Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.
Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。
abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear
The inflorescence is a key yield-determining organ, yet its complex morphology makes manual pixel-level annotation time-consuming, leading to a scarcity of high-quality segmentation datasets. To address this bottleneck, we propose ZCAT (Zero-shot Cross-crop Annotation Transfer), a novel paradigm for zero-annotation cross-crop pseudo-mask screening. ZCAT completely eliminates pixel-level manual annotation of the target crop, requiring only holistic quality assessment of model-generated pseudo-masks (5-10 s per image). Specifically, we train a SegFormer model on public rice panicle datasets (CVRP and RiceSEG) and transfer it across crops to the wheat spike segmentation task. The key innovation is the introduction of a human-defined quality function Q, which circumvents the fundamental challenge in self-learning algorithms: the inability of computers to autonomously distinguish good masks from bad ones. Through iterative human-in-the-loop pseudo-label screening with a curriculum learning strategy, each round adds only a few high-quality pseudo-masks to the training set, continuously improving model performance. After four iterations, ZCAT produces pseudo-masks with an average Spike IoU of 0.7003, evaluated against the GWFSS manual annotations as ground truth. Moreover, the pseudo-mask dataset exhibited higher benchmark performance than the GWFSS manual annotations (Spike IoU 0.7612 vs. 0.7027; mIoU 0.8627 vs. 0.8247), suggesting stronger self-consistency. A generalization test on a strictly held-out set of 100 manually annotated wheat spike images showed that the model trained on ZCAT-generated pseudo-masks achieved marginally better performance than that trained on the GWFSS manual annotations (Spike IoU: 0.5112 vs. 0.4927; mIoU: 0.5627 vs. 0.5247). The time budget of the ZCAT pipeline was substantially lower than that of manual annotation. ZCAT opens a new pathway for rapid annotation of plant reproductive structures or other organs and significantly reduces data preparation costs in plant phenomics. The generated wheat spike pseudo-mask dataset and the mask quality screening tool (Mask Quality Screener) are open-sourced.
Why it matches plant phenotyping methods植物器官セグメンテーションのためのゼロショット転移、擬似マスク品質評価、反復学習パイプラインを開発・検証しており、表現型取得基盤が中心である。
abstractThe key innovation is the introduction of a human-defined quality function Q
Reproduction assets foundThe paper explicitly open-sources two paper-specific assets: the ZCAT-generated wheat spike pseudo-mask dataset and the Mask Quality Screener tool, both with public GitHub URLs in the Data availability statement.Dataset · publicThe wheat spike pseudo-mask dataset and Mask Quality Screener are available at https://github.com/zyxyes1/MaskQualityScreener and https://github.com/zyxyes1/Wheat-Spike-Semantic-Segmentation , respectively.Open asset ↗Wheat-Spike-Semantic-Segmentationlines:415-440Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
Root anatomical barriers, including the suberized and lignified walls of the endodermis and exodermis, and cortical aerenchyma, regulate water and nutrient transport, gas exchange, and rhizosphere interaction. Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin/lignin measurements matching expert annotation at ∼1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.
Why it matches plant phenotyping methods根の解剖学的構造を画像から自動抽出・定量する深層学習フレームワークを開発し、注釈付きベンチマークで検証しているため、植物フェノタイピング手法が中心である。
abstractQuantifying these structures at high resolution is a manual bottleneck that limits experimental scale.
Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.
Why it matches plant phenotyping methods植物フェノタイピングロボット向けに、ゼロショット分割・3D再構成・エッジ処理を開発し、植物形質を基準測定およびベンチマークと比較検証しており、取得・抽出手法が研究の中心である。
abstractwe developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge.
As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .
Why it matches plant phenotyping methods植物点群から茎のインスタンスを分割し、茎長を自動測定する3D表現型解析フレームワークの開発・比較検証が中心である。
abstractwe propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds.
Agriculture plays a pivotal role in ensuring global food security, economic stability, and sustainable development. Plant diseases significantly reduce agricultural productivity, resulting in substantial economic losses and threatening food supply worldwide. Early and accurate identification of plant leaf diseases enables timely intervention, minimizes crop damage, and enhances agricultural yield. Traditional disease diagnosis relies heavily on visual inspection by agricultural experts, making the process labor-intensive, subjective, and unsuitable for large-scale deployment. Recent advances in artificial intelligence, particularly deep learning, have transformed plant disease diagnosis by enabling automatic feature extraction and highly accurate image-based classification. This review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification. Various convolutional neural network (CNN) architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks. The paper also examines transfer learning strategies, object detection methods including YOLO and Faster R-CNN, and semantic segmentation approaches such as U-Net and DeepLabV3+. Publicly available benchmark datasets, including PlantVillage, PlantDoc, AI Challenger, Cassava Leaf Disease, and Rice Leaf Disease datasets, are discussed in terms of dataset diversity, annotation quality, and practical applicability. Furthermore, image preprocessing techniques, data augmentation methods, evaluation metrics, and deployment considerations for mobile and edge devices are comprehensively reviewed. The paper identifies current research challenges, including dataset imbalance, environmental variability, model interpretability, computational complexity, and limited real-world generalization. Finally, emerging research directions such as explainable artificial intelligence, federated learning, multimodal learning, self-supervised learning, lightweight architectures, and edge AI are discussed to provide future research opportunities. This review serves as a valuable resource for researchers, practitioners, and agricultural technologists interested in developing robust, scalable, and intelligent plant disease identification systems.
Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する深層学習手法を中心に、モデル、データセット、評価、展開を体系的にレビューしており、植物表現型計測手法のレビューに該当する。
abstractThis review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification.
This study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024) over the core winter rapeseed production region of the Middle and Lower Yangtze River Plain in China. The data originate from systematic observations at 50 national-level agrometeorological stations across six provinces: Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan. The dataset provides complete records of the specific dates for each phenology stage from sowing to maturity, including eight key phenology periods: Sowing (SO), Emergence (EM), Five-leaf (FV), Bud Formation (BF), Stem Elongation (SE), Flowering (FL), Green Ripening (GR), and Maturity (MA), along with the calculated durations of six distinct growth lengths. We implemented a multi-level quality control protocol encompassing internal logical checks, statistical outlier detection, climatological validation, time series homogenization, and expert arbitration. This protocol effectively constrained data uncertainty and corrected non-climatic discontinuities. Univariate linear regression was further employed to quantify the decadal change trends of each phenology period and growth length, supplemented by Kernel Density Estimation (KDE) to characterize their probability distribution features. The final dataset is presented as structured tables (in xlsx format) and high-resolution diagnostic plots (including trend and density plots), with a total volume of approximately 470 MB, systematically organized by province and station. This dataset fills a critical gap in long-term, standardized rapeseed phenology data for the region. The integrated analysis of phenology dates, growth stage durations, and their trends across the entire network provides an indispensable, high-quality empirical foundation. It is designed to support in-depth investigations into the nonlinear response mechanisms of overwintering crops to climate warming, improve crop model parameterization and validation, and inform regional adaptive management strategies.
Why it matches plant phenotyping methods44年間のナタネの生育段階日を標準化・品質管理して公開するデータセット研究であり、植物状態（フェノロジー）の測定データ整備が中心です。
abstractThis study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024)
Reproduction assets foundThe paper's rapeseed phenology dataset (1981–2024, 50 stations) is openly deposited in Science Data Bank under DOI 10.57760/sciencedb.34086, containing structured xlsx tables and diagnostic plots. No custom code was created per the authors.Dataset · publicThe dataset described in this work has been deposited in the Science Data Bank (ScienceDB) under
accession code https://doi.org/10.57760/sciencedb.34086 [27].Open asset ↗Science Data Bank · 10.57760/sciencedb.34086pdf-page:12 lines:1-68Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.
Why it matches plant phenotyping methods植物の葉画像から病害状態を分類する深層学習手法の開発・交差検証が研究の中心であり、植物表現型（病害状態）の取得・推定に該当する。
abstractThis study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure
Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.
Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。
abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.
Reproduction assets foundThe paper's core asset is the SPVD-Field dataset (SPVD-DET detection images with bounding-box annotations and SPVD-SEG segmentation images with pixel-level masks), explicitly deposited in a public repository via the data availability statement with a DOI link.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://dx.doi.org/10.21227/hq1q-jp43 .Open asset ↗10.21227/hq1q-jp43lines:664-703Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Field / plotLeafRootWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract Background Trait-based ecology has become central for understanding plant form, function and ecosystem processes, but progress has been hampered by biased representation in trait databases. As such, global trait syntheses remain strongly biased towards temperate forest biomes. Tropical savannas are the most extensive, biodiverse and disturbance-driven ecosystems worldwide, yet are poorly represented in functional trait databases, limiting ecological inference and applied decision-making. Scope Here, we introduce the Cerrado Plant Traits (CPT), an open-access initiative compiling and standardising plant functional trait data for the Brazilian Cerrado, the world’s most biodiverse tropical savanna. CPT integrates trait information for all major plant organs (whole-plant, root, shoot, leaf, flower, fruit and seed) across vegetation types in the Cerrado, drawing on a collaborative and inclusive research network. The current version of CPT compiles data from 148 datasets, totalling 113,859 curated trait records for 2,134 taxonomically verified species across 150 families. Trait records span pristine, degraded and restored environments and capture both interspecific and intraspecific variation. Whole-plant and leaf traits dominate the current dataset, while belowground and reproductive traits remain comparatively underrepresented, highlighting key priorities for future research. Conclusions By substantially increasing the representation of savanna species in global trait repositories, CPT enables tests of ecological hypotheses across multiple levels of organization, analyses of trait–environment relationships across fire, soil and climatic gradients, and robust comparisons across forest–savanna transitions. Beyond its scientific value, CPT provides a practical, standardised resource to support conservation planning, restoration programs and evidence-based policy in a biodiversity hotspot facing accelerating land-use and climate pressures.
Why it matches plant phenotyping methods植物の機能形質を標準化して統合した大規模データセットであり、再利用可能な形質リソースの構築が中心です。
abstractHere, we introduce the Cerrado Plant Traits (CPT), an open-access initiative compiling and standardising plant functional trait data for the Brazilian Cerrado
Remote sensing using uncrewed aerial vehicles (UAVs) and AI, particularly machine learning and deep learning, is increasingly applied to crop pest and disease detection. However, the real-world robustness of these models remains uncertain. We conducted a meta-analysis of 121 UAV-based studies published between 2018 and 2024, examining dataset construction and model validation practices. We found that 89% of studies lacked truly independent test datasets, resulting in inflated performance estimates and limited generalisability. Only 11% evaluated models on independent fields, and successful transferability was uncommon. Our analysis identifies key methodological limitations underlying this issue and provides recommendations to improve robustness, reproducibility, and practical relevance. Overall, current validation practices require substantial improvement to ensure reported model performance reflects field-level applicability.
Why it matches plant phenotyping methodsUAV・AIによる作物の病害検出手法を対象に、121研究のデータセット構築とモデル検証をメタ分析し、独立圃場での性能や再現性を評価する方法論的レビューである。病害検出は植物の病態・重症度に関わるため、手法中心の研究として採用する。
abstractWe conducted a meta-analysis of 121 UAV-based studies published between 2018 and 2024, examining dataset construction and model validation practices.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Attempts to deploy computer vision in agricultural tasks often suffer from a shortage of annotated data. One strategy to alleviate the impact of limited data is Self-Supervised Learning (SSL), which involves pre-training a model on a pretext task that utilizes automatically generated annotations. The primary objective of this study is to leverage a multi-camera view dataset of cotton boll images for contrastive learning in order to enable phenotyping tasks with minimal data annotation. This dataset was collected in the field using six camera views. The efficacy of two contrastive learning frameworks (SimCLR and MoCo) in producing representations when positive examples originate from different cameras was investigated, and a comprehensive study of how the camera positions affect performance was conducted. After self-supervised pre-training, linear evaluation and semi-supervised learning experiments were performed on boll detection and plot status downstream tasks. In general, using multiple camera views with SimCLR and MoCo improves cotton boll detection mean average precision by 14% compared to vanilla SimCLR and MoCo. Through careful investigation using synthetic data, it was determined that relative camera poses with an intermediate amount of overlap seem more likely to perform well. Neither MoCo nor SimCLR was consistently superior to the other in this context. The representations embed meaningful features about the cotton plants, such as overall boll density, but also less meaningful ones, such as lighting variations. This technique could potentially accelerate the development of phenotyping algorithms based on data collected from field robots. • A contrastive learning method based on comparing multi-camera views was developed. • The method was tested with images of cotton bolls from a ground robot. • The method outperformed baseline contrastive learning approaches.
Why it matches plant phenotyping methodsマルチカメラ画像とコントラスト学習による植物表現学習・フェノタイピング手法の開発と評価が中心であり、綿花のボール検出性能を検証している。
abstractThe primary objective of this study is to leverage a multi-camera view dataset of cotton boll images for contrastive learning in order to enable phenotyping tasks with minimal data annotation.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' code to reproduce the multi-camera contrastive learning phenotyping experiments. A processed-data Zenodo deposit (10.5281/zenodo.18164649) is also mentioned, but its URL is not among the allowed URLs, so only the code资产Code · publicThe code required to reproduce the above findings are available to download from https://github.com/UGA-BSAIL/self-supervised-learning .Open asset ↗UGA-BSAIL/self-supervised-learninglines:200-224Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring.
Why it matches plant phenotyping methods作物キャノピーのFVCという植物形質を対象に、複数の全球FVC推定プロダクトを多様な参照データで体系的に検証し、UAV・衛星観測を含むCropFVC参照データセットを構築している。形質取得・検証が研究の中心である。
titleMulti-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
This study presents a unified semantic segmentation framework for UAV-based multispectral crop stress mapping, focusing on the integration of water stress and rust disease conditions within a common label space. Unlike conventional approaches that address individual stress factors independently, the proposed framework harmonizes heterogeneous datasets with different annotation schemes into a single multi-class segmentation problem. To achieve this, UAV multispectral orthomosaics are processed using a patch-based strategy and a multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches. In addition, a physics-informed output-space refinement module based on fractional partial differential equations (FPDE) is introduced to enhance spatial coherence and boundary preservation in the predicted maps. Experimental results demonstrate the effectiveness of the proposed framework within the evaluated dataset setting, particularly in terms of boundary delineation, spatial consistency, and minority-class detection. The study highlights the feasibility of integrating heterogeneous stress conditions into a unified segmentation framework and provides a foundation for future research on scalable multi-source agricultural monitoring systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物の水ストレスおよびさび病状態を推定するセマンティックセグメンテーション手法の開発が中心であり、植物状態の取得・抽出に該当する。
abstracta multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches
Plant disease and pest surveillance is undergoing a profound technological transition. Conventional crop protection has historically depended on episodic field scouting, expert visual inspection, and broad-spectrum preventative spraying, all of which are constrained by labour intensity, uneven diagnostic accuracy, and weak temporal resolution. In contrast, recent advances in artificial intelligence, deep learning, the Internet of Things, remote sensing, and edge computing have enabled crop-health monitoring systems that are more continuous, data-rich, and spatially explicit. This review analyses the evolution of AI-driven plant disease and pest surveillance, with particular attention to how image-based deep learning, connected environmental sensing, unmanned aerial vehicle platforms, cloud-edge infrastructures, and multimodal analytics are reshaping next-generation crop protection. The article argues that the central innovation is not merely automated diagnosis, but the emergence of surveillance ecosystems capable of recognising symptoms, estimating risk, localising hotspots, and informing more selective intervention. The review synthesises major developments in convolutional neural networks, object detection, semantic segmentation, transfer learning, domain adaptation, transformer-based computer vision, anomaly detection, environmental time-series modelling, and multimodal analytics. It also evaluates the practical obstacles that still limit real-world deployment, including dataset bias, annotation uncertainty, poor cross-domain generalisation, limited interoperability, energy and connectivity constraints, weak model explainability, and uneven economic accessibility. The article further considers how AI-based surveillance may strengthen integrated pest management by supporting earlier warning, more precise treatment timing, reduced blanket pesticide use, and stronger alignment between biological risk and management action. It concludes that the future of crop protection will depend less on isolated improvements in benchmark accuracy and more on the development of trustworthy, scalable, and biologically meaningful surveillance systems that can support sustainable decisions under real agricultural conditions.
