-CAM (Gradient-weighted Class Activation Mapping) has been implemented to highlight important regions in the leaf images which improves visualization. This can potentially provide an accurate identification and classification of plant diseases based on leaf images.
Why it matches plant phenotyping methodsマンゴー葉画像から植物病害を検出・分類する深層学習手法を比較・融合し、Grad-CAMで病徴領域を可視化しており、植物の病害状態を画像から推定する方法が中心である。
abstractThis paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss.
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudRGB / grayscaleStem / branchMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementArchitecture / morphology / geometry
Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting. While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex. We present a low-cost alternative that only needs a consumer-grade 360 video camera. Our semi-automated pipeline comprises of (i) a dense point cloud reconstruction using Structure from Motion (SfM) photogrammetry software called Agisoft Metashape, (ii) semantic trunk segmentation by projecting Grounded Segment Anything (SAM) masks onto the 3D cloud, and (iii) a robust RANSAC-based technique to estimate cross section shape and DBH. We introduce an interactive visualization tool for inspecting segmented trees and their estimated DBH. On 61 acquisitions of 43 trees under a variety of conditions, our method attains median absolute relative errors of 5-9% with respect to "ground-truth" manual measurements. This is only 2-4% higher than LiDAR-based estimates, while employing a single 360 camera that costs orders of magnitude less, requires minimal setup, and is widely available.
Why it matches plant phenotyping methodsRGB映像から樹木のDBHという明示的な植物形態形質を推定する半自動パイプラインを開発し、手動測定およびLiDARと比較して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。
abstractWe present a low-cost alternative that only needs a consumer-grade 360 video camera.
CottonMicroscopyCell / cellular structureSeed / grainVisualization / data managementGrowth / development / phenology
Cotton fibers, as highly extended, thickened epidermal seed structures, are a crucial renewable resource in textile production. Cotton plants produce two main types of fiber cells: wide, hemisphere-shaped fibers and narrow, tapered fibers. Both types stabilize through secondary cell wall development, with the mature narrow fibers being particularly valued for spinning into fine, strong yarns, suitable for premium cotton fabrics. Traditional methods for studying fiber development and cell types, such as scanning electron microscopy (SEM), are often time-intensive and costly. SEM preparation steps, including fixation, dehydration, and sputter coating, can cause shrinkage and other image distortions, limiting the accuracy of observations. Variable-pressure scanning electron microscopy (VP-SEM) offers an alternative approach, operating under low pressure rather than a high-vacuum environment, which can be advantageous for imaging live samples with minimal sample preparation. In this study, we applied VP-SEM to observe fiber cell initiation and early elongation in the conventional upland cotton cultivar UGA 230 at 0 and 1-day post-anthesis. Two SEM detectors, the ultra-variable-pressure detector and backscattered electrons, were used to capture detailed images. Optimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation. This VP-SEM protocol not only facilitates high-resolution imaging of cotton fibers at early developmental stages but also reduces time and expense, minimizing sample damage. Additionally, this optimized approach can be adapted for other fresh biological samples, making it a versatile tool for real-time imaging across various studies in plant biology and beyond.
Why it matches plant phenotyping methods綿花繊維の発生・伸長を高解像度で取得するVP-SEMプロトコルの条件最適化と技術的利点を中心に扱っており、植物形質取得法が主題である。
abstractOptimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation.
RootVisualization / data managementBiomass / plant weightRoot system architecture
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.
Why it matches plant phenotyping methods熱帯植物の根形態・構造・生理などの表現型特性を標準化して収録した再利用可能なデータベースであり、データセット構築と品質管理が中心です。
abstractHere, we provide a database of tropical root characteristics.
Reproduction assets foundThe paper's core asset is the TropiRoot 1.0 root trait database itself, publicly deposited in ESS-DIVE (DOI 10.15485/2507279) and also provided as Supporting Information (Data S1). This is a paper-specific public phenotype/trait dataset directly reproducing the paper's measurements.Dataset · publich, et al. 2025. “
TropiRoot 1.0: Database of Tropical Root Characteristics across Environments.” Ecology
106(5): e70074. 10.1002/ecy.70074
Handling Editor: Simona Picardi
DATA AVAILABILITY STATEMENT
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.
Associated Data
Supplementary Materials
Data S1.
Data Availability Statement
The dataset is available as Supporting Information to this Ecology data paper and is also accessible in the ESS‐DIVE repository at https://doi.org/10.15485/2507279.Open asset ↗10.15485/2507279html-lines:63-80Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
High-throughput image analysis is a key tool for the efficient assessment of quantitative plant phenotypes. A typical approach to the computation of quantitative plant traits from image data consists of two major steps including (i) image segmentation followed by (ii) calculation of quantitative traits of segmented plant structures. Despite substantial advancements in deep learning-based segmentation techniques, minor artifacts of image segmentation cannot be completely avoided. For several commonly used traits including plant width, height, convex hull, etc., even small inaccuracies in image segmentation can lead to large errors. Ad hoc approaches to cleaning ’small noisy structures’ are, in general, data-dependent and may lead to substantial loss of relevant small plant structures and, consequently, falsified phenotypic traits. Here, we present a straightforward end-to-end approach to direct computation of phenotypic traits from image data using a deep learning regression model. Our experimental results show that image-to-trait regression models outperform a conventional segmentation-based approach for a number of commonly sought plant traits of plant morphology and health including shoot area, linear dimensions and color fingerprints. Since segmentation is missing in predictions of regression models, visualization of activation layer maps can still be used as a blueprint to model explainability. Although end-to-end models have a number of limitations compared to more complex network architectures, they can still be of interest for multiple phenotyping scenarios with fixed optical setups (such as high-throughput greenhouse screenings), where the accuracy of routine trait predictions and not necessarily the generalizability is the primary goal.
Why it matches plant phenotyping methods植物画像から形態・健康形質を直接推定する深層学習手法を開発し、従来のセグメンテーション法と比較検証しており、表現型取得・抽出法が研究の中心である。
abstractHere, we present a straightforward end-to-end approach to direct computation of phenotypic traits from image data using a deep learning regression model.
Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By significantly lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.
Why it matches plant phenotyping methods植物フェノタイピングの画像解析ワークフローを自然言語で自動化するシステムを開発し、ケーススタディと評価タスクで検証しているため、方法が中心である。
abstractwe introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction.
Reproduction assets foundThe paper's authors release PhenoAssistant's code, chat logs, and evaluation results on GitHub, and the winter wheat nutrient-deficiency dataset used in Case Study 3 is publicly available on CodaLab. Case Study 1 demonstration data is request-only (Phenotiki), and Case Study 2 data is on Zenodo, which is not among the审Dataset · publicData for demonstrating Case Study 3 are publicly
available at https://codalab.lisn.upsaclay.fr/competitions/13833.Open asset ↗pdf-page:13 lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published28 Apr 2025Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 3 · OpenAlex ↗
Protein, oil content, stearic acid, linolenic acid, and linoleic acid are key indicators for evaluating the quality of flaxseed in order to optimize the detection method of nutritional quality of flaxseed and to improve the efficiency of the screening of high-quality flax germplasm resources. This study integrated visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging to determine protein, oil, stearic acid, linolenic acid, and linoleic acid contents in diverse flaxseed varieties, along with conducting correlation analyses. After seven data preprocessing methods and three feature selection methods, quantitative prediction models were developed using partial least squares regression (PLSR), principal component regression (PCR), support vector regression (SVR), and multiple linear regression (MLR). Experimental results demonstrated that NIR and fused spectral data outperformed Vis-NIR data across all five quality indices. NIR spectroscopy showed optimal performance for predicting oil content (R p 2 = 0.9671, RMSEP = 0.4364 %), linolenic acid (R p 2 = 0.9517, RMSEP = 0.8795 %), and linoleic acid (R p 2 = 0.9458, RMSEP = 0.3037 %). Fused spectral data achieved superior predictions for protein content (R p 2 = 0.9712, RMSEP = 0.2360 %) and stearic acid (R p 2 = 0.9195, RMSEP = 0.3454 %). And the spatial distribution of flaxseed's internal nutrient contents was also visualized by map. The results showed that the NIR and fusion spectral sets could be successfully used to evaluate multiple nutritional qualities of flaxseed, which provides a new option for nondestructive determination of the nutritional qualities of flaxseed in the future.
Why it matches plant phenotyping methodsVis-NIR/NIRハイパースペクトル画像とデータ融合・回帰モデルを開発し、アマ種子の栄養形質を非破壊推定・可視化する方法が研究の中心である。
abstractThis study integrated visible near-infrared (Vis-NIR) and near-infrared (NIR) hyperspectral imaging to determine protein, oil, stearic acid, linolenic acid, and linoleic acid contents in diverse flaxseed varieties
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data managementGrowth / development / phenology
Plant growth and development rely on a delicate balance between cell proliferation and cell differentiation. The root apical meristem (RAM) of Arabidopsis thaliana is an excellent model to study the cell cycle due to the coordinated relationship between nucleus shape and cell size at each stage, allowing for precise estimation of the cell cycle duration. In this study, we present a method for high-resolution visualization of RAM cells. This is the first protocol that allows for simultaneous high-resolution imaging of cellular and nuclear stains, being compatible with DNA replication markers such as EdU, including fluorescent proteins (H2B::YFP), SYTOX DNA stains, and the cell wall stain SR2200. This protocol includes a clarification procedure that enables the acquisition of high-resolution 3D images, suitable for detailed subsequent analysis.
Why it matches plant phenotyping methodsArabidopsis根端分裂組織の細胞・核形態と増殖状態を取得する高解像度3Dイメージング手法の開発が中心であり、植物状態の定量的解析に再利用可能な方法を提示している。
abstractIn this study, we present a method for high-resolution visualization of RAM cells.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-Range Photogrammetry (CRP) is widely used in 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, Novel View Synthesis (NVS) technology such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using different imaging devices. We then performed dense reconstruction of forest stand using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. Photogrammetric method is less suited to more complex forest stands, resulting in tree point cloud models with issues such as excessive canopy noise, wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, especially in reconstructing canopy regions. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings suggest that NVS methods have significant potential for 3D reconstruction of urban forests, providing further technical support for forest resource visualization, inventory and management tasks.
Why it matches plant phenotyping methods森林の3D再構成手法を比較・検証し、樹高、樹冠径、胸高直径という個体レベルの植物形質を点群から抽出して精度評価しているため、方法中心の植物フェノタイピング研究である。
abstractThe resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods.
Carbon dots (CDs) have emerged as promising nanomaterials for bioimaging and stress monitoring due to their unique optical and functional properties. CDs were synthesized using citric acid and o -phenylenediamine via microwave-assisted heating, named as CP-CDs. High-resolution transmission electron microscopy observed an average particle size of 3.65 ± 0.40 nm with graphitic cores. Raman spectroscopy and Fourier transform infrared spectroscopy confirmed diverse functional groups. The CDs exhibited excitation-dependent fluorescence with a peak emission at 432 nm, a high quantum yield of 54.91%, and a fluorescence lifetime of 9.50 ± 0.15 ns, making them highly suitable for bioimaging. Confocal microscopy demonstrated tissue-specific localization in lettuce plant cells. In stem cells, CP-CDs predominantly targeted mitochondria, confirmed by a colocalization with Mito-Tracker Red. In contrast, leaf cells showed selective accumulation at the stomatal openings. Under salt- and heat-induced stress, stem cells exhibited an increase in mitochondrial fluorescence, indicating stress-responsive interactions, whereas leaf cells maintained consistent stomatal localization. Further, enhanced fluorescence from chloroplasts under stress conditions suggested synergistic effects with chlorophyll. Also, stress conditions caused CP-CDs to accumulate at the cell boundaries in stem cells, highlighting their sensitivity to stress-induced changes. These findings demonstrate the optical properties, tissue-specific uptake, and organelle-level localization of CP-CDs, underlining their potential for bioimaging, stress detection, and targeted delivery systems in plants.
Why it matches plant phenotyping methods植物細胞のストレス応答を蛍光ナノプローブと共焦点イメージングで検出する手法の開発・実証が中心であり、単なる生物学的測定ではない。
titleExploring Carbon Dot as a Fluorescent Nanoprobe for Imaging of Plant Cells under Salt/Heat-Induced Stress Conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate.
Why it matches plant phenotyping methods植物ストレス耐性・作物形質評価のための高スループット表現型解析技術を主題とするレビューであり、方法論と課題を中心に扱っている。
abstractInnovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses
Field / plotLeafRootStem / branchMorphology / geometry measurementPhysiological trait estimationVisualization / data managementArchitecture / morphology / geometryGrowth / development / phenologyPlant / canopy height
Trait-based approaches have proven, and continue to offer strong potential to tackle key issues in ecology, including: (i) understanding the functioning of organisms and how it relates to the environment, (ii) identifying the rules governing the assembly of communities and the coexistence of species, (iii) understanding how the functioning of organisms scales up to that of ecosystems and controls some of the services they deliver to humans. We present FAIRTraits, a data set of plant traits assembled from a set of studies designed to address these issues in the pedo-climatic context of the Northern Mediterranean Basin, considered both a biodiversity and a climatic hotspot. The FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles were followed to ensure maximum visibility and reusability of these data. FAIRTraits compiles and standardizes trait data collected over the 1997-2023 period on six sites by the same research group, ensuring that a consistent methodology was followed. These data were obtained on individuals from 1,955 populations of 240 species belonging to 155 genera and 48 families (172 herbaceous and 39 woody species). It contains 189,452 records for 183 quantitative traits from the different plant organs (leaves, stems, roots, and reproductive parts), which have been grouped into 10 categories: allocation ratio, architecture, chemistry, dynamics, mechanics, microbial associations, morphology, phenology, physiology and plant size. Trait values are given at the level of individual measurements. Species-level values of height and phenology taken from a Mediterranean flora are also given. Species are characterized by plant family, life cycle, Raunkiaer lifeform, photosynthetic pathway and by an original successional stage indicator value. As trait values strongly depend on environmental conditions, we also provide information on the climatic conditions and soil properties of the sites, as well as on disturbance regimes of the plots in which sampled individuals were collected. The following steps were taken to ensure the FAIRness of the data set. Findable: metadata are described using the Ecological Metadata Language, and are deposited both on the InDoRES (CNRS/MNHN) metadata catalogue and on the GBIF data portal (see below); Accessible: FAIRTraits is available on the InDoRES repository, with a subset available on the GBIF data portal (see below); Interoperable: recognized taxonomical and terminological resources to qualify taxa and attributes have been used whenever possible, and fully described sampling protocols and measurement methods are given both for traits and environmental data. A subset of the data could be mapped onto the Darwin Core biodiversity standard, making it possible to display part of the data set on the GBIF portal, more traditionally used for taxonomic occurrence data. Reusable: (meta)data are thoroughly described using domain-relevant community standards, and the full data set is released under the CC-BY 4.0 license. We believe that these multiple efforts, spanning from the very content of the data set to its careful formatting, will make of FAIRTraits a highly valuable resource for trait-based research, both in terms of data analysis and reusability.
Why it matches plant phenotyping methods植物形質183項目を個体レベルで収録し、測定方法とサンプリングプロトコルを明示した再利用可能なデータセットであり、形質取得・標準化が中心的な貢献である。
abstractTrait values are given at the level of individual measurements.
Field / plotWhole plant / canopy / plot / fieldVisualization / data managementStress response / tolerance
An understanding of fire-response traits is essential for predicting how fire regimes structure plant communities and for informing fire management strategies for biodiversity conservation. Quantification of these traits is complex, encompassing several levels of data abstraction scaling up from field observations of individuals, to general categories of species responses. We developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels. Key features include: (a) concise and documented trait and method vocabularies; (b) documented uncertainty in observations and aggregation; and (c) documented origin of data including field observations, laboratory experiments, and expert elicitation. We demonstrated application of our framework using data from new field surveys and existing data sets in New South Wales, Australia. The database includes 14 traits for 6,287 plant species derived from 8,936 field work records from 2007 to 2018, 7,054 field records from surveys after 2019, and 48,306 records from 301 existing sources.
Why it matches plant phenotyping methods火災応答形質を体系的に収集・標準化するデータベースとデータパイプライン自体が中心的な方法論的貢献であり、植物形質データの不確実性・測定法・由来も記録しているため、フェノタイピング用データ基盤として含める。
abstractWe developed the Fire Ecology Database to accommodate this complexity. Its conceptual framework is underpinned by a flexible data pipeline enabling links between fire-related trait data and event information at individual, population, and community levels.
Reproduction assets foundThe paper's core outputs (Fire Ecology Database v1.1 SQL dump, R data frames, CSV/XLSX exports on FigShare/OSF, and the Python import scripts/Jupyter notebooks) are stated to be publicly available, but no concrete repository URL or identifier for them appears in the supplied blocks, and none matches an allowed URL, so Code · publicCustomised scripts were written in Python to automate the importation of field data from the spreadsheets
into the database. These scripts are available for download (see Code availability section)Open asset ↗pdf-page:6 lines:1-78Dataset · publicStatic versions of the Fire Ecology Database, including version 1.1 used in this descriptor, are available via
FigShare or OSF in three different formatsOpen asset ↗FigSharepdf-page:9 lines:1-78Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Mesh / voxelNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionTrackingVisualization / data managementArchitecture / morphology / geometryGrowth / development / phenology
Observing plants across time and diverse scenes is critical in uncovering plant growth patterns. Classic methods often struggle to observe or measure plants against complex backgrounds and at different growth stages. This highlights the need for a universal approach capable of providing realistic plant visualizations across time and scene. Here, we introduce PlantGaussian, an approach for generating realistic three-dimensional (3D) visualization for plants across time and scenes. It marks one of the first applications of 3D Gaussian splatting techniques in plant science, achieving high-quality visualization across species and growth stages. By integrating the Segment Anything Model (SAM) and tracking algorithms, PlantGaussian overcomes the limitations of classic Gaussian reconstruction techniques in complex planting environments. A new mesh partitioning technique is employed to convert Gaussian rendering results into measurable plant meshes, offering a methodology for accurate 3D plant morphology phenotyping. To support this approach, PlantGaussian dataset is developed, which includes images of four crop species captured under multiple conditions and growth stages. Using only plant image sequences as input, it computes high-fidelity plant visualization models and 3D meshes for 3D plant morphological phenotyping. Visualization results indicate that most plant models achieve a Peak Signal-to-Noise Ratio (PSNR) exceeding 25, outperforming all models including the original 3D Gaussian Splatting and enhanced NeRF. The mesh results indicate an average relative error of 4% between the calculated values and the true measurements. As a generic 3D digital plant model, PlantGaussian will support expansion of plant phenotype databases, ecological research, and remote expert consultations.
Why it matches plant phenotyping methods3D Gaussian splattingと画像解析を統合し、植物画像から測定可能な3Dメッシュと形態形質を抽出する手法を開発・検証しており、データセットも構築しているため。
abstractA new mesh partitioning technique is employed to convert Gaussian rendering results into measurable plant meshes, offering a methodology for accurate 3D plant morphology phenotyping.
Multispectral / hyperspectralVisualization / data management
Precision agriculture is rapidly transforming the production workflow for crop monitoring and food quality control through the use of state-of-the-art instrumentation and measurement (I&M) methods that gather information about the crop rapidly and non-destructively. Specifically, the use of hyperspectral imaging (HSI) systems provides the capability to acquire not only spatial but also spectral details. It is no surprise therefore that HSI has become a notable instrument in many areas such as remote sensing and agriculture [1]. Yet despite the usefulness of the instrument, extracting relevant or useful information from HSI data is not an easy task, particularly in uncontrolled lighting conditions which affect the measurement of spectral responses of the object-under-test. In recent years, advancements in Machine Learning (ML) and Deep Learning (DL) have shown success in extracting useful features and performing complex pattern recognition across a range of problems. When used in combination with HSI, it has the potential to extract spectral responses related to plant phenotypes or chemical compounds, thus linking its spectral measurements to crop traits such as ripeness, onset of disease, and nutrient/water deprivation. Fig. 1 illustrates the four key computational steps involved in the use of HSI measurement systems: imaging, data processing, data analysis, and decision-making.
Why it matches plant phenotyping methodsHSIを用いた植物形質抽出と、その画像取得・処理・解析・意思決定ワークフローを中心に扱うロードマップ／レビューであり、植物フェノタイピング手法が主要テーマです。
abstractWhen used in combination with HSI, it has the potential to extract spectral responses related to plant phenotypes or chemical compounds, thus linking its spectral measurements to crop traits such as ripeness, onset of disease, and nutrient/water deprivation.
Stomata are vital for CO 2 and water vapor exchange, with guard cells' aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo-FIB-SEM) to study the guard cell ultrastructure of Vicia faba, a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物のガードセルを対象に、cryo-FIB-SEMによる近天然状態の3D画像取得とオルガネラ形態の再構築・定量を主要な技術貢献として扱っているため、植物フェノタイピング手法研究に該当する。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
ArabidopsisMicroscopyCell / cellular structureRootTissueVisualization / data management
Abstract Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200–300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants' challenging optical properties. Expansion microscopy (ExM) improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that ExM can be applied to increase effective imaging resolution in Arabidopsis root specimens.
Why it matches plant phenotyping methods植物組織に適用可能な超解像イメージング手法を開発し、Arabidopsis根で解像度向上を実証しており、画像取得法が研究の中心である。
abstractHere, we report on whole-mount ExM of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a 4-fold resolution increase over conventional microscopy.
Reproduction assets foundThe paper's PlantEx expansion microscopy imaging data are deposited in ISTA's public repository, and the authors' custom analysis code (including the BigWarp-based expansion-factor script) is publicly available on GitHub. The Click-ExM repository is cited prior work whose method was adapted, not a paper-specific asset.Dataset · publicThe data that support the findings of this study are available via ISTA's data repository at https://doi.org/10.15479/AT:ISTA:18837 .Open asset ↗ISTA's data repository · 10.15479/AT:ISTA:18837lines:219-252Code · publicThe custom-written code used and described in this manuscript is available via Github ( https://github.com/danzllab/PlantEx ).Open asset ↗github.com/danzllab/PlantExlines:219-252Code · publicThe expansion factor was extracted as the linear scaling factor of the similarity transformation minimizing squared landmark residuals using the script https://github.com/danzllab/CATS/tree/master/rcats_image-analysis/bigwarp .Open asset ↗github.com/danzllab/CATSlines:154-159Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Cold hardiness is a crucial physiological parameter that determines the survival of grapevines during the dormant season. Accurate modeling and large-scale prediction of grapevine cold hardiness are essential for assessing the potential geographic distribution of grapevine cultivation, quantifying the impact of climate change on grapevine habitats, and ensuring the sustainability of the grape and wine industries in cool climate regions worldwide. However, until now, no comprehensive database has been available. In this research, we combined advanced automated machine learning techniques with extensive historical and current weather data to create an integrative database for grapevine cold hardiness: VineColD (https://cornell-tree-fruit-physiology.shinyapps.io/VineColD/). We developed the NYUS.2.1 model, an automated machine learning-based system for predicting grapevine cold hardiness and in this study, applied it to global historical weather data from 17,985 curated weather stations spanning 30{degrees} to 55{degrees} in both hemispheres from 1960 to 2024, resulting in the development of an integrative grapevine cold hardiness database and monitoring system. VineColD integrates both a global historical dataset and a daily updated regional cold hardiness system, offering a comprehensive resource to study grape cold hardiness for 54 grapevine cultivars. The platform provides multiple download options, from single-station data to complete datasets, and the interactive multi-functional R Shiny application facilitates data analysis and visualization. VineColD delivers critical insights into the impact of climate change on grapevine cultivation and supports a range of analytical functions, making it a valuable tool for grape growers and researchers.
Why it matches plant phenotyping methodsブドウの耐寒性という植物生理形質を予測する機械学習モデルを開発し、全球データベースと監視プラットフォームとして提供しており、形質推定手法と再利用可能な基盤が研究の中心である。
abstractWe developed the NYUS.2.1 model, an automated machine learning-based system for predicting grapevine cold hardiness
ArabidopsisCherryCell / cellular structureFlowerVisualization / data management
Petal abscission involves cell death and reactive oxygen species (ROS) accumulation in the cells at the base of petals. Visualizing changes in the properties of these cells is crucial for analyzing and understanding petal abscission, a trait with important implications, especially for ornamental flower crops. This protocol describes the guidelines, experimental setups, and conditions for visualizing cell death by trypan blue staining and ROS accumulation by 3,3'-diaminobenzidine (DAB) staining in petals. Additionally, it provides instructions for staining and sectioning the entire Arabidopsis thaliana flower to give an improved view of the cells crucial for abscission. This protocol can be used to study the mechanism of petal abscission, including temporal changes at the base of petals during abscission and comparisons with mutants. Although Arabidopsis thaliana and cherry (Prunus sp.) blossoms are used as examples here, this protocol can easily be adapted for other plant species.
Why it matches plant phenotyping methods花弁離脱に関連する細胞死とROS蓄積を可視化する染色プロトコルが研究の中心であり、植物の状態を測定する方法として実質的に記述されている。
abstractThis protocol describes the guidelines, experimental setups, and conditions for visualizing cell death by trypan blue staining and ROS accumulation by 3,3'-diaminobenzidine (DAB) staining in petals.
RyeWheatFlowerVisualization / data managementFruit / seed / panicle traits
Successful pollination and fertilization are crucial for grain setting in cereals. Wheat is an allohexaploid autogamous species. Due to its evolutionary history, the genetic diversity of current bread wheat ( Triticum aestivum ) cultivars is limited. Introducing favorable alleles from related wild and cultivated wheat species is a promising breeding strategy for resolving this issue. However, wide hybridization between bread wheat and its relatives is hampered by the presence of suppressor genes and difficulties in crossing. Optimized methods for observing pollen tubes are essential for understanding the mechanism of crossability between wheat and its relatives. Here, we improved the crossing procedure between bread wheat and rye ( Secale cereale ) and established an optimized protocol for visualizing pollen tube behavior. Crossing via detached spike culture significantly enhanced crossing efficiency and phenotypic stability. A combination of canonical aniline blue staining and optimized clearing and sectioning allowed us to visualize pollen tube behavior. The proportion of rye pollen tubes reaching the micropyle was lower than that for pollen tubes germinated on the stigmatic hair, explaining why the hybrid seed-setting rate was approximately 75% instead of 100%. This method sheds light on wide hybridization through deeper visualization of the insides of pistils.
Why it matches plant phenotyping methodsコムギ雌ずい内の花粉管挙動を可視化する染色・透明化・切片化手法の最適化が研究の中心であり、植物の生殖状態を測定する実質的な表現型取得法に該当する。
abstractOptimized methods for observing pollen tubes are essential for understanding the mechanism of crossability between wheat and its relatives.
BarleySugar beetWheatLaboratory / benchtopMicroscopyLiDAR / point cloudCell / cellular structureLeafSegmentationVisualization / data management
The ability of laser scanning confocal microscopy to generate high-contrast 2D and 3D images has become essential in studying plant-fungal interactions. Techniques such as visualization of native fluorescence, fluorescent protein tagging of microbes, green fluorescent protein (GFP)/red fluorescent protein (RFP)-fusion proteins, and fluorescent labeling of plant and fungal proteins have been widely used to aid in these investigations. Use of fluorescent proteins has several pitfalls, including variability of expression in planta and the requirement of gene transformation. Here, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline that uses propidium iodide (PI), which stains RNA and DNA, and wheat germ agglutinin labeled with fluorescein isothiocyanate (WGA-FITC), which stains chitin, to visualize fungal colonization of plants. This pipeline relies on the use of KOH to remove the cutin layer of the leaf, increasing its permeability, allowing the different stains to penetrate and effectively bind to their targets, resulting in a consistent visualization of cellular structures. To expand the utility of this pipeline, we used the staining techniques in conjunction with machine learning to analyze fungal biomass through volume analysis, as well as quantifying nuclear breakdown, an early indicator of programmed cell death (PCD). This pipeline is simple to use, robust, consistent across host and fungal species, and can be applied to most plant-fungal interactions. Therefore, this pipeline can be used to characterize model systems as well as nonmodel interactions where transformation is not routine. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.
Why it matches plant phenotyping methods植物-真菌相互作用を可視化し、真菌バイオマスと核崩壊を画像から定量する染色・共焦点顕微鏡・機械学習パイプラインの開発と評価が中心である。
abstractHere, we used the unlabeled pathogens Parastagonospora nodorum , Pyrenophora teres f. teres , and Cercospora beticola infecting wheat, barley, and sugar beet, respectively, to show the utility of a staining and imaging pipeline
Plant diseases significantly impact agricultural productivity, leading to economic losses and food insecurity worldwide. Timely and accurate detection of plant diseases is critical to mitigate these effects and ensure sustainable farming practices. This project explores the use of deep learning techniques for detecting plant diseases based on leaf images. The system leverages convolutional neural networks (CNNs), specifically pre-trained models like ResNet and MobileNet, fine-tuned on the publicly available PlantVillage dataset. The dataset consists of thousands of labeled images of healthy and diseased leaves from various crops. To enhance model performance and generalization, data augmentation techniques such as rotation, flipping, and brightness adjustments were applied during preprocessing. The proposed system achieves high classification accuracy, validated using metrics such as precision, recall, and F1-score. Additionally, visualization tools like Grad-CAM are used to interpret model predictions, highlighting regions of the leaf that influence the decision-making process. The model is further optimized for deployment on mobile and web platforms, enabling real-time disease diagnosis. This approach offers an efficient, scalable, and user-friendly solution for farmers and agricultural experts, aiding in early disease detection and contributing to improved crop management and yield. Future work involves expanding the dataset, incorporating more plant species, and integrating the model with IoT devices for field application
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習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性能評価しているため、植物フェノタイピング手法として含める。
abstractThis project explores the use of deep learning techniques for detecting plant diseases based on leaf images.
PotatoLiDAR / point cloudLeafMorphology / geometry measurementObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementArchitecture / morphology / geometryLeaf traits
In modern agricultural science research, high-fidelity three-dimensional (3D) leaf models are crucial for crop growth analysis. However, reconstructing the complex morphology and texture of leaves from a single viewpoint under varying natural lighting conditions poses a significant challenge. To address the issues associated with this challenge, this paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject. In the camera prediction process, the combination of an explicit point cloud generation technique and an implicit 3D Gaussian rendering technique enables the accurate prediction of camera parameters and the effective capture of leaf phenotypic features. In the synthesis of the 3D model of the leaf, a strategy for optimizing the coarse model UV texture is designed with the objective of achieving spatial consistency of texture details. Furthermore, the model was successfully applied to the reconstruction of other crop leaves and lamellar structural objects, and innovatively constructed a leaf reconstruction model with disease characteristics, aiming to provide a reference for the early 3D detection of crop diseases, as well as a reference for the 3D reconstruction and visualization of other lamellar objects. The results demonstrate that the method is effective in reconstructing the morphological structure and texture details of leaves, as well as thin sheet-like structured objects, achieving fast and high-fidelity single-view reconstruction.
Why it matches plant phenotyping methods単一画像から葉の形態・テクスチャを高精度に3D再構成する手法を開発しており、葉の表現型特徴抽出が中心的な技術貢献である。
abstractthis paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject.
