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:216-267Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
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:335-337Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
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:1-122Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
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:224-268Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
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:528-593Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
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:537-549Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:1-58Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
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:756-834Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:1-70Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published15 Sept 2025International journal of electrical and computer engineering systemsCited by 8 · OpenAlex ↗
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:1-55Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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:1-81Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
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:1-36Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:63-80Code / dataset availability confirmedarXiv · checked 6 Sept 2026
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:1-47Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:1-78Code / dataset availability confirmedCrossref · checked 6 Sept 2026
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:154-159Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:30-40Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
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:138-163Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
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.
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:1-151Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:1-102Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:168-249Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
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
291
directed the research.
292
DATA AVAILABILITY
293
All generated and analyzed data from this study are included in the published article and its
294
Supporting Information (Fig. S2). The code for the FIJI macros as well as the R notebook for
295
filtering cells is available in the GitHub repository: (https://github.com/benjlloyd/CuticleTrace).296
REFERENCES
297
Aono, A. H., J. S. Nagai, G. da S. M. Dickel, R. C. Marinho, P. E. A. M. de Oliveira, J. P. Papa,
298
and F. A. Faria. 2021. A stomata classification and detection system in microscope
299
images of maize cultivars. PLOS ONE 16: e0258679.
300
Barclay, R., J. Mcelwain, D. Dilcher, and B. Sageman. 2007. The COpen asset ↗benjlloyd/CuticleTracepdf-raw-page:13 lines:1-61Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
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:623-661Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
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:556-587Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
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:1-24Code / dataset availability confirmedCrossref · checked 8 Sept 2026
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:500-542Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
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:327-356Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:411-414Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
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
354
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).
356
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
360
This research was supported by the Scientific Service Units of Institute of Science and Technology
361
Austria (ISTA) through resources provided by the Electron Microscopy Facility, Lab Support Facility and
362
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:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
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:431-490Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
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:134-164Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
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:80-91Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:65-81Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Published1 Jul 2021IEEE Transactions on Computational Biology and BioinformaticsCited by 2 · OpenAlex ↗
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:185-261Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
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:198-232Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
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:1-62Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
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:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
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:58-85Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
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:1-46Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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:1217-1303Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
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:357-427Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Annotation / quality controlVisualization / data management
GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.
Why it matches plant phenotyping methods植物フェノミクスデータリポジトリの設計・標準化・相互運用性を扱う方法論的研究であり、フェノタイピングデータ基盤が中心です。
abstractGnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures.
Reproduction assets foundThis is an infrastructure/data-management paper describing the GnpIS phenotyping repository rather than a single measurement study. The paper-specific public assets are the GnpIS repository itself (hosting the curated phenotyping trial datasets the paper describes), the authors' public ontology versioning repository onDataset · publicGnpIS provides phenotyping data discovery capabilities and data aggregation among several datasets. The dedicated query form, available in the phenotyping section of GnpIS ( https://urgi.versailles.inra.fr/gnpis/ ), is based on three tabs: (i) “Genotype” for filtering the plant material by species, genetic panel, and collections, (ii) “Observation variables” that allows variables selection using a Breeding API compliant open source widget ( https://github.com/gnpis/trait-ontology-widget ), and (iii) “Trial” that contains filters for genOpen asset ↗GnpISlines:103-109Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ClassificationStress / disease detectionVisualization / data managementDisease symptoms / severity
Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box . Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.
Why it matches plant phenotyping methods植物病害画像から病徴を分類するCNNの解釈・可視化手法を適用し、特定層への最適化やモデル簡略化まで検討しており、病害状態の取得・推定方法が研究の中心である。
abstractIn this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset.
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 · publicCodes required for feature visualization and attention maps are available at the following GitHub repositories: https://github.com/totti0223/lucid4keras , https://github.com/totti0223/keraswhitebox .Open asset ↗totti0223/lucid4keraslines:55-72Code · publicCodes required for feature visualization and attention maps are available at the following GitHub repositories: https://github.com/totti0223/lucid4keras , https://github.com/totti0223/keraswhitebox .Open asset ↗totti0223/keraswhiteboxlines:55-72Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background High-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales. Findings CropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted. Conclusions As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.
Why it matches plant phenotyping methods分散型植物フェノタイピングのデータ収集・管理プラットフォームを開発し、センサーと画像による作物成長評価を統合しているため、方法が中心的である。
abstractCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations.
Reproduction assets foundThe paper's authors publicly released the CropSight system source code (the software used for the paper's distributed plant phenotyping and IoT crop management) on GitHub under a BSD-3-Clause license, and Additional File 2 contains Python code to replicate the paper's plotted figures with datasets available in the sameCode · publicsimilar subsampling idea can be expanded to a larger and multi-site level, which can then truly help inform decision in crop research and agricultural practices across a country's arable land.