Why it matches plant phenotyping methods植物病害の症状認識・リスク推定・ホットスポット局在化を行う画像解析、センサー、UAV、マルチモーダル手法を中心にレビューしており、植物の病害状態を推定するフェノタイピング手法レビューに該当する。
abstractThis review analyses the evolution of AI-driven plant disease and pest surveillance, with particular attention to how image-based deep learning, connected environmental sensing, unmanned aerial vehicle platforms, cloud-edge infrastructures, and multimodal analytics are reshaping next-generation crop protection.
Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.
Why it matches plant phenotyping methods葉の病害症状を画像解析で定量化するワークフローを開発し、色空間比較と手動アノテーションによる検証を行っており、植物表現型取得法が中心である。
abstractwe present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously
Reproduction assets foundThe paper's authors explicitly state that all code implementing the leaf disease quantification workflow (SegLeaf, including the graphical interface and documentation) is hosted in a public GitHub repository. No separate public phenotype dataset or trained model checkpoint is described in the supplied blocks.Code · publicted by the Agence Nationale de
la Recherche (ANR) (project SCOOP, grant no. ANR-19-CE32-0011; and project MOBIDIV,
grant no. ANR-20-PCPA-0006).
Code and Data Availability
The method and associated scripts developed in this work are freely available to the re-
search community. All code is hosted in a public GitHub repository at https://github.com/titouanlegourrierec/SegLeaf, which includes the full implementation of the method includ-
ing the graphical interface and documentation to guide users through the analysis pipeline.
15
.
CC-BY 4.0 International license
made available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display tOpen asset ↗titouanlegourrierec/SegLeafpdf-raw-page:15 lines:1-39Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Accurate quantification of leaf lesion severity is essential for plant disease research and phenotyping but is often limited by subjective visual scoring and time-intensive manual image analysis. We present LIME, a fully automated, open-source image analysis pipeline for high-throughput quantification of leaf lesions from disease assay images. LIME integrates zero-shot leaf segmentation using the Segment Anything Model with a convolutional neural network for lesion area estimation. Applied to Arabidopsis thaliana leaves infected with Sclerotinia sclerotiorum , the proposed approach achieved a mean absolute percentage error of 12.9%, comparable to observed intrarater variability in manual scoring. Stratified evaluation across lesion-size groups demonstrated consistent prediction accuracy for small, intermediate, and large lesions, and comparative analysis showed that the deep learning–based model substantially outperformed color-based baseline methods. Under GPU-accelerated execution, LIME processed complete assays containing approximately 200 leaves in 15 minutes, representing an approximate 13-fold reduction in processing time relative to manual annotation. Together, these results indicate that LIME enables objective, reproducible, and scalable quantification of leaf lesion severity in standardized plant pathology assays. The pipeline is released as an open-source tool to support quantitative phenotyping studies.
Why it matches plant phenotyping methods植物病斑重症度を画像から定量するオープンソース解析パイプラインを開発・比較評価しており、植物フェノタイピング手法が研究の中心です。
abstractWe present LIME, a fully automated, open-source image analysis pipeline for high-throughput quantification of leaf lesions from disease assay images.
Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole genome sequencing and mapping candidate mutation. This screen led to the identification of a point mutation in CAP-D2 ( capd2-2 ), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery. Highlight We combined AI-powered image analysis with an easy-to-use desktop app to automate plant pollen counting, then used it to identify a new genetic suppressor of meiotic defects.
Why it matches plant phenotyping methods花粉の生存性を画像から自動定量するセグメンテーション手法とソフトウェアPATの開発が中心であり、植物表現型の取得・抽出手法に該当する。
abstractWe integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application.
This paper addresses the problem of automated segmentation of plant green biomass in field crop images aimed at improving the accuracy of crop and weed identification. To construct a training dataset for neural network models, an automatic annotation algorithm is proposed, enabling the generation of polygonal object masks without human intervention. The method is based on adaptive analysis of color characteristics of plant fragments with iterative narrowing of the hue range in the HSV color space, combined with an integral quality metric that accounts for the dynamics of contour area and shape. The proposed method achieved an IoU of 93.22% and a DSC of 96.30%, demonstrating a high level of agreement between automatic and manual annotations. The generated masks are used to train segmentation models of the YOLO11-seg family. Models of different scales (n, s, m, l, x) were trained and evaluated using standard metrics, including Intersection over Union (IoU), mAP@0.5, mAP@0.5–0.95, F1-score, and Precision–Recall (PR) curves. Experimental results demonstrate that models trained on automatically generated annotations achieve stable segmentation performance of plant green biomass. The best results were obtained with the YOLO11m-seg model, achieving an F1-score of 0. 772. The results confirm the effectiveness of the proposed approach and demonstrate acceptable segmentation quality, supported by both quantitative metrics and visual analysis. The developed automatic annotation algorithm can be used to expand training datasets in computer vision tasks for agricultural applications.
Why it matches plant phenotyping methods植物の緑色バイオマスを画像から自動抽出するポリゴン注釈法を開発し、手動注釈との一致度で検証しているため、植物表現型取得・抽出法が中心である。
abstractan automatic annotation algorithm is proposed, enabling the generation of polygonal object masks without human intervention
Reproduction assets foundThe authors publicly released the paper-specific generated dataset of polygonal segmentation annotations (masks and supporting materials) on Hugging Face. CVAT is only a generic annotation tool, and no author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe generated dataset with polygonal segmentation annotations of crop and weed plants, produced using the proposed algorithm and based on the LincolnBeet Dataset, is publicly available on Hugging Face at: https://huggingface.co/datasets/ivliev123/polygonal_marking_plant_objectsOpen asset ↗Hugging Face · ivliev123/polygonal_marking_plant_objectshtml-lines:438-462Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural conditions. Ground mobile robots are emerging as the key carrier for resolving this gap because they combine close-range sensing, autonomous mobility, and physical interaction within real field environments. In this paper, a structured scoping review is presented using a closed-loop perception-decision-action pipeline as the organizing principle. Within this framework, recent advances are synthesized from the perspectives of multimodal fusion, localization-aware sensing, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. The review further clarifies the complementary roles of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and air-ground collaboration in multiscale phenotyping workflows. Beyond summarizing technologies, the article provides three concrete deliverables: a structured taxonomy of mobile phenotyping systems; comparative tables covering sensing modalities, localization/navigation methods, and AI models; and a research agenda linking technical progress to field deployability. The synthesis highlights four persistent bottlenecks, namely environmental generalization, annotation scarcity, limited standardization and reproducibility, and the gap between advanced models and agricultural edge hardware. Overall, ground robots are identified not merely as sensing platforms, but as the central system architecture for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable operation.
Why it matches plant phenotyping methods植物フェノタイピング用移動ロボットについて、センシング、表現型解析、プラットフォーム分類、比較、標準化を中心に扱う方法論レビューであり、対象範囲に明確に合致する。
abstractIn this paper, a structured scoping review is presented using a closed-loop perception-decision-action pipeline as the organizing principle.
Accurate and efficient grain quality assessment is critical for making informed decisions throughout the grain value chain. Early detection of disease enables actions to mitigate spread and further damage, and optimal batch mixing to fulfill specified quality requirements allows for maximizing value and minimizing scrapping. Vision based machine learning and deep learning approaches are gaining attention in the agricultural sector and are useful for the development of automated grain quality assessment. These techniques can reduce the current manual inspection load and are key for objective and precise analysis. Yet, the majority of prior studies are constrained to small or controlled and curated datasets. Practical challenges associated with real-world deployment and reliability are rarely addressed. That is the focus of this work. We present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification. The objective is to determine a CNN model that ensures high and robust classification accuracy, while elucidating and explaining how different image dataset characteristics and training parameters affect performance and reliability. We use a commercial mirror-based imaging system that captures over 90% of each kernel's surface and contrast and compare model architectures, robustness, the effect on pre-processing and image resolution. Our results show similar and high overall performance for ResNet50V2 and EfficientNetV2B0 ([Formula: see text]% accuracy), but per-class analysis indicate that the smaller classes suffer from lack of representative examples, and that most classes benefit from pre-processing including downsampling whereas others benefit from higher resolution. Interactive visualizations reveal that another contributing factor is dubious annotation and multi-class belongingness. Thus, our step-by-step analysis of CNN performance underscores the need for representative data, proper pre-processing, and class-aware evaluation to ensure trustworthy deployment in wheat grain quality assessment.
Why it matches plant phenotyping methods小麦粒画像から品質・病害クラスを推定するCNN画像解析手法の性能、頑健性、前処理、解像度、データ特性を体系的に評価しており、フェノタイピング手法が中心的である。
abstractWe present and demonstrate a structured approach for investigating convolutional neural networks (CNNs) and key factors influencing performance for wheat kernel classification.
Reproduction assets foundThe paper's wheat grain image dataset has a publicly available subset deposited on Zenodo (DOI 10.5281/zenodo.17397123), explicitly stated in the Data Availability statement. The full dataset is proprietary; code is only available upon request, so no qualifying code asset.Dataset · publicA publicly available subset of the segmented wheat grain images used in this study has been deposited in Zenodo to support transparency and reproducibility. The dataset includes representative samples per class collected from instrument and can be accessed at https://doi.org/10.5281/zenodo.17397123.Open asset ↗Zenodo · 10.5281/zenodo.17397123html-lines:337-368Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput phenotyping of biological samples is essential for large-scale studies but is frequently bottlenecked by the need for accurate instance segmentation in crowded images. While deep learning offers powerful solutions, the high cost of manual annotation and the requirement for coding expertise often limit adoption in routine laboratory workflows. Here we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users. The protocol features an interactive annotation GUI that extrapolates full masks from minimal centerline markings, significantly reducing manual labeling effort. It further integrates a configurable U-Net training module and a standalone inference application with a ‘human-in-the-loop’ editing workflow for rapid and intuitive error correction. We employ the pipeline to annotate and train the model on a novel dataset of crowded C. elegans images. Validated on independent datasets, SegBio achieves high segmentation performance (Panoptic quality ∼0.85) and accurately quantifies per-animal morphology and fluorescence. By eliminating external dependencies and streamlining the correction process, SegBio provides a scalable solution for routine phenotyping that is easily generalized to other crowded biological samples, such as cellular organelles, cells, and organisms.
Why it matches plant phenotyping methodsC. elegansの画像から個体インスタンスを分離し、形態と蛍光を定量するエンドツーエンドの表現型解析ツールを開発・検証しており、方法が研究の中心である。
abstractHere we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users.
The errors of human annotation and the noise of the environment such as lighting changes, occlusions and cluttered backdrop limit the correct detection of the plant diseases in the field condition. The research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification. The hybrid EfficientNet-Vision Transformer (ViT) network proposed is based on an EfficientNet-B4 branch of CNN and a branch of Vision Transformer (ViT) network, which focuses on capturing fine-grained lesion features and global contexts information. A data augmentation pipeline based on CycleGAN is used to introduce field-style distortions (e.g., (lighting shifts, shadowing, debris and partial occlusions), to be more robust to environmental noise, and an Adaptive Symmetric Cross-Entropy (ASCE) loss identifies and down-weights uncertain samples with normalized prediction entropy. The training is done in two phases, Stage 1 pretraining with clean images of PlantVillage and Stage 2 with increasingly noisy samples. The framework is tested in two different noise conditions, and these include the controlled synthetic label noise with PlantVillage and the real environmental noise with PlantDoc. The proposed model has an accuracy of 94.5% on the clean PlantVillage test set. It achieves 85.0% accuracy on the PlantVillage dataset under the 20% synthetic label noise protocol, outperforming ResNet-50V2 (76.5%), DenseNet-121 (78.9%), and Co-Teaching (79.5%). Macro-precision, macro-recall and macro-F1 of the model on the external PlantDoc field dataset are 0.718, 0.681, 0.681, respectively with a top-1 accuracy of 72.0, which is a manifestation of cross-domain generalization. The lesion-centric Grad-CAM images indicate that the model places emphasis on symptomatic areas of leaves and represses reactions of background soil, shadows, and clutters. The suggested hybrid EfficientNet-ViT architecture offers, in general, a robust and explainable solution to precision agriculture and intelligent crop tracking systems that are resistant to noise.
Why it matches plant phenotyping methods植物の病徴画像から病害状態を推定する深層学習手法を開発し、ノイズ条件・外部データセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractThe research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
High-throughput plant phenotyping is increasingly constrained by the mismatch between the demand for field-relevant, fine-grained phenotypic data and the limited capability of conventional observation platforms under complex agricultural conditions. In this context, mobile phenotyping systems, particularly ground robots, are emerging as a key technological pathway for bridging macro-scale monitoring and organ-level trait analysis. This review examines the development of mobile phenotyping platforms for high-throughput plant phenotyping, with emphasis on the evolving role of ground robots in field-based sensing, decision-making, and active interaction. We first compare the functional characteristics of unmanned aerial vehicles and unmanned ground vehicles and discuss their complementarity in multiscale phenotypic data acquisition. We then summarize recent advances in the core technical framework of mobile phenotyping robots, including multimodal perception, localization and mapping, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. Major challenges are further discussed, particularly those related to environmental generalization, data annotation, standardization, reproducibility, and long-term field reliability. Finally, future directions are outlined from the perspectives of air–ground collaboration, multi-robot systems, foundation models, and embodied intelligence. This review highlights ground robots as a central carrier for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable systems.
Why it matches plant phenotyping methods植物フェノタイピング用の移動ロボットプラットフォームと、マルチモーダルセンシング・表現型解析などの技術を中心に扱うレビューであり、対象分野の方法論的レビューに該当する。
abstractThis review examines the development of mobile phenotyping platforms for high-throughput plant phenotyping
3D crop phenotyping technology provides critical support for screening morphology-related plant genes and identification of germplasm resource. Organ segmentation or recognition is the first key step in 3D crop phenotyping, where inductive deep learning currently dominates as the mainstream methodology. However, the high requirement for data annotation in inductive learning paradigm has transformed the manual data labeling into a labor-intensive task, thereby in turn restricting the progress of inductive learning. This problem has inspired us to leverage Graph Neural Networks (GNNs) as the transductive learning tool to directly segment organs on sparsely annotated crop point clouds. We propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds that have featureless point features. Different from existing graph-based networks, DBGCN not only carries out the static-feature-space graph convolutions that are good at mining and aggregating on local information on the point cloud, but also incorporates dynamic graph convolutions that captures the potential changes of the graph manifold in deep feature space. Extensive experiments prove that the fusion of two types of graph feature convolutions brings a high node (point) classification accuracy, outperforming mainstream GNNs and even several popular inductive deep architectures. On the PlantNet sub-dataset, DBGCN achieves an mAcc (mean accuracy of node classification) of 93.00% under 1.95% manual annotation ratio. On the Soybean-MVS sub-dataset, DBGCN achieves an mAcc of 91.05% under 4.88% manual annotation ratio. Furthermore, our DBGCN not only works well on crop 3D data but can also serve other applications such as the segmentation of point cloud data for large-scale street view. Our dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.