Background Nitroxyl (HNO) is an emerging signaling molecule that plays a significant regulatory role in various aspects of plant biology, including stress responses and developmental processes. However, understanding the precise actions of HNO in plants has been challenging due to the absence of highly sensitive and real-time in situ monitoring tools. Consequently, it is crucial to develop effective and accurate detection methods for HNO. Establishing such methodologies will enable researchers to elucidate the functional roles of HNO in plant physiological processes, thereby advancing our knowledge of plant resilience and adaptation under environmental stressors. Result Herein, we successfully constructed a near-infrared fluorescent probe, DCIF-HNO, based on the dicyanoisophorone platform as fluorophore and 2-(diphenylphosphino)benzoate as HNO recognition site for identifying HNO in plants. Probe DCIF-HNO exhibited rapid response, excellent selectivity, and high sensitivity to HNO in vitro spectroscopic tests, while also demonstrating low toxicity and biocompatibility. A rapid and portable smartphone sensing platform for HNO in actual samples was successfully constructed based on probe DCIF-HNO and color recognition application. Moreover, probe DCIF-HNO was successfully applied to plant cells and tissues, enabling real-time visualization and detection of HNO and revealing the complex network of HNO interactions during H 2 S/NO crosstalk in plants. Furthermore, the increase in HNO levels in plants response to high salt and Cr stress was observed using probe DCIF-HNO. Transcriptome sequencing and differential metabolites analysis were employed to gain insight into the mechanism of HNO production under Cr stress. Significance Due to the optical properties and high-resolution imaging capabilities of DCIF-HNO, this study offers a novel framework for elucidating the signaling role of HNO in plant stress responses. The precise visualization of HNO dynamics enhances our understanding of the complex molecular pathways involved in plant adaptation to abiotic stressors. This research not only advances plant physiology but also has significant implications for developing strategies to enhance agricultural resilience in challenging environmental conditions.
Why it matches plant phenotyping methods植物内HNOのリアルタイム可視化・検出プローブとスマートフォン計測基盤を開発し、ストレス応答という植物の生理状態を測定しているため、方法が中心的である。
abstractwe successfully constructed a near-infrared fluorescent probe, DCIF-HNO
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The virtual crop stands as a vital content in crop model research field, and has become an indispensable tool for exploring crop phenotypes. The focal objective of this undertaking is to realize three-dimensional (3D) dynamic visualization simulations of rice individual and rice populations, as well as to predict rice phenotype using virtual rice. Leveraging our laboratory's existing research findings, we have realized 3D dynamic visualizations of rice individual and populations across various growth degree days (GDD) by integrating the synchronization relationship between the above-ground parts and the root system in rice plant. The resulting visualization effects are realistic with better predictive capability for rice morphological changes. We conducted a field experiment in Anhui Province in 2019, and obtained leaf area index data for two distinct rice cultivars at the tiller stage, jointing stage, and flowering stage. A method of segmenting leaf based on the virtual rice model is employed to predict the leaf area index. A comparative analysis between the measured and simulated leaf area index yielded relative errors spanning from 7.58% to 12.69%. Additionally, the root mean square error, the mean absolute error, and the coefficient of determination were calculated as 0.56, 0.55, and 0.86, respectively. All the evaluation criteria indicate a commendable level of accuracy. These advancements provide both technical and modeling support for the development of virtual crops and the prediction of crop phenotypes.
Why it matches plant phenotyping methods仮想イネの3D形態モデルと葉分割法を開発・適用し、葉面積指数を予測して実測値と定量比較しているため、表現型取得・推定手法が中心である。
abstractThe focal objective of this undertaking is to realize three-dimensional (3D) dynamic visualization simulations of rice individual and rice populations, as well as to predict rice phenotype using virtual rice.
RiceMicroscopyRaman / spectroscopyStem / branchVisualization / data management
Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.
Why it matches plant phenotyping methods植物細胞壁のリグニン・セルロース・ヘミセルロースを可視化・抽出するCRM画像解析手法を開発し、変異イネで有効性を検証しており、表現型取得法が研究の中心です。
abstractwe propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls.
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchWhole plant / canopy / plot / fieldCountingMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys.
Why it matches plant phenotyping methods森林のマルチビュー画像から点群を再構成し、樹木本数・胸高直径・樹高などの個体形質を抽出する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractUsing multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys.
Plant pathogenic bacteria use various entry strategies to colonize their host, like entering through natural openings and wounds in leaves and roots. The vascular pathogen Xanthomonas campestris pv. campestris (Xcc) enters through hydathodes, organs at the leaf margin involved in guttation. Subsequently, Xcc breaks out from infected hydathodes, progressing into the xylem vessels and causing systemic disease. To elucidate the mechanisms that underpin the different stages of Xcc pathogenesis, a need exists to image Xcc progression in planta in a non-invasive manner. Here, we describe a phenotyping setup and Python image analysis pipeline capturing the Xcc infection in 16 Arabidopsis thaliana plants in parallel over time. The setup used both an RGB to capture disease symptoms and an ultra-sensitive CCD camera to monitor bacterial progression inside the leaves using bioluminescence. We demonstrate that the image analysis pipeline reliably quantifies bacterial growth in planta for two bacterial species, that is vascular Xcc and the mesophyll pathogen Pseudomonas syringae pv. tomato. The resolution of the camera allowed early detection of Xcc in the hydathodes, yielding valuable information on this early stage of the Xcc infection process. The data obtained through the automated image analysis pipeline was robust and validated findings from other bioluminescence imaging methods, while requiring fewer samples. We can thus quantify the resistance level of a large number of Arabidopsis thaliana accessions and mutant lines to different bacterial strains in a non-invasive manner for phenotypic screenings.
Why it matches plant phenotyping methods植物感染の進行・抵抗性を非侵襲的に画像取得・自動定量するフェノタイピング装置とPython解析パイプラインが研究の中心であり、技術的検証も実施している。
abstractHere, we describe a phenotyping setup and Python image analysis pipeline capturing the Xcc infection in 16 Arabidopsis thaliana plants in parallel over time.
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Accurate and efficient 3D reconstruction of trees is crucial for forest resource assessments and management. Close-Range Photogrammetry (CRP) is commonly used for reconstructing forest scenes but faces challenges like low efficiency and poor quality. Recently, Novel View Synthesis (NVS) technologies, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promise for 3D plant reconstruction with limited images. However, existing research mainly focuses on small plants in orchards or individual trees, leaving uncertainty regarding their application in larger, complex forest stands. In this study, we collected sequential images of forest plots with varying complexity and performed dense reconstruction using NeRF and 3DGS. The resulting point clouds were compared with those from photogrammetry and laser scanning. Results indicate that NVS methods significantly enhance reconstruction efficiency. Photogrammetry struggles with complex stands, leading to point clouds with excessive canopy noise and incorrectly reconstructed trees, such as duplicated trunks. NeRF, while better for canopy regions, may produce errors in ground areas with limited views. The 3DGS method generates sparser point clouds, particularly in trunk areas, affecting diameter at breast height (DBH) accuracy. All three methods can extract tree height information, with NeRF yielding the highest accuracy; however, photogrammetry remains superior for DBH accuracy. These findings suggest that NVS methods have significant potential for 3D reconstruction of forest stands, offering valuable support for complex forest resource inventory and visualization tasks.
Why it matches plant phenotyping methods森林スタンドの3D再構成手法を比較・評価し、樹高やDBHという個体樹木形質の抽出精度を検証しているため、植物フェノタイピング手法が中心です。
abstractperformed dense reconstruction using NeRF and 3DGS. The resulting point clouds were compared with those from photogrammetry and laser scanning.
ArabidopsisChlorophyll fluorescenceCell / cellular structureRootVisualization / data management
Polar transport of the phytohormone auxin plays a crucial role in plant growth and response to environmental stimuli. Small-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth. In this study, we developed a new fluorescent auxin probe, BODIPY-IAA2, which effectively visualizes auxin distribution in various plant tissues. We designed this probe to be transported by auxin transporters while lacking the ability to elicit auxin signaling. Using BODIPY as the fluorophore provides bright and stable fluorescence signals, making it suitable for live-imaging under standard fluorescent microscopy. We tested the probe with auxin reporter lines in Arabidopsis and performed yeast two-hybrid assays. The results showed that BODIPY-IAA2 did not activate auxin signaling through the auxin receptor TIR1. However, BODIPY-IAA2 did mildly compete with both exogenous and endogenous auxins for transport, indicating that the probe is transported by auxin transporters in vivo. The probe not only enables visualization of its tissue distribution but also allows sub-cellular staining, including the endoplasmic reticulum and tip regions in elongating cells in moss. We also observed unusual staining patterns in the main root of non-model parasitic plants where genetic transformation is not feasible. Our new fluorescent auxin probe demonstrates significant potential for detailed studies on auxin transport and distribution across diverse plant species.
Why it matches plant phenotyping methods植物体内のオーキシン分布を可視化する蛍光プローブを開発し、植物組織・細胞でのライブイメージング性能を検証しており、表現型取得手法が研究の中心である。
abstractSmall-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth.
Plant health, which affects the nutritional quality and safety of derivative food products, is influenced by symbiotic interactions with microorganisms. These interactions influence the local molecular profile at the tissue level. Therefore, studying the distribution of molecules within plants, microbes, and plant-based food is crucial to assess plant health, ensure the safety and quality of the agricultural products that become part of our food supply, and plan agricultural management practices. Within this framework, the molecular distribution within plant-based samples can be visualized with mass spectrometry imaging (MSI). This review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products. Furthermore, investigations that involve multimodal molecular imaging approaches combining MSI with other imaging techniques are described. The advantages and limitations of the different MSI techniques that influence their applicability in diverse agro-food studies are described to enable informed choices for tailored analyses. For example, some MSI technologies involve meticulous sample preparation while others compromise spatial resolution to gain throughput. Key parameters such as sensitivity, ionization bias and fragmentation, reference database and compound class specificity are described and discussed in this review. With the ongoing refinements in instrumentation, data analysis, and integration of complementary techniques, MSI deepens our insight into the molecular biology of the agricultural ecosystem. This in turn empowers the quest for sustainable and productive agricultural practices.
Why it matches plant phenotyping methods植物・微生物・食品中の分子分布を可視化する質量分析イメージング手法を体系的に解説し、手法の利点・限界・性能パラメータを論じるレビューであり、植物の状態評価に関わる方法論が中心である。
abstractThis review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products.
Stomata are vital for CO2 and water vapor exchange, with guard cells’ aperture and ultrastructure highly responsive to environmental cues. However, traditional methods for studying guard cell ultrastructure, which rely on chemical fixation and embedding, often distort cell morphology and compromise membrane integrity, leaving no suitable methodology until now. In contrast, plunge-freezing in liquid ethane rapidly preserves cells in a near-native vitreous state for cryogenic electron microscopy. Using this approach, we applied Cryo-Focused Ion Beam-Scanning Electron Microscopy (cryo- FIB-SEM) to study the guard cell ultrastructure of Vicia faba , a higher plant model chosen for its sensitivity to external factors and ease of epidermis isolation, advancing beyond previous cryo-FIB-SEM applications in lower plant algae. The results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state. 3D models of organelles such as stromules, chloroplast protrusions, chloroplasts, starch granules, mitochondria, and vacuoles were reconstructed from cryo-FIB-SEM volumetric data, with their surface area and volume initially determined using manual segmentation. Future studies using this near-native volume imaging technique hold promise for investigating how environmental factors like drought or salinity influence stomatal behavior and the morphology of guard cells and their organelles.
Why it matches plant phenotyping methods高等植物の細胞・オルガネラ形態を取得するcryo-FIB-SEM 3Dイメージング手法を導入し、体積データから表面積・体積を定量化しており、表現型取得法が研究の中心です。
abstractThe results firstly introduced cryo-FIB-SEM volume imaging, enabling subcellular ultrastructure visualization of higher plants like V. faba in a vitrified, unaltered state.
Field / plotMicroscopyLeafMorphology / geometry measurementVisualization / data managementLeaf traits
Urban air pollution poses a significant threat to human health, with metropolitan areas particularly affected due to high emissions from human activities. Particulate matter (PMx) is among the most harmful pollutants to human health, being composed of a complex mixture of substances related to severe pulmonary conditions. Urban green spaces play a vital role in mitigating air pollution by capturing PMx, and it is essential to select plant species with a high capacity for PMx accumulation to effectively enhance air quality. This study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves. Light microscopy allowed for the visualization of PMx deposition, revealing the species warranting further analysis using the filtration method. Among the species analyzed, Euonymus japonicus, Ligustrum lucidum, Alnus glutinosa, Rubus ulmifolius, and Laurus nobilis demonstrated the highest total PMx accumulation, exceeding 50 µg cm−2, making them particularly valuable for air pollution mitigation. This study examined the correlation between leaf traits such as specific leaf area (SLA), leaf area (LA), leaf dissection index (LDI), and leaf roundness and PMx accumulation across the 30 different plant species. A multiple linear regression analysis indicated that these leaf traits significantly influenced PMx accumulation, with SLA and LA showing negative correlations and leaf roundness exhibiting a positive correlation with PMx deposition. In conclusion, this study highlights the importance of selecting plant species with specific leaf traits for effective air quality improvement in urban environments particularly in highly polluted areas, to enhance air quality and public health.
Why it matches plant phenotyping methods葉面PM蓄積という植物状態の定量法について、光学顕微鏡法とろ過法の精度・相関を比較評価しており、測定手法の検証が研究の中心である。
abstractThis study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves.
Annotation / quality controlVisualization / data managementDisease symptoms / severity
Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers' livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword "phytoplasma" and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database ( i PhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of i PhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in i PhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections.
Why it matches plant phenotyping methods植物の病徴画像と症状定義を体系的に収録し、植物病害状態の認識・診断に利用するデータベースとウェブインターフェースを開発した研究であり、病徴という植物状態の取得・参照基盤が中心です。
abstractSubsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database ( i PhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicwe established a database that consists of various phytoplasma diseases and their associated symptoms, and we implemented it on a web-based interface ( https://plantpathology.ba.ars.usda.gov/iphydsdb/iphydsdb.html , accessed on 23 May 2024). The database is called the Phytoplasma Disease and Symptom Database ( i PhyDSDB), which includes 1264 links to symptomatic images collected from 372 out of 945 plant speciesOpen asset ↗lines:30-40Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published8 Aug 20242024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)Cited by 6 · OpenAlex ↗
LeafClassificationVisualization / data managementDisease symptoms / severity
Plant leaf disease detection plays a crucial role in agricultural management; this work presents a methodology for precise identification of plant leaf diseases, crucial for effective agricultural management and timely intervention. The proposed approach combines the efficiency of MobileNet, a lightweight convolutional neural network (CNN), with the discriminative power of Local Binary Pattern (LBP) to enhance feature extraction for accurate disease detection. The integration of LBP augments the network's capability to capture essential textural information. Additionally, for improved interpretability, Grad-CAM (Gradient-weighted Class Activation Mapping) is utilized for visualization, highlighting significant regions in input images that influence the model's predictions. This not only aids researchers in validating decisions but also provides transparent insights for farmers and practitioners in plant health monitoring. Evaluation on a comprehensive dataset demonstrates the effectiveness of the approach, achieving impressive results with 96% accuracy, 90% precision, 89% recall, and 89% F1 score. The combination of MobileNet with LBP, along with Grad-CAM, emerges as a robust tool for real-world applications in precision agriculture, fostering trust and informed decision-making in plant disease management.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類・可視化する計算手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractThe proposed approach combines the efficiency of MobileNet, a lightweight convolutional neural network (CNN), with the discriminative power of Local Binary Pattern (LBP) to enhance feature extraction for accurate disease detection.
The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.
Why it matches plant phenotyping methods果樹園・ブドウ園の植物健全性・生育状態を監視するUAVセンサー／ソフトウェアの試作と、植生状態・水ストレスの解析を中心とするため、植物フェノタイピング基盤の開発に該当します。
abstractThe main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards.
RiceRootMorphology / geometry measurement2D/3D reconstructionVisualization / data managementGrowth / development / phenologyRoot system architecture
Root architecture systems (RAS) reflect the spatial structure of roots in soil. To clarify the structure and distribution of rice roots and investigate the coupling between roots and soil, wetland rice was selected as the experimental object, and a three-dimensional (3D) growth model of rice root environment-roots (ERoots) based on the parameter Lindenmayer system (L-system) was proposed. ERoots combines a root morphological structure model with a growth model and defines L-system grammar iteration rules with the unit time and unit step length as parameters. At the same time, the basic growth parameters of rice roots were obtained via destructive detection, and 3D growth visualisation of roots was realised via MATLAB. In the soil coupling process, a soil nutrient simulation map was constructed based on the spatial soil characteristics per unit volume, and an adjustment strategy for roots reaching the growth boundary was designed. The flexibility of the model coupled with soil was reflected in the tropisms of root growth, growth rate and root branching strategy. Finally, combined with soil spatial characteristic simulation, geometric growth boundary and 3D root growth model, the ability of 3D growth visualisation of rice roots was verified under three soil conditions: (1) unconfined root growth, (2) confined spatial root growth, and (3) root growth with tropisms. The results indicated that the ERoots root model basically realised coupling with soil and achieved a satisfactory simulation effect in regard to the rice morphological structure. This study provides a reference for 3D growth modelling and visualisation of other crop roots.
Why it matches plant phenotyping methodsイネ根系の形態構造を3Dでモデル化・可視化し、土壌条件との結合およびシミュレーション能力を検証する手法開発が研究の中心である。
abstracta three-dimensional (3D) growth model of rice root environment-roots (ERoots) based on the parameter Lindenmayer system (L-system) was proposed
WheatMultispectral / hyperspectralSeed / grainPhysiological trait estimationVisualization / data management
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R 2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.
Why it matches plant phenotyping methods小麦粒の栄養素含量と分布を、ハイパースペクトル画像および深層学習で非破壊・高スループットに定量・可視化する手法が研究の中心である。
abstractA high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study.
MicroscopyCell / cellular structureMorphology / geometry measurementVisualization / data management
Ensuring global food security is pressing among challenges like population growth, climate change, soil degradation, and diminishing resources. Meeting the rising food demand while reducing agriculture's environmental impact requires innovative solutions. Nanotechnology, with its potential to revolutionize agriculture, offers novel approaches to these challenges. However, potential risks and regulatory aspects of nanoparticle (NP) utilization in agriculture must be considered to maximize their benefits for human health and the environment. Understanding NP-plant cell interactions is crucial for assessing risks of NP exposure and developing strategies to control NP uptake by treated plants. Insights into NP uptake mechanisms, distribution patterns, subcellular accumulation, and induced alterations in cellular architecture can be effectively drawn using transmission electron microscopy (TEM). TEM allows direct visualization of NPs within plant tissues/cells and their influence on organelles and subcellular structures at high resolution. Moreover, integrating TEM with stereological principles, which has not been previously utilized in NP-plant cell interaction assessments, provides a novel and quantitative framework to assess these interactions. Design-based stereology enhances TEM capability by enabling precise and unbiased quantification of three-dimensional structures from two-dimensional images. This combined approach offers comprehensive data on NP distribution, accumulation, and effects on cellular morphology, providing deeper insights into NP impact on plant physiology and health. This report highlights the efficient use of TEM, enhanced by stereology, in investigating diverse NP-plant tissue/cell interactions. This methodology facilitates detailed visualization of NPs and offers robust quantitative analysis, advancing our understanding of NP behavior in plant systems and their potential implications for agricultural sustainability.
Why it matches plant phenotyping methodsTEMと設計ベースステレオロジーを統合し、植物細胞内のナノ粒子分布・蓄積と細胞形態を定量評価する方法論が中心であるため、植物表現型計測手法として含める。
abstractintegrating TEM with stereological principles, which has not been previously utilized in NP-plant cell interaction assessments, provides a novel and quantitative framework to assess these interactions.
ArabidopsisMicroscopyTissueVisualization / data management
All aerial organs in plants originate from the shoot apical meristem, a specialized tissue at the tip of a plant, enclosing a few stem cells. Understanding developmental dynamics within this tissue in relation to internal and external stimuli is of crucial importance. Imaging the meristem at the cellular level beyond very early stages requires the apex to be detached from the plant body, a procedure that does not allow studies in living, intact plants over longer periods. This protocol describes a new confocal microscopy method with the potential to image the shoot apical meristem of an intact, soil-grown, flowering Arabidopsis plant over several days. The setup opens new avenues to study apical stem cells, their interconnection with the whole plant, and their responses to environmental stimuli. Key features • Novel dissection and imaging method of the shoot apical meristem of Arabidopsis . • Procedure performed with intact, soil-grown, flowering plants. • Possibility of long-term live imaging of the shoot apical meristem. • Protocol can be adapted to different plant species.
Why it matches plant phenotyping methods生きた植物のシュート頂端分裂組織を長期間観察するための新規共焦点イメージング手法・プロトコルが中心であり、植物の形態・発生状態を取得する方法として収載対象です。
abstractThis protocol describes a new confocal microscopy method with the potential to image the shoot apical meristem of an intact, soil-grown, flowering Arabidopsis plant over several days.
RiceMultispectral / hyperspectralSeed / grainPhysiological trait estimationVisualization / data management
This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R 2 C of 0.9393, a validation set R 2 V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection.
Why it matches plant phenotyping methodsイネ籾のタンパク質含量をハイパースペクトル画像から非破壊推定・画素単位で可視化する予測手法を開発し、性能検証しており、表現型取得法が中心である。
abstracta prediction model for rice grain protein content was developed by combining the spectral data with the protein content.
TobaccoMicroscopyLeafTissueVisualization / data management
Abstract Traditional deep fluorescence imaging has primarily focused on red‐shifting imaging wavelengths into the near‐infrared (NIR) windows or implementation of multi‐photon excitation approaches. Here, the advantages of NIR and multiphoton imaging are combined by developing a dual‐infrared two‐photon microscope that enables high‐resolution deep imaging in biological tissues. This study first computationally identifies that photon absorption, as opposed to scattering, is the primary contributor to signal attenuation. A NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues labeled with fluorescent single‐walled carbon nanotubes (SWNTs). Spatial imaging resolutions are achieved close to the Abbe resolution limit and eliminate blur and background autofluorescence of biomolecules, 300 µm deep into brain slices and through the full 120 µm thickness of a Nicotiana benthamiana leaf. NIR‐II two‐photon microscopy can also measure tissue heterogeneity by quantifying how much the fluorescence power law function varies across tissues, a feature this study exploits to distinguish Huntington's Disease afflicted mouse brain tissues from wildtype. These results suggest dual‐infrared two‐photon microscopy can accomplish in‐tissue structural imaging and biochemical sensing with a minimal background, and with high spatial resolution, in optically opaque or highly autofluorescent biological tissues.
Why it matches plant phenotyping methods植物組織を対象に、深部構造イメージングと組織不均一性の測定を可能にする二光子顕微鏡を開発しており、植物組織への適用も明示されているため、方法開発として中心的である。
abstractA NIR two‐photon microscope is constructed next with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues
Cell / cellular structureLeafObject detectionPhysiological trait estimationGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Auxins, particularly indole-3-acetic acid (IAA), is a phytohormone critical for plant growth, development, and response to environmental stimuli. Despite its importance, there is a lack of species-independent sensors that allow direct and reversible detection of IAA. Herein, we introduce a novel near infrared fluorescent nanosensor for spatial and temporal measurement of IAA in planta using Corona Phase Molecular Recognition. The IAA nanosensor shows high specificity to IAA in vitro and was validated to localize and function in plant cells. The sensor works across different plant species without optimization and allows visualization of dynamic changes to IAA distribution and movement in leaf tissues. The results highlighted the utility of IAA nanosensor for understanding IAA dynamics in planta .
Why it matches plant phenotyping methods植物体内のIAAを空間・時間的に可視化する蛍光ナノセンサーを開発し、植物細胞で検証しており、植物生理状態の取得法が研究の中心です。
abstractwe introduce a novel near infrared fluorescent nanosensor for spatial and temporal measurement of IAA in planta using Corona Phase Molecular Recognition.
Abstract Background The sweet potato whitefly ( Bemisia tabaci ) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato. Results We tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals. Conclusions Selective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.
Why it matches plant phenotyping methods植物葉上のコナジラミ卵を染色して定量し、計数値と計数者間一致を改善する方法を評価しており、抵抗性フェノタイピングへの応用が明示された中心的な手法研究。
abstractThe goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's egg-count datasets and the R Markdown analysis code in a Dryad repository, which is a public, paper-specific asset directly reproducing the phenotyping measurements and analysis.Dataset · publicThe datasets generated and analyzed during this study, and an R Markdown document containing the code used to perform these analyses are available in a Dryad repository (DOI: doi: https://doi.org/10.5061/dryad.vmcvdnd1m ).Open asset ↗Dryad · 10.5061/dryad.vmcvdnd1mlines:138-163Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 13 Sept 2026
Field / plotChlorophyll fluorescenceRootWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometryRoot system architecture
Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. In this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the 3D scene within a compact PSF without depth ambiguity. Additionally, we propose a reconstruction algorithm based on the neural fields technique that provides improved reconstruction quality compared to classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by approximately 20 times and captures a 100 mm$^3$ cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.
Why it matches plant phenotyping methods植物根の形態を可視化する新規3D蛍光イメージング hardware と再構成アルゴリズムを開発・検証しており、植物フェノタイピング手法が中心である。
abstractIn this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging.
Societal Impact Statement Parasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface. Summary High‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra ‐cowpea, Phelipanche ‐tomato, Phtheirospermum ‐tomato, Rhamphicarpa ‐tomato, and Striga ‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.
Why it matches plant phenotyping methodsHRXCTによる植物組織の2D・3D画像取得プロトコルを開発し、試料調製・染色を比較検証したうえで、寄生植物と宿主の接触領域を細胞レベルで可視化する方法が中心である。
abstractHere, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution.
MicroscopyCell / cellular structureMorphology / geometry measurementVisualization / data management
The ever increasing breadth of biological knowledge has led to recent efforts to combine information from various fields into cell- or tissue atlases. Anatomical features are the structural basis for such efforts, but unfortunately large scale analysis of subcellular anatomical traits is currently a missing feature. Similarly, small phenotypic alterations of organelle- or cell-specific anatomical traits, such as an increase of the total volume or the number of mitochondria in response to certain stimuli, are currently hard to quantify. To provide tools to extract quantitative information from available 3D microscopic datasets generated with methods such as serial block face scanning electron microscopy we a) developed much improved fixation and embedding protocols for plants to drastically reduce processing artifacts and b) generated an easy-to-use AI tool for quantitative analysis and visualization of large-scale data sets. We make this tool available as open source.
Why it matches plant phenotyping methods植物の3D顕微鏡データから細胞・細胞小器官の構造形質を大規模定量するAIツールを開発しており、植物向け試料調製法も改良しているため、表現型取得・解析手法が研究の中心である。
titleAnatomics MLT, an AI tool for large scale quantification of ultrastructural traits
Cell / cellular structurePhysiological trait estimationTrackingVisualization / data management
The advent of fluorescent probes and the characterization of their photochemical properties in the past years allowed significant advances in the studies of spatiotemporal cellular processes within complex and crowded systems. Dyes are indeed extremely useful tools for the visualization of cellular and subcellular structures present in living cells, as well as to study their dynamic and molecular composition or physiological changes. There are some areas of plant cell biology that have been more challenging to explore due to the physiology and organization of certain endomembrane compartments. In this study we characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments. Its photobleaching, labelling and cytotoxic properties are compared with other well-known and currently most used synthetic and molecular probes.
Why it matches plant phenotyping methods植物細胞内区画の可視化・追跡用蛍光プローブを開発・特性評価し、既存プローブと比較しているため、植物状態の画像取得法が中心です。
abstractwe characterize the labeling properties of cresyl violet as quick and inexpensive imaging agent for tracking endosomes, vacuole compartments which are usually very differentiated and categorized as more acidic as well as acidic plant compartments.
Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly Keywords: Plant leaf disease detection, leaf disease detection, convolutional neural network, deep learning
Why it matches plant phenotyping methods植物葉の病徴を画像・深層学習で検出・分類する手法のレビューであり、植物状態の取得・推定手法が中心です。
abstractThis review provides a comprehensive explanation of DL models used to visualize various plant diseases.
Cell / cellular structureTrackingVisualization / data management
Long-term visualization of changes in plasma membrane dynamics during important physiological processes can provide intuitive and reliable information in a 4D mode. However, molecular tools that can visualize plasma membranes over extended periods are lacking due to the absence of effective design rules that can specifically track plasma membrane fluorescent dye molecules over time. Using plant plasma membranes as a model, we systematically investigated the effects of different alkyl chain lengths of FMR dye molecules on their performance in imaging plasma membranes. Our findings indicate that alkyl chain length can effectively regulate the permeability of dye molecules across plasma membranes. The study confirms that introducing medium-length alkyl chains improves the ability of dye molecules to target and anchor to plasma membranes, allowing for long-term imaging of plasma membranes. This provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes. Using the amphiphilic amino-styryl-pyridine fluorescent skeleton, we discovered that the inclusion of short alkyl chains facilitated rapid crossing of the plasma membrane by the dye molecules, resulting in staining of the cell nucleus and indicating improved cell permeability. Conversely, the inclusion of long alkyl chains hindered the crossing of the cell wall by the dye molecules, preventing staining of the cell membrane and demonstrating membrane impermeability to plant cells. The FMR dyes with medium-length alkyl chains rapidly crossed the cell wall, uniformly stained the cell membrane, and anchored to it for a long period without being transmembrane. This allowed for visualization and tracking of the morphological dynamics of the cell plasma membrane during water loss in a 4D mode. This suggests that the introduction of medium-length alkyl chains into amphiphilic fluorescent dyes can transform them from membrane-permeable fluorescent dyes to membrane-staining fluorescent dyes suitable for long-term imaging of the plasma membrane. In addition, we have successfully converted a membrane-impermeable fluorescent dye molecule into a membrane-staining fluorescent dye by introducing medium-length alkyl chains into the molecule. This molecular engineering of dye molecules with alkyl chains to regulate cell permeability provides a simple and effective design rule for long-term visualization of the plasma membrane, and a convenient and feasible means of chemical modification for efficient transmembrane transport of small molecule drugs.
Why it matches plant phenotyping methods植物細胞膜の長期蛍光イメージング用色素を分子設計・評価し、水分喪失時の膜形態動態を可視化する方法開発が中心である。
abstractThis provides useful design rules for creating dye molecules that enable long-term visualization of plasma membranes.
ArabidopsisLaboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
SUMMARY Gibberellins (GAs) are major regulators of developmental and growth processes in plants. Using the degradation‐based signaling mechanism of GAs, we have built transcriptional regulator (DELLA)‐based, genetically encoded ratiometric biosensors as proxies for hormone quantification at high temporal resolution and sensitivity that allow dynamic, rapid and simple analysis in a plant cell system, i.e. Arabidopsis protoplasts. These ratiometric biosensors incorporate a DELLA protein as a degradation target fused to a firefly luciferase connected via a 2A peptide to a renilla luciferase as a co‐expressed normalization element. We have implemented these biosensors for all five Arabidopsis DELLA proteins, GA‐INSENSITIVE, GAI; REPRESSOR‐of‐ga1‐3, RGA; RGA‐like1, RGL1; RGL2 and RGL3, by applying a modular design. The sensors are highly sensitive (in the low p m range), specific and dynamic. As a proof of concept, we have tested the applicability in three domains: the study of substrate specificity and activity of putative GA‐oxidases, the characterization of GA transporters, and the use as a discrimination platform coupled to a GA agonists' chemical screening. This work demonstrates the development of a genetically encoded quantitative biosensor complementary to existing tools that allow the visualization of GA in planta .
Why it matches plant phenotyping methods植物細胞内のジベレリン量を高時間分解能で定量する遺伝子コード型バイオセンサーを開発し、感度・特異性・動的性能を示しているため、植物の生理状態を測定する方法が中心である。
abstractThis work demonstrates the development of a genetically encoded quantitative biosensor complementary to existing tools that allow the visualization of GA in planta
ArabidopsisTobaccoLaboratory / benchtopMicroscopyCell / cellular structureTrackingVisualization / data management
Super-resolution microscopy techniques have revolutionized cell biology by providing insights into single-molecule dynamics and nanoscale organization within living cells. However, the application of dynamic live-cell methods in plants remains limited by the availability of suitable fluorophores for simultaneous visualization of multiple proteins. To address this challenge, we implemented a dual-color single-particle tracking photoactivated localization microscopy (sptPALM) approach based on codon-optimized photoactivatable fluorescent proteins PA-GFP and PATagRFP. Recently, we demonstrated their individual performance in single-color experiments in Nicotiana benthamiana and Arabidopsis thaliana cells. Here, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell. This approach provides a framework to investigate the coordinated dynamics, interactions, and spatial organization of multiple proteins in living plant cells.