Availability of source code and requirements
Project name: CropSight for wheat prebreeding in Designing Future Wheat
Project home page: https://github.com/Crop-Phenomics-Group/cropsight/releases [ 35 ]
Operating system(s): Platform independent
Programming language: Python, PHP, JavaScript, SQL
Requirements: Apache (or other PHP5+) server, MySQL (or other SQL) server, a recent version of Chrome, Firefox, or Safari
License: BSD-3-Clause available at https://opensource.org/licenses/BSD-3-Clause
RRID:SCR_0Open asset ↗Crop-Phenomics-Group/cropsightlines:79-115Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
The scarce knowledge on phenotypic characterization restricts the usage of genetic diversity of plant genetic resources in research and breeding. We describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions hosted at the Federal ex situ Genebank for Agricultural and Horticultural Plant Species. The dataset gathers records for three traits with agronomic relevance: flowering time, plant height and thousand grain weight. This information was collected for seven decades for winter and spring barley during the seed regeneration routine. The curated data represent a source for research on genetics and genomics of adaptive and yield related traits in cereals due to the importance of barley as model organism. This data could be used to predict the performance of non-phenotyped individuals in other collections through genomic prediction. Moreover, the dataset empowers the utilization of phenotypic diversity of genetic resources for crop improvement.
Why it matches plant phenotyping methods大規模な植物表現型データセットの構築・整理と再利用を主題としており、形質測定自体は再生時の routine だが、データセット提供が中心的な貢献である。
abstractWe describe original and ready-to-use processed data for approximately 60% of ~22,000 barley accessions
Reproduction assets foundThe paper is a data descriptor publishing its own barley phenotypic dataset (FT, PH, TGW; original, outlier-corrected, and BLUEs) in ISA-Tab format at the IPK PGP repository, together with the authors' R/ASReml-R scripts for outlier detection and BLUE estimation, deposited under DOI 10.5447/IPK/2018/10.Code · publicScripts used for outlier detection and estimating BLUEs are included together with the
dataset in the public repository described below (Data Citation 1).Open asset ↗pdf-page:4 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Background: High-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales. Findings: CropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted. Conclusions: As an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.
Why it matches plant phenotyping methods分散型植物フェノタイピング設備とIoTセンサーのデータ収集・管理・比較を中核とするオープンソース基盤であり、植物生育画像や微気候データを用いた再利用可能なワークフローを提供している。
abstractCropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations.
Reproduction assets foundThe authors explicitly state that the CropSurveyor system code and the datasets supporting the paper's results (wheat phenotyping image/sensor data) are openly available on their public GitHub releases page, with a BSD-3-Clause license.Code · publicpanded to a larger and multi-site
397 level, which can then truly help inform decision in crop research and agricultural practices at the
398 national level, across a country’s arable land.
399
400 Availability and requirements
401 Project name: CropSurveyor for wheat prebreeding in Designing Future Wheat
402 Project home page: https://github.com/Crop-Phenomics-Group/cropsurveyor/releases
403 Operating system(s): Platform independent
404 Programming language: Python, PHP, JavaScript, SQL
405 Requirements: Apache (or other PHP5+) server, MySQL (or other SQL) server, a recent version of
406 Chrome, Firefox, or Safari
407 License: BSD-3-Clause available at: https://opensource.org/licenses/BSD-3Open asset ↗Crop-Phenomics-Group/cropsurveyorpdf-layout-page:16 lines:1-73Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( www.try-db.org ) to be included in the next version release.
Why it matches plant phenotyping methods植物の形態・機能形質を大規模に収録した再利用可能なデータベースであり、植物フェノタイピング用データセットとして中心的な貢献がある。
abstractThe Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome.
Reproduction assets foundThe paper's own Tundra Trait Team (TTT) trait database (raw and cleaned csv data) plus the authors' R data-cleaning scripts are publicly released in the authors' GitHub repository, with additional deposition in TRY and the Polar Data Catalogue.Dataset · publicthis cleaning protocol is primarily useful for species with large num‐
bers of observations of a given trait, and that much of the variation
within a species may be due to environmental or other differences
among sites (not error).