Why it matches plant phenotyping methods3D植物点群から器官を分割・推論する深層学習手法を開発し、植物フェノタイピングデータ上で精度検証しているため、表現型取得・抽出法が中心である。
abstractWe propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds
Reproduction assets foundThe authors explicitly state that their dataset (plant point clouds) and DBGCN code are publicly available on GitHub.Code · publicOur data and code are available at: https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:414-455Dataset · publicOur dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:88-94Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
• Novel pipeline simplifying pose annotation • Novel method to quantify the occlusion rate was developed • 99.6% reduction in the amount of manual annotations • Training with an occlusion rate ≤ 95% for the labels lead to the best performance • Improved fruit detection and similar pose estimation as state of the art Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are ≤ 95% occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methods3D Gaussian Splattingによる再構成、アノテーション投影、リンゴの姿勢推定を統合した新規パイプラインが研究の中心であり、果実の位置・向きという植物器官形質を抽出・評価している。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application.
Why it matches plant phenotyping methodsトマト苗の3D表現型を抽出する画像・点群・AI統合手法を開発し、手動測定との精度検証も行っており、表現型取得法が研究の中心である。
abstractThis study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings.
RiceRootAnnotation / quality controlMorphology / geometry measurementSegmentationRoot system architecture
ABSTRACT Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for climate change mitigation. Despite its importance, breeding for root anatomical traits remains limited because manual analysis of root cross-sections is labor-intensive, inconsistent, and poorly scalable, and analysis pipelines do not generalize across heterogeneous imaging conditions. We present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma. The model was trained on a multi-environment dataset of 1,760 annotated rice root cross-sections acquired across growth stages, cultivation systems, and countries, using a collaboratively defined annotation protocol. The model achieved high segmentation performance (mean Intersection-over-Union > 0.92) and near-perfect aerenchyma ratio quantification (R 2 = 0.98), and was evaluated by two experts as performing on par with, and in some cases better than, expert annotators. Delivered as open-source software with an online interactive demonstrator, the pipeline revealed differences in aerenchyma across genotypes, water regimes, environments, and developmental stages. Overall, this work demonstrates that transformer-based segmentation enables high-throughput anatomical phenotyping, supporting scalable and climate-smart rice breeding. HIGHLIGHTS Transformer-based segmentation enables robust aerenchyma phenotyping across environments A SegFormer model achieves expert-level accuracy on diverse rice root cross-sections Automated analysis delivers near-perfect lacuna-to-cortex ratio quantification (R 2 ≈ 0.98) Our online demonstrator supports scalable, climate-smart rice breeding applications
Why it matches plant phenotyping methodsイネ根の画像から通気組織を自動分割・定量する深層学習パイプラインを開発し、異なる環境で性能検証した、中心的な植物フェノタイピング研究である。
abstractWe present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma.
Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82 + % detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.
Why it matches plant phenotyping methods植物病斑の画素レベル分割と病害重症度推定という、植物状態を画像から定量化する手法が研究の中心であり、データセット構築・モデル評価も行っている。
abstractthis study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation
Plant disease adversely impacts food production and quality. Alongside detecting the disease, estimating its severity is important in managing the disease. Artificial intelligence deep learning-based techniques for plant disease detection are emerging. Unlike most of these techniques, which focus on disease recognition, this study addresses various plant disease-related tasks, including annotation, severity classification, lesion detection, and leaf segmentation. We propose a novel approach that learns the disease symptoms, which are then used to segment disease lesions for severity estimation. To demonstrate the work, a dataset of barley images was used. We captured the images of barley plants inoculated with diseases on test-bed paddocks at various growth stages. The dataset was automatically annotated at a pixel level using a trained vision transformer to obtain the ground truth labels. The annotated dataset was applied to train salient object detection (SOD) methods. Two top-performing lightweight SOD models were used to segment the disease lesion areas. To evaluate the performance of the SODs, we have tested them on our dataset and several other datasets, including the Coffee dataset, which has expert pixel-level labels that were unseen during the training step. Several morphological and spectral disease symptoms, including those akin to the widely used ABCD rule for human skin-cancer detection, i.e., asymmetry (A), border irregularity (B), colour variance (C), and diameter (D), are learned. To the best of our knowledge, this is the first study to incorporate these ABCD features in plant disease detection. We further extract visual and texture features using the grey level co-occurrence matrix (GLCM) and fuse them with the ABCD features. For the coffee dataset, our method achieved 82+% detection accuracy on the severity classification task. The results demonstrate the performance of the proposed method in detecting plant diseases and estimating their severity.
Why it matches plant phenotyping methods植物病害の病変を画像から抽出し、病害重症度という植物状態を推定する手法の開発・評価が中心であるため。
titleAutomatic pixel-level annotation for plant disease severity estimation
Field / plotAnnotation / quality controlVisualization / data management
BACKGROUND: Accurate acquisition of phenotypic data is critical for cataloguing and utilising genetic variation in cultivated crops, landraces, and their wild relatives. The collection of phenotypic data using handwritten notes often introduces errors which can and should be avoided. Electronic data collection is crucial for ensuring error prevention and data standardisation and thus ensuring high-quality, reliable data. IMPLEMENTATION: This paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research. Building on its predecessor, GridScore, the development of GridScore NEXT was driven by real life, in the field interactions with expert user groups across a number of crops. This iterative design methodology allowed the development and testing of new features. Collaborators from the 'Biodiversity for Opportunities, Livelihoods and Development' (BOLD) project, focusing on crops including rice, grasspea, and alfalfa, along with barley, potato, vegetable and blueberry teams, provided invaluable insights through training sessions and interviews and in the field use of the application. RESULTS: Key improvements to GridScore NEXT include enhanced data collection tools, supporting individual plant phenotyping within plots and enabling new data types such as GPS coordinates and image traits. GridScore NEXT provides customisable user defined validation rules to help prevent errors and incorporates barcode scanning for accurate, efficient data capture. The application offers an increased toolbox of data visualizations over its predecessor including heatmaps and statistical box plots, which aid in identifying potential data issues and understanding trial performance in the field. GridScore NEXT is cross-platform and can operate without an internet connection, making it ideal for field use in remote areas. Its adoption has led to standardisation of methods, significant error reduction, and the timely sharing of data, enabling quicker decision-making in pre-breeding and characterisation experiments. GridScore NEXT is available under an open-source (Apache 2.0) licence and freely available to all with no restrictions. It offers self-hosting options for enhanced data security and privacy. GridScore NEXT shows broad applicability across a diverse range of not only plant phenotyping experiments, but any experiment that requires the collection of accurate data.
Why it matches plant phenotyping methods植物表現型データ収集アプリケーションの開発と検証が論文の中心であり、個体表現型や画像形質を含む圃場データ取得を支援するため、対象範囲に含める。
abstractThis paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research.
Reproduction assets foundThe paper describes GridScore NEXT and its use in BOLD/CPC phenotyping. Authors' public code (GitHub, Zenodo) and public phenotype datasets (BOLD alfalfa, grasspea, rice; CPC characterisation data) are available; blueberry and UKVGB data are request-only.Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicDatasets used in this study were part of the BOLD project (alfalfa, grasspea and rice) which are available from https://germinate.hutton.ac.uk/cwr/alfalfa/, https://germinate.hutton.ac.uk/cwr/grasspea and https://germinate.hutton.ac.uk/cwr/rice/.Open asset ↗html-lines:528-593Dataset · publicThe CPC datasets used are characterisation datasets which are available from https://germinate.hutton.ac.uk/cpc.Open asset ↗html-lines:528-593Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 15 Sept 2026
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medical and animal cell imaging, which often benefit from temporal redundancy and relatively uniform morphology, studies of microbial and microalgal biodiversity must rely on static snapshots. These snapshots exhibit high variability in cell shape, organelle organisation, and image contrast. Consequently, robust AI-assisted segmentation in this context requires models that learn directly from morphological features and can adapt to heterogeneous sample preparation. In this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets. This framework is specifically designed to address the challenges posed by morphological diversity and contrast variability while remaining within realistic computational constraints. We evaluate multiple lightweight 3D encoder-decoder architectures and identify VNet as the best option for balancing computational efficiency and volumetric accuracy in whole-cell segmentation. Using datasets from two strains of Phaeodactylum tricornutum and extending our analysis to cross-species comparisons, we demonstrate that training on specific regions of interest can lead to an overestimation of model performance. In contrast, performing whole-cell segmentation uncovers significant differences in architectural robustness. Moreover, we show that transfer learning and contrast-aware hybrid strategies allow for efficient adaptation to previously unseen datasets with minimal annotation. The incorporation of boundary-aware loss functions significantly enhances the delineation of closely associated organelles, such as chloroplasts and mitochondria, in multi-class segmentation tasks. Together, these findings establish a scalable, reproducible, and biologically informed AI framework for 3D FIB-SEM segmentation. This framework enables high-throughput analysis of cellular ultrastructure across diverse species and imaging conditions. Author SummaryCells exhibit a wide range of shapes, sizes, and internal structures, particularly among various microbial species. These morphological differences are not arbitrary; they indicate how cells adapt to their environments and manage essential biological functions. Modern three-dimensional electron microscopy can capture this structural diversity at the nanometre scale, but analysing the resulting data is often slow. This is due to the time-consuming process of manually outlining cellular structures, which also requires expert knowledge. Artificial intelligence (AI) has made significant advances in accelerating image analysis in medical and animal cell studies, typically by learning from repeated observations over time. However, studies focusing on microbial and microalgal biodiversity often rely on single snapshots of diverse cells prepared under varying imaging conditions. This complicates automated analysis since AI systems must learn from morphology directly rather than from temporal repetition. In this study, we developed and evaluated an AI-assisted segmentation framework specifically for whole-cell 3D electron microscopy data. By systematically comparing efficient neural network architectures and incorporating transfer learning and contrast-aware strategies, we demonstrate that accurate segmentation can be achieved even with limited training data and standard computing resources. Our approach facilitates faster, scalable, and reproducible analysis of cellular ultrastructure, paving the way for large-scale investigations into cell morphology, adaptation, and diversity across species. Significance statementQuantitative analysis of cellular ultrastructure across species is currently limited by challenges in segmenting large three-dimensional electron microscopy datasets. Unlike medical imaging, which often benefits from artificial intelligence due to its use of temporal repetition and consistent morphology, studies of microbial biodiversity depend on single snapshots that display extreme variations in cell shape and image contrast. This work presents a scalable, morphology-driven AI framework for whole-cell 3D segmentation that is resilient to biological diversity and variations in sample preparation. By enabling accurate analysis with minimal annotations and standard computational resources, this approach enhances access to high-throughput ultrastructural studies and facilitates comparative investigations of cellular adaptation across different species.
Why it matches plant phenotyping methods珪藻の細胞形態・細胞内構造を定量化する3D電子顕微鏡画像のAIセグメンテーション手法を開発・比較検証しており、表現型取得・抽出が研究の中心である。
abstractIn this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets.
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection. It examines UAV platforms, sensor technologies, flight protocols, image preprocessing pipelines, DL architectures, and existing datasets, and it evaluates how these components interact within UAV–DL disease-monitoring frameworks. The review also compares model performance across convolutional neural network-based and Transformer-based architectures, highlighting metrics such as accuracy, recall, F1-score, inference speed, and deployment feasibility. Persistent challenges—such as limited UAV-acquired datasets, annotation inconsistencies, geographic model bias, and inadequate real-time deployment—are identified and discussed. Finally, the paper proposes a structured research agenda including lightweight edge-deployable models, UAV-ready benchmarking protocols, and multimodal data fusion. This review provides a consolidated reference for researchers and practitioners seeking to develop practical and scalable cassava-disease detection systems.
Why it matches plant phenotyping methodsUAV画像と深層学習によるカッサバ病害の検出手法を中心に、センサー、撮影プロトコル、画像処理、モデル、データセット、性能指標を体系的にレビューしているため、植物の病害状態を対象とするフェノタイピング手法レビューに該当する。
abstractThis review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection.
Industrial hemp cultivation is expanding and requires reliable monitoring for legal compliance and agricultural management. This paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L. It integrates RGB, multispectral, and thermal imaging as core modules, with hyperspectral and LiDAR as optional extensions. The framework sets protocols for sensor integration, flight planning, field measurements, and annotation, ensuring datasets that meet EU altitude limits (≤120 m AGL). Multi-altitude and multi-time-of-day acquisitions are proposed to capture spatial and diurnal variability. These data improve model robustness for phenotyping, stress detection, and THC compliance verification. Potential applications include precision agriculture, breeding, regulatory monitoring, environmental assessment, and illicit crop detection. Open-access datasets generated through this framework will support reproducibility, machine learning development, and collaboration among researchers, farmers, and regulators.
Why it matches plant phenotyping methodsUAVマルチセンサーフレームワークの設計、取得プロトコル、アノテーション、オープンデータセット開発が中心で、植物表現型やストレスを測定する方法論的貢献が明確です。
abstractThis paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 7 Sept 2026
Plain Language Summary The lack of low-cost, user-friendly and expedient methods for plant phenotyping challenges researchers’ ability to efficiently collect accurate phenotypic data in large field experiments. Here, we demonstrate the use of a novel, smartphone-based image capture system and two user-friendly image analysis pipelines (utilizing PlantCV or Biodock AI) to increase the throughput of plant phenotyping in two large, spaced plant populations. We showed that the image capture system collected images of adequate quality for downstream analysis using either the PlantCV or Biodock AI pipeline. Both image analysis pipelines produced phenotype values in line with those obtained using manual image annotation. Together, these results provide researchers with a low-cost, user-friendly image-based phenotyping method that can be widely applied to increase phenotyping throughout in field experiments.
Why it matches plant phenotyping methodsスマートフォン画像取得システムと画像解析パイプラインを開発・検証し、手動アノテーションとの比較で植物表現型測定の精度とスループットを評価しており、方法が研究の中心である。
abstractHere, we demonstrate the use of a novel, smartphone-based image capture system and two user-friendly image analysis pipelines (utilizing PlantCV or Biodock AI) to increase the throughput of plant phenotyping in two large, spaced plant populations.
Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are $\leq95\%$ occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methodsリンゴの姿勢推定アノテーションを大幅に効率化する3D再構成・自動ラベル投影パイプラインを開発し、姿勢推定性能も評価しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
Reproduction assets foundThe paper explicitly provides two paper-specific public assets: the authors' phenotyping/pose-estimation pipeline code on GitHub and the collected apple orchard image dataset on a 4TU DOI. Both are directly used for the paper's measurements and analysis.Dataset · publicIn total, 367 images were collected. The dataset is available at https://doi.org/10.4121/976c94f2-028f-4291-adfd-20eb82b0f647Open asset ↗10.4121/976c94f2-028f-4291-adfd-20eb82b0f647lines:92-108Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
FlowerAnnotation / quality controlObject detectionGrowth / development / phenology
ABSTRACT Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found in-house expert-curated annotations were essential for producing reliable results. Expert validation found relatively strong accuracy for detecting present floral structures, but still had moderately high false negative rates. Applying the ensemble to our filtered final image dataset of 22 million records resulted in 11.1 million records labeled with flowers present. However, only 2.9 million of these contained complete metadata necessary for downstream phenology research, highlighting the need for full label digitization efforts. Still, this dataset represents a large compilation of historical herbarium-derived phenology records available as a resource for the phenology community. We end by demonstrating how integrating these machine-labeled records into Phenobase, a publicly-available phenology database, expands taxonomic and temporal coverage for large-scale phenological analyses, and discuss remaining challenges and next steps.