Why it matches plant phenotyping methods植物細胞内の2種類のタンパク質を同時追跡するデュアルカラーsptPALM法を開発・確立した研究であり、生細胞の動態・空間配置という植物状態の取得法が中心です。
abstractHere, we establish their combined use for dual-color sptPALM, enabling the simultaneous tracking of two distinct protein species within the same plant cell.
MicroscopyCell / cellular structureTissueVisualization / data managementGrowth / development / phenology
Abstract Plants continuously face various environmental stressors throughout their lifetime. To be able to grow and adapt in different environments, they developed specialized tissues that allowed them to maintain a protected yet interconnected body. These tissues undergo specific primary and secondary cell wall modifications that are essential to ensure normal plant growth, adaptation and successful land colonization. The composition of cell walls can vary among different plant species, organs and tissues. The ability to remodel their cell walls is fundamental for plants to be able to cope with multiple biotic and abiotic stressors. A better understanding of the changes taking place in plant cell walls may help identify and develop new strategies as well as tools to enhance plants’ survival under environmental stresses or prevent pathogen attack. Since the invention of microscopy, numerous imaging techniques have been developed to determine the composition and dynamics of plant cell walls during normal growth and in response to environmental stimuli. In this review, we discuss the main advances in imaging plant cell walls, with a particular focus on fluorescent stains for different cell wall components and their compatibility with tissue clearing techniques. Lay Description : Plants are continuously subjected to various environmental stresses during their lifespan. They evolved specialized tissues that thrive in different environments, enabling them to maintain a protected yet interconnected body. Such tissues undergo distinct primary and secondary cell wall alterations essential to normal plant growth, their adaptability and successful land colonization. Cell wall composition may differ among various plant species, organs and even tissues. To deal with various biotic and abiotic stresses, plants must have the capacity to remodel their cell walls. Gaining insight into changes that take place in plant cell walls will help identify and create novel tools and strategies to improve plants’ ability to withstand environmental challenges. Multiple imaging techniques have been developed since the introduction of microscopy to analyse the composition and dynamics of plant cell walls during growth and in response to environmental changes. Advancements in plant tissue cleaning procedures and their compatibility with cell wall stains have significantly enhanced our ability to perform high‐resolution cell wall imaging. At the same time, several factors influence the effectiveness of cleaning and staining plant specimens, as well as the time necessary for the process, including the specimen's size, thickness, tissue complexity and the presence of autofluorescence. In this review, we will discuss the major advances in imaging plant cell walls, with a particular emphasis on fluorescent stains for diverse cell wall components and their compatibility with tissue clearing techniques. We hope that this review will assist readers in selecting the most appropriate stain or combination of stains to highlight specific cell wall components of interest.
Why it matches plant phenotyping methods植物細胞壁の蛍光染色・組織透明化とイメージング技術を中心に整理した方法論レビューであり、植物の形態・状態の画像取得手法が主題である。
abstractIn this review, we discuss the main advances in imaging plant cell walls, with a particular focus on fluorescent stains for different cell wall components and their compatibility with tissue clearing techniques.
MicroscopyCell / cellular structureTissueMorphology / geometry measurementSegmentationVisualization / data management
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We developed a novel approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg, and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading script that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues. Summary StatementWe present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation.
Why it matches plant phenotyping methods植物器官の3D核・細胞形態を定量化する画像解析ツール、学習モデル、データセット、セグメンテーション改良法が研究の中心であり、植物の形態状態を抽出するフェノタイピング手法に該当する。
abstractWe present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs.
Expansion microscopy (ExM) has revolutionized biological imaging by physically enlarging samples, surpassing the light diffraction limit and enabling nanoscale visualization using standard microscopes. While extensively employed across a wide range of biological samples, its application to plant tissues is sparse. In this work, we present ROOT-ExM, an expansion method suited for stiff and intricate multicellular plant tissues, focusing on the primary root of Arabidopsis thaliana. ROOT-ExM achieves isotropic expansion with a fourfold increase in resolution, enabling super-resolution microscopy comparable to STimulated Emission Depletion (STED) microscopy. Labelling is achieved through immunolocalization, compartment-specific dyes, and native fluorescence preservation, while N-Hydroxysuccinimide (NHS) ester-dye conjugates reveal the ultrastructural context of cells alongside specific labelling. We successfully applied ROOT-ExM to image various cellular structures, including the Golgi apparatus, the endoplasmic reticulum, the cytoskeleton, and wall-embedded structures such as plasmodesmata. When combined with lattice light sheet microscopy (LLSM), ROOT-ExM achieves 3D quantitative analysis of nanoscale cellular process, revealing increased vesicular fusion in close proximity of the cell plate during cell division. Achieving super-resolution fluorescence imaging in plant biology remains a formidable challenge. Our findings underscore that ROOT-ExM provides a remarkable, cost-effective solution to this challenge, paving the way for unprecedented insights into plant cellular subcellular architecture. One sentence summaryROOT-ExM achieves super-resolution expansion microscopy in plants
Why it matches plant phenotyping methods植物組織向けの超解像イメージング手法そのものを開発し、細胞構造の3D定量解析に応用しており、画像取得法が研究の中心である。
abstractIn this work, we present ROOT-ExM, an expansion method suited for stiff and intricate multicellular plant tissues, focusing on the primary root of Arabidopsis thaliana.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Brassica vegetablesLeafPhysiological trait estimationGrowth / time-series analysisVisualization / data managementGrowth / development / phenologyStress response / tolerancePlant / canopy temperature
Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.
Why it matches plant phenotyping methods植物に非侵襲的に装着する有機e-skinセンサーを開発し、葉のひずみと表面温度を取得して表現型解析プラットフォームとして実証しているため、方法が中心的である。
abstractHere, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
CottonField / plotFlowerObject detectionVisualization / data management
In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.
Why it matches plant phenotyping methods綿花の花の検出を対象とする高スループット表現型解析用の分散計算パイプラインを開発し、異なるクラスタ構成の性能比較も行っており、表現型取得・処理手法が中心である。
abstractthe development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning
Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.
Why it matches plant phenotyping methodsUAV LiDAR点群から圃場区画ごとの作物高を自動抽出するソフトウェアと処理アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractwe present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicsoftware (available freely for non-commercial use here: https://github.com/PalackyUniversity/Open asset ↗pdf-page:9 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The fungus Botrytis cinerea causes severe diseases in many crops. In grapevines, it causes Botrytis bunch rot (BBR), one of the most reported diseases worldwide. It affects all herbaceous organs of the vine, especially the ripe berries, causing significant reductions in yield and wine quality. Botrytis detection models traditionally focus on temporal analysis at a specific spatial location, ignoring the study of the spatial variability of the crop. Unmanned aerial vehicles (UAVs) equipped with multispectral cameras can provide high-resolution images that can be valuable information to develop a tool for aerial pest detection. This paper proposes an algorithm to assess the risk of Botrytis development in a vineyard in Spain, using as input products generated by UAV imagery: DTM (Digital Terrain Model), NDVI (Normalised Difference Vegetation Index), CHM (Canopy Height Model) and LAI (Leaf Area Index). They represent the height and architecture of the canopy, the topography and the plant status. Healthy vines were significantly different from vines affected by Botrytis (p 0.7) that may support vineyard managers in understanding the spatial variability of the disease, allowing the spatial 2D visualisation of the risk of BBR disease development and, potentially, resulting in higher operational efficiency and reducing phytosanitary treatments, as well as economic costs. Furthermore, the present work takes advantage of imaging technologies that provide information about any location in the field, not only about specific points in the vineyard, suggesting that UAV imagery is appropriate to measure the likelihood of BBR development within the vineyard, highlighting the importance of efficient disease management based on spatial variability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からキャノピー形状・植物状態を抽出し、ブドウの灰色かび病リスクを空間推定するアルゴリズムが研究の中心であり、植物の病害状態を測定する実質的なフェノタイピング手法である。
abstractThis paper proposes an algorithm to assess the risk of Botrytis development in a vineyard in Spain, using as input products generated by UAV imagery: DTM (Digital Terrain Model), NDVI (Normalised Difference Vegetation Index), CHM (Canopy Height Model) and LAI (Leaf Area Index).
Laboratory / benchtopSeed / grainVisualization / data managementGrowth / development / phenology
Seed germination is a crucial phase of plant responses in early life to current and future environmental conditions. However, germination data are still scarce or disaggregated for many plant lineages and regions, including global biodiversity hotspots such as the Mediterranean Basin. We present MedGermDB, the first germination database for characteristic species of Mediterranean habitats, as defined by the EUNIS classification. We also present a systematic approach to build germination databases using automatic and semi‐automatic data extraction from the literature. MedGermDB contains germination data for 4680 laboratory tests performed with 236 angiosperm species from 43 families, extracted from 125 literature sources (2837 sources screened). Each test is associated to a seed lot (i.e., a seed collection of a plant species obtained from a specific location at a specific time) and its metadata, recording geographical information and experimental conditions (storage, dormancy‐breaking treatments, incubation temperature, and photoperiod). MedGermDB is available as a csv file, and through a web app: https://dianamariacruztejada.shinyapps.io/medgermdb/. MedGermDB can be used to explore eco‐evolutionary questions and provides a backbone data set for informing effective seed‐based conservation and ecological restoration activities targeting EUNIS habitats. Our methodological approach to data extraction can be extended to other study systems, contributing to global efforts to mobilize germination data.
Why it matches plant phenotyping methods植物の発芽状態に関する大規模データセットを構築し、文献からの自動・半自動データ抽出手法とWebアプリを提示しており、単なる生物学的実験のルーチン測定ではない。
abstractWe present MedGermDB, the first germination database for characteristic species of Mediterranean habitats
Reproduction assets foundThe paper's MedGermDB germination database (supplementary CSVs) and the code/workflow to join database files are publicly available in the authors' GitHub repository, with a Zenodo version of record and a Shiny app for visualization.Code · publicty and Research (MUR) as part of the PON 2014–
2020 “Research and Innovation” resources—Green/Innovation
Action—DM MUR 1061/2022, Number: DOT13GFICX-
2.
CONFLICT OF INTEREST STATEMENT
None.
DATA AVAILABILITY STATEMENT
All data are available as supplementary materials. The data and codes
to join the database files are stored at https://github.com/DianaCruzT
ejada/
MedGe
rmDB and visualized with the shiny app at https://diana
mariacruztejada.shinyapps.io/medgermdb/. A version of record of the
repository can be found at https://
doi.
org/
10.
5281/
zenodo.
10915154.
All people interested in contributing to the growth of this germination
database are encouraged to contact the correspOpen asset ↗MedGermDBpdf-raw-page:6 lines:1-151Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R2 of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change.
Why it matches plant phenotyping methodsSFM-MVSによる3次元再構成とPLSRを組み合わせ、ポプラ個体のクロロフィル含量を3次元的に推定・可視化する手法が研究の中心である。
abstractPlant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics
Laboratory / benchtopSeed / grainTissueVisualization / data management
Abstract Motivation The propensity of plant tissues to burn (i.e. their flammability) is a key trait to understand fire regimes in many ecosystems across the globe. Measuring plant flammability under laboratory conditions allows us to improve both our understanding of plant evolutionary processes and modelling tools for simulating fire hazard and behaviour. Plant flammability has been studied from different but complementary disciplines (e.g. physics, chemistry, ecology, evolution, forestry). However, information is scattered and standardized terminology is lacking, which slows down the progress of research on plant flammability. Here we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology; and (c) find geographical, ecological, and taxonomic gaps in our knowledge on plant flammability. We hope this database will stimulate transdisciplinary research and provide useful information to better cope with an increasingly flammable planet. Main Types of Variables Contained The FLAMITS database contains 19,972 records of 40 flammability variables (classified according to the measured component of flammability). For each record, relevant details of the flammability experiment are given, such as the burning device, the ignition source, and the burnt plant part. In addition, FLAMITS compiles taxonomic and functional data of the studied species and information on the study site (i.e. locality, geographic coordinates, biome, biogeographic realm, and fire activity). Spatial Location and Grain We compiled data from 295 studies in 39 countries and distributed across 12 biomes worldwide. Time Period and Grain The last 62.5 years (1961 to 15th May 2023). Major Taxa and Level of Measurement 1790 plant taxa from 186 families, 883 genera, and 1784 species. Software Format Five text files (.csv), relationally linked.
Why it matches plant phenotyping methods植物の可燃性という観察可能な形質を対象に、測定法の多様性を整理したグローバルデータベースを構築しており、形質取得・方法標準化が中心です。
abstractHere we provide an open access global database on plant flammability traits measured under laboratory conditions aiming to: (a) identify the diversity of methodologies to measure plant flammability under laboratory conditions; (b) standardize the associated terminology
Reproduction assets foundThe paper's core asset is the FLAMITS database itself: five text files (Data, Taxa, Synonymy, Site, Source) containing 19,972 flammability trait records for 1790 taxa. The Data Availability Statement explicitly deposits these files openly in DRYAD (DOI 10.5061/dryad.h18931zr3). The exact Dryad URL is not among the whitDataset · publicDATA AVAILABILITY STATEMENT
The five text files composing the database are openly available in
DRYAD at https://
doi.
org/
10.
5061/
dryad.
h1893
1zr3.Open asset ↗DRYADpdf-raw-page:11 lines:1-102Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.
Why it matches plant phenotyping methods植物構造パラメータの合成画像データ生成と、植物形質検出・検証用の学習パイプラインを開発しており、フェノタイピング手法が中心的である。
titleA scalable pipeline to create synthetic datasets from functional–structural plant models for deep learning
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 15 Sept 2026
Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree reconstruction and the exported point cloud models are compared with point cloud derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has higher successful reconstruction rate, better reconstruction in the canopy area, it requires less amount of images as input. Compared with photogrammetric reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tends to be noisy and low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those of derived from the NeRF point cloud. The results of this study illustrate the great potential of NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes.
Why it matches plant phenotyping methodsNeRFによる個体樹木の3D再構成を中心に、写真測量・レーザースキャンと比較検証し、樹高や胸高直径という植物形質を抽出しているため。
abstractthe Neural Radiance Fields (NeRF) method was used for individual tree reconstruction and the exported point cloud models are compared with point cloud derived from photogrammetric reconstruction and laser scanning methods
Digital imaging technology has gained significant interest in recent decades, particularly in the field of high-throughput phenotyping (HTP) for plant breeding. Breeding programs generates thousands of new crop lines that require evaluation under multiple environments. Considerable efforts have been made in utilizing genome wide association studies (GWAS) and genomic selection (GS) to identify genetic markers and improve desirable crop characteristics. Selecting key phenotypes is an essential component of plant breeding, and traditional methods require considerable resources and are subjective. Therefore, breeders and geneticists are in an urge of a robust technology to identify desirable crop traits. HTP using advanced sensors is a promising approach to evaluate improved crop genotypes for traits of agronomic importance. In this project, six Research and development Centers (RDCs) of Agriculture and Agri-food Canada have been utilizing University of Saskatchewan built Field Phenotyping System ("UFPS Cart") to phenotype a heritage bread wheat panel. The UFPS cart is a proximal sensing mobile platform equipped with multiple payloads (RTK GPS, RGB, NIR, and LiDAR sensor). For diverse climatic data collection, the panel consisting of 30 Canadian western spring wheat varieties were grown under six environments. This study aims to develop large-scale data management and image analysis pipelines to quantify different crop growth characteristics representing agronomic and physiological traits. It support data-driven decision making under genotype × environment effect. The multi-location imagery and ground observation data from six environments are currently being processed using the internal General Public Science Cluster (GPSC) for deep learning training to develop prediction models and extract phenotypic traits of interest (canopy height, crop lodging, heading, maturity, grain yield and protein content). The developed tools and associated models will aid to accelerate advances in cereal breeding programs.
Why it matches plant phenotyping methods植物形質を取得する移動型センシングプラットフォームと、大規模画像解析・データ処理パイプラインの開発が研究の中心であり、複数の農業・生理形質を抽出する。
abstractThe UFPS cart is a proximal sensing mobile platform equipped with multiple payloads (RTK GPS, RGB, NIR, and LiDAR sensor).
Field / plotThermalLeafWhole plant / canopy / plot / fieldVisualization / data managementGrowth / development / phenologyPlant / canopy temperature
Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 × 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters.
Why it matches plant phenotyping methods植物葉温を低コスト熱カメラで測定し、IoT-SCADAによる連続モニタリング基盤と通信・可視化手法を開発しており、植物表現型取得法が中心である。
abstractThis paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition).
Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.
Why it matches plant phenotyping methods植物病害の状態をハイパースペクトル画像とオミクスデータから推定するEG-CNNモデルを開発・評価しており、病害表現型の取得・抽出手法が中心である。
abstractWe propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study.
ArabidopsisMicroscopyCell / cellular structureRootVisualization / data management
Abstract Capturing images of the nuclear dynamics within live cells is an essential technique for comprehending the intricate biological processes inherent to plant cell nuclei. While various methods exist for imaging nuclei, including combining fluorescent proteins and dyes with microscopy, there is a dearth of commercially available dyes for live-cell imaging. In Arabidopsis thaliana , we discovered that nuclei emit autofluorescence in the near-infrared (NIR) range of the spectrum and devised a non-invasive technique for the visualization of live cell nuclei using this inherent NIR autofluorescence. Our studies demonstrated the capability of the NIR imaging technique to visualize the dynamic behavior of nuclei within primary roots, root hairs, and pollen tubes, which are tissues that harbor a limited number of other organelles displaying autofluorescence. We further demonstrated the applicability of NIR autofluorescence imaging in various other tissues by incorporating fluorescence lifetime imaging techniques. Nuclear autofluorescence was also detected across a wide range of plant species, enabling analyses without the need for transformation. The nuclear autofluorescence in the NIR wavelength range was not observed in animal or yeast cells. Genetic analysis revealed that this autofluorescence was caused by the phytochrome protein. Our studies demonstrated that nuclear autofluorescence imaging can be effectively employed not only in model plants but also for studying nuclei in non-model plant species.
Why it matches plant phenotyping methods植物の生体核を可視化する非侵襲的NIR自家蛍光イメージング法を開発し、複数組織・種で適用性を示した研究であり、画像取得法が中心的です。
abstractdevised a non-invasive technique for the visualization of live cell nuclei using this inherent NIR autofluorescence
ClassificationStress / disease detectionVisualization / data managementDisease symptoms / severity
Abstract The concept of weight initialization technique for transfer learning refers to the practice of using pre-trained models that can be modified to solve new problems, instead of starting the training process from scratch. By using pre-trained models as a starting point, the network can learn from patterns and features present in the original data, improving overall accuracy and allowing for faster convergence during training. In this study, four different transfer learning weight initialization strategies are proposed for plant disease detection: random initialization, pre-trained model on different domain (ImageNet), model trained on related domain (ISIC 2019), and model trained on same domain (PlantVillage). Weights from each strategy are transferred to a target dataset, Plant Pathology 2021. These strategies were implemented using four state-of-the-art CNN-based architectures: AlexNet, DenseNet, MobileNetV2, and VGG. The best result was obtained when both the target and source datasets included images of plant diseases. In this case, VGG was used and resulted in an 85.9% weighted f-score, which is a 9% improvement from random initialization. The transfer of knowledge from small-sized, related domain data (skin cancer data) was almost as successful as the transfer from ImageNet. Transferring from ImageNet yielded an f-score of 85.7%, while transferring from skin cancer data resulted in an f-score of 85.2%. This indicates that ImageNet, which is widely favored in the literature, may not necessarily represent the most optimal transfer source for the given context. Finally, the classifications made by the proposed models were visualized using Grad-CAM to better understand the decision-making process.
Why it matches plant phenotyping methods植物病害画像から病害状態を推定するCNN手法について、転移学習の初期化戦略を比較評価しており、病害表現型の取得・分類方法が研究の中心である。
titleDo different weight initialization strategies have an impact on transfer learning for plant disease detection?
PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.
Why it matches plant phenotyping methods葉の表皮細胞形態を画像からセグメンテーション・測定するソフトウェアを開発し、代替手法および専門家の手トレースと比較検証しており、植物表現型取得法が研究の中心です。
abstractCuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe authors publicly release the CuticleTrace FIJI macros and R filtering notebook used for the paper's epidermal cell phenotyping analysis on GitHub, with explicit availability language and URL.Code · publicSB and SWP supervised and
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DATA AVAILABILITY
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All generated and analyzed data from this study are included in the published article and its
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Supporting Information (Fig. S2). The code for the FIJI macros as well as the R notebook for
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filtering cells is available in the GitHub repository: (https://github.com/benjlloyd/CuticleTrace).296
REFERENCES
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Aono, A. H., J. S. Nagai, G. da S. M. Dickel, R. C. Marinho, P. E. A. M. de Oliveira, J. P. Papa,
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and F. A. Faria. 2021. A stomata classification and detection system in microscope
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images of maize cultivars. PLOS ONE 16: e0258679.
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Barclay, R., J. Mcelwain, D. Dilcher, and B. Sageman. 2007. The COpen asset ↗benjlloyd/CuticleTracepdf-raw-page:13 lines:1-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R 2 ), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.
Why it matches plant phenotyping methodsVNIR-HSIと波長選択・回帰モデルにより、トマト葉の光合成色素量を定量・可視化する手法が研究の中心であるため。
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionVisualization / data managementPlant / canopy height
Introduction Rubber trees are an important cash crop in Hainan Province; thus, monitoring sample plots of these trees provides important data for determining growth conditions. However, existing monitoring technology and rubber forest sample plot analysis methods are relatively simple and present widespread issues, such as limited monitoring equipment, transportation difficulties, and relatively poor three-dimensional visualization effects in complex environments. These limitations have complicated the development of rubber forest sample plot monitoring. Method This study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of sample plot information were performed in the study area for trees to explore the practical significance of this method for monitoring rubber forest sample plots. Furthermore, the relationship between the height of the first branch, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree standard volume model was established. Results The Bias, relative Bias, RMSE, and RRMSE of the height of the first branch measured by this method were −0.018 m, −0.371%, 0.562 m, and 11.573%, respectively. The Bias, relative Bias, RMSE, and RRMSE of DBH were −0.484 cm, −1.943%, −2.454 cm, and 9.859%, respectively, which proved that the method had high monitoring accuracy and met the monitoring requirements of rubber forest sample plots. The fitting results of rubber tree standard volume model had an R2 value of 0.541, and the estimated values of each parameter were 1.745, 0.115, and 0.714. The standard volume model accurately estimated the volume of rubber trees and forests using the first branch height and DBH. Discussion This study proposed an innovative planning scheme for a terrestrial photogrammetry system for 3D visual monitoring of rubber tree forests, thus providing a novel solution to issues observed in current sample plot monitoring practices. In the future, the application of terrestrial photogrammetry systems to monitor other types of forests will be explored.
Why it matches plant phenotyping methods地上 photogrammetry と3D点群再構成を用いて樹高関連形質、DBH、樹木体積を取得・検証する方法を開発し、精度評価も行っており、植物形質計測が中心である。
abstractThis study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data.
Reproduction assets foundThe paper's data availability statement deposits the study's dataset (3D visual sustainable management of rubber forest based on terrestrial photogrammetry system) on Figshare with a public DOI, making the paper-specific phenotyping data (DBH, first branch height, point-cloud measurements) publicly available.Dataset · publics in the future. Such monitoring is important for the sustainable development of tropical agriculture and forestry in Hainan Province.
Statements
Data availability statement
The datasets [3D Visual Sustainable Management of Rubber Forest Based on Terrestrial Photogrammetry System] for this study can be found in the [FIGSHARE] [ https://doi.org/10.6084/m9.figshare.22133126 ].
Author contributions
ZQ and SL contributed to the conception and design of the study and wrote the first draft of the manuscript. SL, LL, YX, CW, NL, RL, and DY organized the database and performed the statistical analysis. LL, YX, CW, NL, RL, and DY wrote the sections of the manuscript. All authors contributed to the maOpen asset ↗FIGSHARE · 10.6084/m9.figshare.22133126lines:623-661Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Field / plotLaboratory / benchtopX-ray / CTLeafRootTissueWhole plant / canopy / plot / field2D/3D reconstructionSegmentationVisualization / data management
Studies visualizing plant tissues and organs in three-dimension (3D) using micro-computed tomography (CT) published since approximately 2015 are reviewed. In this period, the number of publications in the field of plant sciences dealing with micro-CT has increased along with the development of high-performance lab-based micro-CT systems as well as the continuous development of cutting-edge technologies at synchrotron radiation facilities. The widespread use of commercially available lab-based micro-CT systems enabling phase-contrast imaging technique, which is suitable for the visualization of biological specimens composed of light elements, appears to have facilitated these studies. Unique features of the plant body, which are particularly utilized for the imaging of plant organs and tissues by micro-CT, are having functional air spaces and specialized cell walls, such as lignified ones. In this review, we briefly describe the basis of micro-CT technology first and then get down into details of its application in 3D visualization in plant sciences, which are categorized as follows: imaging of various organs, caryopses, seeds, other organs (reproductive organs, leaves, stems and petioles), various tissues (leaf venations, xylems, air-filled tissues, cell boundaries, cell walls), embolisms and root systems, hoping that wide users of microscopes and other imaging technologies will be interested also in micro-CT and obtain some hints for a deeper understanding of the structure of plant tissues and organs in 3D. Majority of the current morphological studies using micro-CT still appear to be at a qualitative level. Development of methodology for accurate 3D segmentation is needed for the transition of the studies from a qualitative level to a quantitative level in the future.
Why it matches plant phenotyping methods植物組織・器官の3D形態を取得するマイクロCT技術を中心に扱い、定量化に向けたセグメンテーション手法の必要性も論じる方法レビューである。
titleThree-dimensional visualization of plant tissues and organs by X-ray micro–computed tomography
MicroscopyTissueCalibration / preprocessingVisualization / data management
Motivation Quantitative descriptions of multi-cellular structures from optical microscopy imaging are prime to understand the variety of three-dimensional (3D) shapes in living organisms. Experimental models of vertebrates, invertebrates and plants, such as zebrafish, killifish, Drosophila or Marchantia, mainly comprise multilayer tissues, and even if microscopes can reach the needed depth, their geometry hinders the selection and subsequent analysis of the optical volumes of interest. Computational tools to "peel" tissues by removing specific layers and reducing 3D volume into planar images, can critically improve visualization and analysis. Results We developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks. The plugin implements spherical and spline surface projections. We applied VolumePeeler to perform peeling in 3D images of spherical embryos, as well as non-spherical tissue layers. The produced images improve the 3D volume visualization and enable analysis and quantification of geometrically challenging microscopy datasets. Availability ImageJ/FIJI software, source code, examples, and tutorials are openly available in https://cimt.uchile.cl/mcerda.
Why it matches plant phenotyping methods植物を含む3D組織画像の層構造を仮想的に展開し、可視化・定量化するFIJIプラグインの開発研究であり、植物組織形態の画像解析に再利用可能な手法が中心である。
abstractWe developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks.
Reproduction assets foundThis is a software paper for VolumePeeler, a FIJI plugin for 3D volume peeling applied to zebrafish, killifish, and the plant model Marchantia. The authors' plugin source code and example data/tutorials are explicitly and publicly available, covering the paper's computational analysis including the Marchantia (plant) 3Code · publicSource code is available from https://github.com/busmangit/volume-peeler . Examples and video tutorials are available under Creative Commons license (CC BY-NC).Open asset ↗busmangit/volume-peelerlines:556-587Plant phenotyping relevance match · UnverifiedCrossref · checked 8 Sept 2026
MicroscopyLiDAR / point cloudCell / cellular structureVisualization / data management
In this paper, we present the use of multiplex click/bioorthogonal chemistry combined with super-resolution Airyscan microscopy to track biomolecules in living systems with a focus on studying lignin formation in plant cell walls. While laser scanning confocal microscopy (LSCM) provided insights into the tissue-scale dynamics of lignin formation and distribution in our previous reports, its limited resolution precluded an in-depth analysis of lignin composition at the unique cell wall or substructure level. To overcome this limitation, we explored the use of Airyscan microscopy, which, among the super-resolution techniques available, offers an optimal balance between performance, cost, accessibility, and ease of implementation. Our study demonstrates that a triple labeling strategy using copper-catalyzed azide-alkyne cycloaddition (CuAAC), strain-promoted azide-alkyne cycloaddition (SPAAC), and inverse electronic-demand Diels-Alder cycloaddition (IEDDA) to label modified lignin metabolic precursors can be combined with Airyscan microscopy to reveal the zones of active lignification at the single cell level with improved sensitivity and resolution. This approach enables insights into the lignin composition in wall substructures, such as pits or in wall layers that are otherwise not distinguishable by classical LSCM. Our work emphasizes the importance of studying lignin formation in plant cell walls and demonstrates the potential of combining bioorthogonal chemistry and super-resolution microscopy techniques for studying biomolecules in living systems.
Why it matches plant phenotyping methods植物細胞壁のリグニン形成状態を単一細胞レベルで可視化・抽出するため、バイオオーソゴナル化学とAiryscan超解像顕微鏡を組み合わせた手法が研究の中心である。
abstractOur study demonstrates that a triple labeling strategy using copper-catalyzed azide-alkyne cycloaddition (CuAAC), strain-promoted azide-alkyne cycloaddition (SPAAC), and inverse electronic-demand Diels-Alder cycloaddition (IEDDA) to label modified lignin metabolic precursors can be combined with Airyscan microscopy to reveal the zones of active lignification at the single cell level with improved sensitivity and resolution.
Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.
Why it matches plant phenotyping methodsVNIR-HSIを用いて植物葉の光合成色素量と分布を定量・可視化し、波長選択と回帰モデルによる抽出・予測手法を構築しているため、植物フェノタイピング手法が中心である。
abstractwe utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Carbohydrate binding modules (CBMs) are noncatalytic domains that assist tethered catalytic domains in substrate targeting. CBMs have therefore been used to visualize distinct polysaccharides present in the cell wall of plant cells and tissues. However, most previous studies provide a qualitative analysis of CBM‐polysaccharide interactions, with limited characterization of engineered tandem CBM designs for recognizing polysaccharides like cellulose and limited application of CBM‐based probes to visualize cellulose fibrils synthesis in model plant protoplasts with regenerating cell walls. Here, we examine the dynamic interactions of engineered type‐A CBMs from families 3a and 64 with crystalline cellulose‐I and phosphoric acid swollen cellulose. We generated tandem CBM designs to determine various characteristic properties including binding reversibility toward cellulose‐I using equilibrium binding assays. To compute the adsorption ( nk on ) and desorption ( k off ) rate constants of single versus tandem CBM designs toward nanocrystalline cellulose, we employed dynamic kinetic binding assays using quartz crystal microbalance with dissipation. Our results indicate that tandem CBM3a exhibited the highest adsorption rate to cellulose and displayed reversible binding to both crystalline/amorphous cellulose, unlike other CBM designs, making tandem CBM3a better suited for live plant cell wall biosynthesis imaging applications. We used several engineered CBMs to visualize Arabidopsis thaliana protoplasts with regenerated cell walls using confocal laser scanning microscopy and wide‐field fluorescence microscopy. Lastly, we also demonstrated how CBMs as probe reagents can enable in situ visualization of cellulose fibrils during cell wall regeneration in Arabidopsis protoplasts.
Why it matches plant phenotyping methods植物細胞壁のセルロース線維を可視化するCBMプローブを設計・動力学的に検証し、植物プロトプラストでの画像化に応用しており、表現型取得法が研究の中心である。
titleEngineering and characterization of carbohydrate‐binding modules for imaging cellulose fibrils biosynthesis in plant protoplasts
Salt stress easily leads to oxidative stress and promotes the catalase (CAT) response in tomato leaves. For the changes in catalase activity in leaf subcells, there is a need for a visual in situ detection method and mechanism analysis. This paper, taking catalase in leaf subcells under salt stress as the starting point, describes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective, and lay the theoretical foundation for exploring the detection limit of catalase activity under salt stress. In this study, a total of 298 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400-1000 nm. With the increase in salt solution concentration and the advancement of the growth period, the CAT activity value increased. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining CAT activity. The characteristic wavelength was extracted by five methods (SPA, IVISSA, IRFJ, GAPLSR and CARS), and four models (PLSR, PCR, CNN and LSSVM) were established according to the characteristic wavelengths. The results show that the random sampling (RS) method was better for the selection samples of the correction set and prediction set. Raw wavelengths are optimized as the pretreatment method. The partial least-squares regression model based on the IRFJ method is the best, and the coefficient of correlation (R p ) and root mean square error of the prediction set (RMSEP) are 0.81 and 58.03 U/g, respectively. According to the ratio of microarea area to the area of the macroscopic tomato leaf slice, the R p and RMSEP of the prediction model for the detection of microarea cells are 0.71 and 23.00 U/g, respectively. Finally, the optimal model was used for quantitative visualization analysis of CAT activity in tomato leaves, and the distribution of CAT activity was consistent with its color trend. The results show that it is feasible to detect the CAT activity in tomato leaves by microhyperspectral imaging combined with stoichiometry.