2.3 | Data availability and access
The TTT database will be maintained at the GitHub repository
(https://github.com/TundraTraitTeam/TraitHub). Trait data collec‐
tion is ongoing; thus, we will periodically release updated versions
of the database. A new version number will be assigned every time
there is a database update, and old database versions will be ar‐
chived for reference. A static version of the cleaned database (v. 1.0)
will also be available at the PolOpen asset ↗TundraTraitTeam/TraitHubpdf-raw-page:7 lines:1-59Code · publicSoftware format: csv file and GitHub repository with data cleaning scripts in R; con‐
tribution to TRY plant trait database (www.try-db.org) to be included in the next ver‐
sion release.Open asset ↗pdf-raw-page:4 lines:1-79Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 13 Sept 2026
Field / plotGreenhouseWhole plant / canopy / plot / fieldVisualization / data management
Summary Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open‐source Phenotyping Hybrid Information System ( PHIS ) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology‐driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi‐source and multi‐scale data, but also because it is based on 10 yr of trial and error in our groups.
Why it matches plant phenotyping methods植物フェノタイピング実験の多ソース・多スケールデータを統合、管理、可視化するオントロジー駆動システム自体が中心的な方法論的貢献である。
abstractThe open‐source Phenotyping Hybrid Information System ( PHIS ) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse).
Reproduction assets foundThe paper's PHIS software (the ontology-driven phenotyping information system that manages the paper's phenotyping data and analysis) is publicly released by the authors on GitHub under an open license. The application-example datasets are stated to be available at http://www.phis.inra.fr/under, but that exact URL is不在Code · publicThe source code and user and developer documentation of the latest version of PHIS are available at https://github.com/OpenSILEX under a GNU Affero General Public License version 2.Open asset ↗https://github.com/OpenSILEXlines:105-112Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 10 Sept 2026
A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.
Why it matches plant phenotyping methods植物画像の自動取得、機械学習によるセグメンテーション、成長形質の解析を一体化した高スループット表現型解析システムの開発・比較評価が中心である。
abstractwe developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation.
Reproduction assets foundThe paper's Data Availability statement and Results section explicitly deposit the authors' plant-image training/validation data, raw test data, and analysis code in a public GitHub repository, directly supporting this paper's phenotyping pipeline.Dataset · publicThe training and validation data are available at: https://github.com/ektf1130/high-throughput-plant-phenotyping-system/tree/master/image_processing/train_dataOpen asset ↗https://github.com/ektf1130/high-throughput-plant-phenotyping-systemlines:135-147Dataset · publicThe raw data for testing are available at: https://github.com/ektf1130/high-throughput-plant-phenotyping-system/tree/master/image_processing/raw_dataOpen asset ↗https://github.com/ektf1130/high-throughput-plant-phenotyping-systemlines:135-147Code · publicAll data, code and description have been uploaded at: https://github.com/ektf1130/high-throughput-plant-phenotyping-systemOpen asset ↗https://github.com/ektf1130/high-throughput-plant-phenotyping-systemlines:135-147Code / dataset availability confirmedarXiv · checked 14 Sept 2026
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 paper, 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・解析するアルゴリズムとオープンソースソフトウェアを開発し、植物データセットで評価しているため、フェノタイピング解析手法が中心である。
abstractIn this paper, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data
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:185-261Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
Multispectral / hyperspectralVisualization / data management
This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules.
Why it matches plant phenotyping methods植物育種向けの高解像度スペクトル反射データを探索・品質評価し、255種の反射スペクトル指数を適用するソフトウェア自体が中心的な成果であり、植物形質解析の再利用可能なツールに該当する。
abstractThis article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data.
Reproduction assets foundThe paper describes SK-UTALCA, authors' Matlab software for exploratory analysis of spectral reflectance phenotyping data. Source code is explicitly available as supplementary material at the Frontiers supplementary-material URL, and a compiled version is stated to be freely downloadable from the authors' fenomica.utalCode · publicThe compiled version of the software will be available for free downloading at http://www.fenomica.utalca.cl/ and source code is available as Supplementary Material.Open asset ↗lines:179-210Code · publicThe compiled version of the software will be available for free downloading at http://www.fenomica.utalca.cl/ and source code is available as Supplementary Material.Open asset ↗lines:179-210Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.
Why it matches plant phenotyping methods非侵襲的ハイパースペクトル画像から植物の病害進展・生理状態を抽出し、トピックモデルで自動追跡する新しい表現・解析手法が研究の中心である。
abstractwe present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques
Reproduction assets foundThe paper's authors publicly released the Python implementation of online regularized LDA used for their hyperspectral plant phenotyping analysis on GitHub. No public phenotype dataset or image deposit is stated; the hyperspectral data itself is only described, not deposited.Code · publicThe Python implementation of online regularized LDA is freely available at https://github.com/mirwaes/sclda .Open asset ↗mirwaes/scldalines:78-87