Why it matches plant phenotyping methods植物標本画像から花の存在を自動検出し、精度検証と大規模な phenology データセット化を行う機械学習手法が研究の中心であるため、植物フェノタイピング手法として適格です。
abstractwe present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the ensemble models, training/validation/test images, training data and final ensemble output on Zenodo, and the analysis code on GitHub; machine-labeled records are also served via the public Phenobase portal. All are paper-specific, public, and actionable.Dataset · publicors contributed to drafts and gave final
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The ensemble data models and a corresponding JSON file with model metadata data are
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the SupportinOpen asset ↗Zenodo · 17675089pdf-raw-page:19 lines:1-55Dataset · publictps://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotate genera and families removed from training and
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Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
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The ensemble data models and a corresponding JSON file with model metadata data are
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housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
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validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
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this project can be found on github (https://github.com/rafelafrance/phenobase/tree/v1.0.0).461
Training data and final ensemble output can be found on Zenodo
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(https://doi.org/10.5281/zenodo.17675089).463
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Additional Supporting Information may be found online in the Supporting Information section at
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the end of the article.
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Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labelling and categorisation of NSC's digital assets. To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine-learning models for complex tasks, such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine-learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine-learning models, such as Mask R-CNN and YOLO11, are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. The determination of quantitative characteristics of plant specimens, such as measuring leaf area or the timestamp of the floral transition, opens up herbarium data for reuse in the large prognosis platforms currently developed in the framework of the Common European Data Spaces. In this way, plant trait data mobilised from natural science collections can improve the predictive capability of the vegetation model components of climate-related data spaces.
Why it matches plant phenotyping methodsハーバリウム画像から植物器官を検出・セグメンテーションし、葉面積などの形質を定量化する機械学習手法と基盤の応用が研究の中心である。
abstractThis study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens.
MultimodalLiDAR / point cloudAnnotation / quality controlClassificationObject detectionCalibration / preprocessingSegmentationTracking
Plant phenomics, the comprehensive study of plant phenotypes, has gained prominence as a vital tool for understanding the intricate relationships between genotypes and the environment. Image-based plant phenomics has progressed rapidly, and three-dimensional (3D) phenotyping is a valuable extension of traditional 2D phenomics. However, the increased data dimensionality poses challenges to feature extraction and phenotyping. In recent decades, deep learning has led to remarkable progress in revolutionizing 3D phenotyping. Therefore, this review highlights the importance of using deep learning in 3D plant phenomics. It systematically overviews the capabilities of deep learning for 3D computer vision, covering 3D representation, classification, detection and tracking, semantic segmentation, instance segmentation, and generation. Additionally, deep learning techniques for 3D point preprocessing (e.g., annotation, downsampling, and dataset organization) and various plant phenotyping tasks are discussed. Finally, the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized, including (1) benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning; (2) accurate and efficient 3D point cloud analysis by leveraging multitask learning, lightweight models, and self-supervised learning; and (3) deep learning for 3D plant phenomics by exploring interpretability, extensibility, and multimodal data utilization. The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
Why it matches plant phenotyping methods3D植物フェノミクスにおける深層学習手法を体系的にレビューしており、植物形質の抽出・推定手法が中心である。
abstractTherefore, this review highlights the importance of using deep learning in 3D plant phenomics.
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 · publicThe dataset can be downloaded from https://github.com/Jinlab-AiPhenomics/Mazie3D.Open asset ↗Jinlab-AiPhenomics/Mazie3Dhtml-lines:332-336Plant phenotyping relevance match · UnverifiedbioRxiv · checked 6 Sept 2026
In nearly all plants, pores on the leaf surface called stomata are essential for photosynthesis and gas exchange. The shape and distribution of stomata on the leaf varies widely between plants and is directly connected to photosynthetic efficiency. However, our understanding of the factors, both genetic and environmental, that exert subtle but significant effects on stomatal morphology is limited by the time required to manually annotate stomata in large imaging datasets. Here, we present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images. First, we establish QuickSpotters ability to automatically and accurately annotate mature stomata across developmental time. We also introduce an optional, speedy proofreading utility, StomEdit, that allows the researcher to quickly validate and correct machine-generated annotations. We use QuickSpotter and StomEdit to quantify how stomatal morphology evolves at the population level during cotyledon development and demonstrate how the programs can be used to extract subtle differences in stomatal development following pharmacological treatments. Finally, we describe PairCaller, a pair-calling classifier that accompanies QuickSpotter and can be used to identify stomatal clusters, a physiologically relevant and widely studied developmental phenotype. Taken together, our suite of programs facilitates quantitative analyses of stomatal development at scale, enabling high-throughput analyses of leaf phenotypes under varied conditions.
Why it matches plant phenotyping methods蛍光画像から気孔形態・分布を半自動抽出するソフトウェア群を開発し、精度検証と植物表現型への適用を行っており、フェノタイピング手法が研究の中心である。
abstractwe present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images.
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Accurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies. While RGB-D cameras enable high-throughput scanning in operational settings like harvesting conveyors, they produce incomplete, low-quality 3D models. Conversely, close-range Structure-from-Motion (SfM) produces high-quality reconstructions but is not suitable for high-throughput field application. This study bridges this gap through 3DPotatoTwin , a paired dataset containing 339 tuber samples across three cultivars collected in Hokkaido, Japan. Our dataset uniquely combines: (1) conveyor-acquired RGB-D point clouds, (2) ground measurement, (3) SfM reconstructions under indoor controlled environment, and (4) aligned model pairs with transformation matrices. The multi-sensory alignment employs an semi-supervised pin-guided pipeline incorporating single-pin extraction and referencing, cross-strip matching, and binary-color-enhanced ICP, achieving 0.59 ± 0.11 mm registration accuracy. Beyond serving as a benchmark for 3D phenotyping algorithms, the dataset enables training of 3D completion networks to reconstruct high-quality 3D models from partial RGB-D point clouds. Meanwhile, the proposed semi-automated annotation pipeline has the potential to accelerate 3D dataset generation for similar studies. The presented methodology demonstrates broader applicability for multi-sensor data fusion across crop phenotyping applications. The dataset and pipeline source code are publicly available at HuggingFace and GitHub, respectively.
Why it matches plant phenotyping methodsジャガイモ塊茎の3D表現型計測を対象に、RGB-D・SfM・地上計測を統合したデータセット、位置合わせパイプライン、ベンチマークを開発しており、表現型取得手法が中心である。
abstractAccurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies.
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 · publicAll the batch processing scripts mentioned in this section were provided in the 3dscan folder at Github (https://github.com/UTokyo-FieldPhenomics-Lab/PotatoScan/).Open asset ↗UTokyo-FieldPhenomics-Lab/PotatoScanhtml-lines:119-131Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
RGB / grayscaleLeafAnnotation / quality controlClassificationGrowth / development / phenology
ABSTRACT Plant phenology dictates many aspects of community function and ecosystem dynamics. Yet, global phenology data are still limited, especially in areas lacking monitoring programs. Here we present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data for deciduous, woody genera. We first discuss our implementation of a new human annotation framework for leaf phenology on iNaturalist, aligning with phenophase definitions used by the larger phenology community. We then showcase the use of 165,988 crowdsourced annotated records to train a Vision Transformer model with a two-stage regime to maximize accuracy across single- and multi-image records. This approach extends Phenovision from scoring individual images to aggregating at the iNaturalist record level, better aligning with human annotation processes. Post-hoc validation showed high performance for detecting present green and colored leaves (>98% accuracy), and reasonable accuracy for breaking leaf buds (>87% accuracy). Applying PhenoVision–Leaf to over 26 million iNaturalist records yielded 5.6 million record-level phenology observations across 6,500 species and 57 families, filling geographic and taxonomic gaps. These data, now accessible through the Phenobase portal, establish a foundation for near real-time monitoring of leaf phenology, supporting global-scale synthesis analyses.
Why it matches plant phenotyping methods葉のフェノロジー状態を画像から推定するコンピュータビジョン手法と、注釈・学習・検証・大規模データ生成基盤が研究の中心であるため。
abstractwe present a new data resource, PhenoVision–Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data
Reproduction assets foundThe paper's PhenoVision–Leaf record-level leaf phenology dataset (5.6M machine-labeled observations) is publicly available via the Phenobase portal, and the underlying iNaturalist images used for training and machine labeling are available through the iNaturalist open data repository on AWS. No author analysis code or Dataset · publicAll images associated with these records were
downloaded using iNaturalist’s open data repository on AWS
(https://registry.opendata.aws/inaturalist-open-data/).Open asset ↗pdf-page:4 lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Obtaining the complete morphology of tomato fruits under non-destructive conditions is essential for phenotype research, yet fruit occlusions often hinder deep learning-based image segmentation methods from capturing the true shape of occluded regions. This limitation reduces prediction accuracy and adversely impacts phenotype data acquisition. To overcome this challenge, we propose CGA-ASNet, an RGB-D amodal segmentation network incorporating a Contextual and Global Attention (CGA) module. A synthetic tomato dataset (Tomato-sim) was constructed using NVIDIA Isaac Sim's Replicator Composer (ISRC) to realistically simulate tomato morphology and greenhouse environments, and the network was trained on this dataset. To evaluate generalization, CGA-ASNet was tested on both the synthetic and a separate real-world dataset. While no explicit domain adaptation techniques were adopted, diverse lighting conditions (strong, normal, and weak illumination) were simulated to implicitly reduce the domain gap, and a mean coordinate fusion algorithm was introduced to improve annotation completeness in real-world occlusion scenarios. By leveraging contextual information among feature input keys for self-attention learning, capturing global information, and expanding the receptive field, CGA-ASNet enhanced representation capacity, semantic understanding, and localization accuracy. Experimental results demonstrated that CGA-ASNet achieved an F@0.75 score of 94.2 and a mean Intersection over Union (mIoU) of 82.4% in greenhouse amodal segmentation tasks. These findings indicate that training with well-designed synthetic datasets can effectively support accurate occlusion-aware segmentation in real environments, providing a practical solution for tomato phenotyping in greenhouse conditions.
Why it matches plant phenotyping methodsトマト果実の遮蔽領域を復元して完全形態を取得するRGB-D画像解析手法を開発し、合成・実画像データセットで技術検証しているため、植物表現型取得が中心的である。
abstractObtaining the complete morphology of tomato fruits under non-destructive conditions is essential for phenotype research
Automated seed phenotyping has wide applications in research and agriculture and relies on easy-to-use platforms and pipelines. Seed phenotyping in the model species Arabidopsis thaliana poses a significant challenge due to the large number of tiny seeds produced by individual plants, which are difficult to manually separate and count. Manual counting methods are time-consuming and prone to user bias, particularly for subtle phenotypic changes. To address these limitations, we developed Samplify , a scalable, automated pipeline for seed segmentation and classification. By integrating classical image processing techniques with Meta’s Segment Anything Model (SAM), Samplify effectively segments Arabidopsis seeds, even in dense clusters where conventional methods fail. To demonstrate its versatility, we quantified the seed abortion occurring in interploidy crossings in Arabidopsis, often referred to as ‘triploid block’. Samplify includes a Random Forest classifier trained on a set of computed seed shape features that enables the categorization of seeds into normal, partially aborted, and fully aborted seeds, automating the manual classification process. The tool, designed as a command-line application, significantly reduces manual annotation workload. Our validation across multiple datasets demonstrates high segmentation and classification reliability, making Samplify a valuable resource for the plant research community.
Why it matches plant phenotyping methods種子の画像セグメンテーション・分類による表現型抽出パイプラインを開発し、複数データセットで検証しており、方法論が研究の中心である。
abstractwe developed Samplify , a scalable, automated pipeline for seed segmentation and classification.
A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 m2 field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an F1 score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation.
Why it matches plant phenotyping methodsUAV画像から個体分割と植物形質（冠径）を自動抽出する手法の開発・評価が研究の中心であり、精度指標と手動測定との一致も検証している。
abstractwe propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery
Object detection is essential for precision agriculture applications like automated plant counting, but the minimum dataset requirements for effective model deployment remain poorly understood for arable crop seedling detection on orthomosaics. This study investigated how much annotated data is required to achieve standard counting accuracy (R2 = 0.85) for maize seedlings across different object detection approaches. We systematically evaluated traditional deep learning models requiring many training examples (YOLOv5, YOLOv8, YOLO11, RT-DETR), newer approaches requiring few examples (CD-ViTO), and methods requiring zero labeled examples (OWLv2) using drone-captured orthomosaic RGB imagery. We also implemented a handcrafted computer graphics algorithm as baseline. Models were tested with varying training sources (in-domain vs. out-of-distribution data), training dataset sizes (10–150 images), and annotation quality levels (10–100%). Our results demonstrate that no model trained on out-of-distribution data achieved acceptable performance, regardless of dataset size. In contrast, models trained on in-domain data reached the benchmark with as few as 60–130 annotated images, depending on architecture. Transformer-based models (RT-DETR) required significantly fewer samples (60) than CNN-based models (110–130), though they showed different tolerances to annotation quality reduction. Models maintained acceptable performance with only 65–90% of original annotation quality. Despite recent advances, neither few-shot nor zero-shot approaches met minimum performance requirements for precision agriculture deployment. These findings provide practical guidance for developing maize seedling detection systems, demonstrating that successful deployment requires in-domain training data, with minimum dataset requirements varying by model architecture.
Why it matches plant phenotyping methodsトウモロコシ幼苗の個体数という植物形質を画像から推定する物体検出手法について、複数モデル、データ量、アノテーション品質を系統的に比較・評価しており、手法の性能検証が中心である。
abstractThis study investigated how much annotated data is required to achieve standard counting accuracy (R2 = 0.85) for maize seedlings across different object detection approaches.
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' handcrafted-method analysis code on a GitHub gist and the ID (in-distribution) annotation datasets created for this study on Zenodo. Both have explicit availability language and public URLs.Code · publicThe code for the handcrafted methods used in this study is available at
https://gist.github.com/SamueleBumbaca/4a227bbe7b78d6be3424899c16c60bb4 (accessed on 20
June 2025).Open asset ↗gist.github.com/SamueleBumbacapdf-page:23 lines:1-52Dataset · publicThe datasets created during this study (ID datasets) are available at the Zenodo repository
https://doi.org/10.5281/zenodo.15235602 (accessed on 20 June 2025)Open asset ↗Zenodo · 10.5281/zenodo.15235602pdf-page:23 lines:1-52Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Quantitative descriptions of the complete canopy architecture are essential for accurately evaluating crop photosynthesis and yield performance to guide ideotype design. Although various sensing technologies have been developed for three-dimensional (3D) reconstruction of individual plants and canopies, they failed to obtain an accurate description of canopy architectures due to severe occlusion among complex canopy architectures. We proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model. A complete point cloud generation framework was developed for automated annotation of the training dataset by distinguishing surface points from occluded points within canopies. The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points. To further enhance feature extraction, a dynamic graph convolutional feature extractor (DGCFE) module was proposed to capture structural variations over the whole rapeseed growth period. The results demonstrated that CP-PCN achieved chamfer distance (CD) values of 3.35 cm -4.51 cm over four growth stages, outperforming the state-of-the-art transformer-based method (PoinTr). Ablation studies confirmed the effectiveness of the MRDG and DGCFE modules. Moreover, the validation experiment demonstrated that the silique efficiency index developed from CP-PCN improved the overall accuracy of rapeseed yield prediction by 11.2% compared to that of using incomplete point clouds. The CP-PCN pipeline has the potential to be extended to other crops, significantly advancing the quantitatively analysis of in-field population canopy architectures.