Why it matches plant phenotyping methodsトマト葉のCAT活性という生理形質を、顕微ハイパースペクトル画像と回帰モデルで定量・可視化する手法が研究の中心であり、モデル性能も検証している。
abstractdescribes the use of microscopic hyperspectral imaging technology to dynamically detect and study catalase activity from a microscopic perspective
Natural compounds in plants are often unevenly distributed, and determining the best sampling locations to obtain the most representative results is technically challenging. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide the basis for formulating sampling guideline. For a succulent plant sample, ensuring the authenticity and in situ nature of the spatial distribution analysis results during MSI analysis also needs to be thoroughly considered. In this study, we developed a well-established and reliable MALDI-MSI method based on preservation methods, slice conditions, auxiliary matrices, and MALDI parameters to detect and visualize the spatial distribution of mescaline in situ in Lophophora williamsii . The MALDI-MSI results were validated using liquid chromatography-tandem mass spectrometry. Low-temperature storage at -80°C and drying of "bookmarks" were the appropriate storage methods for succulent plant samples and their flower samples, and cutting into 40 μm thick sections at -20°C using gelatin as the embedding medium is the appropriate sectioning method. The use of DCTB (trans-2-[3-(4-tert-butylphenyl)-2-methyl-2-propenylidene]malononitrile) as an auxiliary matrix and a laser intensity of 45 are favourable MALDI parameter conditions for mescaline analysis. The region of interest semi-quantitative analysis revealed that mescaline is concentrated in the epidermal tissues of L. williamsii as well as in the meristematic tissues of the crown. The study findings not only help to provide a basis for determining the best sampling locations for mescaline in L. williamsii , but they also provide a reference for the optimization of storage and preparation conditions for raw plant organs before MALDI detection. Key points An accurate in situ MSI method for fresh water-rich succulent plants was obtained based on multi-parameter comparative experiments.Spatial imaging analysis of mescaline in Lophophora williamsii was performed using the above method.Based on the above results and previous results, a sampling proposal for forensic medicine practice is tentatively proposed.
Why it matches plant phenotyping methods植物組織内の化合物空間分布を可視化・半定量するMALDI-MSI法の開発とLC-MS/MSによる検証が研究の中心であり、植物器官の状態・分布を測定する方法論的貢献に該当する。
abstractwe developed a well-established and reliable MALDI-MSI method based on preservation methods, slice conditions, auxiliary matrices, and MALDI parameters to detect and visualize the spatial distribution of mescaline in situ in Lophophora williamsii
Sainfoin ( Onobrychis spp.) is a perennial forage legume that is also attracting attention as a perennial pulse with potential for human consumption. The dual use of sainfoin underpins diverse research and breeding programs focused on improving sainfoin lines for forage and pulses, which is driving the generation of complex datasets describing high dimensional phenotypes in the post-omics era. To ensure that multiple user groups, for example, breeders selecting for forage and those selecting for edible seed, can utilize these rich datasets, it is necessary to develop common ontologies and accessible ontology platforms. One such platform, Crop Ontology, was created in 2008 by the Consortium of International Agricultural Research Centers (CGIAR) to host crop-specific trait ontologies that support standardized plant breeding databases. In the present study, we describe the sainfoin crop ontology (CO). An in-depth literature review was performed to develop a comprehensive list of traits measured and reported in sainfoin. Because the same traits can be measured in different ways, ultimately, a set of 98 variables (variable = plant trait + method of measurement + scale of measurement) used to describe variation in sainfoin were identified. Variables were formatted and standardized based on guidelines provided here for inclusion in the sainfoin CO. The 98 variables contained a total of 82 traits from four trait classes of which 24 were agronomic, 31 were morphological, 19 were seed and forage quality related, and 8 were phenological. In addition to the developed variables, we have provided a roadmap for developing and submission of new traits to the sainfoin CO.
Why it matches plant phenotyping methods植物形質の測定方法と尺度を含む変数を標準化し、育種データベースで再利用可能な作物オントロジーを開発した研究であり、形質データ基盤が中心です。
abstractwe describe the sainfoin crop ontology (CO)
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1
List of important Traits and Variables in sainfoin as determined by literature review.Open asset ↗lines:500-542Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Sweet potatoMultispectral / hyperspectralPhysiological trait estimationVisualization / data management
This study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion. The hyperspectral images of the sweet potato samples containing 900-1700 nm spectral information within every pixel were collected. The spectra were preprocessed, analyzed and the 18 informative wavelengths were finally extracted to relate to the measured starch content using the multiple linear regression (MLR) algorithm, producing a good quantitative prediction accuracy with a correlation coefficient of prediction (r P ) of 0.970 and a root-mean-square error of prediction (RMSE P ) of 0.874 g/100 g by an external validation using a set of dependent samples. The MLR model was further verified in terms of soundness and predictive validity via F-test and t-test, and then transferred to each pixel of the original two dimensional images with the help of a developed algorithm, generating color distribution maps to achieve the vivid visualization of the starch distribution. The study demonstrated that the fusion of the NIR spectral and image data provided a good strategy for the rapidly and nondestructively monitoring the starch content of sweet potato. This technique can be applied to industrial use in the future.
Why it matches plant phenotyping methodsサツマイモ試料のデンプン含量という植物器官形質を、NIRハイパースペクトル画像とデータ融合・回帰モデルで非破壊推定し、外部検証と画素単位可視化まで行っており、形質取得法が研究の中心です。
abstractThis study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion.
MicroscopyX-ray / CTLeafTissueCalibration / preprocessingVisualization / data management
Abstract Leaves of the majority of plants contain calcium oxalate (CaOx) crystals or druses which often occur in spectacular distribution patterns. Numerous studies on CaOx in plant tissues across many different plant groups have been published, since it can be visualised readily under a light microscope (LM). However, there is surprisingly limited knowledge on the actual, precise distribution of CaOx in the leaves of quite ordinary plants such as common native and exotic trees. Traditional sample preparation for the documentation of the distribution of CaOx crystals in a given sample – including overall distribution – requires time‐consuming clearing procedures. Here we present a refined fast preparation method to visualise the overall CaOx complement in a sample: The plant material is ashed and the ash viewed under the polarising microscope. This is a rapid method which overcomes many shortcomings of other methods and permits the visualisation of the entire CaOx content in most leaf samples. Pros and cons in comparison with the conventional clearing technique are discussed. Further aspects for CaOx investigations by micro‐CT and scanning electron microscopy are discussed.
Why it matches plant phenotyping methods葉中のシュウ酸カルシウム結晶の分布を可視化する試料調製法を開発・改良し、従来法と比較しているため、植物形質取得法が中心である。
abstractHere we present a refined fast preparation method to visualise the overall CaOx complement in a sample: The plant material is ashed and the ash viewed under the polarising microscope.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Annotation / quality controlVisualization / data management
As part of the BioHackathon Germany 2022, we hereby report on the success of the two projects “MIAPPE Wizard: Enabling easy creation of MIAPPE-compliant ISA metadata for Plant Phenotyping Experiments” and “DataPLANT - Facilitating Research Data Management to combat the reproducibility crisis”. Shortly before the actual hackathon, it became apparent to the participants that close coordination between the projects would be very beneficial. Both projects aimed to improve the process of collecting and aggregating metadata on plant experiments, but with different approaches.
Why it matches plant phenotyping methods植物フェノタイピング実験のメタデータ収集・集約を改善するソフトウェア／基盤の報告であり、フェノタイピング研究の再利用可能なデータ管理手法が中心である。
titleImproving Metadata Collection and Aggregation in Plant Phenotyping Experiments with MIAPPE Wizard and DataPLANT
StrawberryMultispectral / hyperspectralFruitPhysiological trait estimationSegmentationVisualization / data management
In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed. NIR-HSI data collected from 180 samples of "Tochigi iW1 go" white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and R2p values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.
Why it matches plant phenotyping methods近赤外ハイパースペクトル画像からイチゴ果実の糖含量分布を推定・可視化する画像解析および回帰モデルが研究の中心であり、植物器官の品質形質を直接取得するフェノタイピング手法に該当する。
abstractan approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913-2166 nm) is developed
Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.
Why it matches plant phenotyping methods作物品種試験の表現型データ取得・処理・標準化・再利用を一体化するプラットフォームが中心であり、標準化データベースの構築と大規模適用も報告している。
abstractThis study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing.
MicroscopyCell / cellular structureVisualization / data management
Fluorescent probes are valuable tools to visualize plasma membranes intuitively and clearly and their related physiological processes in a spatiotemporal manner. However, most existing probes have only realized the specific staining of the plasma membranes of animal/human cells within a very short time period, while almost no fluorescent probes have been developed for the long-term imaging of the plasma membranes of plant cells. Herein, we designed an AIE-active probe with NIR emission to achieve four-dimensional spatiotemporal imaging of the plasma membranes of plant cells based on a collaboration approach involving multiple strategies, demonstrated long-term real-time monitoring of morphological changes of plasma membranes for the first time, and further proved its wide applicability to plant cells of different types and diverse plant species. In the design concept, three effective strategies including the similarity and intermiscibility principle, antipermeability strategy and strong electrostatic interactions were combined to allow the probe to specifically target and anchor the plasma membrane for an ultralong amount of time on the premise of guaranteeing its sufficiently high aqueous solubility. The designed APMem-1 can quickly penetrate cell walls to specifically stain the plasma membranes of all plant cells in a very short time with advanced features (ultrafast staining, wash-free, and desirable biocompatibility) and the probe shows excellent plasma membrane specificity without staining other areas of the cell in comparison to commercial FM dyes. The longest imaging time of APMem-1 can be up to 10 h with comparable performance in both imaging contrast and imaging integrity. The validation experiments on different types of plant cells and diverse plants convincingly proved the universality of APMem-1. The development of plasma membrane probes with four-dimensional spatial and ultralong-term imaging ability provides a valuable tool to monitor the dynamic processes of plasma membrane-related events in an intuitive and real-time manner.
Why it matches plant phenotyping methods植物細胞の形態変化を長時間・リアルタイムに取得する蛍光イメージングプローブを開発し、異なる細胞型・植物種で検証しており、表現型取得法が研究の中心である。
abstractHerein, we designed an AIE-active probe with NIR emission to achieve four-dimensional spatiotemporal imaging of the plasma membranes of plant cells
In the past decade, the potential of positioning LED lamps in between the canopy (intra-canopy) to enhance crop growth and yield has been explored in greenhouse cultivation. Changes in spatial heterogeneity of light absorption that come with the introduction of intra-canopy lighting have not been thoroughly explored. We calibrated and validated an existing functional structural plant model (FSPM), which combines plant morphology with a ray tracing model to estimate light absorption at leaflet level. This FSPM was used to visualize the light environment in a tomato crop illuminated with intra-canopy lighting, top lighting or a combination of both. Model validation of light absorption of individual leaves showed a good fit (R 2 = 0.93) between measured and modelled light absorption of the canopy. Canopy light distribution was then quantified and visualized in three voxel directions by means of average absorbed photosynthetic photon flux density (PPFD) and coefficient of variation (CV) within that voxel. Simulations showed that the variation coefficient within horizontal direction was higher for intra-canopy lighting than top lighting (CV=48% versus CV= 43%), while the combination of intra-canopy lighting and top lighting yielded the lowest CV (37%). Combined intra-canopy and top lighting (50/50%) had in all directions a more uniform light absorption than intra-canopy or top lighting alone. The variation was minimal when the ratio of PPFD from intra-canopy to top lighting was about 1, and increased when this ratio increased or decreased. Intra-canopy lighting resulted in 8% higher total light absorption than top lighting, while combining 50% intra-canopy lighting with 50% top lighting, increased light absorption by 4%. Variation in light distribution was further reduced when the intra-canopy LEDs were distributed over strings at four instead of two heights. When positioning LED lamps to illuminate a canopy both total light absorption and light distribution have to be considered.
Why it matches plant phenotyping methodsトマト葉レベルの光吸収を推定する機能構造植物モデルを校正・検証し、光環境分布を定量化・可視化しており、植物状態の取得・推定手法が中心である。
abstractWe calibrated and validated an existing functional structural plant model (FSPM), which combines plant morphology with a ray tracing model to estimate light absorption at leaflet level.
Proanthocyanidins (PAs) are polymeric phenolic compounds found in plants and used in many industrial applications. Despite strong evidence of herbivore and pathogen resistance-related properties of PAs, their in planta function is not fully understood. Determining the location and dynamics of PAs in plant tissues and cellular compartments is crucial to understand their mode of action. Such an approach requires microscopic localization with fluorescent dyes that specifically bind to PAs. Such dyes have hitherto been lacking. Here, we show that 4-dimethylaminocinnamaldehyde (DMACA) can be used as a PA-specific fluorescent dye that allows localization of PAs at high resolution in cell walls and inside cells using confocal microscopy, revealing features of previously unreported wall-bound PAs. We demonstrate several novel usages of DMACA as a fluorophore by taking advantage of its double staining compatibility with other fluorescent dyes. We illustrate the use of the dye alone and its co-localization with cell wall polymers in different Populus root tissues. The easy-to-use fluorescent staining method, together with its high photostability and compatibility with other fluorogenic dyes, makes DMACA a valuable tool for uncovering the biological function of PAs at a cellular level in plant tissues. DMACA can also be used in other plant tissues than roots, however care needs to be taken when tissues contain compounds that autofluoresce in the red spectral region which can be confounded with the PA-specific DMACA signal.
Why it matches plant phenotyping methods植物組織中のプロアントシアニジンを高解像度で可視化・局在化する蛍光染色法を開発し、共焦点顕微鏡で検証・適用しているため、植物フェノタイピング手法が中心である。
abstractHere, we show that 4-dimethylaminocinnamaldehyde (DMACA) can be used as a PA-specific fluorescent dye that allows localization of PAs at high resolution in cell walls and inside cells using confocal microscopy
Owing to iron chlorosis, pear trees are some of the most severely impacted by iron deficiency, and they suffer significant losses every year. While it is possible to determine the iron content of leaves using laboratory-standard analytical techniques, the sampling and analysis process is time-consuming and labor-intensive, and it does not quickly and accurately identify the physiological state of iron-deficient leaves. Therefore, it is crucial to find a precise and quick visualization approach for metabolites linked to leaf iron to comprehend the mechanism of iron deficiency and create management strategies for pear-tree planting. In this paper, we propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves. According to our findings, iron deficiency significantly decreased the Raman peak intensities of chlorophylls and lipids in leaves. The spatial distributions of chlorophylls and lipids in the leaves changed significantly as the symptoms of iron insufficiency worsened. The technique offers a new, prospective tool for rapid recognition of iron deficiency in pear trees because it is capable of visual detection of plant physiological metabolites induced by iron deficiency.
Why it matches plant phenotyping methodsナシ葉の鉄欠乏状態に関連する代謝物をマイクロラマン分光イメージングで非破壊・迅速に可視化する手法を提案しており、植物の生理状態を取得する方法開発が中心である。
abstractwe propose a micro-Raman spectral imaging method for non-destructive, rapid, and precise visual characterization of iron-deficiency-related metabolites in pear leaves.
O_LICarbohydrate binding modules (CBMs) are non-catalytic domains associated with cell wall degrading carbohydrate-active enzymes (CAZymes) that are often present in nature tethered to distinct catalytic domains (CD). Fluorescently labeled CBMs have been also used to visualize the presence of specific polysaccharides present in the cell wall of plant cells and tissues. C_LIO_LIPrevious studies have provided a qualitative analysis of CBM-polysaccharide interactions, with limited characterization of optimal CBM designs for recognizing specific plant cell wall glycans. Furthermore, CBMs also have not been used to study cell wall regeneration in plant protoplasts. C_LIO_LIHere, we examine the dynamic interactions of engineered type-A CBMs (from families 3a and 64) with crystalline cellulose-I and phosphoric acid swollen cellulose (PASC). We generated tandem CBM designs to determine their binding parameters and reversibility towards cellulose-I using equilibrium binding assays. Kinetic parameters - adsorption (kon) and desorption (koff) rate constants-for CBMs towards nanocrystalline cellulose were determined using quartz crystal microbalance with dissipation (QCM-D). Our results indicate that tandem CBM3a exhibits a five-fold increased adsorption rate to cellulose compared to single CBM3a, making tandem CBM3a suitable for live-cell imaging applications. We next used engineered CBMs to visualize Arabidopsis thaliana protoplasts with regenerated cell walls using wide-field fluorescence and confocal laser scanning microscopy (CLSM). C_LIO_LIIn summary, tandem CBMs offer a novel polysaccharide labeling probe for real-time visualization of growing cellulose chains in living Arabidopsis protoplasts. C_LI
Why it matches plant phenotyping methods植物プロトプラストのセルロース鎖をリアルタイム可視化する蛍光プローブを設計・評価し、生細胞イメージングへ適用しているため、植物状態の取得方法が中心的です。
abstractWe generated tandem CBM designs to determine their binding parameters and reversibility towards cellulose-I using equilibrium binding assays.
Chlorophyll fluorescenceCell / cellular structureVisualization / data management
The plant cell wall comprises various types of macromolecules whose abundance and spatial distribution change dynamically and are crucial for plant architecture. High-resolution live cell imaging of plant cell wall components is, therefore, a powerful tool for plant cell biology and plant developmental biology. To acquire suitable data, the experimental setup for staining and imaging of non-fixed samples must be straightforward and avoid creating stress-induced artifacts. We present a detailed sample preparation and live image acquisition protocol for fluorescence visualization of cell wall components using commercially available probes and stains.
Why it matches plant phenotyping methods植物細胞壁成分の空間分布を取得するためのライブ蛍光染色・画像取得プロトコルが研究の中心であり、単なる生物学的測定ではない。
abstractWe present a detailed sample preparation and live image acquisition protocol for fluorescence visualization of cell wall components using commercially available probes and stains.
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
Labeling of the nucleolus in Arabidopsis thaliana can be achieved by incorporation of 5'-ethynyl uridine (EU) into bulk RNA. Although EU does not selectively label the nucleolus, the abundance of ribosomal transcripts results in the predominant accumulation of the signal in the nucleolus. Ethynyl uridine has the advantage of being detected via Click-iT chemistry providing a specific signal and low background. While the protocol presented here employs fluorescent dye and allows visualization of the nucleolus by microscopy, this method can also be used for other downstream applications. Though we tested nucleolar labeling only in A. thaliana, in principle it can be applied to other plant species.
Why it matches plant phenotyping methodsArabidopsisの核小体を蛍光顕微鏡で可視化するためのEU標識プロトコルが中心であり、植物細胞状態の画像取得法を提示している。
abstractLabeling of the nucleolus in Arabidopsis thaliana can be achieved by incorporation of 5'-ethynyl uridine (EU) into bulk RNA.
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
The plant cytoskeleton is instrumental in cellular processes such as cell growth, differentiation, and immune response. Microtubules, in particular, play a crucial role in morphogenesis by governing the deposition of plant cell wall polysaccharides and, in consequence, the cell wall mechanics and cell shape. Scrutinizing the microtubule dynamics is therefore integral to understanding the spatiotemporal regulation of cellular activities. In this chapter, we outline steps to acquire 3D images of microtubules in epidermal pavement cells of Arabidopsis thaliana cotyledons using a confocal microscope. We introduce the steps to assess the microtubule distribution and organization using image processing software Bitplane Imaris and ImageJ. We also demonstrate how the interpretation of image material can be facilitated by post-processing with general-purpose image enhancement software using methods trained by artificial intelligence-based algorithms.
Why it matches plant phenotyping methods植物細胞の微小管分布・組織化を3D画像から取得・解析する技術プロトコルが中心であり、植物細胞の形態形成に関わる状態を画像処理で評価するため、方法論文として収録する。
abstractwe outline steps to acquire 3D images of microtubules in epidermal pavement cells of Arabidopsis thaliana cotyledons using a confocal microscope.
Background Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. Results This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. Conclusion In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.
Why it matches plant phenotyping methods与えられた表現型情報から作物画像を生成するGANベースの手法とソフトウェアを開発しており、植物表現型の可視化・再現が研究の中心である。
abstractThis study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information.
Reproduction assets foundThe paper explicitly states that supplementary files including datasets, trained models, software, and source code are publicly available at the authors' HZAU plant phenotyping download site and a GitHub repository. These directly reproduce the paper's phenotyping datasets and CropPainter analysis.Code · publicSupplementary files for this article, which include datasets, trained models, software as well as the source codes used in this study, are available on website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download and https://github.com/zhwang-hzau/CropPainter-master .Open asset ↗zhwang-hzau/CropPainter-masterlines:157-211Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution.
Why it matches plant phenotyping methodsリンゴ葉のセグメンテーション、マルチスペクトル画像、CNN、データセット、後処理アプリケーションを統合した植物健康状態評価プラットフォームの開発が中心であり、植物表現型の取得・抽出手法に該当する。
abstractWe developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture.
MaizeSorghumMesh / voxelPhotogrammetry / SfM / MVSLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionSkeletonization / topologyVisualization / data management
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional RSA, and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods大型作物の根系を3Dで取得・再構築し、根系形質を抽出するメソッドとメソコスム基盤を開発・検証しており、植物フェノタイピング手法が中心である。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
ArabidopsisMicroscopyCell / cellular structureTissueObject detectionVisualization / data management
Protein activities depend heavily on protein complex formation and dynamic post-translational modifications, such as phosphorylation. Their dynamic nature is notoriously difficult to monitor in planta at cellular resolution, often requiring extensive optimization and high-end microscopy. Here, we generated and exploited the SYnthetic Multivalency in PLants (SYMPL)-vector set to study protein-protein interactions (PPIs) and kinase activities in planta based on phase separation. This technology enabled easy detection of inducible, binary and ternary protein-protein interactions among cytoplasmic, nuclear and plasma membrane proteins in plant cells via a robust image-based readout. Moreover, we applied the SYMPL toolbox to develop an in vivo reporter for SnRK1 kinase activity, allowing us to visualize tissue-specific, dynamic SnRK1 activation upon energy deprivation in stable transgenic Arabidopsis plants. The applications of the SYMPL cloning toolbox lay the foundation for the exploration of PPIs, phosphorylation and other post-translational modifications with unprecedented ease and sensitivity.
Why it matches plant phenotyping methods植物体内のタンパク質相互作用とキナーゼ活性を画像で可視化するSYMPL技術を開発し、植物の組織特異的・動的な生理状態を測定しているため、方法開発が中心である。
abstractThis technology enabled easy detection of inducible, binary and ternary protein-protein interactions among cytoplasmic, nuclear and plasma membrane proteins in plant cells via a robust image-based readout.
TomatoSeed / grainVisualization / data managementGrowth / development / phenology
Tomato (Solanum lycopersicum L.) is one of the major cash crops worldwide. The tomato seed is an important model for studying genetics and developmental biology during plant reproduction. Visualization of finer embryonic structure within a tomato seed is often hampered by seed coat mucilage, multi-cell-layered integument, and a thick-walled endosperm, which needs to be resolved by laborious embedding-sectioning. A simpler alternative is to employ tissue clearing techniques that turn the seed almost transparent using chemical agents. Although conventional clearing procedures allow deep insight into smaller seeds with a thinner seed coat, clearing tomato seeds continues to be technically challenging, especially in the late developmental stages. Presented here is a rapid and labor-saving clearing protocol to observe tomato seed development from 3 to 23 days after flowering when embryonic morphology is nearly complete. This method combines chloral hydrate-based clearing solution widely used in Arabidopsis with other modifications, including the omission of Formalin-Aceto-Alcohol (FAA) fixation, the addition of sodium hypochlorite treatment of seeds, removal of the softened seed coat mucilage, and washing and vacuum treatment. This method can be applied for efficient clearing of tomato seeds at different developmental stages and is useful in full monitoring of the developmental process of mutant seeds with good spatial resolution. This clearing protocol may also be applied to deep imaging of other commercially important species in the Solanaceae.
Why it matches plant phenotyping methodsトマト種子の胚形態を高効率・高空間分解能で可視化する組織透明化プロトコル自体が中心的な技術貢献であり、発生形態という植物表現型の取得に直接関与するため。
abstractPresented here is a rapid and labor-saving clearing protocol to observe tomato seed development from 3 to 23 days after flowering when embryonic morphology is nearly complete.
Cell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenologyStress response / tolerance
High-throughput profiling of key enzyme activities of carbon, nitrogen, and antioxidant metabolism is emerging as a valuable approach to integrate cell physiological phenotyping into a holistic functional phenomics approach. However, the analyses of the large datasets generated by this method represent a bottleneck, often keeping researchers from exploiting the full potential of their studies. We address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study. This includes the introduction of multivariate statistical analyses that can easily be implemented in similar studies, allowing researchers to extract more valuable information to identify enzymatic biosignatures. Through a literature meta-analysis, we demonstrate how enzyme activity profiling has already provided functional information on the mechanisms regulating plant development and response mechanisms to abiotic stress and pathogen attack. The high robustness of the distinct enzymatic biosignatures observed during developmental processes and under stress conditions underpins the enormous potential of enzyme activity profiling for future applications in both basic and applied research. Enzyme activity profiling will complement molecular -omics approaches to contribute to the mechanistic understanding required to narrow the genotype-to-phenotype knowledge gap and to identify predictive biomarkers for plant breeding to develop climate-resilient crops.
Why it matches plant phenotyping methods植物の酵素活性プロファイリングを生理的フェノタイピングとして扱い、データ評価・可視化ツールと多変量解析を適用して表現型シグネチャー抽出を支援する方法論が中心である。
abstractWe address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study.
As a globally popular leafy vegetable and a representative plant of the Asteraceae family, lettuce has great economic and academic significance. In the last decade, high-throughput sequencing, phenotyping, and other multi-omics data in lettuce have accumulated on a large scale, thus increasing the demand for an integrative lettuce database. Here, we report the establishment of a comprehensive lettuce database, LettuceGDB (https://www.lettucegdb.com/). As an omics data hub, the current LettuceGDB includes two reference genomes with detailed annotations; re-sequencing data from over 1000 lettuce varieties; a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies; re-analyses of 256 RNA sequencing datasets; a complete miRNAome; extensive metabolite information for representative varieties and wild relatives; epigenetic data on the genome-wide chromatin accessibility landscape; and various lettuce research papers published in the last decade. Five hierarchically accessible functions (Genome, Genotype, Germplasm, Phenotype, and O-Omics) have been developed with a user-friendly interface to enable convenient data access. Eight built-in tools (Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, Gene Annotation, Tissue Expression, Literature, and Data) are available for data downloading and browsing, functional gene exploration, and experimental practice. A community forum is also available for information sharing, and a summary of current research progress on different aspects of lettuce is included. We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration, useful for both scientific research and lettuce breeding.
Why it matches plant phenotyping methodsレタスの表現型記録とフェノミクスデータを統合・提供する、再利用可能なコミュニティデータベース／プラットフォームであり、表現型データ基盤が中心的な貢献である。
abstracta collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies
Brassica vegetablesChlorophyll fluorescenceMicroscopyCell / cellular structureRootVisualization / data management
Infection of Brassica crops by the soilborne protist Plasmodiophora brassicae leads to gall formation on the underground organs. The formation of galls requires cellular reprogramming and changes in the metabolism of the infected plant. This is necessary to establish a pathogen-oriented physiological sink toward which the host nutrients are redirected. For a complete understanding of this particular plant-pathogen interaction and the mechanisms by which host growth and development are subverted and repatterned, it is essential to track and observe the internal changes accompanying gall formation with cellular resolution. Methods combining fluorescent stains and fluorescent proteins are often employed to study anatomical and physiological responses in plants. Unfortunately, the large size of galls and their low transparency act as major hurdles in performing whole-mount observations under the microscope. Moreover, low transparency limits the employment of fluorescence microscopy to study clubroot disease progression and gall formation. This article presents an optimized method for fixing and clearing galls to facilitate epifluorescence and confocal microscopy for inspecting P. brassicae-infected galls. A tissue-clearing protocol for rapid optical clearing was used followed by vibratome sectioning to detect anatomical changes and localize gene expression with promoter fusions and reporter lines tagged with fluorescent proteins. This method will prove useful for studying cellular and physiological responses in other pathogen-triggered structures in plants, such as nematode-induced syncytia and root knots, as well as leaf galls and deformations caused by insects.
Why it matches plant phenotyping methods感染ゴールの解剖学的変化や生理応答を可視化するための組織透明化・蛍光顕微鏡法を最適化しており、植物病態の表現型取得が中心である。
abstractThis article presents an optimized method for fixing and clearing galls to facilitate epifluorescence and confocal microscopy for inspecting P. brassicae-infected galls.
Benefits of independent learning and extraction of features have received a lot of attention in recent years from both academic and professional circles. A subcategory of artificial intelligence is deep learning. The use of deep learning towards plant disease recognition can prevent the drawbacks associated with crop disease and production losses. In order to identify and characterize the signs of plant diseases, numerous established machine learning and deep learning architectures are used in conjunction with a number of visualization tools. The detection of leaf disease using image processing has been covered in this survey. Leaf disease diagnosis is enhanced when image segmentation is used in combination with deep learning or machine learning models. A big data collection can be segmented with the use of image segmentation, and the output is then fed to the AI algorithms on disease detection. Additionally, this survey covers the performance metrics of prior studies, which offered guidance for future advancements in plant disease detection and prevention methods.
Why it matches plant phenotyping methods植物葉の病徴を画像処理・機械学習で検出する方法を中心に整理したレビューであり、植物の病害状態を観測・推定するフェノタイピング手法レビューに該当する。
titleState of Art Survey on Plant Leaf Disease Detection
Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.
Why it matches plant phenotyping methodsイネ葉の無症状病害状態をハイパースペクトル画像と深層学習で直接推定する方法が研究の中心であり、技術評価も実施している。
abstractThe purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN).
Laboratory / benchtopMicroscopyX-ray / CTRoot2D/3D reconstructionSegmentationSkeletonization / topologyVisualization / data managementRoot system architecture
Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems are different, how much they exploit common systems or distinct systems to architect their structures are largely unknown. To understand the regulatory mechanism how bryophytes architect rhizoid system responding to an environmental factor, such as gravity, and compare it with the root system of vascular plants, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D. The rhizoids having the diameter of 21.3 m on the average were visualized by refraction-contrast X-ray micro-CT using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line, black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids which appeared in different three form types in tomograms was tested by a method using Canny edge detector or machine learning. Accuracy of output images was evaluated by comparing with the manually-segmented ground truth images using measures such as F1 score and IoU, revealing that the automatic segmentation using the machine learning was more effective than that using Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids, which was similar to root system architecture in vascular plants. We successfully visualized the moss rhizoid system in 3D for the first time.