Why it matches plant phenotyping methods作物群落キャノピーの3D形態を復元する点群補完法を開発し、既存法との比較、アブレーション、収量予測への有効性検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractWe proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model.
Reproduction assets foundThe paper's availability statement explicitly deposits all source code and test data (rapeseed canopy point cloud completion, CP-PCN) on GitHub at the allowed URL.Code · publicn Wang, Yi Feng,
Mengjie Gong and Guangyu Wu, for their participation in the experiments, and
to the Jiaxing Academy of Agricultural Sciences for their assistance with the
experimental data acquisition.
Availability of supporting data and source code
All source codes and test data involved in this study are available on
GitHub (https://github.com/Ziyue-Guo/RP-PCN.git).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests
or personal relationships that could have appeared to influence the work
reported in this paper.
Contributions
Z. G. designed the study, conducted the experiments, and wrote the
manuscript. Y. S. contributed to the expeOpen asset ↗Ziyue-Guo/RP-PCNpdf-layout-page:42 lines:1-42Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Soybeans are important due to their nutritional benefits, economic role, agricultural contributions, and various industrial applications. Effective leaf detection plays a crucial role in analyzing soybean growth within precision agriculture. This study examines the influence of different labeling methods on the efficiency of artificial intelligence (AI) based soybean leaf detection. We compare a traditional general labeling technique against a new context-aware method that utilizes information about leaf length and bottom extremities. Both approaches were employed to train a YOLOv5L deep learning model using high-resolution soybean imagery. Results show that the general labeling method excelled with soybean varieties that have wider internodes and distinctly separated leaves. In contrast, the context-aware labeling method outperformed the general approach for medium soybean varieties characterized by narrower internodes and overlapping leaves. By optimizing labeling strategies, the accuracy and efficiency of AI-based soybean growth analysis can be significantly improved, particularly in high-throughput phenotyping systems. Ultimately, the findings suggest that a thoughtful approach to labeling can enhance agricultural management practices, contributing to better crop monitoring and improved yields.
Why it matches plant phenotyping methods大豆葉の検出精度を高めるラベリング手法を比較・評価し、AI画像解析とハイスループット表現型解析への適用を中心に扱うため、植物フェノタイピング手法研究に該当する。
abstractThis study examines the influence of different labeling methods on the efficiency of artificial intelligence (AI) based soybean leaf detection.
Deep learning has transformed computer vision for precision agriculture, yet apple orchard monitoring remains limited by dataset constraints. The lack of diverse, realistic datasets and the difficulty of annotating dense, heterogeneous scenes. Existing datasets overlook different growth stages and stereo imagery, both essential for realistic 3D modeling of orchards and tasks like fruit localization, yield estimation, and structural analysis. To address these gaps, we present AppleGrowthVision, a large-scale dataset comprising two subsets. The first includes 9,317 high resolution stereo images collected from a farm in Brandenburg (Germany), covering six agriculturally validated growth stages over a full growth cycle. The second subset consists of 1,125 densely annotated images from the same farm in Brandenburg and one in Pillnitz (Germany), containing a total of 31,084 apple labels. AppleGrowthVision provides stereo-image data with agriculturally validated growth stages, enabling precise phenological analysis and 3D reconstructions. Extending MinneApple with our data improves YOLOv8 performance by 7.69 % in terms of F1-score, while adding it to MinneApple and MAD boosts Faster R-CNN F1-score by 31.06 %. Additionally, six BBCH stages were predicted with over 95 % accuracy using VGG16, ResNet152, DenseNet201, and MobileNetv2. AppleGrowthVision bridges the gap between agricultural science and computer vision, by enabling the development of robust models for fruit detection, growth modeling, and 3D analysis in precision agriculture. Future work includes improving annotation, enhancing 3D reconstruction, and extending multimodal analysis across all growth stages.
Why it matches plant phenotyping methodsリンゴの生育段階・果実・樹体構造を対象とする大規模ステレオ画像データセットを構築し、果実検出、フェノロジー分析、3D再構成モデルの評価に用いており、表現型取得・解析基盤が中心である。
titleAppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Background Arabidopsis thaliana is the leading model plant used to study plant-pathogen interactions. High-throughput phenotyping allows for the simultaneous study of many plants with high-frequency image acquisition. Nevertheless, the segmentation of symptomatic plants on natural soil remains challenging, requiring the annotation of hundreds of images and the subsequent training of specialized models for each pathosystem considered. This paper presents a novel approach to segmenting A. thaliana plants' time series using a single annotated image. Results Images of A. thaliana plants infected with Pseudomonas syringae pathovar tomato strain DC3000 were annotated with precise segmentation masks. We compared various mask segmentation methods; our one-shot learning approach obtained a Dice score of 0.977 on our test dataset. Variables extracted from the segmented images allowed statistical discrimination between infected and control plants. We used our one-shot learning approach without further fine-tuning on a new pathosystem; A. thaliana infected with Ralstonia pseudosolanacearum , strain GMI1000. We obtained a Dice score of 0.966 in the second test dataset. We also obtained a Pearson correlation coefficient of -0.928 between the annotated quantitative disease index and the variable generated with our method. Conclusion This work provides a pipeline to segment symptomatic A. thaliana plants by leveraging a visual foundation model. The method has been used successfully on two different pathogens, is fast to train, and does not need a large dedicated graphical processing unit. Our method has characterized plant-pathogen interactions of two pathosystems without fine-tuning for the second pathosystem. Its ease of use and low computing requirements should make adapting our approach to other high-throughput phenotyping platforms easy.
Why it matches plant phenotyping methods植物画像の症状セグメンテーションと定量的病害指数推定のパイプライン開発・検証が中心であり、2病原体で性能評価も行っているため。
abstractThis paper presents a novel approach to segmenting A. thaliana plants' time series using a single annotated image.
Extracting the quantity and geolocation data of small objects at the organ level via large-scale aerial drone monitoring is both essential and challenging for precision agriculture. The quality of reconstructed digital orthophoto maps (DOMs) often suffers from seamline distortion and ghost effects, making it difficult to meet the requirements for organ-level detection. While raw images do not exhibit these issues, they pose challenges in accurately obtaining the geolocation data of detected small objects. The detection of small objects was improved in this study through the fusion of orthophoto maps with raw images using the EasyIDP tool, thereby establishing a mapping relationship from the raw images to geolocation data. Small object detection was conducted by using the Slicing-Aided Hyper Inference (SAHI) framework and YOLOv10n on raw images to accelerate the inferencing speed for large-scale farmland. As a result, comparing detection directly using a DOM, the speed of detection was accelerated and the accuracy was improved. The proposed SAHI-YOLOv10n achieved precision and mean average precision (mAP) scores of 0.825 and 0.864, respectively. It also achieved a processing latency of 1.84 milliseconds on 640×640 resolution frames for large-scale application. Subsequently, a novel crop canopy organ-level object detection dataset (CCOD-Dataset) was created via interactive annotation with SAHI-YOLOv10n, featuring 3986 images and 410,910 annotated boxes. The proposed fusion method demonstrated feasibility for detecting small objects at the organ level in three large-scale in-field farmlands, potentially benefiting future wide-range applications.
Why it matches plant phenotyping methodsUAV画像と生画像の融合、SAHI-YOLOv10nによる作物器官の検出・位置推定を中心に開発・評価し、器官レベルの大規模データセットも構築しているため、植物表現型取得法が研究の中心である。
abstractThe detection of small objects was improved in this study through the fusion of orthophoto maps with raw images using the EasyIDP tool, thereby establishing a mapping relationship from the raw images to geolocation data.
Reproduction assets foundThe paper's CCOD-Dataset (3986 UAV images, 410,910 annotated bounding boxes of crop canopy organs) is publicly released on Hugging Face, and the authors' SAHI-YOLOv10 detection framework code is hosted in a public GitHub repository. Other code (fusion/EasyIDP pipeline) is only available upon request.Dataset · publicThe CCOD-Dataset link
is publicly available at Hugging Face at https://huggingface.co/datasets/Nir-Open asset ↗CCOD-Datasetpdf-page:8 lines:1-51Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist, they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations. These workflows make use of an LLM to intelligently parse phenotypes into short concepts, followed by finding appropriate ontology terms via embedding vector similarity or via Retrieval-Augmented Generation (RAG). The RAG model is a state-of-the-art approach that augments conversational prompts to the LLM with context-specific data to empower it beyond its pre-trained parameter space. We show that the RAG produces the most accurate automated annotations that are often highly similar or identical to expert-curated annotations.
Why it matches plant phenotyping methods植物の表現型観察記述をオントロジー語に自動アノテーションするLLM/RAGワークフローの開発・精度評価が中心であり、再利用可能な計算ツールとして植物表現型データを処理する。
abstractHere we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms
Reproduction assets foundThe paper's phenotype descriptors, gold-standard annotations, LLM-parsed concepts, auto-annotations, and evaluation scores are publicly available as supplementary files, and the authors' analysis code (DE, DCE, DCRAG workflows) is publicly deposited on GitHub. A specific AraPheno trait (#278) used as an input example/`Code · publicCode to execute the DE, DCE and DCRAG workflows is available at https://github.com/dkainer/LLMannotator .Open asset ↗github.com/dkainer/LLMannotatorlines:229-257Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.
Why it matches plant phenotyping methods植物の出芽速度を対象とする大規模RGB深度画像・アノテーションデータセットを提供し、深層学習および高スループット表現型解析への利用性を実証しており、表現型取得基盤が中心である。
abstracta unique annotated dataset of seedling emergence kinetics is proposed
Reproduction assets foundThis is a data paper whose core contribution is a public annotated RGB-depth seedling dataset (~70,000 frames, >700,000 annotations) deposited in DATA INRAE with DOI 10.57745/AMFJTK, explicitly stated as publicly accessible. Other allowed URLs (license, Intel datasheet, Jülich record) are not paper-specific assets.Dataset · publicSynthesis of the full time-lapse and RGB-Depth full frame quantity per species
Species
Pots time-lapse
Labelled pots time-lapse
RGB-depth full frame
Rapeseed
1 760
336
15 218
Tomatoes
1 960
480
33 283
Beans
2 320
400
21 445
Total
6 040
1 216
69 946
The dataset is publicly accessible in the DATA INRAE repository, DOI: https://doi.org/10.57745/AMFJTK . The file tree structure is illustrated in Fig. 4 . The dataset is organized into 11 compressed .zip files, each corresponding to a distinct trial. Within these files, images are sorted chronologically by acquisition start date, then by camera, and stored in .png format within dedicated color and depth folders. Labels are alsoOpen asset ↗DATA INRAE · 10.57745/AMFJTKlines:105-195Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published30 Dec 2024International Journal of Experimental Research and ReviewCited by 1 · OpenAlex ↗
: Enabling visual plant disease diagnosis through deep learning that analyses big data is essential to diagnose diseases quickly. It helps the farmers and enables them to treat early, reducing the crop losses needed for a sustainable increase in agriculture. Farmers’ losses were also reduced using these technologies. However, deep learning still has great potential for plant disease diagnosis, though many challenges are associated with it. For example, it requires large, annotated data sets of symptoms and processing resources. This study proposes a novel Cloud-based Image Annotation Plant Disease Detection (C-IAPDD), which employs cloud platforms such as Google Cloud Vision API for image annotation and plant disease detection. Instead of creating such datasets manually or using those non-annotated ones saved by farmers onto their mobile phones since sensors in the device can detect disease on a particular leaf whenever placed close to it. The proposed solution provides a connection to the Internet and offline as well. The ability of C-IAPDD to simplify large-scale envision dataset collection and annotation enables powerful deep-learning models. Using cloud infrastructure’s processing power and scalability makes this a highly efficient method of identifying plant diseases without compromising accuracy. Several simulation experiments have proved that C-IAPDD could recognize a wide range of plant diseases across different types of crops. This simulation shows that C-IAPDD performs better than other methods in precision, swiftness, and expandability. The results indicate that C-IAPDD may improve plant disease detection and control, leading to healthier harvests. These findings endorse I-CIAPDD for artificial intelligence in agriculture.
Why it matches plant phenotyping methods植物の病徴画像を対象としたクラウド画像アノテーションおよび疾病検出手法の開発が中心であり、植物の病害状態を直接推定するため、方法論文として含める。
abstractThis study proposes a novel Cloud-based Image Annotation Plant Disease Detection (C-IAPDD), which employs cloud platforms such as Google Cloud Vision API for image annotation and plant disease detection.
Phenomics research, driven by advancements in imaging and image processing, enables high-throughput measurements of plant traits, providing insights into growth, tissue development, and biochemical states. However, data accuracy is critical to reliable outcomes, especially in complex methods like 3D reconstruction and hyperspectral imaging. This study demonstrates the role of Quality Management Systems (QMS) in enhancing the research process in plant phenotyping. The study emphasizes the importance of a robust data quality assurance pipeline, focusing on error identification and improving data labeling processes through semi-automation. Root Cause Analysis (RCA) was employed to address discrepancies in annotated datasets and identify critical issues, such as misalignment in experimental protocols and operational errors, including the misplacement of irrigation hoses during data collection. Corrective actions, such as photo documentation and procedural revisions, significantly improved data quality. Additionally, algorithmic support streamlined the annotation process, increasing efficiency and data reliability. This integrated approach underscores the necessity of quality control in research, especially for geographically distributed teams working under variable conditions, and highlights the broader applicability of QMS in optimizing research outputs.
Why it matches plant phenotyping methods植物フェノタイピング研究におけるデータ品質保証、アノテーション半自動化、誤差分析と改善を中心に扱う方法論的研究であり、表現型取得・解析ワークフローの技術的信頼性を評価している。
abstractThis study demonstrates the role of Quality Management Systems (QMS) in enhancing the research process in plant phenotyping.
Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed-scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Down-stream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach not only provides a scalable solution for spectral segmentation but also facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.
Why it matches plant phenotyping methods植物のハイパースペクトル画像を対象に、少量アノテーションで高精度に分割する解析ワークフローを開発し、植物状態のモニタリングに用いる方法研究である。
abstractThis study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed-scale convolution neural networks.
ArabidopsisRootAnnotation / quality controlMorphology / geometry measurementRoot system architecture
Plant phenotyping is essential in agricultural research for identifying resilient traits critical for global food security. Analyzing root growth quantitatively is increasingly vital for evaluating a plant's resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images poses significant challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootTracer” a software tool that offers a variety of functionalities. RootTracer enables users to quickly and easily create RSML files that capture the structure of a root system by inputting the image to be analyzed and marking or modifying key points within the image. Additionally, it allows for the modification of previously created RSML files (using any state-of-the-art creation tool) through an intuitive and user-friendly interface. The program also provides the capability to automatically extract various plant and root measurements from the RSML file. Furthermore, we present a new image dataset, named TILLMore CDC (Compact Disk Case), that includes ground truth annotations manually generated with the support of RootTracer, designed to advance the development of automated root recognition systems. This dataset, which will be publicly released, can be used by researchers to train machine learning models for accurate root image analysis, helping to overcome the challenges posed by complex root structures and varied imaging conditions. By leveraging this dataset, we aim to enhance the accuracy and robustness of root phenotyping methods, thereby contributing to the broader field of plant phenotyping and agricultural research. The RootTracer tool and the TILLMore CDC dataset are available on GitHub. • RootTracer is a novel software tool for efficient analysis and modification of plant root system architectures. • The software enables quick RSML file creation and editing via an intuitive interface. • RootTracer extracts diverse plant and root measurements, addressing challenges for complex root structures. • A new dataset with ground truth annotations generated using RootTracer is introduced for advancing root recognition systems. • The dataset provides resources for training machine learning models and improving root phenotyping in agriculture.
Why it matches plant phenotyping methods根系画像からRSMLを作成・編集し、根系形態計測を抽出するソフトウェアと、教師付き画像データセットを開発・提供しており、植物フェノタイピング手法が研究の中心である。
abstractIn this study, we introduce “RootTracer” a software tool that offers a variety of functionalities.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPT’s capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations. These workflows make use of an LLM to intelligently parse phenotypes into short concepts, followed by finding appropriate ontology terms via embedding vector similarity or via Retrieval-Augmented Generation (RAG). The RAG model is a state-of-the-art approach that augments conversational prompts to the LLM with context-specific data to empower it beyond its pre-trained parameter space. We show that the RAG produces the most accurate automated annotations that are often highly similar or identical to expert-curated annotations. Short description Large Language Models excel at tasks that require semantic understanding of text. Here we use that capability to auto-annotate plant phenotypes with ontological terms and compare to expert annotation.
Why it matches plant phenotyping methods植物の表現型記述をオントロジー語へ自動注釈するLLM/RAGワークフローを開発し、専門家注釈との比較で精度を評価しており、表現型情報処理手法が中心である。
abstractHere we describe a series of workflows incorporating OpenAI GPT’s capabilities to annotate Arabidopsis thaliana and forest tree phenotypic observations with ontology terms, aiming for results that resemble manually curated annotations.
In an era where global climate change is shifting plant phenology, global meta-analyses of multiple species are required more than ever. Common language or references for enhanced data compatibility are key for such analyses. Although the Plant Phenology Ontology (PPO) addresses this challenge, it does not capture several relevant reproductive structures that are critical in species with long reproductive cycles, like many Eucalyptus species. We reviewed the terminology and concepts that describe reproductive structures in eucalypts and compared them with the existing classes of the PPO to explore the PPO's potential for harmonizing disparate eucalypt datasets. We identified incongruencies within and between eucalypt terminology and the PPO. We tested the sensitivity of the PPO for capturing key eucalypt phenological structures and found it sensitive to classification of certain structures. To address these limitations, we developed the Eucalyptus Phenology Ontology (EPO), a new ontology that builds on the PPO and captures key reproductive structures using a more refined classification. The EPO integrates the relationships between reproductive structures, phenological stages, and phenological traits. The vocabulary is species-neutral so it can be applied to other taxa but specifies the synonyms and descriptions required to capture the complexity of eucalypt phenology.
Why it matches plant phenotyping methodsユーカリの生殖構造・フェノロジー形質を標準化して記録する新規オントロジーを開発しており、植物フェノタイピングデータの取得・統合基盤が中心である。
abstractTo address these limitations, we developed the Eucalyptus Phenology Ontology (EPO), a new ontology that builds on the PPO and captures key reproductive structures using a more refined classification.
Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.
Why it matches plant phenotyping methodsドローン画像、物体検出、写真測量を統合し、ナシ樹のさび病症状を検出・局在化して樹体ごとの感染強度を推定するデジタル表現型解析システムの開発が中心である。
abstracta digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the annotated UAV image dataset on Mendeley Data and the trained model, detection workflow, and Metashape loading script on figshare, both with public URLs matching allowed entries.Dataset · publicsource repository Mendeley Data (https://data.mendeley.com/datasets/44kjgc4gkc/1, accessed on 8Open asset ↗Mendeley Data · 44kjgc4gkc/1pdf-page:15 lines:1-67Code · publicThe model, the detection workflow with instructions and the script for
loading the detections into Agisoft’s Metashape are available in the open-source figshare repository
(https://doi.org/10.6084/m9.figshare.27225312.v2, accessed on 28 October 2024).Open asset ↗figshare · 10.6084/m9.figshare.27225312.v2pdf-page:15 lines:1-67Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
INTRODUCTION: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. MATERIALS AND METHODS: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. RESULTS: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. CONCLUSION: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
Why it matches plant phenotyping methods作物ストレスという植物状態を推定するUAVマルチセンサーの取得、校正、前処理、データ融合、品質管理の実践的方法を主題としており、フェノタイピング手法が中心である。
abstractThis paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
Why it matches plant phenotyping methods航空画像・オルソモザイクから作物列を自動セグメンテーションし、時系列の生育・成長を推定する画像解析パイプラインが研究の中心であり、植物形質の取得手法として適格。
abstractThis study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public Mendeley Data repository (GobhiSet, DOI 10.17632/dcjjcwc5dh.4), which contains the raw, manually, and automatically annotated RGB aerial images and ortho-mosaics of cauliflower used for the YOLOv8x-seg and Grounded SAM training and growth analysis inDataset · publicon of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data was created. However, the data that were used to perform
this research can be found in the article published at https://doi.org/10.1016/j.dib.2024.110506 and
available in the repository DOI: 10.17632/dcjjcwc5dh.4 (https://data.mendeley.com/drafts/dcjjcwc5dh).Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Di, L.; Ustundag, B. Crop Growth Modeling and Yield Forecasting. In Agro-Geoinformatics; Springer: Cham, Switzerland, 2021.
[CrossRef]
2. Mithen, S.; Jenkins, E.; Jamjoum, K.; Nuimat, S.; Nortcliff, S.; Finlayson, B. Experimental crop growingOpen asset ↗data.mendeley.com · 10.17632/dcjjcwc5dh.4pdf-raw-page:17 lines:1-52Code / 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:30-40Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published17 Jun 20242024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Cited by 12 · OpenAlex ↗
Robotics paired with computer vision are widely used in precision agriculture. Simulations are critical for safety and performance estimation by verifying their routine in a virtual world before real-world testing and deployment. However, many simulators used in agricultural robots lack photorealism in their virtual worlds compared to the real world. We implemented Unreal Engine 5 (UE5) and the Robot Operating System (ROS) to develop a robot simulator tailored to agricultural tasks and synthetic data generation with RGB, segmentation, and depth images. We designed a method for assigning multiple segmentation labels within a single plant mesh. We experimented with a semi-spherical routine for two robot arms to perform 3D point cloud reconstruction across 10 plant assets. We showed our simulator produces much more accurate segmentation images and reconstruction compared to existing UE5 solutions. We extend our results with Neural Radiance Field (NeRF) reconstructions. The packaged simulator, UE5 project, and ROS package with the Python routine can be found at https://github.com/NCSU-BAE-ARLab/AgriRoboSimUE5.
Why it matches plant phenotyping methods植物の3D再構成・自動アノテーションのためのロボットシミュレータと合成画像生成手法を開発し、精度比較も行っており、表現型取得・抽出基盤が研究の中心である。
titlePhotorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.
Why it matches plant phenotyping methods小麦の形質・表現型抽出とエンティティ linking のための手動アノテーションコーパスを構築し、言語モデルの訓練・評価に用いており、表現型情報の取得手法とデータセットが研究の中心である。
titleA manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature
Reproduction assets foundThe paper's core assets are publicly available: the TaeC annotated corpus (trait/phenotype/species annotations of 528 PubMed wheat references) on Recherche Data Gouv, the Wheat Trait and Phenotype Ontology on AgroPortal, the AlvisNLP bread wheat workflow on Forgemia, and the ToMap method code on GitHub, all with authorDataset · publicThe corpus dataset TaeC is available under CC-BY-ND License at: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3QOpen asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qlines:142-152Code · publicThe code of the ToMap method is available under Apache License at https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-bibliome/src/main/java/fr/inra/maiage/bibliome/alvisnlp/bibliomefactory/modules/tomapOpen asset ↗github.comlines:142-152Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
Common object detection and image segmentation methods are unable to accurately estimate the shape of the occluded fruit. Monitoring the growth status of shaded crops in a specific environment is challenging, and certain studies related to crop harvesting and pest detection are constrained by the natural shadow conditions. Amodal segmentation can focus on the occluded part of the fruit and complete the overall shape of the fruit. We proposed a Transformer-based amodal segmentation algorithm to infer the amodal shape of occluded tomatoes. Considering the high cost of amodal annotation, we only needed modal dataset to train the model. The dataset was taken from two greenhouses on the farm and contains rich occlusion information. We introduced boundary estimation in the hourglass structured network to provide a priori information about the completion of the amodal shapes, and reconstructed the occluded objects using a GAN network (with discriminator) and GAN loss. The model in this study showed accuracy, with average pairwise accuracy of 96.07%, mean intersection-over-union (mIoU) of 94.13% and invisible mIoU of 57.79%. We also examined the quality of pseudo-amodal annotations generated by our proposed model using Mask R-CNN. Its average precision (AP) and average precision with intersection over union (IoU) 0.5 (AP50) reached 63.91%,86.91% respectively. This method accurately and rationally achieves the shape of occluded tomatoes, saving the cost of manual annotation, and is able to deal with the boundary information of occlusion while decoupling the relationship of occluded objects from each other. Future work considers how to complete the amodal segmentation task without overly relying on the occlusion order and the quality of the modal mask, thus promising applications to provide technical support for the advancement of ecological monitoring techniques and ecological cultivation.
Why it matches plant phenotyping methods遮蔽トマト果実の形状を画像から再構成・推定する手法を開発し、精度検証も行っているため、植物フェノタイピング手法が中心である。
abstractWe proposed a Transformer-based amodal segmentation algorithm to infer the amodal shape of occluded tomatoes.
Abstract. Benefiting from advancements in algorithms and computing capabilities, supervised deep learning models offer significant advantages in accurately mapping individual tree canopy cover, which is a fundamental component of forestry management. In contrast to traditional field measurement methods, deep learning models leveraging remote sensing data circumvent access limitations and are more cost-effective. However, the efficiency of models depends on the accuracy of the tree crown annotations, which are often obtained through manual labeling. The intricate features of the tree crown, characterized by irregular contours, overlapping foliage, and frequent shadowing, pose a challenge for annotators. Therefore, this study explores a novel approach that integrates the annotations of multiple annotators for the same region of interest. It further refines the labels by leveraging information extracted from multi-spectral aerial images. This approach aims to reduce annotation inaccuracies caused by personal preference and bias and obtain a more balanced integrated annotation.
Why it matches plant phenotyping methods個体樹冠被覆の推定に用いるマルチスペクトル画像と複数 annotator のラベル統合・精密化手法が研究の中心であり、植物の樹冠形態を抽出する画像解析法に該当する。
abstractIt further refines the labels by leveraging information extracted from multi-spectral aerial images.
Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.
Why it matches plant phenotyping methods植物のRGB・マルチ/ハイパースペクトル・熱・深度画像と植物形質ラベルを生成するシミュレーション基盤を開発しており、表現型取得・学習用データ生成が中心的な方法論的貢献である。
abstractwe proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation.
Reproduction assets foundThe paper's phenotyping analysis relies on three public, paper-specific assets: the Helios framework code (used to generate the synthetic annotated images), the MSU-PID bean image dataset, and the strawberry.00 annotated dataset, all with explicit open-availability statements and URLs.Dataset · publicThe Bean data that support the findings of this study are openly available in MSU-PID at https://www.cse.msu.edu/computervision/MVA15-MSU-PID.zipOpen asset ↗MSU-PIDlines:255-283Dataset · publicThe strawberry data that support the findings of this study are openly available in strawberry.00 at https://universe.roboflow.com/skripsie/strawberry.00Open asset ↗strawberry.00lines:255-283Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.
Why it matches plant phenotyping methods植物器官の3D点群セグメンテーションと形質抽出を目的とする軽量深層学習ネットワークを開発し、複数作物で性能検証しており、フェノタイピング手法が中心です。
abstractthis study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Plant phenomics has become one of the most significant scientific fields in recent years. However, typical phenotyping procedures have low accuracy, low throughput, and are labor-intensive and time-consuming. Large-scale phenotypic collection equipment, on the other hand, is pricy, rigid, and inconvenient. The advancement of phenomics has been hampered by these restrictions. Lightweight picture collection equipment can now be used to capture plant phenotypic data thanks to the development of deep learning-based image identification. For the purpose of training the model, this approach needs high-quality annotated datasets. In this study, we used a handheld camera to gather multi-angle, multi-time series images and an unmanned aerial vehicle (UAV) to create a maize image phenotyping database (MIPDB). Over 30,000 high-resolution photos are available in the MIPDB, with 17,631 of those images having been carefully tagged with point-line method. The MIPDB can be accessed by the general public at http://phenomics.agis.org.cn . We anticipate that the availability of this superior dataset will stimulate a new revolution in crop breeding and advance deep learning-based phenomics research.
Why it matches plant phenotyping methodsトウモロコシの画像表現型データベースを構築し、アノテーション済み画像を公開する研究であり、再利用可能な表現型取得・データセットが中心です。
abstractwe used a handheld camera to gather multi-angle, multi-time series images and an unmanned aerial vehicle (UAV) to create a maize image phenotyping database (MIPDB).
Identification of plant diseases is crucial for preserving crops and ensuring food security. Analysis of detectable chemicals in plants is essential to understand transmission mechanisms and develop effective strategies for disease control measures to conserve agricultural products and prevent losses. However, manual monitoring of plant health is labor-intensive and time-consuming, requiring specialized skills and knowledge. To overcome these challenges, random forest systems are emerging as a powerful tool for disease detection and classification in plants. The process involves several steps, including image acquisition, preprocessing, and segmentation, followed by feature extraction, model training, and testing. Leveraging machine learning techniques, the random forest algorithm enables accurate classification of healthy and diseased leaves based on selected features. Image classification techniques are utilized to extract color information, while global features such as size and texture are captured through annotation. The dataset used for model training and testing comprises diverse samples, encompassing healthy and diseased plants. The random forest model is trained on 70% of the data to ensure robust learning, while the remaining 30% is reserved for testing, facilitating the exploration of model performance and overall feasibility
Why it matches plant phenotyping methods植物葉の画像から健全・罹病状態を推定する画像取得、前処理、特徴抽出、機械学習分類を中心に扱うため、植物フェノタイピング手法として収録する。
abstractThe process involves several steps, including image acquisition, preprocessing, and segmentation, followed by feature extraction, model training, and testing.
Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers’ backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants’ behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.
Why it matches plant phenotyping methods植物表現型画像のアノテーションソフトウェアについて、利用者課題・要求を調査し、AI支援システム設計への示唆を導出しており、方法・ツール設計が研究の中心です。
abstractDesigning a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation
BarleyRootAnnotation / quality controlMorphology / geometry measurementSegmentationRoot system architecture
Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new automated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicating that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify SNPs significantly associated with angle and length, shedding light on the genetic basis of root architecture.
Why it matches plant phenotyping methods根の表現型である根角度を画像から自動抽出する手法・オープンソースツールを開発し、手動測定との妥当性検証も行っているため、方法が研究の中心である。
abstractWe introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images.
MaizeSoybeanSugar beetSunflowerAerial / UAVField / plotLiDAR / point cloudRootWhole plant / canopy / plot / fieldAnnotation / quality control
Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.
Why it matches plant phenotyping methodsUAS搭載LiDARの運用パラメータ最適化と多時期点群からの作物キャノピー高さ推定を検証する、植物フェノタイピング手法の開発・ベンチマーク研究である。
abstractThis article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis.
BACKGROUND: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS: We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION: The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.
Why it matches plant phenotyping methodsCNNによる葉のセグメンテーション・追跡手法を開発し、検出・追跡精度を検証した植物フェノタイピング研究である。
abstractHere, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants.