Why it matches plant phenotyping methodsコケの根茎系を3D可視化・定量化するX線マイクロCTと自動セグメンテーション手法を開発し、教師データとの比較で精度検証しているため、植物表現型取得法が中心である。
abstractwe have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D
The leaf area index (LAI) is an important indicator reflecting the growth status of vegetation and is widely used in agriculture, ecology, climate change, and other fields. The shortcomings of the currently available methods for manually measuring LAI include labor-intensive, low sampling frequency, and asynchronous data collection. Focusing on these issues, a LAI sensor based on hemispherical photogrammetry and an automatic network observation system (LAI-NOS) for LAI were developed, which consists of four parts: LAI sensor, sensor node, sink node, and online data management system. The LAI sensor measures LAI values based on hemispherical photography. The sensor node is responsible for controlling the sensor and obtaining the data measured by the LAI sensor. The sink node is responsible for local networking and communication with the remote server. Data storage, data management, data display, and sampling frequency are managed by the online data management system. Comparative studies with LAI-2200C and satellite products were also conducted in this study. The comparative study with LAI-2200C showed that the LAI measurements of different vegetation types from both sources were highly significantly correlated whether based on Pearson regression or Passing & Bablok regression. A preliminary study comparing LAI-NOS measurements with Sentinel-2 inversion LAI and MODIS LAI products (MOD15A2H) showed (1) all LAI-NOS nodes measurements agreed very well with Sentinel-2 inversion LAI in the experimental period (average R²=0.94, RMSE=0.41); (2) the possible overestimate of Sentinel-2 inversion LAI was found in the middle stage of wheat (jointing-anthesis); (3) MOD15A2H and LAI-NOS measurements showed similar crop growth trends in long-term observations.
Why it matches plant phenotyping methods植物のLAIを自動取得するセンサーおよびネットワーク観測システムを開発し、既存センサーや衛星推定値との比較検証を行っており、表現型取得手法が研究の中心です。
abstracta LAI sensor based on hemispherical photogrammetry and an automatic network observation system (LAI-NOS) for LAI were developed
Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.
Why it matches plant phenotyping methodsBreedbaseは植物育種データベースおよびオープンソースのソフトウェア基盤で、表現型測定の記録・管理を中核機能として提供するため、表現型研究の方法基盤として含める。
abstractHere, we present a breeding database system, Breedbase
Reproduction assets foundThis is a software/database description paper for Breedbase, not a phenotyping study with its own measurements. The qualifying paper-specific assets are the authors' open-source codebase and Docker deployment image, explicitly stated in the Data Availability Statement. Cassavabase and other instance URLs are the systemCode · publicStrickler SR, Powell AF, Mabry ME, An H, Mirzaei M, York T, Holland CK, Kumar P, Erb M, et al. Independent evolution of ancestral and novel defenses in a genus of toxic plants ( Erysimum , Brassicaceae). eLife. 2020;9(April). 10.7554/eLife.51712.
Associated Data
Data Availability Statement
All codes are available from Github ( https://github.com/solgenomics ) and docker hub ( https://hub.docker.com/r/breedbase/breedbase# ).Open asset ↗solgenomicslines:411-414Code · publicM, York T, Holland CK, Kumar P, Erb M, et al. Independent evolution of ancestral and novel defenses in a genus of toxic plants ( Erysimum , Brassicaceae). eLife. 2020;9(April). 10.7554/eLife.51712.
Associated Data
Data Availability Statement
All codes are available from Github ( https://github.com/solgenomics ) and docker hub ( https://hub.docker.com/r/breedbase/breedbase# ).Open asset ↗breedbase/breedbaselines:411-414Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
RiceLaboratory / benchtopMicroscopyPanicle / ear / spikeStem / branchVisualization / data managementGrowth / development / phenology
The recently developed clearing technology that eliminates refractive index mismatches and diminishes auto-fluorescent material has made it possible to observe plant tissues in three dimensions (3D) while preserving their internal structures. In rice (Oryza sativa L.), a monocot model plant and a globally important crop, clearing technology has been reported in organs that are relatively easy to observe, such as the roots and leaves. Applications of clearing technology in shoot apical meristem (SAM) and stems have also been reported, but only to a limited degree because of the poor penetration of the clearing solution (CS) in these tissues. The limited efficiency of the clearing solutions in these tissues has been attributed to auto-fluorescence, thickening, and hardening of the tissues in the stem as the vascular bundles and epidermis develop and layering of the SAM with water-repellent leaves. The present protocol reports the optimization of a clearing approach for continuous and 3D observation of gene expression from the SAM/young panicle to the base of the shoots during development. Fixed tissue samples expressing a fluorescent protein reporter were trimmed into sections using a vibrating micro-slicer. When an appropriate thickness was achieved, the CS was applied. By specifically targeting the central tissue, the penetration rate and uniformity of the CS increased, and the time required to make the tissue transparent decreased. Additionally, clearing of the trimmed sections enabled the observation of the internal structure of the whole shoot from a macro perspective. This method has potential applications in deep imaging of tissues of other plant species that are difficult to clear.
Why it matches plant phenotyping methodsイネ茎内組織を透明化し、蛍光レポーター発現を含む内部構造を3D観察するための試料調製・深部イメージング法を最適化した研究であり、植物表現型取得の方法が中心である。
abstractThe present protocol reports the optimization of a clearing approach for continuous and 3D observation of gene expression from the SAM/young panicle to the base of the shoots during development.
Field / plotAnnotation / quality controlVisualization / data management
BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. RESULTS: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. CONCLUSION: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
Why it matches plant phenotyping methodsGridScoreは、植物表現型データの収集・可視化を目的とするオープンソースの横断的アプリであり、手動表現型計測ワークフロー自体が中心的な技術貢献です。
abstractWe introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition.
Reproduction assets foundThis is a software paper describing GridScore, a phenotyping data-collection app. The authors explicitly state the source code is publicly available on GitHub and a Docker container on Docker Hub, with the project home page at ics.hutton.ac.uk. No phenotype datasets or images from the paper's exemplar trials are sharedCode · publicThe source code is available on GitHub [ 19 ] and a Docker container is available on Docker Hub [ 20 ].Open asset ↗lines:182-244Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Recently many methods have been induced for plant disease detection by the influence of Deep Neural Networks in Computer Vision. However, the dearth of transparency in these types of research makes their acquisition in the real-world scenario less approving. We propose an architecture named ResTS (Residual Teacher/Student) that can be used as visualization and a classification technique for diagnosis of the plant disease. ResTS is a tertiary adaptation of formerly suggested Teacher/Student architecture. ResTS is grounded on a Convolutional Neural Network (CNN) structure that comprises two classifiers (ResTeacher and ResStudent) and a decoder. This architecture trains both the classifiers in a reciprocal mode and the conveyed representation between ResTeacher and ResStudent is used as a proxy to envision the dominant areas in the image for categorization. The experiments have shown that the proposed structure ResTS (F1 score: 0.991) has surpassed the Teacher/Student architecture (F1 score: 0.972) and can yield finer visualizations of symptoms of the disease. Novel ResTS architecture incorporates the residual connections in all the constituents and it executes batch normalization after each convolution operation which is dissimilar to the formerly proposed Teacher/Student architecture for plant disease diagnosis. Residual connections in ResTS help in preserving the gradients and circumvent the problem of vanishing or exploding gradients. In addition, batch normalization after each convolution operation aids in swift convergence and increased reliability. All test results are attained on the PlantVillage dataset comprising 54 306 images of 14 crop species.
Why it matches plant phenotyping methods植物病徴を画像から分類・可視化するResTS深層学習アーキテクチャの開発と比較検証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。
abstractWe propose an architecture named ResTS (Residual Teacher/Student) that can be used as visualization and a classification technique for diagnosis of the plant disease.
Reproduction assets foundThe paper uses the public PlantVillage leaf-image dataset and explicitly provides its public URL; the authors' source code URL exists in the text but is not among the allowed_urls, so only the dataset is reported.Dataset · publicl relationships that could have
appeared to influence the work reported in this paper.
Acknowledgements
The authors are grateful to Vishwakarma Government Engi-
neering College for the permission to publish this research.
Appendix A. . Dataset and source code access
The PlantVillage dataset used in this research is available at
https://github.com/spMohanty/PlantVillage-Dataset/I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 9 ( 2 0 2 2 ) 2 1 2 –2 2 3 221Open asset ↗PlantVillage-Datasetpdf-raw-page:10 lines:93-100Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Results In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. Conclusion The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
Why it matches plant phenotyping methodsUAV画像から個体を時空間的に同定・個別化し、植物画像データセットを抽出するコンピュータビジョン手法が研究の中心であり、精度検証も行っている。
abstractwe present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs
Reproduction assets foundThe paper's authors publicly released their plant cataloging workflow code on GitHub and deposited a supporting subset of the sugar beet UAV image data with code snapshots in GigaDB (10.5524/102225). The GitHub repository URL is in the allowed list; the GigaDB DOI is not, so only the code asset is listed with an exact-Code · publicponding data. By automatizing the plant cataloging and providing a data framework, our work helps to exploit the full potential of UAV imaging in agricultural contexts.
Availability of Source Code
The source code of our workflow is available in the following repository:
Project name: Plant Cataloging Workflow
GitHub repository: https://github.com/mrcgndr/plant_cataloging_workflow
RRID: SCR_022276
Operating system(s): Platform independent (with conda), Linux (with Docker)
Programming language: Python (3.9 or higher)
License: Apache License 2.0
Data Availability
A subset of the sugar beet data is available in order to run the workflow and reproduce our results. The data have been uploaded to tOpen asset ↗https://github.com/mrcgndr/plant_cataloging_workflowlines:172-190Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.
Why it matches plant phenotyping methods植物細胞内オルガネラの3D形態を取得・解析する電子顕微鏡プロトコルと深層学習ワークフローが研究の中心であり、植物CCV形態の技術的スクリーニング手法を提供している。
abstractHere we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs)
Reproduction assets foundThe paper's Data Availability statement explicitly deposits example data (SEM replica images, training image pairs) and the analysis code (Cellpose-based CCV segmentation workflow) generated in this study at a public Zenodo DOI, making it a paper-specific, publicly actionable asset. The temography.com URLs are vendor/mCode · publicand round; LF, large and
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flat) using an area threshold of 8500 nm2
(a CCV diameter of 105 nm) and a 3D value of 1.52 (the
355
average of the 3 smallest CCVs in control conditions determined to be spherical by the experimenter).
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Data Availability
357
Example data and the code generated in this study is available at:
358
https://doi.org/10.5281/zenodo.6563819
359
Acknowledgements
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This research was supported by the Scientific Service Units of Institute of Science and Technology
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Austria (ISTA) through resources provided by the Electron Microscopy Facility, Lab Support Facility and
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the Imaging and Optics Facility. A.J. is supported by funding from the Austrian Science FundOpen asset ↗zenodo · 10.5281/zenodo.6563819pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published22 May 2022INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTCited by 1 · OpenAlex ↗
Plant diseases influence the growth of their respective species; therefore, their early identification is very important. Many Machines Learning (ML) models have been utilized for the recognition and order of plant infections yet, after the progressions in a subset of ML, or at least, Deep Learning (DL), this area of exploration seems to have extraordinary potential with regards to expanded precision. This audit gives a complete clarification of DL models used to picture different plant infections. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the side effects of plant illnesses. Key Words: Plant Disease; Deep Learning (DL); Convolution Neural Networks (CNN); Machine Learning (ML); F1-score, Tensorflow tf, FastAPI.
Why it matches plant phenotyping methods植物病害の画像認識・分類に用いる深層学習手法を体系的に扱うレビューであり、植物の病徴・病害状態の表現型推定が中心である。
abstractThis audit gives a complete clarification of DL models used to picture different plant infections.
MicroscopySeed / grainTissueVisualization / data management
Structural botany is an indispensable perspective to fully understand the ecology, physiology, development, and evolution of plants. When researching mycoheterotrophic plants (i.e., plants that obtain carbon from fungi), remarkable aspects of their structural adaptations, the patterns of tissue colonization by fungi, and the morphoanatomy of subterranean organs can enlighten their developmental strategies and their relationships with hyphae, the source of nutrients. Another important role of symbiotic fungi is related to the germination of orchid seeds; all Orchidaceae species are mycoheterotrophic during germination and seedling stage (initial mycoheterotrophy), even the ones that photosynthesize in adult stages. Due to the lack of nutritional reserves in orchid seeds, fungal symbionts are essential to provide substrates and enable germination. Analyzing germination stages by structural perspectives can also answer important questions regarding the fungi interaction with the seeds. Different imaging techniques can be applied to unveil fungi endophytes in plant tissues, as are proposed in this article. Freehand and thin sections of plant organs can be stained and then observed using light microscopy. A fluorochrome conjugated to wheat germ agglutinin can be applied to the fungi and co-incubated with Calcofluor White to highlight plant cell walls in confocal microscopy. In addition, the methodologies of scanning and transmission electron microscopy are detailed for mycoheterotrophic orchids, and the possibilities of applying such protocols in related plants is explored. Symbiotic germination of orchid seeds (i.e., in the presence of mycorrhizal fungi) is described in the protocol in detail, along with possibilities of preparing the structures obtained from different stages of germination for analyses with light, confocal, and electron microscopy.
Why it matches plant phenotyping methods植物組織内の菌類定着や発芽構造を可視化・評価するための光学、共焦点、走査型・透過型電子顕微鏡プロトコルが中心であり、植物の構造・共生状態を取得する方法論的研究である。
abstractDifferent imaging techniques can be applied to unveil fungi endophytes in plant tissues, as are proposed in this article.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever-increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data-driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.
Why it matches plant phenotyping methods植物フェノタイピング自体の測定法開発ではないが、植物フェノタイピングにおけるデータ管理プロセスとベストプラクティスを具体的ユースケースに基づいてレビューしており、方法論的レビューとして中心的である。
abstractBased on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.
MaizeMicroscopyMRI / PETStem / branchMorphology / geometry measurementVisualization / data managementWater status / transpiration
In plants, water flows are the major driving force behind the growth and play a crucial role in the life cycle. To study hydrodynamics, methods based on tracking small particles inside water flows occupy a special place. Due to these tools, it is possible to get information about the dynamics of the spatial distribution of the fluxes characteristics. In this paper, using contrast-enhanced MRI, we have shown that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of xylem bundles of maize stem nodes and internodes. Supplementing MRI data, a high-precision visualization of xylem vessels by laser scanning microscopy was used to reveal structural and dimensional characteristics of the stem vascular system. In addition, we proposed the concept of using the prototype "Y-type xylem vascular bundles" as a model of the elementary connection of vessels within the vascular system. A Reynolds number can match the microchannel model with the real xylem vessels.
Why it matches plant phenotyping methodsMRIとレーザー走査顕微鏡を用いてトウモロコシの木部構造・寸法と水流動態を可視化する手法を提示しており、植物の生理・構造形質の取得が中心である。
abstractusing contrast-enhanced MRI, we have shown that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of xylem bundles of maize stem nodes and internodes.
Abstract: Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;
Why it matches plant phenotyping methods植物病害症状の画像認識を深層学習で行う方法を扱い、複数モデルの性能評価も含むため、植物の病害状態を推定するフェノタイピング手法のレビューとして中心的です。
abstractA variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant-parasitic nematodes are a significant cause of yield losses and food security issues. Specifically, nematodes of the genus Meloidogyne can cause significant production losses in horticultural crops around the world. Understanding the mechanisms of the ever-changing physiology of plant roots by imaging the galls induced by nematodes could provide a great insight into their control. However, infected roots are unsuitable for light microscopy investigation due to the opacity of plant tissues. Thus, samples must be cleared to visualize the interior of whole plants in order to make them transparent using clearing agents. This work aims to identify which clearing protocol and microscopy system is the most appropriate to obtain 3D images of tomato cv. Durinta and eggplant cv. Cristal samples infected with Meloidogyne incognita to visualize and study the root–nematode interaction. To that extent, two clearing solutions (BABB and ECi), combined with three different dehydration solvents (ethanol, methanol and 1-propanol), are tested. In addition, the advantages and disadvantages of alternative imaging techniques to confocal microscopy are analyzed by employing an experimental custom-made setup that combines two microscopic techniques, light sheet fluorescence microscopy and optical projection tomography, on a single instrument.
Why it matches plant phenotyping methods根部の感染状態を可視化するための透明化プロトコルと3D顕微鏡法を開発・比較しており、植物病害状態の画像取得手法が中心である。
abstractThis work aims to identify which clearing protocol and microscopy system is the most appropriate to obtain 3D images of tomato cv. Durinta and eggplant cv. Cristal samples infected with Meloidogyne incognita to visualize and study the root–nematode interaction.
Stripe rust (caused by Puccinia striiformis f. sp. tritici) is one of the most devastating diseases of wheat and causes large-scale epidemics and severe yield loss. Applying fungicides during early epidemic development is crucial to controlling the disease but is often challenged by resource-limited human visual scouting. Deep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust for timely application of fungicides and improve control efficiency. Here, we developed RustNet, a neural network-based image classifier, for efficiently monitoring fields for stripe rust. RustNet was built on a ResNet-18 architecture pre-trained with ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset using transfer learning. RGB images and videos of multiple wheat fields with different wheat types (winter and spring wheat), conditions (irrigated and non-irrigated), and locations were acquired using smartphones or unmanned aerial vehicles near the canopy. A semi-automated image labeling approach was conducted to improve labeling efficiency by combining automated machine labeling and human correction. Cross-validations across multiple categories (sensor platforms, wheat types, and locations) achieved Area Under Curve from 0.72 to 0.87. Independent validation on a published dataset from Germany achieved accuracies ranging from 0.79 to 0.86. The visualization of the last convolutional layer of RustNet demonstrated the identification of pixels with stripe rust. RustNet is freely available at https://zzlab.net/RustNet.
Why it matches plant phenotyping methods小麦のストライプさび病という植物状態を画像から検出する深層学習手法を開発し、複数条件で交差検証・独立検証しており、表現型取得手法が研究の中心です。
abstractDeep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust
Reproduction assets foundThe authors publicly released the RustNet trained model (integrated into Rooster) and the Rooster semi-automated image-labeling software used to produce this paper's wheat stripe rust phenotyping analysis, with explicit availability statements and URLs.Code · publicwere calculated based on its gradient to the disease prediction, which
was equal to the weights of the last fully connected layer. A ReLU function was applied to filter
negative input (Figure 2b). A python package was used to visualize the Grad-CAM
(https://github.com/jacobgil/pytorch-grad-cam).Image labeling
Rooster software (https://github.com/12HuYang/Rooster) was used to label tile images into
disease or non-disease classes by easily clicking it with a mouse. Rooster was developed with
python and can split raw images into tiles (e.g., 224 × 224 pixels) by defining column and row
numbers. A semi-automatic image labeling that combines machine- and human labeling was
implemented in RoOpen asset ↗12HuYang/Roosterpdf-raw-page:18 lines:1-30Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analysis techniques. However such approaches are not always easily practicable in particular when faced with mixed datasets. Moreover displaying large numbers of individuals leads to cluttered visualisations which are difficult to interpret. Results We introduced a new methodology to overcome these limits. Its main feature is a new semantic distance tailored for both quantitative and qualitative variables which allows for a realistic representation of the relationships between individuals (phenotypic descriptions in our case). This semantic distance is based on ontologies which are engineered to represent real-life knowledge regarding the underlying variables. For easier handling by biologists, we incorporated its use into a complete tool, from raw data file to visualisation. Following the distance calculation, the next steps performed by the tool consist in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) building sparse visualisations based on these archetypes. Our approach was implemented as a Python pipeline and applied to a rosebush dataset including passport and phenotypic data. Conclusions The introduction of our new semantic distance and of the archetype concept allowed us to build a comprehensive representation of an incomplete dataset characterised by a large proportion of qualitative data. The methodology described here could have wider use beyond information characterizing organisms or species and beyond plant science. Indeed we could apply the same approach to any mixed dataset.
Why it matches plant phenotyping methods植物の表現型データを対象に、混合型・不完全データを可視化する新しい意味距離とPythonパイプラインを開発しており、表現型解析手法が研究の中心である。
abstractWe introduced a new methodology to overcome these limits.
Reproduction assets foundThe authors' DIVIS Python pipeline (semantic distance, clustering, archetype visualisation) is publicly available on Forgemia with explicit availability language; the rosebush phenotype dataset itself is only available on request, so it is not a qualifying public asset.Code · publicLoire”, supported by the French Region Pays de la Loire, Angers Loire Métropole and the European Regional Development Fund, as part of the DIVIS project.
Availability of data and materials
The software developed to implement the pipeline presented in this paper is available as follows:
• Project name: DIVIS
• Project home page: https://forgemia.inra.fr/irhs-bioinfo/Divis
• Archived version: v1.2
• Operating system(s): Platform independent
• Programming language: Python 3.7
• Other requirements: Described as requirement.txt file for pip in the code repository
• License: CeCILL. See LICENCE file in the code repository
• Any restrictions to use by non-academics: None
The OWL ontology (in FrenchOpen asset ↗irhs-bioinfo/Divis · v1.2lines:431-490Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Apr 2022Computers and Electronics in Agriculture.
RiceRootMorphology / geometry measurement2D/3D reconstructionVisualization / data managementRoot system architecture
In agronomy sciences and researches, root system architecture (RSA) of Oryza sativa L. (rice) is often used to reflect the spatial configuration of rice roots. But, because soil has its own opacity, the cognition and expression of the spatial morphology and structure of rice roots have become the bottleneck in the in-depth study. The three-dimensional (3D) modeling and visualization can be used to help further study and recognize the morphological, structural and functional traits of rice roots. To clarify the rules governing the structure and distribution of the rice root system, and so to understand the relationship between RSA and the functionality of rice roots, a method combined the improved dual-scale automaton with Lindenmayer-system (L-system) for the three-dimensional modeling and visualization of the rice root system is hereby proposed. The basic parameters of the rice root growth were firstly measured by the destructive detection, and various appropriate growth functions were selected according to the rice root growth rule and development so as to control the rice root growth rate in the modeling. In the dual-scale automaton modeling, the identifiers of micro-states and macro-states were redefined with numbers and characters, and hence the improved dual-scale automaton model was used to describe the growth process of rice roots, which was then combined with L-system grammar. According to the morphological structure characteristics of the rice root growth, the direction and radius of the rice root growth were constrained, and a single three-dimensional morphological model was thus constructed. Then, the visualization of the rice root growth was realized by MATLAB. By calculating the total rice root length, total rice root surface area, total rice root volume, maximum rice root width, maximum rice root depth and other indicators of the model, as well as analyzing and comparing the experimental measurement data, it was finally found that the average accuracy of the simulation of the total rice root length, total rice root surface area and total rice root volume was 94.27%, 93.68% and 92.35%, respectively. Besides, the maximum rice root width and maximum rice root depth had strong correlation with the corresponding measured data. So, the results showed that the model had good simulation effects on the rice morphological structure. Again, based on this, the relationship between the rice root structure and its functions was further analyzed by quantifying the structural parameters such as the rice root length fraction, solidity, convex hull volume, rice root volume density and rice root surface area density, etc. This model laid a foundation for the coupling of both the morphological structure model of the rice root growth as well as the model of physiological and ecological factors of next stage, hoping to provide a reference for the 3D modeling and visualization research of the root growth of other crops.
Why it matches plant phenotyping methodsイネ根系の形態・構造形質を3Dモデル化・可視化する手法を開発し、実測値との比較で精度を検証しているため、植物フェノタイピング手法が研究の中心である。
abstracta method combined the improved dual-scale automaton with Lindenmayer-system (L-system) for the three-dimensional modeling and visualization of the rice root system is hereby proposed.
TomatoMultispectral / hyperspectralFruitPhysiological trait estimationVisualization / data management
The quality of tomatoes is usually predicted by measuring a single index, rather than a comprehensive index. To find a comprehensive index, visible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes, and twelve quality indexes were measured as the reference standards. The changing trends and correlations of different indexes were analyzed, and comprehensive quality index (CQI) was proposed through factor analysis. The characteristic wavelengths were selected by successive projection algorithm (SPA) based on the hyperspectral data, which was used to establish three regression models for CQI prediction. The result indicated that MLR achieved good performance withR V 2 = 0.87, RMSEV = 1.33 and RPD = 2.58. After that, spatial distribution map was generated to visualize the CQI in tomato fruit. This study indicated that the comprehensive quality of tomatoes can be predicted non-destructively based on hyperspectral imaging and chemometrics, determining the optimal harvesting period.
Why it matches plant phenotyping methodsトマト果実の品質という植物器官の形質を、ハイパースペクトル画像とケモメトリクスで非破壊推定・可視化する手法が研究の中心である。
abstractvisible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes
ClassificationVisualization / data managementArchitecture / morphology / geometry
The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.
Why it matches plant phenotyping methodsCNNとCAMを用いて樹皮画像から識別に有用な形態的特徴を抽出・可視化する方法が研究の中心であり、植物器官の観察可能な形質の推定に該当する。
abstractwe trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species.
Reproduction assets foundThe paper's own bark image dataset (BARK-KR) is publicly deposited on Zenodo, the authors' analysis scripts are on GitHub, and the CAM extended figures are hosted on Figshare. The BarkNet 1.0 dataset is cited prior work and excluded.Dataset · publicthe bark image data collected in this study were published and are available on Zenodo ( https://doi.org/10.5281/zenodo.4749062 ) 48 .Open asset ↗Zenodo · 10.5281/zenodo.4749062lines:134-164Code · publicThe python scripts used in this study are available on GitHub ( https://github.com/snutp/TBKFE ).Open asset ↗GitHub · snutp/TBKFElines:134-164Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
ArabidopsisMicroscopyCell / cellular structureRootVisualization / data management
Protein tracking in living plant cells has become routine with the emergence of reporter genes encoding fluorescent tags. Unfortunately, this imaging strategy is not applicable to glycans because they are not directly encoded by the genome. Indeed, complex glycans result from sequential additions and/or removals of monosaccharides by the glycosyltransferases and glycosidases of the cell's biosynthetic machinery. Currently, the imaging of cell wall polymers mainly relies on the use of antibodies or dyes that exhibit variable specificities. However, as immunolocalization typically requires sample fixation, it does not provide access to the dynamics of living cells. The development of click chemistry in plant cell wall biology offers an alternative for live-cell labeling. It consists of the incorporation of a carbohydrate containing a bio-orthogonal chemical reporter into the target polysaccharide using the endogenous biosynthetic machinery of the cell. Once synthesized and deposited in the cell wall, the polysaccharide containing the analog monosaccharide is covalently coupled to an exogenous fluorescent probe. Here, we developed a metabolic click labeling approach which allows the imaging of cell wall polysaccharides in living and elongating cells without affecting cell viability. The protocol was established using the pollen tube, a useful model to follow cell wall dynamics due to its fast and tip-polarized growth, but was also successfully tested on Arabidopsis root cells and root hairs. This method offers the possibility of imaging metabolically incorporated sugars of viable and elongating cells, allowing the study of the long-term dynamics of labeled extracellular polysaccharides.
Why it matches plant phenotyping methods生細胞中の細胞壁多糖を代謝クリック標識して動態を画像化する手法を開発しており、植物細胞の状態・形態取得が中心的な方法論的貢献である。
abstractHere, we developed a metabolic click labeling approach which allows the imaging of cell wall polysaccharides in living and elongating cells without affecting cell viability.
Phenotypic traits of crops are an important basis for cultivating new crop varieties. Breeding experts expect to use artificial intelligence (AI) technology and obtain many accurate phenotypic data at a lower cost for the design of breeding programs. Computer vision (CV) has a higher resolution than human vision and has the potential to achieve large-scale, low-cost, and accurate analysis and identification of crop phenotypes. The existing criteria for investigating phenotypic traits are oriented to artificial species examination, among these are a few traits type that cannot meet the needs of machine learning even if the data are complete. Therefore, the research starts from the need to collect phenotypic data based on CV technology to expand, respectively, the four types of traits in the "Guide to Plant Variety Specificity, Consistency and Stability Testing: Soybean": main agronomic traits in field investigation, main agronomic traits in the indoor survey, resistance traits, and soybean seed phenotypic traits. This paper expounds on the role of the newly added phenotypic traits and shows the necessity of adding them with some instances. The expanded traits are important additions and improvements to the existing criteria. Databases containing expanded traits are important sources of data for Soybean AI Breeding Platforms. They are necessary to provide convenience for deep learning and support the experts to design accurate breeding programs.
Why it matches plant phenotyping methodsCVに基づく大規模な作物表現型データの収集・拡張とデータベース化が中心であり、育種用AIプラットフォームを支える表現型基盤として扱っている。
abstractthe research starts from the need to collect phenotypic data based on CV technology to expand, respectively, the four types of traits
RiceAnnotation / quality controlCalibration / preprocessingVisualization / data management
Background Developing a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators. Results We reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline . Conclusion The analysis workflow and document presented are not limited to IRRI's RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI's RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.
Why it matches plant phenotyping methods作物育種における表現型データの前処理・品質管理・統計解析・可視化を一貫して行う、再現可能なオープンソース解析パイプラインが中心である。
abstractHere, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline
Reproduction assets foundThe paper's authors publicly release their phenotypic data analysis pipeline source codes, sample HTML workflow documents, and demo phenotypic dataset on GitHub, directly reproducing this paper's computational analysis.Code · publicThe analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:1-75Dataset · publicAll the instructions, R source codes, examples, and the data sets are freely available in the GitHub repository at https://github.com/whussain2/Analysis-pipelineOpen asset ↗whussain2/Analysis-pipelinelines:80-91Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Field / plotGrowth chamberObject detectionVisualization / data management
In the upcoming years, global changes in agricultural and environmental systems will require innovative approaches in crop research to ensure more efficient use of natural resources and food security. Cutting-edge technologies for precision agriculture are fundamental to improve in a non-invasive manner, the efficiency of detection of environmental parameters, and to assess complex traits in plants with high accuracy. The application of sensing devices and the implementation of strategies of artificial intelligence for the acquisition and management of high-dimensional data will play a key role to address the needs of next-generation agriculture and boosting breeding in crops. To that end, closing the gap with the knowledge from the other ‘omics’ sciences is the primary objective to relieve the bottleneck that still hinders the potential of thousands of accessions existing for each crop. Although it is an emerging discipline, phenomics does not rely only on technological advances but embraces several other scientific fields including biology, statistics and bioinformatics. Therefore, establishing synergies among research groups and transnational efforts able to facilitate access to new computational methodologies and related information to the community, are needed. In this review, we illustrate the main concepts of plant phenotyping along with sensing devices and mechanisms underpinning imaging analysis in both controlled environments and open fields. We then describe the role of artificial intelligence and machine learning for data analysis and their implication for next-generation breeding, highlighting the ongoing efforts toward big-data management.
Why it matches plant phenotyping methods植物フェノタイピングの概念、センシング機器、画像解析、AI・機械学習を体系的に扱うレビューであり、方法論が中心です。
abstractIn this review, we illustrate the main concepts of plant phenotyping along with sensing devices and mechanisms underpinning imaging analysis in both controlled environments and open fields.
MicroscopyMorphology / geometry measurementTrackingVisualization / data management
A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.
Why it matches plant phenotyping methodsデジタルホログラフィーと数値アルゴリズムを用いて、Tetraselmis微細藻類の3D運動軌跡、運動学的形質、形態を自動・半自動で抽出する方法が研究の中心である。
abstracta fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume.
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureTissueVisualization / data management
The accumulation of the cell wall component callose at plasmodesmata (PD) is crucial for the regulation of symplastic intercellular transport in plants. Here we describe protocols to fluorescently image callose in sectioned plant tissue using monoclonal antibodies. This protocol achieves high-resolution images by the fixation, embedding, and sectioning of plant material to expose internal cell walls. By using this protocol in combination with high-resolution confocal microscopy, we can detect PD callose in a variety of plant tissues and species.
Why it matches plant phenotyping methods植物組織内のPD calloseという細胞状態を蛍光・共焦点画像で取得するプロトコル自体が中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere we describe protocols to fluorescently image callose in sectioned plant tissue using monoclonal antibodies.
Why it matches plant phenotyping methods発生中の葉の細胞状態・組織分化を可視化するための共焦点イメージング手順と撮像条件最適化が中心であり、植物状態の取得法を実質的に扱っている。
abstractHere we address this limitation (1) by providing robust, step-by-step protocols for the local application of the plant hormone auxin to developing leaves and for the routine dissection and mounting of leaves and leaf primordia, and (2) by offering practical guidelines for the optimization of imaging parameters for confocal microscopy.