Reproduction assets foundThe paper's authors explicitly state that the full project library (leaf detection/tracking code and dataset) is available as a public GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicof the PyTorch-Vision GitHub repository was used for the model training, specifically the reference scripts found in the folder detection. These scripts are included in the project GitHub under the modelTraining folder without relevant modifications. The full library of this project is available as a public repository at GitHub https://github.com/Fedjurrui/Leaf-Tracking .
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Consent for publication
Not applicable.
Competing interests
No competing interests declared.
References
1.Open asset ↗Fedjurrui/Leaf-Trackinglines:174-269Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. Newly desirable wheat variety traits include disease resistance to reduce pesticide use, adaptation to climate change, resistance to heat and drought stresses, or low gluten content of grains. Wheat breeding experiments are documented by a large body of scientific literature and observational data obtained in-field and under controlled conditions. The cross-referencing of complementary information from the literature and observational data is essential to the study of the genotype-phenotype relationship and to the improvement of wheat selection. The scientific literature on genetic marker-assisted selection describes much information about the genotype-phenotype relationship. However, the variety of expressions used to refer to traits and phenotype values in scientific articles is a hinder to finding information and cross-referencing it. When trained adequately by annotated examples, recent text mining methods perform highly in named entity recognition and linking in the scientific domain. While several corpora contain annotations of human and animal phenotypes, currently, no corpus is available for training and evaluating named entity recognition and entity-linking methods in plant phenotype literature. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 540 PubMed references fully annotated for trait, phenotype, and species named entities using the Wheat Trait and Phenotype Ontology and the species taxonomy of the National Center for Biotechnology Information. A study of the performance of tools trained on the Triticum aestivum trait Corpus shows that the corpus is suitable for the training and evaluation of named entity recognition and linking.
Why it matches plant phenotyping methods小麦の形質・表現型を文献から抽出・リンクするための注釈付きデータセットを開発し、ツール性能も評価しており、植物表現型情報の計算的抽出が中心である。
abstractThe Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat.
Reproduction assets foundThe paper's core asset, the TaeC annotated wheat trait/phenotype corpus, is publicly deposited on Recherche Data Gouv under CC-BY-ND. The authors' AlvisNLP wheat text-mining workflow and the ToMap method code are also publicly available. The WTO ontology used for annotation is public on AgroPortal.Dataset · publicTaeC is
available under CC-BY-ND License at:
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/GCYG3Q.Open asset ↗entrepot.recherche.data.gouv.fr · doi:10.57745/GCYG3Qpdf-page:9 lines:1-56Code · publicThe AlvisNLP bread wheat workflow is available at : https://forgemia.inra.fr/migale/wheat-tm.
It includes the wheat-specific lexica of ToMap.Open asset ↗forgemia.inra.frpdf-page:13 lines:1-53Code · publicThe code of the ToMap method is available at
https://github.com/Bibliome/alvisnlp/tree/master/alvisnlp-Open asset ↗github.com/Bibliome/alvisnlppdf-page:13 lines:1-53Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework’s performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework’s performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework’s applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.
Why it matches plant phenotyping methods植物葉画像から病変画素率を算出して病害重症度を推定する画像解析手法を開発し、データセット上で比較・統計検証しており、植物表現型取得が中心である。
abstracta lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances.
Reproduction assets foundThe paper's plant-phenotyping measurements (apple leaf disease detection and severity estimation) were performed on the publicly available Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset, which the authors link via a Science Data Bank deposit. No authors' analysis code or trained model checkpoints are explicitlydeDataset · publicrk of this research also includes the deployment of the PDSE-Lite framework on different IoT devices, such as Unmanned Aerial Vehicles (UAVs), to enable real-time monitoring of plant diseases in agricultural fields.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
PB: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing. PG: Conceptualization, Methodology, Software, Visualization, Writing – original draft. SM: Formal analysis, Resources, Writing – review & editing.
Funding Open asset ↗lines:501-513Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/ .
Why it matches plant phenotyping methods植物根の形態ランドマークをポーズ推定で検出し、根系形質を抽出する手法とソフトウェアを開発・検証した研究であり、植物フェノタイピング手法が中心である。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe paper makes its root phenotyping assets public: labeled training data, trained SLEAP models, and analysis files on OSF, the sleap-roots trait-extraction codebase on GitHub, and a separate figure-replication code repository.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files which can be accessed via the following link: https://osf.io/k7j9g/ .Open asset ↗osf.io/k7j9glines:752-811Code · publicAdditionally, the specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-roots .Open asset ↗talmolab/Berrigan_et_al_sleap-rootslines:752-811Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published21 Nov 2023Journal of Computing and Electronic Information ManagementCited by 2 · OpenAlex ↗
With the breakthrough progress of deep learning technology in multiple fields, its application in specialized areas such as plant leaf disease recognition is constrained by the cost of data annotation and the lack of sample diversity. This study proposes an enhanced few-shot learning method that integrates self-supervised learning and semi-supervised learning to improve the model's generalization ability in plant leaf disease recognition tasks. Through self-supervised pre-training and semi-supervised fine-tuning, the model can effectively utilize limited annotated data and expand the training set by generating high-quality pseudo-labels. Experimental results show that this method significantly improves the model's recognition performance on unseen categories. Future research will explore more self-supervised tasks and complex pseudo-label generation algorithms to further enhance the model's accuracy and robustness, promoting the application of few-shot learning technology in the field of agriculture.
Why it matches plant phenotyping methods植物葉の病徴・病害状態を画像認識するための少数ショット学習手法を開発し、自己教師あり事前学習と半教師あり微調整で認識性能を評価しているため、植物表現型取得・抽出法が中心である。
abstractThis study proposes an enhanced few-shot learning method that integrates self-supervised learning and semi-supervised learning to improve the model's generalization ability in plant leaf disease recognition tasks.
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと対話的アノテーション機能を開発・紹介し、植物画像から器官数・サイズ・色などの形質を半自動抽出する方法が中心である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
The Embedded Automated Generator of Labeled Images (EAGL-I) system is a tool for generating labeled images, particularly for data-driven methods, such as deep learning models. The system has already generated hundreds of thousands of images of weeds and crops. We present modifications made to the original system that are based on the experiences gathered from generating such large-scale datasets. The improvements relate to lighting conditions, ease of use, refined image segmentation, and pathfinding for camera-movements. To address lighting conditions, we made three major changes to the hardware. First, the blue keying fabric was replaced by solid black panels, mitigating reflections and achieving reliable color accuracy; second, sunlight entering the room through a window is diffused and partially blocked by a screen, achieving consistent and uniform lighting of the imaging environment; third, dimmable LED lights are installed allowing us to image with lower ISO and to reduce noise in the resulting images. A YOLO machine learning model was trained to replace the previous methods of estimating bounding boxes around the plants. This new way of creating bounding boxes adapts to different plant architectures, such as grasses or different kind of dicots. Finally, we implemented a version of the A* pathfinding algorithm to define save zones through which the camera will not be moved. Overall, these modifications improved system performance and image quality significantly, while making EAGL-I easier to use. We have extended potential applications of EAGL-I, particularly for plant phenotyping research and in fine-tuning machine learning models for image analysis.
Why it matches plant phenotyping methods植物画像の大規模生成、照明・画像分割・植物境界ボックス推定・カメラ経路制御を改良するフェノタイピング用システムの開発が中心である。
abstractWe present modifications made to the original system that are based on the experiences gathered from generating such large-scale datasets.
Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requires much expert knowledge and meticulous work, which is expensive and time-consuming. This paper aims to learn robust feature representations with inaccurate bounding boxes, thereby reducing the model requirements for annotation quality. Specifically, we analyze the distribution of noisy annotations in the real world. A teacher-student learning paradigm is proposed to correct inaccurate bounding boxes. The teacher model is used to rectify the degraded bounding boxes, and the student model extracts more robust feature representations from the corrected bounding boxes. Furthermore, the method can be easily generalized to semi-supervised learning paradigms and auto-labeling techniques. Experimental results show that applying our method to the Faster-RCNN detector achieves a 26% performance improvement on the noisy dataset. Besides, our method achieves approximately 75% of the performance of a fully supervised object detector when 1% of the labels are available. Overall, this work provides a robust solution to real-world location noise. It alleviates the challenges posed by noisy data to precision agriculture, optimizes data labeling technology, and encourages practitioners to further investigate plant disease detection and intelligent agriculture at a lower cost. The code will be released at https://github.com/JiuqingDong/TS_OAMIL-for-Plant-disease-detection.
Why it matches plant phenotyping methods植物病害を画像から検出する手法のためのノイズ注釈補正モデルを開発し、検出性能を評価しており、植物病害状態の取得・抽出が中心的な方法論的貢献である。
titleAn iterative noisy annotation correction model for robust plant disease detection.
The Embedded Automated Generator of Labeled Images (EAGL-I) system is a tool for generating labeled images, particularly for data-driven methods, such as deep learning models. The system has already generated hundreds of thousands of images of weeds and crops. We present modifications made to the original system that are based on the experiences gathered from generating such large-scale datasets. The improvements relate to lighting conditions, ease of use, refined image segmentation, and pathfinding for camera-movements. To address lighting conditions, we made three major changes to the hardware. First, the blue keying fabric was replaced by solid black panels, mitigating reflections and achieving reliable color accuracy; second, sunlight entering the room through a window is diffused and partially blocked by a screen, achieving consistent and uniform lighting of the imaging environment; third, dimmable LED lights are installed allowing us to image with lower ISO and to reduce noise in the resulting images. A YOLO machine learning model was trained to replace the previous methods of estimating bounding boxes around the plants. This new way of creating bounding boxes adapts to different plant architectures, such as grasses or different kind of dicots. Finally, we implemented a version of the A* pathfinding algorithm to define save zones through which the camera will not be moved. Overall, these modifications improved system performance and image quality significantly, while making EAGL-I easier to use. We have extended potential applications of EAGL-I, particularly for plant phenotyping research and in fine-tuning machine learning models for image analysis.
Why it matches plant phenotyping methods植物画像の大規模生成・セグメンテーション・植物形態に適応したバウンディングボックス推定を行うEAGL-Iの改良であり、植物フェノタイピング用画像取得基盤の開発が中心。
abstractThe improvements relate to lighting conditions, ease of use, refined image segmentation, and pathfinding for camera-movements.
PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.
Why it matches plant phenotyping methodsPlantCVの画像解析ソフトウェアと点アノテーション機能の開発・適用を中心に扱い、植物器官の数・サイズ・色などの形質抽出を半自動化する方法論的研究である。
abstractPlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python
Taxonomic literature keeps records of the planet's biodiversity and gives access to the knowledge needed for research and sustainable management. The number of publications generated is quite large: the corpus of biodiversity literature includes tens of millions of figures and taxonomic treatments. Unfortunately, most of the taxonomic descriptions are from scientific publications in text format. With more than 61 million digitized pages in the Biodiversity Heritage Library (BHL), only 467,265 taxonomic treatments are available in the Biodiversity Literature Repository. To obtain highly structured texts from digitized text has been shown to be complex and very expensive (Cui et al. 2021). The scientific community has described over 1.2 million species, but studies suggest that 86% of existing species on Earth and 91% of species in the ocean still await description (Mora et al. 2011). The published descriptions synthesize observations made by taxonomists over centuries of research and include detailed morphological aspects (i.e., shape and structure) of species useful to identify specimens, to improve information search mechanisms, to perform data analysis of species having particular characteristics, and to compare species descriptions. To take full advantage of this information and to work towards integrating it with repositories of biodiversity knowledge, the biodiversity informatics community first needs to convert plain text into a machine-processable format. More precisely, there is a need to identify structures and substructure names and the characters that describe them (Fig. 1). Open information extraction (OIE) is a research area of Natural Language Processing (NLP), which aims to automatically extract structured, machine-readable representations of data available in unstructured text; usually the result is handled as n-ary propositions, for instance, triples of the form (Shen et al. 2022). OIE is continuously evolving with advancements in NLP and machine learning techniques. The state of the art in OIE involves the use of neural approaches, pre-trained language models, and integration of dependency parsing and semantic role labeling. Neural solutions mainly formulate OIE as a sequence tagging problem or a sequence generation problem. Ongoing research focuses on improving extraction accuracy; handling complex linguistic phenomena, for instance, addressing challenges like coreference resolution; and more open information extraction, because most existing neural solutions work in English texts (Zhou et al. 2022). The main objective of this project is to evaluate and compare the results of automatic data extraction from plant morphological descriptions using pre-trained language models (PLM) and a language model trained on data from plant morphological descriptions written in Spanish. The research data for this study were sourced from the species records database of the National Biodiversity Institute of Costa Rica (INBio). Specifically, the project focused on selecting records of morphological descriptions of plant species written in Spanish. The system processes the morphological descriptions using a workflow that includes phases like data selection and pre-processing, feature extraction, test PLM, local language model training, and test and evaluate results. Fig. 2 shows the general workflow used in this research. Pre-processing and Annotation: Descriptions were standardized by removing special characters like double and single quotes, replacing abbreviations, tokenizing text, and other transformations. Some records of the dataset were annotated with the ground-truth structured information in the form of triples that were extracted from each paragraph. Additionally, structured data from the project carried out by Mora and Araya (Mora and Araya 2018) were included in the dataset. Feature extraction: The token vectorization was done using word embedding directly by the language models. Test PLM: The evaluation process of PLM models used the zero-shot approach and involved applying the models to the test dataset, extracting information, and comparing it to annotated ground truth. Local Language Model Training: The annotated data was split into 80% training data and 20% test data. Using the training data, a language model based on the Transformers architecture was trained. Evaluate results: Evaluation metrics such as precision, recall, and F1 (a meaure of the model's accuracy) were calculated comparing the extracted information and the ground truth. The results were analyzed to understand the models' performance, identify strengths and weaknesses, and gain insights into their ability to extract accurate and relevant information. Based on the analysis, the evaluation process iteratively improved models results. The main contributions of this project are: A Transformers-based language model to extract information from morphological descriptions of plants written in Spanish available on the project website.*1 A corpus of morphological descriptions of plants, written in Spanish, labeled for information extraction, and made available on the project website. The results of the project, the first of its kind applied to morphological descriptions of plants written in Spanish, published on the project website. A Transformers-based language model to extract information from morphological descriptions of plants written in Spanish available on the project website.*1 A corpus of morphological descriptions of plants, written in Spanish, labeled for information extraction, and made available on the project website. The results of the project, the first of its kind applied to morphological descriptions of plants written in Spanish, published on the project website.
Why it matches plant phenotyping methods植物の形態記述から形質情報を自動抽出する言語モデル、評価ワークフロー、注釈付きコーパスを開発・検証しており、植物形質の取得・構造化手法が研究の中心である。
abstractThe main objective of this project is to evaluate and compare the results of automatic data extraction from plant morphological descriptions using pre-trained language models (PLM) and a language model trained on data from plant morphological descriptions written in Spanish.
Reliable and automated 3-dimensional (3D) plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly supervised deep learning method was proposed for plant organ segmentation. The method contained (a) pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and (b) fine-tuning the pretrained model with about only 0.5% points being annotated to implement plant organ segmentation. After, 3 phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting. Our method achieved 95.1%, 96.6%, 95.8%, and 92.2% in the precision, recall, F1 score, and mIoU for stem-leaf segmentation for the soybean dataset and 53%, 62.8%, and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation for the Pheno4D dataset. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes. The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.
Why it matches plant phenotyping methods3D植物点群の器官セグメンテーション手法を開発・評価し、植物形質の抽出まで実施しており、フェノタイピング手法が研究の中心である。
abstractwe proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation.