TobaccoMicroscopyCell / cellular structureGrowth / time-series analysisVisualization / data management
Transgenic tobacco BY-2 cell lines stably expressing fluorescent protein-tagged marker proteins have been used to visualize the dynamic behaviors of cytoskeletons and organelles during plant cell division. Using time-lapse confocal imaging, we recently revealed that the pharmacological disruption of actin filaments results in the abnormal organization of phragmoplast microtubules during the early phase of cytokinesis in cell cycle-synchronized BY-2 cells. Additionally, disrupting the actin filaments shortens the time from cell plate emergence to the accumulation of green fluorescent protein-tagged NACK1 kinesin on the cell plate, suggesting that there are two functionally diverse types of microtubules in the phragmoplast. We herein describe a protocol for the cell cycle synchronization of BY-2 cells and the time-lapse confocal imaging of cytokinesis combined with a treatment with an actin polymerization inhibitor and the visualization of an emerging cell plate with a vital stain. This protocol is useful for examining the dynamic changes in protein localization or the intracellular architecture and the effects of actin disruption during plant cell division.
Why it matches plant phenotyping methods植物細胞分裂中の細胞板形成や細胞内構造の動態を取得するタイムラプス共焦点 imaging protocol が論文の中心であり、植物細胞状態の画像ベース計測法として収載可能。
abstractthe time-lapse confocal imaging of cytokinesis combined with a treatment with an actin polymerization inhibitor and the visualization of an emerging cell plate with a vital stain
ArabidopsisMicroscopyCell / cellular structureVisualization / data management
Live-cell imaging is a powerful method to obtain insights into cellular processes, particularly with respect to their dynamics. This is especially true for meiosis, where chromosomes and other cellular components such as the cytoskeleton follow an elaborate choreography over a relatively short period of time. Making these dynamics visible expands understanding of the regulation of meiosis and its underlying molecular forces. However, the analysis of meiosis by live-cell imaging is challenging; specifically in plants, a temporally resolved understanding of chromosome segregation and recombination events is lacking. Recent advances in live-cell imaging now allow the analysis of meiotic events in plants in real time. These new microscopy methods rely on the generation of reporter lines for meiotic regulators and on the establishment of ex vivo culture and imaging conditions, which stabilize the specimen and keep it alive for several hours or even days. In this review, we combine an overview of the technical aspects of live-cell imaging in plants with a summary of outstanding questions that can now be addressed to promote live-cell imaging in Arabidopsis and other plant species and stimulate ideas on the topics that can be addressed in the context of plant meiotic recombination.
Why it matches plant phenotyping methods植物の減数分裂を対象とするライブセル画像化の技術的側面を中心に扱うレビューであり、植物試料の時系列画像取得法が主要テーマである。
abstractIn this review, we combine an overview of the technical aspects of live-cell imaging in plants with a summary of outstanding questions
MicroscopyCell / cellular structureRootVisualization / data management
Hydrophobic cell wall depositions in roots play a key role in plant development and interaction with the soil environment, as they generate barriers that regulate bidirectional nutrient flux. Techniques to label the respective polymers are emerging, but are efficient only in thin roots or sections. Moreover, simultaneous imaging of the barrier constituents lignin and suberin remains problematic owing to their similar chemical compositions. Here, we describe a staining method compatible with single- and multiphoton confocal microscopy that allows for concurrent visualization of primary cell walls and distinct secondary depositions in one workflow. This protocol permits efficient separation of suberin- and lignin-specific signals with high resolution, enabling precise dissection of barrier constituents. Our approach is compatible with imaging of fluorescent proteins, and can thus complement genetic markers or aid the dissection of barriers in biotic root interactions. We further demonstrate applicability in deep root tissues of plant models and crops across phylogenetic lineages. Our optimized toolset will significantly advance our understanding of root barrier dynamics and function, and of their role in plant interactions with the rhizospheric environment.
Why it matches plant phenotyping methods植物根のリグニン・スベリン等の構造を共焦点顕微鏡で可視化・分離する染色およびイメージング手法の開発が中心であり、根の形態・状態の表現型取得に直接関わる。
abstractHere, we describe a staining method compatible with single- and multiphoton confocal microscopy that allows for concurrent visualization of primary cell walls and distinct secondary depositions in one workflow.
Field / plotLaboratory / benchtopChlorophyll fluorescenceStress / disease detectionVisualization / data managementDisease symptoms / severity
Fungal microparasites (here chytrids) are widely distributed and yet, they are often overlooked in aquatic environments. To facilitate the detection of microparasites, we revisited the applicability of two fungal cell wall markers, Calcofluor White (CFW) and wheat germ agglutinin (WGA), for the direct visualization of chytrid infections on phytoplankton in laboratory-maintained isolates and field-sampled communities. Using a comprehensive set of chytrid-phytoplankton model pathosystems, we verified the staining pattern on diverse morphological structures of chytrids via fluorescence microscopy. Empty sporangia were stained most effectively, followed by encysted zoospores and im-/mature sporangia, while the staining success was more variable for rhizoids, stalks, and resting spores. In a few instances, the staining was unsuccessful (mostly with WGA), presumably due to insufficient cell fixation, gelatinous cell coatings, and multilayered cell walls. CFW and WGA staining could be done in Utermöhl chambers or on polycarbonate filters, but CFW staining on filters seemed less advisable due to high background fluorescence. To visualize chytrids, 1 µg dye mL -1 was sufficient (but 5 µg mL -1 are recommended). Using a dual CFW-WGA staining protocol, we detected multiple, mostly undescribed chytrids in two natural systems (freshwater and coastal), while falsely positive or negative stained cells were well detectable. As a proof-of-concept, we moreover conducted imaging flow cytometry, as a potential high-throughput technology for quantifying chytrid infections. Our guidelines and recommendations are expected to facilitate the detection of chytrid epidemics and to unveil their ecological and economical imprint in natural and engineered aquatic systems.
Why it matches plant phenotyping methods植物プランクトン上の寄生菌感染を蛍光染色で可視化・検出する方法を比較検証し、画像フローサイトメトリーによる高スループット定量も実証しており、感染状態の取得法が中心である。
abstractTo facilitate the detection of microparasites, we revisited the applicability of two fungal cell wall markers, Calcofluor White (CFW) and wheat germ agglutinin (WGA), for the direct visualization of chytrid infections on phytoplankton in laboratory-maintained isolates and field-sampled communities.
The biggest challenge in the classification of plant water stress conditions is the similar appearance of different stress conditions. We introduce HortNet417v1 with 417 layers for rapid recognition, classification, and visualization of plant stress conditions, such as no stress, low stress, middle stress, high stress, and very high stress, in real time with higher accuracy and a lower computing condition. We evaluated the classification performance by training more than 50,632 augmented images and found that HortNet417v1 has 90.77% training, 90.52% cross validation, and 93.00% test accuracy without any overfitting issue, while other networks like Xception, ShuffleNet, and MobileNetv2 have an overfitting issue, although they achieved 100% training accuracy. This research will motivate and encourage the further use of deep learning techniques to automatically detect and classify plant stress conditions and provide farmers with the necessary information to manage irrigation practices in a timely manner.
Why it matches plant phenotyping methods植物の水ストレス状態を画像から自動検出・分類する深層学習手法を開発し、複数データセットで性能評価しており、植物フェノタイピング手法が中心である。
titleA Deep-Learning Architecture for the Automatic Detection of Pot-Cultivated Peach Plant Water Stress
Abstract Digital images are an intuitive way to capture, store and analyse organismal phenotypes. Many biologists are taking images to collect high‐dimensional phenotypic information from specimens to investigate complex ecological, evolutionary and developmental phenomena, such as relationships between trait diversity and ecosystem function, multivariate natural selection or developmental plasticity. As a consequence, images are being collected at ever‐increasing rates, but extraction of the contained phenotypic information poses a veritable analytical bottleneck. phenopype is a high‐throughput phenotyping pipeline for the programming language Python that aims at alleviating this bottleneck. The package facilitates immediate extraction of high‐dimensional phenotypic data from digital images with low levels of background noise and complexity. At the core, phenopype provides functions for rapid signal processing‐based image preprocessing and segmentation, data extraction, as well as visualization and data export. This functionality is provided by wrapping low‐level computer vision libraries (such as OpenCV) into accessible functions to facilitate scientific image analysis. In addition, phenopype provides a project management ecosystem to streamline data collection and to increase reproducibility. phenopype offers two different workflows that support users during different stages of scientific image analysis. The low‐throughput workflow uses regular Python syntax and has greater flexibility at the cost of reproducibility, which is suitable for prototyping during the initial stages of a research project. The high‐throughput workflow allows users to specify and store image‐specific settings for analysis in human‐readable YAML format, and then execute all functions in one step by means of an interactive parser. This approach facilitates rapid program‐user interactions during batch processing, and greatly increases scientific reproducibility. Overall, phenopype intends to make the features of powerful but technically involved low‐level CV libraries available to biologists with little or no Python coding experience. Therefore, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection. Furthermore, image annotations produced by phenopype can be used as training data, thus presenting a stepping stone towards the application of deep learning architectures.
Why it matches plant phenotyping methods画像から表現型データを抽出するPythonパイプラインの開発が研究の中心であり、画像前処理・セグメンテーション・データ抽出・再現可能なワークフローを提供する。
abstractphenopype is a high‐throughput phenotyping pipeline for the programming language Python that aims at alleviating this bottleneck.
GreenhousePhotogrammetry / SfM / MVSX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionVisualization / data managementBiomass / plant weightRoot system architectureWater status / transpiration
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed with an internal volume of 45 ft3 (1.27 m3). Mesocosms allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. Sensors arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at depths of 1.25, 2.75, & 4.25 ft to assess environmental fluxes at regular intervals. Additionally, 3-dimensional water availability can be measured using ERT inside of root mesocosms. Methods of 3D data visualization of fluxes were developed to allow for comparison with root architectural traits. Following harvest, the recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Initial metrics inferred from the 3D models include root system biomass (occupied voxels), volume, flatness, convex hull volume and solidity with depth. Root systems are finally dissected and biomass measurements are made in a 3-dimensional matrix of the growth zone, while the crown is saved for X-ray CT analysis.
Why it matches plant phenotyping methods根系の3次元可視化・再構成と環境センシングを統合したメソコスムおよび表現型抽出手法の開発が中心であり、根系形態形質を定量化している。
abstractTo facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed
In-field fruit monitoring at different growth stages provides important information for farmers. Recent advances have focused on the detection and location of fruits, although the development of accurate fruit size estimation systems is still a challenge that requires further attention. This work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation. Four techniques were evaluated in the fruit size estimation step. The first consisted of obtaining the fruit diameter by measuring the two most distant points of an apple detection (largest segment technique). The second and third techniques were based on fitting a sphere to apple points using least squares (LS) and M−estimator sample consensus (MSAC) algorithms, respectively. Finally, template matching (TM) was applied for fitting an apple 3D model to apple points. The best results were obtained with the LS, MSAC and TM techniques, which showed mean absolute errors of 4.5 mm, 3.7 mm and 4.2 mm, and coefficients of determination (R2) of 0.88, 0.91 and 0.88, respectively. Besides fruit size, the proposed method also estimated the visibility percentage of apples detected. This step showed an R2 of 0.92 with respect to the ground truth visibility. This allowed automatic identification and discrimination of the measurements of highly occluded apples. The main disadvantage of the method is the high processing time required (in this work 2760 s for 3D modelling of 6 trees), which limits its direct application in large agricultural areas. The code and the dataset have been made publicly available and a 3D visualization of results is accessible at http://www.grap.udl.cat/en/publications/apple_size_estimation_SfM.
Why it matches plant phenotyping methods果実径と可視性という植物器官の形態特性を、SfM/MVS点群と複数推定手法で自動抽出・検証する方法論研究であり、フェノタイピング手法が中心です。
abstractThis work proposes a novel methodology for automatic in-field apple size estimation which is based on four main steps: 1) fruit detection; 2) point cloud generation using structure-from-motion (SfM) and multi-view stereo (MVS); 3) fruit size estimation; and 4) fruit visibility estimation.
Embrapa has led breeding programs for irrigated and upland rice (Oryza sativa L.) since 1977, generating a large amount of pedigree and phenotypic data. However, there were no systematic standards for data recording nor long‐term data preservation and reuse strategies. With the new aim of making data reuse practical, we recovered all data available and structured it into the Embrapa Rice Breeding Dataset (ERBD). In its current version, the ERBD includes 20,504 crosses involving 9,974 parents, the pedigrees of most of the 4,532 inbred lines that took part in advanced field trials, and phenotypic data from 2,711 field trials (1,118 irrigated, 1,593 upland trials), representing 226,458 field plots. Those trials were conducted over 38 years (1982–2019), in 247 locations, in latitudes ranging from 3°N to 33°S. Phenotypic traits included grain yield, days to flowering, plant height, canopy lodging, and five important fungal diseases: leaf blast, panicle blast, brown spot, leaf scald, and grain discoloration. The total number of data points surpasses 1.27 million. Descriptive statistics were computed over the dataset, split by cropping systems (irrigated or upland). The mean heritability of grain yield was high for both systems, at around .7, whereas the mean coefficient of variation was 13.9% for irrigated trials and 18.7% for upland trials. The ERBD offers the possibility of conducting studies on different aspects of rice breeding and genetics, including genetic gain, G×E analysis, genome‐wide association studies and genomic prediction.
Why it matches plant phenotyping methodsイネ育種の長期フェノタイプ記録を体系化した再利用可能な大規模データセットの構築が中心であり、植物形質データセットとして収録対象。
abstractwe recovered all data available and structured it into the Embrapa Rice Breeding Dataset (ERBD).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
Why it matches plant phenotyping methodsキノアの表現型計測 framework を中心に、計測カード、リモートセンシングによる高スループット計測、種子計測ツール、データベースを提示しており、植物フェノタイピング手法の方法論的研究である。
abstractHere, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Low temperature freezing stress has adverse effects on wheat seedling growth and final yield. The traditional method to evaluate the wheat injury caused by the freezing stress is by visual observations, which is time-consuming and laborious. Therefore, to effectively and efficiently quantify the wheat freezing injury in the field environments, a high-throughput phenotyping system was developed in this paper , namely, RGB FREEZING INJURY SYSTEM. The system is able to automatically collect, processing, and analyze the wheat images collected using a mobile phenotype cabin in the field conditions. A data management system was also developed to store and manage the original images and the calculated phenotypic data in the system. A group of 128 wheat varieties were planted with replicates under a freezing environment. Canopy images of the wheat were collected at the seedling stage and three image features were extracted for each wheat samples, including ExG, ExR and ExV. The results show that the developed methods can clearly distinguish wheat samples with different wheat freezing injury scores. The automatic phenotypic analysis method of freezing injury provides a solution for high-throughput phenotypic analysis of field wheat and can quantify the stress caused by freezing injury at the seedling stage. The method has a certain guiding significance for wheat breeding.
Why it matches plant phenotyping methodsRGBカメラ、移動式フェノタイピングキャビン、画像特徴量抽出、ソフトウェア制御を用いて、圃場コムギの凍害表現型を自動定量する方法とシステムを開発しており、フェノタイピング手法が研究の中心である。
abstracta high-throughput phenotyping system was developed in this paper , namely, RGB FREEZING INJURY SYSTEM.
We introduce a simple approach to understanding the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. The pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on. We demonstrate the capacity of deep CNNs at performing this classification task, and show the utility of these visualizations on RGB imagery of biomass sorghum captured by the TERRA-REF gantry. We focus on several different genetic markers with known phenotypic expression, and discuss the possibilities of using this approach to uncover genotype x phenotype relationships.
Why it matches plant phenotyping methods植物画像からSNPに対応する表現型をCNNで分類・可視化する解析パイプラインが研究の中心であり、画像に基づく表現型抽出手法として適格です。
abstractThe pipeline involves training deep convolutional neural networks (CNNs) to differentiate between images of plants with reference and alternate versions of various SNPs, and then using visualization approaches to highlight what the classification networks key on.
Abstract BackgroundThanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analyses. However such approaches are not always easily practicable in particular when faced with mixed datasets with missing values. Moreover displaying large numbers of individuals leads to cluttered visualizations which are difficult to interpret. ResultsWe introduce a new methodology to overcome these limits. The underlying principle consists in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) build sparse visualizations based on these archetypes. As a preliminary step to the clustering we design a new semantic distance tailored for both quantitative and qualitative variables which allows a realistic representation of the relationships between individuals. This semantic distance is based on ontologies which are engineered to represent real life knowledge regarding the underlying variables. Our approach is implemented as a Python pipeline and illustrated by a rosebush dataset including passport and phenotypic data. ConclusionsThe introduction of our new semantic distance and of the archetype concept allows us to build a comprehensive representation of an incomplete dataset characterized by large proportion of qualitative data. The methodology described here could have wider use beyond information characterizing organisms or species and beyond plant science. Indeed we could apply the same approach to any incomplete mixed dataset.
Why it matches plant phenotyping methods不完全で異種の植物表現型データを可視化するための距離尺度・クラスタリング・アーキタイプ表現・Pythonパイプラインを中心に開発しており、表現型解析手法が研究の中核である。
abstractWe introduce a new methodology to overcome these limits.
Reproduction assets foundThe authors' DIVIS Python pipeline (semantic distance, clustering, archetype visualization) is publicly available on Forgemia with an archived v1.0 release and bundled OWL ontology. The rosebush phenotypic dataset itself is only available on request, so it is not a public asset.Code · publicis
• MCA: Multiple Correspondance Analysis
• MDS: Multi-Dimensional Scaling
• OWL: Web Ontology Language
• SPARQL: SPARQL Protocol and RDF Query Language
Availability of data and materials
The software developed to implement the pipeline presented in this paper is available as follows:
• Project name: DIVIS
• Project home page: https://forgemia.inra.fr/irhs-bioinfo/Divis
• Archived version: v1.0
• Operating system(s): Platform independent
• Programming language: Python 3.7
• Other requirements: Described as requirement.txt file for pip in the code repository
• License: CeCILL. See LICENCE file in the code repository
• Any restrictions to use by non-academics: None
The OWL ontology (in FrenchOpen asset ↗irhs-bioinfo/Divis · v1.0pdf-raw-page:17 lines:1-69Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Laboratory / benchtopChlorophyll fluorescenceMicroscopyRootGrowth / time-series analysisVisualization / data management
Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root–microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.
Why it matches plant phenotyping methods植物根系全体を高解像度・経時的に撮像するための新規チャンバーを開発し、その撮像性能と用途を示しており、表現型取得法が研究の中心である。
abstractHere, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period.
Reproduction assets foundThe paper's computational analysis (K-means clustering and segmentation/cell counting of the 40× multispectral root image) is explicitly stated to have its environment, code, and parent data file available in the Supplementary Materials, hosted at the MDPI S1 link. Additionally, the 3D-printing-ready Imaging EcoFAB 3D-Code · publicThe environment, code, and parent data file are available in the Supplementary Materials .Open asset ↗lines:65-81Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Drone is a UAV vehicle which is currently widely used in various activities, one of which is for aerial photography (photogrammetry). The concept of efficiency is the main goal of using this drone, which is to produce detailed and up-to-date aerial photo images with adjustable area coverage, relatively short time, affordable costs and minimal personnel required. The output is an orthophoto image that already has coordinates and can be used as primary data for convenience in the tree counting process so that tree populations in blocks based on design and area statements can be known in detail and accountably. The aerial mapping process using a copter unmanned vehicle with a height of 80-meters above ground level at an image resolution of 2.23 cm/pixel produces 3,795 photos with side overlap and front overlap photos of 70% and 80% with an area covering 2.72 km2 or 272 ha. The results of the calculation of oil palm trees as many as 3,147 pkk with a statement area of 29.08 ha in Block M06 and obtained an SpH of 108 pkk which is smaller than the ideal SpH ranging from 135-143 pkk/ha so that there is a need for compaction activities or plant fulfillment in the block.
Why it matches plant phenotyping methodsUAV空撮・フォトグラメトリによるオルソ画像生成とヤシ個体数・植栽密度の抽出が研究の中心であり、圃場レベルの植物状態を画像から定量化する実質的なフェノタイピング応用である。
abstractThe output is an orthophoto image that already has coordinates and can be used as primary data for convenience in the tree counting process so that tree populations in blocks based on design and area statements can be known in detail and accountably.
Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.
Why it matches plant phenotyping methods植物フェノミクスデータを探索・解析し、表現型特性を抽出するオープンソースソフトウェアの開発と評価が中心であるため。
abstractIn this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe tool is available as open source in the GitHub repository [ 18 ] .Open asset ↗lines:185-261Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.
Why it matches plant phenotyping methods画像ベースHTPの資源・データセット・ツール性能評価を扱うレビューであり、植物表現型計測手法と再利用可能なデータ資源が中心です。
titleResources for image-based high-throughput phenotyping in crops and data sharing challenges
PotatoMultispectral / hyperspectralPhysiological trait estimationVisualization / data management
Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.
Why it matches plant phenotyping methodsジャガイモ塊茎のデンプン含量という植物器官形質を、ハイパースペクトル画像とケモメトリクスで非破壊推定・可視化する方法が研究の中心であり、モデル性能の検証も行っている。
abstractThis study combined hyperspectral imaging with chemometrics to predict potato starch content.
CoffeeLeafClassificationVisualization / data managementDisease symptoms / severity
Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model "sees" as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.
Why it matches plant phenotyping methodsコーヒー葉の病害領域を画像から可視化・分類する手法が研究の中心であり、Grad-CAM系手法の比較と分類精度の評価を行っているため、植物病害状態の画像ベース表現型解析に該当する。
abstractIn this study, we present a methodology for visualizing coffee diseases using different visualization approaches.
Reproduction assets foundThe paper's plant-phenotyping input data (the Robusta coffee leaf image dataset, RoCoLe, used for disease classification and visualization experiments) is explicitly declared openly available in Mendeley Data. No author analysis code or trained model checkpoints are stated as publicly available. The only allowed URL isDataset · publicThe data presented in this study are openly available in Mendeley Data at doi:10.17632/c5yvn32dzg.2, reference number 36.Mendeley Data · doi:10.17632/c5yvn32dzg.2lines:198-232Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
MicroscopyCell / cellular structureVisualization / data management
Premise Fluorescence microscopy is an effective tool for viewing plant internal anatomy. However, using fluorescent antibodies or labels hinders throughput. We present a minimal protocol that takes advantage of inherent autofluorescence and aldehyde-induced fluorescence in plant cellular and subcellular structures to markedly increase throughput in cellular and ultrastructural visualization. Methods and results Twelve species distributed across the plant phylogeny were each subjected to five fixative treatments: 1% paraformaldehyde and 2% glutaraldehyde, 2% paraformaldehyde, 2% glutaraldehyde, formalin-acid-alcohol (FAA), and 70% ethanol. Samples were prepared by embedding and mechanically sectioning or via whole mount. A confocal laser scanning system was used to collect micrographs. We evaluated and compared fixative influence on sample structural preservation and tissue autofluorescence. Conclusions Formaldehyde fixation of Viridiplantae taxa samples generates useful structural data while requiring no additional histological staining or clearing. In addition, a fluorescence-capable microscope is the only specialized equipment required for image acquisition. The minimal protocol developed in this experiment enables high-throughput sample processing by eliminating the need for multi-day preparations.
Why it matches plant phenotyping methods植物組織の細胞・超微細構造を自家蛍光で可視化する高スループット画像取得プロトコルの開発が中心であり、植物解剖学的状態の表現型取得に該当する。
abstractWe present a minimal protocol that takes advantage of inherent autofluorescence and aldehyde-induced fluorescence in plant cellular and subcellular structures to markedly increase throughput in cellular and ultrastructural visualization.
The primary plant cell wall is a complex matrix composed of interconnected polysaccharides including cellulose, hemicellulose, and pectin. Changes of this dynamic polysaccharide system play a critical role during plant cell development and differentiation. A better understanding of cell wall architectures can provide insight into the plant cell development. In this study, a Raman spectroscopic imaging approach was developed to visualize the distribution of plant cell wall polysaccharides. In this approach, Surface-enhanced Raman scattering (SERS through self-assembled silver nanoparticles) was combined with Raman labels (4-Aminothiophenol. 4ATP) and targeted enzymatic hydrolysis to improve the sensitivity, specificity, and throughput of the Raman imaging technique, and to reveal the distribution of pectin and its co-localization with xyloglucan inside onion epidermal cell (OEC) wall. This technique significantly decreased the required spectral acquisition time. The resulted Raman spectra showed a high Raman signal. The resulted Raman images successfully revealed and characterized the pectin distribution and its co-localization pattern with xyloglucan in OEC wall.
Why it matches plant phenotyping methods植物細胞壁多糖類の分布を可視化・特徴づけるラマン分光イメージング法を開発しており、フェノタイピング手法の開発が研究の中心である。
abstractIn this study, a Raman spectroscopic imaging approach was developed to visualize the distribution of plant cell wall polysaccharides.
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data management
A critical shortage of 'big' agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network.
Why it matches plant phenotyping methods大規模な機械測定フェノタイプデータセットの構築・提供が中心であり、植物フェノタイピング研究用データ基盤に該当する。
abstractproviding a large non-relational database of agronomic trials
Reproduction assets foundThe paper is a data descriptor for a million-phenotype agronomic dataset (Australian National Variety Trials) with associated annotated R processing code, both deposited publicly on figshare as a collection by the authors. The figshare deposit directly contains the paper's phenotype data, environmental time series, andCode · publicCode availability
All data and code is available without restrictions from figshare19 and from the corresponding author on request.Open asset ↗figsharepdf-page:7 lines:1-62Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
BlueberryLaboratory / benchtopMRI / PETTissueObject detectionCalibration / preprocessingStress / disease detectionVisualization / data managementStress response / toleranceWater status / transpiration
Abstract Background Investigating plant mechanisms to tolerate freezing temperatures is critical to developing crops with superior cold hardiness. However, the lack of imaging methods that allow the visualization of freezing events in complex plant tissues remains a key limitation. Magnetic resonance imaging (MRI) has been successfully used to study many different plant models, including the study of in vivo changes during freezing. However, despite its benefits and past successes, the use of MRI in plant sciences remains low, likely due to limited access, high costs, and associated engineering challenges, such as keeping samples frozen for cold hardiness studies. To address this latter need, a novel device for keeping plant specimens at freezing temperatures during MRI is described. Results The device consists of commercial and custom parts. All custom parts were 3D printed and made available as open source to increase accessibility to research groups who wish to reproduce or iterate on this work. Calibration tests documented that, upon temperature equilibration for a given experimental temperature, conditions between the circulating coolant bath and inside the device seated within the bore of the magnet varied by less than 0.1 °C. The device was tested on plant material by imaging buds from Vaccinium macrocarpon in a small animal MRI system, at four temperatures, 20 °C, − 7 °C, − 14 °C, and − 21 °C. Results were compared to those obtained by independent controlled freezing test (CFT) evaluations. Non-damaging freezing events in inner bud structures were detected from the imaging data collected using this device, phenomena that are undetectable using CFT. Conclusions The use of this novel cooling and freezing device in conjunction with MRI facilitated the detection of freezing events in intact plant tissues through the observation of the presence and absence of water in liquid state. The device represents an important addition to plant imaging tools currently available to researchers. Furthermore, its open-source and customizable design ensures that it will be accessible to a wide range of researchers and applications.
Why it matches plant phenotyping methods植物組織の凍結イベントをMRIで可視化するための冷却・凍結デバイスを開発し、温度校正と植物試料での検証を行った、中心的なフェノタイピング手法研究である。
abstracta novel device for keeping plant specimens at freezing temperatures during MRI is described.
The Minimal Information About Plant Phenotyping Experiment, MIAPPE (www.miappe.org) has been designed by ELIXIR, EMPHASIS and Bioversity international to guide plant scientists in the management of experimental data and to facilitate integration with genotyping data.The webinar gives an overview of the current practices and methods for plant phenotyping data standardization. A recording of the webinar is available:https://youtu.be/Fq2j16jzdBA The webinar was organized in the frame of the AGENT and GenRes Bridge projects that received funding from the European Union’s Horizon 2020 research and innovation programme under respective grant agreements No 862613, and No 817580.
Why it matches plant phenotyping methods植物フェノタイピング実験のデータ管理・標準化手法を中心に扱うウェビナーであり、方法論的レビュー／標準化リソースとして適格。
abstractThe webinar gives an overview of the current practices and methods for plant phenotyping data standardization.
CoffeeField / plotFruitClassificationObject detectionVisualization / data management
In this study, an algorithm is implemented with a computer vision model to detect and classify coffee fruits and map the fruits maturation stage during harvest. The main contribution of this study is with respect to the assignment of geographic coordinates to each frame, which enables the mapping of detection summaries across coffee rows. The model used to detect and classify coffee fruits was implemented using the Darknet, an open source framework for neural networks written in C. The coffee fruits detection and classification were performed using the object detection system named YOLOv3-tiny. For this study, 90 videos were recorded at the end of the discharge conveyor of a coffee harvester during the 2020 harvest of arabica coffee (Catuaí 144) at a commercial area in the region of Patos de Minas, in the state of Minas Gerais, Brazil. The model performance peaked around the ~3300th iteration when considering an image input resolution of 800 × 800 pixels. The model presented an mAP of 84%, F1-Score of 82%, precision of 83%, and recall of 82% for the validation set. The average precision for the classes of unripe, ripe, and overripe coffee fruits was 86%, 85%, and 80%, respectively. As the algorithm enabled the detection and classification in videos collected during the harvest, it was possible to map the qualitative attributes regarding the coffee maturation stage along the crop lines. These attribute maps provide managers important spatial information for the application of precision agriculture techniques in crop management. Additionally, this study should incentive future research to customize the deep learning model for certain tasks in agriculture and precision agriculture.
Why it matches plant phenotyping methodsコーヒー果実の成熟段階という植物器官の状態を、コンピュータビジョンで検出・分類・マッピングする手法が研究の中心であり、性能評価も行っている。単なる収穫対象の位置検出を超えて成熟状態を推定しているため、植物フェノタイピング手法に該当する。
abstractan algorithm is implemented with a computer vision model to detect and classify coffee fruits and map the fruits maturation stage during harvest.
Field / plotMicroscopyCell / cellular structureWhole plant / canopy / plot / fieldVisualization / data managementBiomass / plant weight
Abstract Plant cell wall-derived biomass serves as a renewable source of energy and materials with increasing importance. The cell walls are biomacromolecular assemblies defined by a fine arrangement of different classes of polysaccharides, proteoglycans, and aromatic polymers and are one of the most complex structures in Nature. One of the most challenging tasks of cell biology and biomass biotechnology research is to image the structure and organization of this complex matrix, as well as to visualize the compartmentalized, multiplayer biosynthetic machineries that build the elaborate cell wall architecture. Better knowledge of the plant cells, cell walls, and whole tissue is essential for bioengineering efforts and for designing efficient strategies of industrial deconstruction of the cell wall-derived biomass and its saccharification. Cell wall-directed molecular probes and analysis by light microscopy, which is capable of imaging with a high level of specificity, little sample processing, and often in real time, are important tools to understand cell wall assemblies. This review provides a comprehensive overview about the possibilities for fluorescence label-based imaging techniques and a variety of probing methods, discussing both well-established and emerging tools. Examples of applications of these tools are provided. We also list and discuss the advantages and limitations of the methods. Specifically, we elaborate on what are the most important considerations when applying a particular technique for plants, the potential for future development, and how the plant cell wall field might be inspired by advances in the biomedical and general cell biology fields.
Why it matches plant phenotyping methods植物細胞壁の構造・生合成機構を可視化する蛍光イメージング手法を包括的にレビューしており、植物の形態・状態の取得技術が中心です。
abstractThis review provides a comprehensive overview about the possibilities for fluorescence label-based imaging techniques and a variety of probing methods, discussing both well-established and emerging tools.
Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.
Why it matches plant phenotyping methods超音波アレイによる葉に隠れたブドウ房の画像化と、果房体積・樹冠体積の推定手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThis paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes.
MicroscopyRaman / spectroscopyCell / cellular structureVisualization / data management
Abstract Background New cell wall imaging tools permit direct visualization of the molecular architecture of cell walls and provide detailed chemical information on wall polymers, which will aid efforts to use these polymers in multiple applications; however, detailed imaging and quantification of the native composition and architecture in the cell wall remains challenging. Results Here, we describe a label-free imaging technology, coherent Raman scattering (CRS) microscopy, including coherent anti-Stokes Raman scattering (CARS) microscopy and stimulated Raman scattering (SRS) microscopy, which can be used to visualize the major structures and chemical composition of plant cell walls. We outline the major steps of the procedure, including sample preparation, setting the mapping parameters, analysis of spectral data, and image generation. Applying this rapid approach will help researchers understand the highly heterogeneous structures and organization of plant cell walls. Conclusions This method can potentially be incorporated into label-free microanalyses of plant cell wall chemical composition based on the in situ vibrations of molecules.
Why it matches plant phenotyping methods植物細胞壁の構造と化学組成を可視化・定量するラベルフリー画像化技術と解析手順が中心であり、植物の形態・組成状態を取得するフェノタイピング手法に該当する。
abstractHere, we describe a label-free imaging technology, coherent Raman scattering (CRS) microscopy, including coherent anti-Stokes Raman scattering (CARS) microscopy and stimulated Raman scattering (SRS) microscopy, which can be used to visualize the major structures and chemical composition of plant cell walls.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Historical hard-rock mine activities have resulted in nearly half a million mining-impacted sites scattered around the US. Compared to conventional remediation, (aided) phytostabilization is generally cost-effective and ecologically productive approach, particularly for large-scale sites. Native species act to maintain higher local biodiversity, providing a foundation for natural ecological succession. Due to heterogeneity of mine waste, revegetation strategies are inconsistent in approach, and to avoid failure scenarios, greenhouse screening studies can identify candidate plants and amendment strategies before scaling up. This greenhouse study aimed to concurrently screen a variety of native species for their potential to revegetate Cu/Pb/Zn mine tailings and develop a high throughput and non-destructive approach utilizing computer vision and image-based phenotyping technologies to quantify plant responses. A total number of 34 species were screened in this study, which included: 5 trees, 8 grasses, and 21 forbs and legumes. Most of the species tested were Missouri native and prairie species. Plants were non-destructively imaged, and 15 shape and color phenotypic attributes were extracted utilizing computer vision techniques of PlantCV. Compared to reference soil, all species tested were negatively impacted by the tailings' characteristics, with lowest tolerance generally observed in tree species. However, significant improvement in plant growth and tolerance generally observed with biosolids addition with biomass surpassing reference soil for most legumes. Accumulation of Cu, Pb, and Zn was below Domestic Animal Toxicity Limits in most species. Statistically robust differences in species responses were observed using phenotypic data, such as area, height, width, color, and 9 other morphological attributes. Correlations with destructive data indicated that area displayed the greatest positive correlation with biomass and color the greatest negative correlation with shoot metals. Computer visualization greatly increased the phenotypic data and offers a breakthrough in rapid, high throughput data collection to project site-specific phytostabilization strategies to efficiently restore mine-impacted sites.
Why it matches plant phenotyping methodsPlantCVを用いた画像ベースの植物形質抽出と非破壊・ハイスループット計測が研究の中心であり、復元候補種のスクリーニングに実質的に適用されているため。
abstractdevelop a high throughput and non-destructive approach utilizing computer vision and image-based phenotyping technologies to quantify plant responses
Laboratory / benchtopMicroscopyCell / cellular structureRootVisualization / data management
Abstract Background The formation of infection threads in the symbiotic infection of rhizobacteria in legumes is a unique, fascinating, and poorly understood process. Infection threads are tubes of cell wall material that transport rhizobacteria from root hair cells to developing nodules in host roots. They form in a type of reverse tip-growth from an inversion of the root hair cell wall, but the mechanism driving this growth is unknown, and the composition of the thread wall remains unclear. High resolution, 3-dimensional imaging of infection threads, and cell wall component specific labelling, would greatly aid in our understanding of the nature and development of these structures. To date, such imaging has not been done, with infection threads typically imaged by GFP-tagged rhizobia within them, or histochemically in thin sections. Results We have developed new methods of imaging infection threads using novel and traditional cell wall fluorescent labels, and laser confocal scanning microscopy. We applied a new Periodic Acid Schiff (PAS) stain using rhodamine-123 to the labelling of whole cleared infected roots of Medicago truncatula ; which allowed for imaging of infection threads in greater 3D detail than had previously been achieved. By the combination of the above method and a calcofluor-white counter-stain, we also succeeded in labelling infection threads and plant cell walls separately, and have potentially discovered a way in which the infection thread matrix can be visualized. Conclusions Our methods have made the imaging and study of infection threads more effective and informative, and present exciting new opportunities for future research in the area.
Why it matches plant phenotyping methods感染スレッドという植物組織・構造の3D画像取得法を開発し、蛍光標識と共焦点顕微鏡で可視化性能を改善した研究であり、表現型取得法が中心です。
abstractWe have developed new methods of imaging infection threads using novel and traditional cell wall fluorescent labels, and laser confocal scanning microscopy.
Optical microscopy techniques for plant inspection benefit from the fact that at least one of the multiple properties of light (intensity, phase, wavelength, polarization) may be modified by vegetal tissues. Paradoxically, polarimetric microscopy although being a mature technique in biophotonics, is not so commonly used in botany. Importantly, only specific polarimetric observables, as birefringence or dichroism, have some presence in botany studies, and other relevant metrics, as those based on depolarization, are underused. We present a versatile method, based on a representative selection of polarimetric observables, to obtain and to analyse images of plants which bring significant information about their structure and/or the spatial organization of their constituents (cells, organelles, among other structures). We provide a thorough analysis of polarimetric microscopy images of sections of plant leaves which are compared with those obtained by other commonly used microscopy techniques in plant biology. Our results show the interest of polarimetric microscopy for plant inspection, as it is non-destructive technique, highly competitive in economical and time consumption, and providing advantages compared to standard non-polarizing techniques.
Why it matches plant phenotyping methods植物組織の構造や構成要素の空間組織を画像化・解析する偏光顕微鏡法を開発し、他の顕微鏡法と比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe present a versatile method, based on a representative selection of polarimetric observables, to obtain and to analyse images of plants which bring significant information about their structure and/or the spatial organization of their constituents (cells, organelles, among other structures).
Cotton is a significant economic crop. It is vulnerable to aphids ( Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376-1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.
Why it matches plant phenotyping methods綿葉のアブラムシ感染状態を、ハイパースペクトル画像とCNNで直接推定する画像ベースの植物状態フェノタイピング手法を開発・比較しており、手法が中心的である。
abstractthe visible/near-infrared (Vis/NIR) hyperspectral imaging system (376-1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.13668314) containing the original contributions — the cotton leaf hyperspectral images/spectra used for the 1D/2D/3D CNN aphid-infection analysis. No code availability is stated.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://doi.org/10.6084/m9.figshare.13668314 .Open asset ↗figshare · 10.6084/m9.figshare.13668314lines:721-728Code / dataset availability confirmedOpenAlex · checked 8 Sept 2026
ABSTRACT Similarities in phenotypic descriptions can be indicative of shared genetics, metabolism, and stress responses, to name a few. Finding and measuring similarity across descriptions of phenotype is not straightforward, with previous successes in computation requiring a great deal of expert data curation. Natural language processing of free text descriptions of phenotype is often less resource intensive than applying expert curation. It is therefore critical to understand the performance of natural language processing techniques for organizing and analyzing biological datasets and for enabling biological discovery. For predicting similar phenotypes, a wide variety of approaches from the natural language processing domain perform as well as curation-based methods. These computational approaches also show promise both for helping curators organize and work with large datasets and for enabling researchers to explore relationships among available phenotype descriptions. Here we generate networks of phenotype similarity and share a web application for querying a dataset of associated plant genes using these text mining approaches. Example situations and species for which application of these techniques is most useful are discussed. Database URLs The database and analytical tool called QuOATS are available at https://quoats.dill-picl.org/ . Code for the web application is available at https://git.io/Jtv9J . Datasets are available for direct access via https://zenodo.org/record/7947342#.ZGwAKOzMK3I . The code for the analyses performed for the publication is available at https://github.com/Dill-PICL/Plant-data and https://github.com/Dill-PICL/NLP-Plant-Phenotypes .
Why it matches plant phenotyping methods植物表現型の自然言語記述をNLPで類似性解析し、遺伝子データセット探索用のWebアプリケーションを開発・提供しており、表現型データの計算的整理・解析手法が中心です。
abstractNatural language processing of free text descriptions of phenotype is often less resource intensive than applying expert curation.
Reproduction assets foundThe paper's phenotype description dataset and analysis code are explicitly deposited with public git.io URLs, and the QuOATS web application is publicly hosted. All are paper-specific and actionable.Dataset · publicThe dataset used in this work is available at https://git.io/JTutQ.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the analysis performed here
is available at https://git.io/JTutN and https://git.io/JTuqv.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the analysis performed here
is available at https://git.io/JTutN and https://git.io/JTuqv.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the web application discussed here
is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.Open asset ↗pdf-page:1 lines:1-60Code · publicThe code for the web application discussed here
is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.Open asset ↗pdf-page:1 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
MicroscopyCell / cellular structureRootVisualization / data management
Histological stains are useful tools for characterizing cell shape, arrangement and the material they are made from. Stains can be used individually or simultaneously to mark different cell structures or polymers within the same cells, and to visualize them in different colors. Histological stains can be combined with genetically-encoded fluorescent proteins, which are useful for understanding of plant development. To visualize suberin lamellae by fluorescent microscopy, we improved a histological staining procedure with the dyes Fluorol Yellow 088 and aniline blue. In the complex plant organs such as roots, suberin lamellae are deposited deep within the root on the endodermal cell wall. Our procedure yields reliable and detailed images that can be used to determine the suberin pattern in root cells. The main advantage of this protocol is its efficiency, the detailed visualization of suberin localization it generates in the root, and the possibility of returning to the confocal images to analyze and re-evaluate data if necessary.
Why it matches plant phenotyping methods根のスベリン局在を可視化・再解析する染色プロトコルを改良しており、植物組織の状態を取得する方法自体が中心である。
abstractwe improved a histological staining procedure with the dyes Fluorol Yellow 088 and aniline blue.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Studies on plant–pathogen interactions often involve monitoring disease symptoms or responses of the host plant to pathogen‐derived immunogenic patterns, either visually or by staining the plant tissue. Both these methods have limitations with respect to resolution, reproducibility, and the ability to quantify the results. In this study we show that red light detection by the red fluorescent protein (RFP) channel of a multipurpose fluorescence imaging system that is probably available in many laboratories can be used to visualize plant tissue undergoing cell death. Red light emission is the result of chlorophyll fluorescence on thylakoid membrane disassembly during the development of a programmed cell death process. The activation of programmed cell death can occur during either a hypersensitive response to a biotrophic pathogen or an apoptotic cell death triggered by a necrotrophic pathogen. Quantifying the intensity of the red light signal enables the magnitude of programmed cell death to be evaluated and provides a readout of the plant immune response in a faster, safer, and nondestructive manner when compared to previously developed chemical staining methodologies. This application can be implemented to screen for differences in symptom severity in plant–pathogen interactions, and to visualize and quantify in a more sensitive and objective manner the intensity of the plant response on perception of a given immunological pattern. We illustrate the utility and versatility of the method using diverse immunogenic patterns and pathogens.
Why it matches plant phenotyping methods植物組織の細胞死と病徴重症度を蛍光イメージングで可視化・定量する方法が研究の中心であり、植物病害状態という表現型を取得する技術として明確に扱われている。
abstractQuantifying the intensity of the red light signal enables the magnitude of programmed cell death to be evaluated
TissueCalibration / preprocessingVisualization / data management
DRPPP (Deep-Resolution Plant Phenotyping Platform) is a combination of protocols for plant tissue preparation, labeling, scanning, and open-source software to visualize and analyze 4D biological datasets. Here we describe a step-by-step procedure, including sample preparation and data analysis.
Why it matches plant phenotyping methods植物組織のスキャンとオープンソース解析ソフトを組み合わせた4D表現型解析プラットフォームの手順を中心に記述しており、方法論的貢献が明確です。
abstractDRPPP (Deep-Resolution Plant Phenotyping Platform) is a combination of protocols for plant tissue preparation, labeling, scanning, and open-source software to visualize and analyze 4D biological datasets.
MicroscopyCell / cellular structureVisualization / data management
Cortical microtubules (CMTs) play pivotal roles during plant cell growth and division. The organization of CMTs undergoes important changes during different cellular and developmental processes. Here, we describe two methods for the visualization of CMT organization in plant cells using confocal laser scanning microscopy. CMT networks in the outer tissue layers can be directly visualized by live imaging of a fluorescent reporter line, and a protocol combining sectioning and immunostaining is applied for visualization of CMTs throughout tissues. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2020).
Why it matches plant phenotyping methods植物細胞内の微小管ネットワーク構成を可視化・評価するための共焦点イメージングおよび免疫染色プロトコルが主題であり、植物の細胞状態を取得する方法論が中心である。
abstractHere, we describe two methods for the visualization of CMT organization in plant cells using confocal laser scanning microscopy.
Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining and enables high-quality 3D imaging of embryo morphology and expression of fluorescent reporters with the cellular resolution. Our protocol provides a reliable method that is applicable to the analysis of morphogenesis and gene expression patterns in Arabidopsis thaliana embryos.
Why it matches plant phenotyping methodsArabidopsis胚の形態を細胞解像度で3D取得するClearSeeベースのクリアリング・画像化プロトコルが研究の中心であり、植物表現型の取得方法を開発している。
abstractHere, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos.
Reproduction assets foundThe paper's supplementary materials include Movie S1, the Z-stack confocal image data (157 serial optical sections) used for the paper's 3D embryo visualization analysis, publicly available at the MDPI supplementary URL. No author analysis code or trained models are reported.Supplement · publicThe following are available online at https://www.mdpi.com/2223-7747/10/2/190/s1 , Movie S1: Z-stack images of 157 serial optical sections used for Figure 2 ; Table S1: Primers used in this study.
Click here for additional data file.
Author Contributions
Conceptualization, M.A.; methodology, M.A.; validation, A.I. and M.A.; formal analysis, A.I.; investigation, A.I., M.Y., T.S., A.O., and M.A.; resources, TOpen asset ↗lines:58-85Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Background Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress. Results In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents. Conclusions It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.
Why it matches plant phenotyping methodsラマン分光と校正モデル移転を用いて茶葉のクロロフィル・カロテノイド濃度を画素単位で定量・画像化する手法を開発し、携帯型装置への移転と実測値との相関も検証しているため、植物フェノタイピング手法が中心である。
abstractin situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer
Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here we describe a ClearSee-based clearing protocol, which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining, and enables high quality 3D imaging of embryo morphology and expression of a fluorescent reporter with the cellular resolution.
Why it matches plant phenotyping methodsArabidopsis胚の形態を細胞解像度で3D取得するためのClearSeeベースの組織透明化・イメージングプロトコルが研究の中心であり、植物表現型の取得法に該当する。
abstractHere we describe a ClearSee-based clearing protocol, which is suitable for obtaining 3D images of Arabidopsis thaliana embryos.
The mechanical properties (like sensory texture etc.) of plants/fruits directly depend on their microstructures. Therefore, it is very important to well understand the geometry and topology of cells in order to control the microstructure for better mechanical response. In this research, techniques of digital image processing and segmentation in conjunction with mathematical morphology models are used to visualize and analyze the 3D cells of potato. ImageJ and MATLAB are used throughout in this study. The labeled image stacks are essential for studying quantitative characterization of 3D cells, MATLAB is used to label each image stacks. By using MATLAB 12420 cells were segmented within a short period of time and labeled each cell uniquely.
Why it matches plant phenotyping methodsジャガイモ細胞の3D形状・トポロジーを画像処理、セグメンテーション、数学的形態学で定量化する手法が研究の中心であり、植物組織の形態的表現型を抽出している。
abstracttechniques of digital image processing and segmentation in conjunction with mathematical morphology models are used to visualize and analyze the 3D cells of potato.
We recently reported that Viburnum tinus fruit generates its metallic blue color using globular lipid inclusions embedded in its epicarpal cell walls. This protocol describes steps to visualize the lipidic nature of the nanostructure using cryo-ultramicrotomy, chloroform extraction, and transmission electron microscopy (TEM) imaging. This method is useful to localize and characterize novel lipidic nanostructures embedded in both plant and animal tissues at the TEM resolution. For complete details on the use and execution of this protocol, please refer to Middleton et al. (2020).
Why it matches plant phenotyping methods植物果実組織内の脂質ナノ構造を低温超薄切片法とTEMで可視化・局在化するプロトコルであり、植物器官の構造的形質取得が中心です。
abstractThis protocol describes steps to visualize the lipidic nature of the nanostructure using cryo-ultramicrotomy, chloroform extraction, and transmission electron microscopy (TEM) imaging.
RGB / grayscaleFruitClassificationVisualization / data managementDisease symptoms / severity
Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.
Why it matches plant phenotyping methods柿果実の内部障害をRGB画像と深層学習で診断し、Grad-CAMやLRPで診断根拠を可視化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含める。
abstractwe propose an application of deep neural networks to make an image-based internal disorder diagnosis
Morphometrics has been applied in several fields of science including botany. Plant leaves are been one of the most important organs in the identification of plants due to its high variability across different plant groups. The differences between and within plant species reflect variations in genotypes, development, evolution, and environment. While traditional morphometrics has contributed tremendously to reducing the problems that come with the identification of plants and delimitation of species based on morphology, technological advancements have led to the creation of deep learning digital solutions that made it easy to study leaves and detect more characters to complement already existing leaf datasets. In this study, we demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, height of (each position of the) tooth from tip, and the height of each (position of the) tooth from base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can quantitatively track diversity in leaf specimens, and it can be applied to functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to apply and carry out more accurate image-based researches in diverse areas including, but not limited to, plant development, evolution, and phenotyping.
Why it matches plant phenotyping methodsMorphoLeafによる葉画像のスキャン、ランドマーク抽出、形態計測データ化を中心に、葉の形状形質を定量化するソフトウェア／ワークフローを実証しているため。
abstractwe demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours.
Reproduction assets foundThe preprint states its supplementary data (the Cucurbitaceae leaf morphometric dataset from MorphoLeaf analysis) is available online in the authors' GitHub repository, matching an allowed URL.Dataset · public411 The Data for this article is available online at: https://github.com/osooluwatobia/cucurbitaceae-Open asset ↗cucurbitaceae-pdf-page:19 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Abstract A method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue. CDTA chelates divalent cations that cross‐link the pectic polysaccharides of the middle lamella, allowing gentle separation of the cells without harsh physical treatments. These individual cells are ideal for observing starch granules in situ by microscopy without the requirement for fixation, embedding, sectioning, or prior starch extraction. Starch can easily be observed either unstained, or by polarizing optics, or after staining with iodide (I 2 /KI). Staining with I 2 /KI in combination with polarizing optics gives information on polarizing colors that indicate compositional differences within granules. Examples of the starch complement in developing, mature, and cooked rr wrinkled pea cells, and in banana and potato tissue are shown. The CDTA‐separation method is ideal for the survey of starch mutants and other cell components as it preserves cytoplasmic organization and prevents microbial degradation during storage.
Why it matches plant phenotyping methods植物組織から細胞を分離し、顕微鏡でデンプン顆粒を観察する方法自体が中心であり、デンプン変異体の調査など植物形質評価への再利用性が示されている。
abstractA method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue.
Abstract The plant cell wall is a complex network of polysaccharides. A better understanding of the plant cell wall polysaccharide content, distribution, and interactions among them could be instrumental for our further understanding of plant cell wall in general. Confocal Raman microscopy (CRM) is a powerful tool that could reveal details about chemical landscape of the plant cell wall with micrometer resolution. However, the low signal‐to‐noise (S/N) ratio of Raman spectral signal led to low throughput of Raman imaging, which limited its application in plant cell wall study. In this study, the interaction between the cell wall polysaccharides and the pectin enzyme endo‐polygalacturonase (EPG) was characterized by analyzing Raman images collected before and after EPG treatment at high scanning speed. The obtained low S/N ratio Raman spectra were processed with principal component analysis (PCA) and hierarchical clustering analysis (HCA) to recover information‐rich signature changes in Raman signal dataset that reflected the changes in the polysaccharides caused specifically by the enzymatic hydrolysis. The PCA reconstruction technique significantly improved the S/N ratio of the spectra dataset while kept the Raman signal intact. Further analysis of principal components (PCs) revealed the pectin distribution and its interaction with the enzyme, which provided organizational details of pectin inside the onion plant cell wall. The technique could be used to reveal polysaccharide organization and distribution inside plant cell walls.
Why it matches plant phenotyping methods植物細胞壁内のペクチン分布を取得するラマン分光イメージングと、低S/Nデータを改善するPCA再構成・HCA解析が研究の中心であり、植物組織の状態・化学的分布を画像化する方法開発に該当する。
abstractConfocal Raman microscopy (CRM) is a powerful tool that could reveal details about chemical landscape of the plant cell wall with micrometer resolution.
Field / plotLeafWhole plant / canopy / plot / fieldVisualization / data managementBiomass / plant weightLeaf traitsPlant / canopy height
Abstract. The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).
Why it matches plant phenotyping methods植物のバイオマス、草丈、LAI、フェノロジーなどの再利用可能な形質データを含む包括的データセットを構築し、リモートセンシング手法の開発・検証にも利用できるため、植物フェノタイピングデータセットとして中心的です。
abstractHere, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany
Reproduction assets foundThis is a data description paper whose core contribution is a public plant-phenotyping dataset (vegetation states, biomass, LAI, phenology, fluxes, weather, management, soil) hosted at the TR32 database with a DOI. The dataset is directly downloadable via the authors' public URLs; no code or models are described.Dataset · publicments start with “#”. Comments can contain additional
information on yield, management of harvest residues, additional contents of
agrochemicals, etc.
9 Data availability
The dataset can be downloaded from the TR32
database ( https://www.tr32db.uni-koeln.de/data.php?dataID=1889 , last access: 29 September 2020) or using
the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020). The dataset is provided as a zip-compressed
container. All files are plain text files organized in a folder per site as
shown in Fig. 2 and as explained in Sect. 3. Technical details on file
formats and data structure within files are presented for the different kinds
of data in Sects. 4.4, 5.4, 6.4, 7Open asset ↗TR32DB · 10.5880/TR32DB.39lines:1217-1303Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract In this work, several attributes of the internal morphology of drupaceous fruits found in the archaeological site Monte Castelo (Rondonia, Brazil) are analyzed by means of two different imaging methods. The aim is to explore similarities and differences in the visualization and analytical properties of the images obtained via High Resolution Light Microscopy and X-ray micro-computed tomography (X-ray MicroCT) methods. Both provide data about the three-layered pericarp (exo-, meso- and endocarp) of the studied exemplars, defined by cell differentiation, vascularisation, cellular contents, presence of sclerenchyma cells and secretory cavities. However, it is possible to identify a series of differences between the information that can be obtained through each of the methods. These variations are related to the definition of contours and fine details of some characteristics, their spatial distribution, size attributes, optical properties and material preservation. The results obtained from both imaging methods are complementary, contributing to a more exhaustive morphological study of the plant remains. X-ray MicroCT in phase-contrast mode represents a suitable non-destructive analytic technique when sample preservation is required.
Why it matches plant phenotyping methods植物果実の内部形態を対象に、光学顕微鏡とX線microCTを比較し、画像から得られる形態情報と各手法の特性を評価しているため、画像ベースの植物形態計測手法の検証として中心的です。
abstractThe aim is to explore similarities and differences in the visualization and analytical properties of the images obtained via High Resolution Light Microscopy and X-ray micro-computed tomography (X-ray MicroCT) methods.
Studies on plant-pathogen interactions often involve monitoring disease symptoms or responses of the host plant to pathogen-derived immunogenic patterns, either visually or by staining the plant tissue. Both these methods have limitations with respect to resolution, reproducibility and the ability to quantify the results. In this study we show that red light detection in a multi-purpose fluorescence imaging system that is probably available in many labs can be used to visualize plant tissue undergoing cell death. Red light emission is the result of chlorophyll fluorescence upon thylakoid membrane disassembly during the development of a programmed cell death process. The activation of programmed cell death can occur either during a hypersensitive response to a biotrophic pathogen or an apoptotic cell death triggered by a necrotrophic pathogen. Quantifying the intensity of the red light signal enables to evaluate the magnitude of programmed cell death and provides a non-invasive readout of the plant immune response in a faster and safer manner as compared to chemical staining methodologies previously developed. This application can be implemented to screen for differences in symptom severity in plant-pathogen interactions, and to visualize and quantify in a sensitive and objective manner the intensity of a plant response upon perception of a given immunological pattern. We illustrate the utility and versatility of the method using diverse immunogenic patterns and pathogens.
Why it matches plant phenotyping methods植物組織の細胞死と病害応答を赤色蛍光で非侵襲的・定量的に可視化する手法を開発し、病徴重症度や免疫応答の評価への適用を示しており、フェノタイピング手法が中心です。
abstractQuantifying the intensity of the red light signal enables to evaluate the magnitude of programmed cell death and provides a non-invasive readout of the plant immune response
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 9 Sept 2026
GreenhouseLaboratory / benchtopMultispectral / hyperspectralCalibration / preprocessingVisualization / data management
BACKGROUND: The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up. RESULTS: This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices. CONCLUSIONS: This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.
Why it matches plant phenotyping methods植物のハイパースペクトル画像を用いた表現型取得・処理ワークフロー、センサー校正、誤差対策、形質抽出を中心に扱う方法論レビューであり、植物フェノタイピング手法が中核です。
abstractThis review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale.
WheatField / plotSeed / grainVisualization / data management
Motivation In 2005, researchers from the French National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, INRAE) started a collaboration with the French farmers' seed network Réseau Semences Paysannes (RSP) on bread wheat participatory breeding (PPB). The aims were: (1) to study on-farm management of crop diversity, (2) to develop population-varieties adapted to organic and low-inputs agriculture, (3) to co-develop tools and methods adapted to on-farm experiments. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using network formalism to represent relationships between seed lots. All this information had to be centralized and stored in a database. Results We describe here SHiNeMaS (Seeds History and Network Management System) a web tool database. SHiNeMaS aims to provide useful interfaces to track seed lot history and related data (phenotyping, environment, cultural practices). Although SHiNeMaS has been developed in the context of a bread wheat participatory breeding program, the database has been designed to manage any kind and even multiple cultivated plant species. SHiNeMaS is available under Affero GPL licence and uses free technologies such as the Python language, Django framework or PostgreSQL database management system (DBMS). Conclusion We developed SHiNeMaS, a web tool database, dedicated to the management of the history of seed lots and related data like phenotyping, environmental information and cultural practices. SHiNeMaS has been used in production in our laboratory for 5 years and farmers' organizations facilitators manage their own information in the system.
Why it matches plant phenotyping methods種子ロット履歴と関連する表現型データを管理する専用Webツールを開発しており、植物表現型データ基盤としてのソフトウェアが中心です。
abstractWe describe here SHiNeMaS (Seeds History and Network Management System) a web tool database.
ArabidopsisLaboratory / benchtopMicroscopyRootVisualization / data management
Plant roots adapt their development and metabolism to changing environmental conditions. In order to understand the response mechanisms of roots to the dynamic availability of water or nutrients, to biotic and abiotic stress conditions or to mechanical stimuli, microfluidic platforms have been developed that offer microscopic access and novel experimental means. Here, we describe the design, fabrication and use of microfluidic devices suitable for imaging growing Arabidopsis roots over several days under controlled perfusion. We present a detailed protocol for the use of our exemplar platform-the RootChip-8S-and offer a guide for troubleshooting, which is also largely applicable to related device designs. We further discuss considerations regarding the design of custom-made plant microdevices, the choice of suitable materials and technologies as well as the handling of the specimen.
Why it matches plant phenotyping methods植物根を顕微鏡で長期間イメージングするマイクロ流体プラットフォームの設計・製作・使用手順を中心に扱う方法論的研究であり、根の表現型取得が中核です。
abstractmicrofluidic platforms have been developed that offer microscopic access and novel experimental means
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.
Why it matches plant phenotyping methods植物表現型プラットフォーム向けのデータワークフロー、Pythonアプリケーション、データベース、品質検証を開発しており、表現型データ処理が中心的な方法論的貢献である。
abstracta data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database.
Laboratory / benchtopChlorophyll fluorescenceRootPhysiological trait estimationVisualization / data management
Phosphorus (P) is an essential macronutrient for plant growth, but bioavailable P in soils is often limited due to immobilization resulting from pH and geochemical interactions. Understanding the dynamics of P in soils and elucidating the mechanisms by which plants access P from their environment are critical to evaluating productivity, particularly in nutrient poor environments. Phosphorus from organic matter can act as a major source of P for organisms in soil systems. Phosphatases, enzymes that liberate inorganic P from organic sources, are produced by both plants and microbes and are considered one of the most active classes of enzymes in soil. We developed a root blotting method to spatially image phosphatase activity in the rhizosphere. Proteins from the rhizosphere are transferred to a nitrocellulose membrane while retaining their enzymatic activity and two-dimensional spatial distribution. Subsequent application of a fluorogenic phosphatase indicator, DDAO phosphate, enables visualization of the distribution of phosphatase activity in the sample. The proteins can then be fixed to the membrane and treated with SYPRO® Ruby Protein Blot Stain, a fluorescent total protein stain, allowing for visualization of total protein distribution. Taken together, the images of phosphatase activity and total protein localization can be mapped back to the root architecture and provide insight into factors affecting the spatial distribution of enzymatic activity and protein accumulation in the rhizosphere. Notably, this method can be applied to plants growing in rhizoboxes containing soil or soilless growth mixtures (e.g., sand or various potting mixes) and, because of the non-destructive nature of this approach, be performed over time to track changes. We anticipate that this fluorescent indicator imaging technique on root blots can be used in diverse plant-microbe-soil systems to better understand the role of phosphatases in P acquisition and soil P cycling.
Why it matches plant phenotyping methods植物根圏のホスファターゼ活性とタンパク質分布を非破壊・空間イメージングする根ブロッティング法を開発しており、表現型取得法が研究の中心である。
abstractWe developed a root blotting method to spatially image phosphatase activity in the rhizosphere.
Field / plotMRI / PETWhole plant / canopy / plot / fieldVisualization / data management
This Expert View provides an update on the recent development of new microsensors, and briefly summarizes some novel applications of existing microsensors, in plant biology research. Two major topics are covered: (i) sensors for gaseous analytes (O2, CO2, and H2S); and (ii) those for measuring concentrations and fluxes of ions (macro- and micronutrients and environmental pollutants such as heavy metals). We show that application of such microsensors may significantly advance understanding of mechanisms of plant-environmental interaction and regulation of plant developmental and adaptive responses under adverse environmental conditions via non-destructive visualization of key analytes with high spatial and/or temporal resolution. Examples included cover a broad range of environmental situations including hypoxia, salinity, and heavy metal toxicity. We highlight the power of combining microsensor technology with other advanced biophysical (patch-clamp, voltage-clamp, and single-cell pressure probe), imaging (MRI and fluorescent dyes), and genetic techniques and approaches. We conclude that future progress in the field may be achieved by applying existing microsensors for important signalling molecules such as NO and H2O2, by improving selectivity of existing microsensors for some key analytes (e.g. Na, Mg, and Zn), and by developing new microsensors for P.
Why it matches plant phenotyping methods植物内の無機分析物を高空間・時間分解能で可視化するマイクロセンサー技術の開発と応用を中心に扱うレビューであり、植物の生理状態を測定する方法論が主題である。
abstractThis Expert View provides an update on the recent development of new microsensors, and briefly summarizes some novel applications of existing microsensors, in plant biology research.
WheatField / plotSeed / grainVisualization / data management
Abstract Motivation In 2005, researchers from the French National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l’agriculture, l’alimentation et l’environnement, INRAE) started a collaboration with the French farmers' seed network Réseau Semences Paysannes (RSP) on bread wheat participatory breeding (PPB). The aims were: (1) to study on-farm management of crop diversity, (2) to develop population-varieties adapted to organic and low-inputs agriculture, (3) to co-develop tools and methods adapted to on-farm experiments. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using network formalism to represent relationships between seed lots. All this information had to be centralized and stored in a database. Results We describe here SHiNeMaS (Seeds History and Network Management System) a web tool database. SHiNeMaS aims to provide useful interfaces to track seed lot history and related data (phenotyping, environment, cultural practices). Although SHiNeMaS has been developed in the context of a bread wheat participatory breeding program, the database has been designed to manage any kind and even multiple cultivated plant species. SHiNeMaS is available under Affero GPL licence and uses free technologies such as the Python language, Django framework or PostgreSQL database management system (DBMS). Conclusion We developed SHiNeMaS, a web tool database, dedicated to the management of the history of seed lots and related data like phenotyping, environmental information and cultural practices. SHiNeMaS has been used in production in our laboratory for 5 years and farmers' organizations facilitators manage their own information in the system.
Why it matches plant phenotyping methods種子ロット履歴とフェノタイピング関連データを管理する専用Webデータベースであり、植物フェノタイピング情報を扱う再利用可能なソフトウェア基盤が中心です。
abstractWe describe here SHiNeMaS (Seeds History and Network Management System) a web tool database.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.
Why it matches plant phenotyping methodsジャガイモのSPAD値（葉緑素・栄養状態の指標）をスペクトル画像からリアルタイム推定するセンサーシステムを開発・検証しており、表現型取得手法が研究の中心である。
abstracta SPAD value detection system was developed based on a 25-wavelength spectral sensor
MicroscopyLeafVisualization / data managementDisease symptoms / severity
Peronospora salviae‐officinalis, the causal agent of downy mildew on common sage, is an obligate biotrophic pathogen. It grows in the intercellular spaces of the leaf tissue of sage and forms intracellular haustoria to interface with host cells. Although P. salviae‐officinalis was described as a species of its own 10 years ago, the infection process remains obscure. To address this, a histological study of various infection events, from the adhesion of conidia on the leaf surface to de novo sporulation is presented here. As histological studies of oomycetes are challenging due to the lack of chitin in their cell wall, we also present an improved method for staining downy mildews for confocal laser scanning microscopy as well as evaluating the potential of autofluorescence of fixed nonstained samples. For staining, a 1:1 mixture of aniline blue and trypan blue was found most suitable and was used for staining of oomycete and plant structures, allowing discrimination between them as well as the visualization of plant immune responses. The method was also used to examine samples of Peronospora lamii on Lamium purpureum and Peronospora belbahrii on Ocimum basilicum, demonstrating the potential of the presented histological method for studying the infection processes of downy mildews in general.
Why it matches plant phenotyping methodsダウンyミルデューの感染過程と植物免疫応答を可視化するための共焦点顕微鏡用染色法を改良・評価しており、植物病害状態の画像取得法が中心です。
abstractwe also present an improved method for staining downy mildews for confocal laser scanning microscopy
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureTissueVisualization / data management
Understanding cell and tissue level regulation of growth and morphogenesis has been at the forefront of biological research for many decades. Advances in molecular and imaging technologies allowed us to gain insights into how biochemical signals influence morphogenetic events. However, it is increasingly evident that apart from biochemical signals, mechanical cues also impact several aspects of cell and tissue growth. The Arabidopsis shoot apical meristem (SAM) is a dome-shaped structure responsible for the generation of all aboveground organs. The organization of the cortical microtubule cytoskeleton that mediates apoplastic cellulose deposition in plant cells is spatially distinct. Visualization and quantitative assessment of patterns of cortical microtubules are necessary for understanding the biophysical nature of cells at the SAM, as cellulose is the stiffest component of the plant cell wall. The stereotypical form of cortical microtubule organization is also a consequence of tissue-wide physical forces existing at the SAM. Perturbation of these physical forces and subsequent monitoring of cortical microtubule organization allows for the identification of candidate proteins involved in mediating mechano-perception and transduction. Here we describe a protocol that helps investigate such processes.
Why it matches plant phenotyping methods植物のシュート頂端分裂組織における微小管パターンをライブイメージングで可視化・定量するプロトコルが研究の中心であり、植物の細胞・組織状態を抽出する測定法に該当する。
abstractVisualization and quantitative assessment of patterns of cortical microtubules are necessary for understanding the biophysical nature of cells at the SAM
BarleyMicroscopyLeafStomata / guard-cell complexVisualization / data management
Background In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the spatial distribution pattern of HvGAPDH and the low expressed disease resistance gene Rpg1 in leaf tissue sections of barley and discuss precautions that should be followed during image analysis. Results We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell associated subsidiary cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAscope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Conclusions RNAscope multiplex fluorescent RNA-ISH detection is well described and adapted for animal tissue samples, however due to morphological and structural differences in the plant tissues the standard protocol is deficient and required optimization. Utilizing barley specific HvGAPDH and Rpg1 RNA probes we report an optimized method which can be used for RNAscope detection to determine the spatial expression and semi-quantification of target RNAs. This optimized method will be immensely useful in other plant species such as the widely utilized Arabidopsis.
Why it matches plant phenotyping methods植物組織における空間的遺伝子発現の蛍光RNA-ISH法を、サンプル前処理・切片保持・画像解析を含めて最適化した研究であり、植物の状態を取得する方法が中心である。
abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.
Chlorophyll fluorescenceCell / cellular structureTissueVisualization / data management
Summary Plant cell walls constitute the extracellular matrix surrounding plant cells and are composed mainly of polysaccharides. The chemical makeup of the primary plant cell wall, and specifically, the abundance, localization and arrangement of the constituting polysaccharides are intimately linked with growth, morphogenesis and differentiation in plant cells. Visualization of the cell wall components is, therefore, a crucial tool in plant cell developmental studies. In this technical update, we present protocols for fluorescence visualization of cellulose and pectin in selected plant tissues and illustrate examples of some of the available labels that hold promise for live imaging of plant cell wall expansion and morphogenesis.
Why it matches plant phenotyping methods植物細胞壁成分を蛍光可視化する具体的プロトコルを提示し、生細胞での細胞壁拡張・形態形成の観察に用いる技術的方法が中心である。
abstractIn this technical update, we present protocols for fluorescence visualization of cellulose and pectin in selected plant tissues and illustrate examples of some of the available labels that hold promise for live imaging of plant cell wall expansion and morphogenesis.
LiDAR / point cloudLeafWhole plant / canopy / plot / fieldSegmentationGrowth / time-series analysisVisualization / data managementGrowth / development / phenologyYield / yield components
Abstract Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment‐wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.
Why it matches plant phenotyping methods植物の3Dスキャン点群から成長状態を推定・補間する計算手法の開発が中心であり、植物構造の時系列表現を直接扱うため、フェノタイピング手法として適格です。
abstractWe present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment‐wise computation of optimal transport.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldObject detectionVisualization / data managementYield / yield components
Abstract Background To ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations. Results Key element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weather-guided phenotyping and data analysis for outlier or hot-spot detection are demonstrated. Conclusions The DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.
Why it matches plant phenotyping methods植物フェノタイピングを含む圃場試験データを、空間・時間情報と統合管理するシステムを開発しており、再利用可能なフェノタイピング基盤が中心である。
abstractwe propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data.
ArabidopsisBarleyChlorophyll fluorescenceMicroscopyLeafStomata / guard-cell complexTissueObject detectionVisualization / data managementStomatal traits
In situ analysis of biomarkers such as DNA, RNA and proteins are important for research and diagnostic purposes. At the RNA level, plant gene expression studies rely on qPCR, RNAseq and probe-based in situ hybridization (ISH). However, for ISH experiments poor stability of RNA and RNA based probes commonly results in poor detection or poor reproducibility. Recently, the development and availability of the RNAscope RNA-ISH method addressed these problems by novel signal amplification and background suppression. This method is capable of simultaneous detection of multiple target RNAs down to the single molecule level in individual cells, allowing researchers to study spatio-temporal patterning of gene expression. However, this method has not been optimized thus poorly utilized for plant specific gene expression studies which would allow for fluorescent multiplex detection. Here we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley. We have shown the ubiquitous HvGAPH and predominantly stomatal guard cell expressed Rpg1 expression pattern in barley leaf sections and described the improve RNAcope methodology suitable for plant tissues using confocal laser microscope. By addressing the problems in the sample collection and incorporating additional sample backing steps we have significantly reduced the section detachment and experiment failure problems. Further, by reducing the time of protease treatment, we minimized the sample disintegration due to over digestion of barley tissues. Thus, we optimized the RNAscope detection method in plants to visualize the spatial expression and semi-quantification of target RNAs which can be employed in other plants such as the widely utilized model dicot plant Arabidopsis.
Why it matches plant phenotyping methods植物組織内のRNA発現を空間的に可視化・半定量するRNAscope法を、植物組織向けに最適化・実証した方法開発研究であり、表現型取得法が中心である。
abstractHere we provide a step-by-step method for sample collection and pretreatment optimization to perform the RNAscope assay in the leaf tissues of model monocot plant barley.
SoybeanMRI / PETLeafRootStem / branchWhole plant / canopy / plot / field2D/3D reconstructionVisualization / data managementWater status / transpiration
Abstract The observation of initial transport of fixed nitrogen in intact soybean plants in real-time was conducted by using the positron-emitting tracer imaging system (PETIS). Soybean root nodules were fed with [ 13 N]N 2 for 10 minutes, and the radioactivity of [ 13 N]N tracer was recorded for 60 minutes. The serial images of nitrogen fixation activity and translocation of fixed nitrogen in the soybean plant were reconstructed to estimate the fixed-N transport to the upper shoot. As a result, the signal of nitrogen radiotracer moving upward through the intact stem was successfully observed. This is the first report that the translocation of fixed-N is visualized in real-time in soybean plant by a moving image. The signal of nitrogen radiotracer appeared at the base stem at about 20 minutes after the feeding of tracer gas and it took 40 minutes to reach the upper stem. The velocity of fixed nitrogen translocation was estimated approximately at 1.63 cm min -1 . The autoradiography taken after PETIS experiment showed a clear picture of transport of fixed 13 N in the whole plant that the fixed-N moved not only via xylem system but also via the phloem system to the shoot after transferring from xylem to phloem in the stem although it has been generally considered that the fixed-N in nodule is transported dominantly via xylem by transpiration stream toward mature leaves. This result also suggests that the initial transport of fixed-N was mainly into the stem and subsequently translocated to young leaves and buds via the phloem system. These new findings in the initial transport of fixed nitrogen of soybean by PETIS observation will become the basis for future study of fixed-N transport in the whole legume plants.
Why it matches plant phenotyping methodsPETISを用いて、根粒で固定された窒素の植物体内輸送をリアルタイム画像化・定量推定しており、植物の生理状態の取得方法が研究の中心である。
abstractThe observation of initial transport of fixed nitrogen in intact soybean plants in real-time was conducted by using the positron-emitting tracer imaging system (PETIS).
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform's device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient ( R 2 ) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
Why it matches plant phenotyping methodsトウモロコシの器官・個体形質を抽出する低コストMVS画像計測プラットフォームを開発し、手動測定との技術検証も行っており、フェノタイピング手法が中心である。
abstractMVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed.
ApplePhotogrammetry / SfM / MVSLiDAR / point cloudFruitObject detection2D/3D reconstructionSegmentationVisualization / data management
The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentation neural network; (2) 3D point cloud generation of detected apples using structure-from-motion (SfM) photogrammetry; (3) projection of 2D image detections onto 3D space; (4) false positives removal using a trained support vector machine. This methodology was tested on 11 Fuji apple trees containing a total of 1455 apples. Results showed that, by combining instance segmentation with SfM the system performance increased from an F1-score of 0.816 (2D fruit detection) to 0.881 (3D fruit detection and location) with respect to the total amount of fruits. The main advantages of this methodology are the reduced number of false positives and the higher detection rate, while the main disadvantage is the high processing time required for SfM, which makes it presently unsuitable for real-time work. From these results, it can be concluded that the combination of instance segmentation and SfM provides high performance fruit detection with high 3D data precision. The dataset has been made publicly available and an interactive visualization of fruit detection results is accessible at http://www.grap.udl.cat/documents/photogrammetry_fruit_detection.html.
Why it matches plant phenotyping methodsリンゴ果実の検出・3D位置推定という植物器官の形態情報を抽出する画像解析・3D再構成手法を開発し、性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentation neural network; (2) 3D point cloud generation of detected apples using structure-from-motion (SfM) photogrammetry; (3) projection of 2D image detections onto 3D space; (4) false positives removal using a trained support vector machine.
AppleRaman / spectroscopyCell / cellular structureVisualization / data managementWater status / transpiration
Raman spectroscopy has been employed for studying the hydrogen bonding states of water molecules for decades, however, Raman imaging data contain thousands of spectra, making it challenging to obtain information on water with different hydrogen bonds. In the current study, a novel method combining confocal Raman microscopy (CRM) imaging with the iterative curve fitting algorithms was developed to determine the distribution of water contents at the cellular level and water states with different hydrogen bonds in apple tissues. Raman imaging data ranging from 2700 to 3800 cm-1 were acquired from whole cells in the apple tissue, which were then decomposed into seven sub-peaks using the fixed-position Gaussian iterative curve fitting (FPGICF) algorithm. The content and hydrogen bonding states of cellular water were calculated as the area sum of the OH stretching vibration and the area ratio of DA-OH over DDAA-OH stretching vibration or the number of hydrogen bonds of each water molecule, respectively. Finally, the area of each sub-peak, the area sum of the OH stretching vibration, and the area ratio of DA-OH over DDAA-OH stretching vibration were used to visualize the distribution of each sub-peak, water contents and water states with different hydrogen bonds, respectively. In addition, it was found that the number of hydrogen bonds of each water molecule could also be considered as a criterion to describe the hydrogen bond states of water in apple tissues. The availability of such information should provide new insights for future study of cellular water in other food materials.
Why it matches plant phenotyping methodsリンゴ組織の細胞レベルの水分量・水素結合状態を可視化するため、共焦点ラマン画像と反復曲線フィッティングを組み合わせた新規の表現型取得・推定手法を開発しており、測定が生物学的実験の単なる補助ではない。
abstracta novel method combining confocal Raman microscopy (CRM) imaging with the iterative curve fitting algorithms was developed to determine the distribution of water contents at the cellular level and water states with different hydrogen bonds in apple tissues.
Abstract Background: Rice quality research attracts attention worldwide. Rice chalkiness is one of the key indexes determining rice kernel quality. The traditional rice chalkiness measurement methods are mainly based on naked-eye observation or two-dimensional (2D) image analysis and the results could not represent the three-dimensional (3D) characteristics of chalkiness in the rice kernel. These methods are neither in vivo thus are unable to provide technical support for high throughput screening of rice chalkiness phenotype. Results: Here, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT). This approach not only develops a novel method to measure the rice chalkiness index, but also provides a high throughput solution for rice chalkiness phenotype analysis. Conclusions: Our method could be a new powerful tool for rice chalkiness measurement, which would greatly help the research of rice chalkiness traits as well as the quality evaluation in rice production practice.
Why it matches plant phenotyping methodsイネ玄米の胴割れではなく白未熟粒（chalkiness）を、マイクロCTで3D可視化・体積定量する植物表現型取得法を開発しており、方法自体が中心的である。
abstractHere, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT).
Summary Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.
Why it matches plant phenotyping methods植物フェノタイピング向けのクラウド基盤とデータベースを設計・比較評価しており、研究の中心がフェノタイピングデータ処理基盤の技術開発・検証である。
titleCloud architecture for plant phenotyping research
Although it is widely accepted that actin plays an important role in regulating pollen germination and pollen tube growth, how actin exactly performs functions remains incompletely understood. As the function of actin is dictated by its spatial organization, it is the key to reveal how exactly actin distributes in space in pollen cells. Here we describe the protocol of revealing and quantifying the spatial organization of actin using fluorescent phalloidin-staining in fixed Arabidopsis pollen grains and pollen tubes. We also introduce the method of assessing the stability and/or turnover rate of actin filaments in pollen cells using the treatment of latrunculin B.
Why it matches plant phenotyping methods固定したシロイヌナズナ花粉のアクチン空間構成を蛍光画像で可視化・定量する手法と、フィラメント安定性・ターンオーバー評価法を中心に記述した技術プロトコルである。
abstractHere we describe the protocol of revealing and quantifying the spatial organization of actin using fluorescent phalloidin-staining in fixed Arabidopsis pollen grains and pollen tubes.
ArabidopsisMicroscopyCell / cellular structureVisualization / data managementGrowth / development / phenology
Virtually all growth, developmental, physiological, and defense responses in plants are accompanied by reorganization of subcellular structures to enable altered cellular growth, differentiation or function. Visualizing cellular reorganization is therefore critical to understand plant biology at the cellular scale. Fluorescently labeled markers for organelles, or for cellular components are widely used in combination with confocal microscopy to visualize cellular reorganization. Early during plant embryogenesis, the precursors for all major tissues of the seedling are established, and in Arabidopsis, this entails a set of nearly invariant switches in cell division orientation and directional cell expansion. Given that these cellular reorganization events are genetically regulated and coupled to formative events in plant development, they offer a good model to understand the genetic control of cellular reorganization in plant development. Until recently, it has been challenging to visualize subcellular structures in the early Arabidopsis embryo for two reasons: embryos are deeply embedded in seed coat and fruit, and in addition, no dedicated fluorescent markers, expressed in the embryo, were available. We recently established both an imaging approach and a set of markers for the early Arabidopsis embryo. Here, we describe a detailed protocol to use these new tools in imaging cellular reorganization.
Why it matches plant phenotyping methods初期シロイヌナズナ胚の細胞内構造を可視化する専用イメージング手法と蛍光マーカーを開発し、その使用プロトコルを提示しているため、植物表現型取得法が中心である。
abstractWe recently established both an imaging approach and a set of markers for the early Arabidopsis embryo.
Plastids are cell organelles that, beside other functions, have the capability to store carotenoids in specialized structures, which may vary among the different plant species, tissues or according to the carotenoid complement. Fruits are an important source of carotenoids, and during ripening, chloroplasts differentiate into chromoplasts that are able to accumulate large amounts of carotenoids, rendering then the characteristic fruit coloration. Whereas lycopene or β-carotene may accumulate as crystal in the chromoplasts of some fruit, other xanthophyll-accumulating fruits differentiate plastoglobuli as a preferred system to enhance carotenoids stability and storage. Visualization of plastid ultrastructure and their transformation during ripening or in fruit of contrasting coloration are fundamental objectives within carotenoids research in fruits. Therefore, in this chapter, we describe a protocol for the visualization and analysis of plastid ultrastructure by transmission electron microscopy (TEM), specially designed and adapted to fruit tissues.
Why it matches plant phenotyping methods果実組織向けにTEM観察法を適応し、色素体超微細構造の可視化・解析を中心とする方法論的プロトコルであるため、植物表現型の取得手法として適格。
abstractwe describe a protocol for the visualization and analysis of plastid ultrastructure by transmission electron microscopy (TEM), specially designed and adapted to fruit tissues.
BarleyWheatLaboratory / benchtopMicroscopyCell / cellular structureVisualization / data management
Wheat and barley have large genomes of 15 Gb and 5.1 Gb, respectively, which is much larger than the human genome (3.3 Gb). The release of their respective genomes has been a tremendous advance the understanding of the genome organization and the ability for deeper functional analysis in particular meiosis. Meiosis is the cell division required during sexual reproduction. One major event of meiosis is called recombination, or the formation of crossing over, a tight link between homologous chromosomes, ensuring gene exchange and faithful chromosome segregation. Recombination is a major driver of genetic diversity but in these large genome crops, the vast majority of these events is constrained at the end of their chromosomes. It is estimated that in barley, about 30% of the genes are located within the poor recombining centromeric regions, making important traits, such as resistance to pest and disease for example, difficult to access. Increasing recombination in these crops has the potential to speed up breeding program and requires a good understand of the meiotic mechanism. However, most research on recombination in plant has been carried in Arabidopsis thaliana which despite many of the advantages it brings for plant research, has a small genome and more spread out of recombination compare to barley or wheat. Advance in microscopy and cytological procedures have emerged in the last few years, allowing to follow meiotic events in these crops. This protocol provides the steps required for cytological preparation of barley and wheat pollen mother cells for light microscopy, highlighting some of the differences between the two cereals.
Why it matches plant phenotyping methods小麦・オオムギの減数分裂過程を可視化する細胞学的調製と高解像度顕微鏡法のプロトコルが中心であり、植物の生殖細胞状態を観察する測定手法に該当する。
abstractThis protocol provides the steps required for cytological preparation of barley and wheat pollen mother cells for light microscopy
ArabidopsisMicroscopyCell / cellular structureSegmentationVisualization / data management
We have recently installed a serial block face imaging system and have collected our first major data set. The project was a proof of concept to track the progress of pollen tubes towards specialist cells involved in plant reproduction. Pollen tubes target synergid cells and in particular the basal membrane of those cells which is known as the filiform apparatus. Little to nothing in known about the behavior of the filiform apparatus during pollen tube reception. By examining the ovules of Arabidopsis in 3D volumes we hope to examine whether the pollen tube enters the receptive synergid through the filiform apparatus, In addition we hope to determine if the pollen tube enters the receptive synergid at all. We prepared samples using an enhanced protocol that has been designed to improve signal and contrast in samples for serial block face imaging. We collected close to 1800 40nm slices from 3 different regions. We successfully found and oriented the two synergids in different ovules. We used Amira for visualization and have carried out segmentation of the filiform apparatus.
Why it matches plant phenotyping methods植物の生殖組織を対象に、シリアルブロックフェイス画像取得、3D可視化、セグメンテーションを用いて花粉管と助細胞構造を解析する画像ベース手法が中心であり、単なる生物学的測定ではない。
abstractThe project was a proof of concept to track the progress of pollen tubes towards specialist cells involved in plant reproduction.
Raman / spectroscopyRootStem / branchTissueVisualization / data management
Profiling the spatial distributions and dynamic changes of metabolites in plant tissue is critical to elucidate the complex metabolic regulation during plant growth, development, and responses to abiotic or biotic stresses. In this study, we developed a high-coverage MALDI-MS imaging method to visualize the spatial locations of a wide spectrum of metabolites in Salvia miltiorrhiza Bge. 1,5-Diaminonaphthalene (DAN) and 1,1'-binaphthyl-2,2'-diamine (BNDM) were optimized as MALDI matrices in positive and negative ion modes respectively, due to their low background interference and high sensitivity. Moreover, a simple organic washing protocol using acetone was shown to significantly improve the sensitivity of MALDI-MSI. Using this method, we successfully imaged the spatial locations of amino acids, phenolic acids, fatty acids, oligosaccharides, cholines, polyamines, tanshinones, and phospholipids in Salvia miltiorrhiza Bge. In addition, the distributions of some metabolites in Salvia miltiorrhiza Bge root and stem were found to exhibit good spatial match with plant tissue structure. Thus, our method provides a spatially-resolved way to map the plant metabolic networks and to understand the physiological roles of several plant metabolites.
Why it matches plant phenotyping methods植物組織内の代謝物分布を可視化するMALDI-MSイメージング法の開発が中心であり、植物の生理状態を空間的に表現する手法に該当する。
abstractIn this study, we developed a high-coverage MALDI-MS imaging method to visualize the spatial locations of a wide spectrum of metabolites in Salvia miltiorrhiza Bge.
Field / plotMorphology / geometry measurementPhysiological trait estimationCalibration / preprocessingVisualization / data managementArchitecture / morphology / geometry
Abstract Cryptomeria japonica (sugi) and Chamaecyparis obtusa (hinoki) are major Japanese timber species whose plantation area accounts for 44 and 25%, respectively, of the plantation forests in Japan. Physiology, anatomy and ecology of the species have been intensively studied for this half century, which now forms a huge stock of information. These data, however, were scattered in diverse sources, including papers, bulletins of research institutes, reports of other kinds and books, and were presented in nonstandardized, diverse styles in each source. This paper provides a database (SugiHinoki DB) that compiles 177 plant traits of sugi and hinoki from 364 primary sources published since 1950. The compiled traits include physiological, morphological, anatomical and biochemical features that are recognized as relevant to life history strategies, vegetation modeling and global change responses. Collected data have been obtained under different environmental conditions, for plants with different ages and for organs with a different age or different position, which provide information of within‐species variation for a given trait. Each data entry is accompanied by detailed ancillary information describing the site of measurement, stand of measurement and detailed measurement conditions, which help users account for data variations as a consequence of phenotypic plasticity and genetic variations and to filter data. To provide data in a consistent format, the data and the ancillary information were standardized, and units were converted. After data compilation, outliers were detected by calculating interquartile range for each trait per each species. As of August 2019, SugiHinokiDB contains 24,683 data entries (16,410 for sugi, 8,273 for hinoki). As sugi and hinoki are major plantation species in Japan, the data mostly came from study sites in Japan (from Hokkaido in the north to Yakushima island in Kyushu to the south) but were also obtained from arboretums or plantation forests in Taiwan, Korea and China. Data were largely obtained from plants in plantation forests but also from plants in natural forests and under experimental conditions. The improved availability of trait data offered by SugiHinokiDB provides new research opportunities, such as the intensive parameterization of vegetation models for a more accurate prediction of climate change impacts.
Why it matches plant phenotyping methods植物の生理・形態・解剖・生化学的形質を標準化して収録した再利用可能なデータベースの構築が中心であり、単なる生物学的実験の routine measurement ではない。
abstractThis paper provides a database (SugiHinoki DB) that compiles 177 plant traits of sugi and hinoki from 364 primary sources published since 1950.
Nitric oxide (NO), is a redox-active, endogenous signalling molecule involved in the regulation of numerous processes. It plays a crucial role in adaptation and tolerance to various abiotic and biotic stresses. In higher plants, NO is produced either by enzymatic or non-enzymatic reduction of nitrite and an oxidative pathway requiring a putative nitric oxide synthase (NOS)-like enzyme. There are several methods to measure NO production: mass spectrometry, tissue localization by DAF-FM dye. Electron paramagnetic resonance (EPR) also known as electron spin resonance (ESR) and spectrophotometric assays. The activity of NOS can be measured by L-citrulline based assay and spectroscopic method (NADPH utilization method). A major route for the transfer of NO bioactivity is S-nitrosylation, the addition of a NO moiety to a protein cysteine thiol forming an S-nitrosothiol (SNO). This experimental method describes visualization of NO using DAF-FM dye by fluorescence microscopy (Zeiss AXIOSKOP 2). The whole procedure is simplified, so it is easy to perform but has a high sensitivity for NO detection. In addition, spectrophotometry based protocols for assay of NOS, Nitrate Reductase (NR) and the content of S-nitrosothiols are also described. These spectrophotometric protocols are easy to perform, less expensive and sufficiently sensitive assays which provide adequate information on NO based regulation of physiological processes depending on the treatments of interest.
Why it matches plant phenotyping methods植物中のNO可視化とNOS、NR、S-ニトロソチオール測定プロトコル自体が中心であり、植物の生理状態を取得する方法論研究に該当する。
abstractThis experimental method describes visualization of NO using DAF-FM dye by fluorescence microscopy (Zeiss AXIOSKOP 2).
A study was conducted to assess yield variability of paddy crop using automatic yield monitoring (AYM) system fitted on an indigenous grain combine harvester. Algorithms were developed to assess the on-farm yield variability and classify the field in different yield zones. Yield maps were created using ArcGIS and open source GeoDa software for four years (2014, 2015, 2016 and 2017). The calibration factor (CF) of AYM system for paddy crop was 6.79. Yield variations were recorded in one ha plot, and classified in five different yield zones of 5000 kg.ha-1. Variation in actual yield and yield measured by automatic yield monitor varied from (+) 2.8 to (+)5.1 per cent. Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017. The AYM system could show the real-time temporal and spatial variability classification and quantification in paddy field.
Why it matches plant phenotyping methods自動収量モニタリングシステムの開発・校正と、収量の空間的・時間的変動の定量化が中心であり、植物の収量形質を取得するフェノタイピング手法に該当する。
abstractusing automatic yield monitoring (AYM) system fitted on an indigenous grain combine harvester
Background: An organism can be described by its observable features (phenotypes) and the genes and genomic information (genotypes) that cause these phenotypes. For many decades, researchers have tried to find relationships between genotypes and phenotypes, and great strides have been made. However, improved methods and tools for discovering and visualizing these phenotypic relationships are still needed. The maize genetics and genomics database (MaizeGDB, www.maizegdb.org) provides an array of useful resources for diverse data types including thousands of images related to mutant phenotypes in Zea mays ssp. mays (maize). To integrate mutant phenotype images with genomics information, we implemented and enhanced the web-based software package BioDIG (Biological Database of Images and Genomes). Findings: We developed a genotype-phenotype database for maize called MaizeDIG. MaizeDIG has several enhancements over the original BioDIG package. MaizeDIG, which supports multiple reference genome assemblies, is seamlessly integrated with genome browsers to accommodate custom tracks showing tagged mutant phenotypes images in their genomic context and allows for custom tagging of images to highlight the phenotype. This is accomplished through an updated interface allowing users to create image-to-gene links and is accessible via the image search tool. Conclusions: We have created a user-friendly and extensible web-based resource called MaizeDIG. MaizeDIG is preloaded with 2,396 images that are available on genome browsers for 10 different maize reference genomes. Approximately 90 images of classically defined maize genes have been manually annotated. MaizeDIG is available at http://maizedig.maizegdb.org/. The code is free and open source and can be found at https://github.com/Maize-Genetics-and-Genomics-Database/maizedig.
Why it matches plant phenotyping methodsトウモロコシの変異体表現型画像とゲノム情報を統合・検索・注釈するデータベース／ソフトウェアの開発が中心であり、植物表現型取得・活用基盤に該当する。
abstractWe developed a genotype-phenotype database for maize called MaizeDIG.
Reproduction assets foundThe paper describes MaizeDIG, a public genotype-phenotype image database preloaded with 2,396 maize mutant phenotype images, and its open-source code on GitHub. Both the database (phenotype image dataset) and the authors' code repository are explicitly public with URLs.Code · publicThe source code is free and open source and can be found at ( https://github.com/Maize-Genetics-and-Genomics-Database/maizedig ).Open asset ↗Maize-Genetics-and-Genomics-Database/maizediglines:357-427Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
ArabidopsisMaizeMicroscopyCell / cellular structureLeafTissueVisualization / data managementDisease symptoms / severity
Observing pathogen colonization and localization within specific plant tissues is a critical component of plant pathology research. High-resolution imaging, in which the researcher can clearly view the plant pathogen interacting with a specific plant cell, is needed to enhance our understanding of pathogen lifestyle and virulence mechanisms. However, it can be challenging to find the pathogen along the plant surface or in a specific cell type. Because of the time-consuming and expensive nature of high-resolution microscopy, techniques that allow a researcher to find a region of pathogen colonization more quickly at low resolution and subsequently move to a high-resolution microscope for detailed observation are needed. Here we present paraffin scanning electron microscopy (PSEM), a technique in which paraffin-embedded samples are first sectioned to identify a region of interest. Subsequently the same block is recut, deparaffinized, and used in scanning electron microscopy (SEM) to generate high-resolution images of plant-pathogen interactions in specific plant cell types. This method has several additional advantages over traditional SEM techniques, including reduced noise and better image quality. Here we use this technique to show that Fusarium oxysporum f. sp. lycopersici colonization is restricted in resistant Solanum pimpinellifolium and that PSEM works well in additional pathosystems, including maize leaves and Clavibacter michiganensis subsp. nebraskensis and Arabidopsis leaves and Pseudomonas syringae.
Why it matches plant phenotyping methods植物組織内の病原体定着・局在という植物病態を可視化するPSEM画像法を開発し、複数の病原系で有効性を実証しており、画像取得法が研究の中心である。
abstractHere we present paraffin scanning electron microscopy (PSEM), a technique in which paraffin-embedded samples are first sectioned to identify a region of interest.