Stay green (SG) in wheat, a beneficial trait for increasing yield and stress resistance, needs to be supported by analysis of the underlying genetic basis. Spectral reflectance indices (SIs) provide non-destructive tools to evaluate crop temporal senescence. However, few SI-based SG quantification pipelines for analyzing diverse wheat panels in the field are available. Here, we first applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages during two field seasons. Based on over 12,000 SIs data set, four SIs (NDVI, GNDVI, NDRE and OSAVI) were selected to develop relative stay green scores (RSGS) and the senescence of wheat populations occurs mainly at four developmental stages stage 1 (S1) to S4, accounting for the final SG indicators. A RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 SNPs significantly associated with RSGS and 1,085 corresponding candidate genes in the two seasons; 15 QTL overlapped or were adjacent to known SG-related QTL or genes and the remaining QTL were novel. Finally, we selected three superior candidate genes ( TraesCS6B03G0356400 , TraesCS2B03G1299500 , and TraesCS2A03G1081100 ) as examples by transcriptomes, gene annotation, and gene-based association analysis for further analysis and found that utilization of superior SG-related variation in China gradually increased following the Green Revolution. The study provides a useful reference for further SG-related gene discovery of favorable variations in diverse wheat panels.
Why it matches plant phenotyping methodsUAVスペクトル指標を用いたコムギの老化・stay-green形質の時系列定量パイプラインを開発・適用しており、形質抽出法が実質的な研究要素である。
abstractfew SI-based SG quantification pipelines for analyzing diverse wheat panels in the field are available
Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.
Why it matches plant phenotyping methods作物RGB画像から植生を分割し、キャノピー形質推定に用いる注釈付きデータセットを作成・ベンチマークする研究であり、フェノタイピング用データ基盤が中心である。
abstractThis motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions.
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 · publicThe dataset can be downloaded from Zenodo: https://doi.org/10.5281/zenodo.763640828 and is under the
CC-BY license, allowing for reuse without restrictions.Open asset ↗Zenodo · 10.5281/zenodo.7636408pdf-page:8 lines:1-50Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Why it matches plant phenotyping methods植物組織培養の形質を自動・非破壊・連続測定するマルチセンサーフェノタイピングシステムを開発し、画像分割や深度計測を検証しており、方法が研究の中心である。
abstractAn automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated.
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 dataset supporting the conclusions of this article (Hard- and Software of “Phenomenon” phenotyping system) are available in an open-access Github repository, https://github.com/halube/Phenomenon .Open asset ↗halube/Phenomenonlines:224-282Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published15 Apr 2023International Journal of Advanced Research in Science, Communication and TechnologyCited by 1 · OpenAlex ↗
Agriculture was the backbone of India now it is facing several difficulties which includes diseases, selection of quality seed, water scarcity etc. One of the main issues of agriculture field is plant diseases which causes farmers a huge loss either in loss of crop or unnecessary use of drugs. Early detection of a plant disease can prevent its spreading hence the loss of yield This paper proposes a deep learning approach that is based on improved convolutional neural networks z(CNNs) for the real-time detection of apple leaf diseases. In this paper, the apple leaf disease dataset (ALDD), which is composed of laboratory images and complex images under real field conditions, is first constructed via data augmentation and image annotation technologies. The experimental results show that the INAR-SSD model realizesa detection performance of 78.80 .The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-timedetection of these diseases with higher accuracy and faster detection speed than previous methods.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から検出する深層学習手法を開発し、データセット構築と性能評価も行っており、植物フェノタイピング手法が中心である。
abstractThis paper proposes a deep learning approach that is based on improved convolutional neural networks z(CNNs) for the real-time detection of apple leaf diseases.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Annotation / quality controlVisualization / data management
As part of the BioHackathon Germany 2022, we hereby report on the success of the two projects “MIAPPE Wizard: Enabling easy creation of MIAPPE-compliant ISA metadata for Plant Phenotyping Experiments” and “DataPLANT - Facilitating Research Data Management to combat the reproducibility crisis”. Shortly before the actual hackathon, it became apparent to the participants that close coordination between the projects would be very beneficial. Both projects aimed to improve the process of collecting and aggregating metadata on plant experiments, but with different approaches.
Why it matches plant phenotyping methods植物フェノタイピング実験のメタデータ収集・集約を改善するソフトウェア／基盤の報告であり、フェノタイピング研究の再利用可能なデータ管理手法が中心である。
titleImproving Metadata Collection and Aggregation in Plant Phenotyping Experiments with MIAPPE Wizard and DataPLANT
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, the area under the receiver operating characteristic (ROC) curves, 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コムギ葉のさび病状態を画像から推定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、植物病害表現型の取得・抽出が中心である。
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
Maize is one of the most important staple food and cash crops that are largely produced by majority of smallholder farmers throughout the humid and sub-humid tropic of Africa. Despite its significance in the household food security and income, diseases, especially Maize Lethal Necrosis and Maize Streak, have been significantly affecting production of this crop. This paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania. The dataset is the largest publicly accessible dataset for maize leaves with a total of 18,148 images, which can be used to develop machine learning models for the early detection of diseases affecting maize. Moreover, the dataset can be used to support computer vision applications such as image segmentation, object detection and classification. The goal of generating this dataset is to assist the development of comprehensive tools that will help farmers in the diagnosis of diseases and the enhancement of maize yields thus eradicating the problem of fod security in Tanzania and other parts in Africa.
Why it matches plant phenotyping methodsトウモロコシの健全・罹病葉画像を収録した公開データセットで、植物病徴の画像ベース判定モデル開発を直接支援するため、フェノタイピング用データセットが中心です。
abstractThis paper offers a dataset of well curated images of maize crop for both healthy and diseased leaves captured using smartphone camera in Tanzania.
Reproduction assets foundThe paper's core asset is its own maize leaf imagery dataset (18,148 images of healthy/MLN/MSV leaves), publicly deposited by the authors on Harvard Dataverse with an explicit DOI and direct URL. The annotation tools cited (VisiPics, LabelMe, Makerere web annotation tool) are generic third-party tools, not paper assetsDataset · publica source location
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Institution: The Nelson Mandela African Institution of Science and Technology (NM-AIST), Tanzania Agricultural Research Institute (TARI)
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City/Town/Region: Arusha
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Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/GDON8Q
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•Open asset ↗Harvard Dataverse · doi:10.7910/DVN/GDON8Qlines:1-98Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published31 Mar 2023International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
Abstract: Brown spot, Mosaic, Grey spot, and Rust all significantly reduce apple yield. Rust is a sign of Foliar illness in this instance. The primary factor influencing apple output is the occurrence of apple leaf diseases, which results in significant yearly economic losses. Therefore, it is very important to research apple leaf disease identification. Plants are frequently attacked by pests, bacterial diseases, and other microorganisms. Inspection of the leaves, stem, or fruit usually identifies the attack's signs. Powdery Mildew and Leaf Blight are two common plant diseases that can cause severe harm if not treated quickly. In the realm of agriculture, image processing is frequently utilized for classification, detection, grading, and quality control. Finding and identifying plant diseases is crucial, especially when trying to produce fruit of the highest caliber. The real-time identification of apple leaf diseases is addressed in this research using a deep learning strategy that is based on enhanced convolutional neural networks (CNNs). This study uses data augmentation and image annotation tools to create the foliar disease dataset, which is made up of complex images captured in the field and laboratories. Overall, we can identify the illness present in plants on a massive scale by utilizing machine learning to train the vast data sets that are publically available. The project explains how to identify plant leaf diseases, how they affect plant yield, and which pesticides should be used to treat them. in agriculture. To monitor huge plant fields and automatically identify disease symptoms as soon as they develop on plant leaves, research on automatic plant disease is crucial. In this essay, we'll demonstrate how to identify plant illnesses by obtaining photos of their leaves
Why it matches plant phenotyping methodsリンゴ葉の画像から病害状態を推定する深層学習手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThe real-time identification of apple leaf diseases is addressed in this research using a deep learning strategy that is based on enhanced convolutional neural networks (CNNs).
Background Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R 2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning .
Why it matches plant phenotyping methods3D深層学習による綿花の器官分割と建築形質抽出ワークフローを開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractThe goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits.
Reproduction assets foundThe paper's plant part segmentation code is explicitly stated as publicly available in the authors' GitHub repository. The underlying datasets are only available on request, so they are noted as request-only.Code · publicThe plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning .Open asset ↗UGA-BSAIL/plant_3d_deep_learninglines:1-72Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 7 Sept 2026
Maintenance of sufficient healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semi-synthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time-series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (i) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (ii) investigation of the elusive interaction between biotic stress and physiological senescence. The potentially high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.
Why it matches plant phenotyping methods深層学習による画像分割と時系列解析で、コムギ群落の葉・穂の黄化および壊死を定量化する方法を開発・適用し、スコアリングとの相関で検証している。植物病害・老化という状態の取得が研究の中心である。
abstractHere, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation.
Abstract Introduction Assessing intraspecific variation in plant volatile organic compounds (VOCs) involves pitfalls that may bias biological interpretation, particularly when several laboratories collaborate on joint projects. Comparative, inter-laboratory ring trials can inform on the reproducibility of such analyses. Objectives In a ring trial involving five laboratories, we investigated the reproducibility of VOC collections with polydimethylsiloxane (PDMS) and analyses by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). As model plant we used Tanacetum vulgare , which shows a remarkable diversity in terpenoids, forming so-called chemotypes. We performed our ring-trial with two chemotypes to examine the sources of technical variation in plant VOC measurements during pre-analytical, analytical, and post-analytical steps. Methods Monoclonal root cuttings were generated in one laboratory and distributed to five laboratories, in which plants were grown under laboratory-specific conditions. VOCs were collected on PDMS tubes from all plants before and after a jasmonic acid (JA) treatment. Thereafter, each laboratory (donors) sent a subset of tubes to four of the other laboratories (recipients), which performed TD-GC-MS with their own established procedures. Results Chemotype-specific differences in VOC profiles were detected but with an overall high variation both across donor and recipient laboratories. JA-induced changes in VOC profiles were not reproducible. Laboratory-specific growth conditions led to phenotypic variation that affected the resulting VOC profiles. Conclusion Our ring trial shows that despite large efforts to standardise each VOC measurement step, the outcomes differed both qualitatively and quantitatively. Our results reveal sources of variation in plant VOC research and may help to avoid systematic errors in similar experiments.
Why it matches plant phenotyping methods植物VOC測定の再現性を5施設間リング試験で検証し、前処理・分析・後処理における技術的変動を評価しているため、測定法の妥当性検証が中心である。
abstractComparative, inter-laboratory ring trials can inform on the reproducibility of such analyses.
A large amount of training data is usually lacking at the beginning of system development and labeling such a large number of RGB (red, green, blue) images is laborious. Interactive recurrent annotation is beneficial to incrementally gain training images in the stream of the system development and provides an opportunity to reduce human workload. We developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems. Predictions can be performed under both single-image mode and batch mode for multiple images. The prediction results can be used as the initial image labeling and manually adjusted under a single image mode. Human labeling and machine predictions are visualized on the same image. ROOSTER provides fully automatic labeling for abundantly available initial images of wheat stripe rust to gain essential predictability. The navigation of integrating prediction with labeling benefits human adjustment to iteratively improve predictability. The development of a detection system for wheat stripe rust was presented as a use case to demonstrate the efficiency of using interactive deep learning to develop machine vision systems.
Why it matches plant phenotyping methods植物病害（コムギ縞萎縮病）の画像検出を対象とした対話型画像ラベリング・分類ソフトウェアを開発しており、植物病害状態の取得手法が中心である。
abstractWe developed a software package, ROOSTER, to integrate both labeling and prediction in a single user-friendly graphic user interface with interactive deep learning to reduce the laborious human labeling for fast development of machine vision systems.
Reproduction assets foundThe paper's wheat stripe rust use case is supported by a public Zenodo underlying dataset (400 author-captured training images and use case output files) and public author source code (zzlab.net, GitHub, archived Zenodo). The independent test data from Schirrmann et al. is only available on request.Dataset · publicilability
Underlying data
The independent data used to test ROOSTER was sourced from Schirrmann et al.,10
see here: https://doi.org/10.3389/fpls.2021.469689). Please contact the corresponding author of this article (mschirrmann@atb-potsdam.de) to request
access to the test data if interested.
Zenodo: ROOSTER underlying dataset. https://doi.org/10.5281/zenodo.7530460.11
This project contains the following underlying data:
- RawImages.zip (400 input training images used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software aOpen asset ↗Zenodo · 10.5281/zenodo.7530460pdf-raw-page:5 lines:1-44Code · publicmages used to develop the model, and captured by the authors of this
article).
- UseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗GitHub · 12HuYang/ROOSTERpdf-layout-page:5 lines:1-63Code · publicseCase.zip (use case output files).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
Software availability
Software available from: https://zzlab.net/ROOSTER.
Source code available from: https://github.com/12HuYang/ROOSTER.
Archived source code at time of publication: https://doi.org/10.5281/zenodo.7320405.12
License: MIT
Page 5 of 9Open asset ↗Zenodo · 10.5281/zenodo.7320405pdf-layout-page:5 lines:1-63Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 8 Sept 2026
Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly-supervised deep learning method was proposed for plant organ segmentation. The method contained: (1) Pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds; (2) Fine-tuning the pre-trained model with about only 0.5% points being annotated to implement plant organ segmentation. After, three phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly-supervised network obtained similar segmentation performance compared with the fully-supervised setting. Our method achieved 95.1%, 96.6%, 95.8% and 92.2% in the Precision, Recall, F1-score, and mIoU for stem leaf segmentation and 53%, 62.8% and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes.
Why it matches plant phenotyping methods3D点群による植物器官セグメンテーションと形質抽出手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractReliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level.
Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improved the performance by ameliorating networks and optimizing the loss function. However, because of the vast influence of data annotation quality and the cost of annotation, the data-centric part of a project also needs more investigation. We should further consider the relationship between data annotation strategies, annotation quality, and the model's performance. In this paper, a systematic strategy with four annotation strategies for plant disease detection is proposed: local, semi-global, global, and symptom-adaptive annotation. Labels with different annotation strategies will result in distinct models' performance, and their contrasts are remarkable. An interpretability study of the annotation strategy is conducted by using class activation maps. In addition, we define five types of inconsistencies in the annotation process and investigate the severity of the impact of inconsistent labels on model's performance. Finally, we discuss the problem of label inconsistency during data augmentation. Overall, this data-centric quantitative analysis helps us to understand the significance of annotation strategies, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection. Our work encourages researchers to pay more attention to annotation consistency and the essential issues of annotation strategy. The code will be released at: https://github.com/JiuqingDong/PlantDiseaseDetection_Yolov5 .
Why it matches plant phenotyping methods植物病害の画像検出におけるアノテーション戦略・一貫性とモデル性能を体系的に評価する手法研究であり、植物の病徴・病害状態の抽出方法が中心的である。
abstracta systematic strategy with four annotation strategies for plant disease detection is proposed: local, semi-global, global, and symptom-adaptive annotation.
Reproduction assets foundThe paper's authors explicitly state a public GitHub repository for the plant disease detection analysis code (YOLOv5-based annotation strategy/consistency experiments). No separate phenotype dataset deposit by the authors is stated; cited datasets (PlantVillage, etc.) are prior work.Code · publicThe code will be released at: https://github.com/JiuqingDong/PlantDiseaseDetection_Yolov5 .Open asset ↗JiuqingDong/PlantDiseaseDetection_Yolov5lines:224-322Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.
Why it matches plant phenotyping methods植物形質の大規模再利用可能データセットを構築し、確率的品質管理と外部情報による検証を実施しており、形質データ基盤が研究の中心である。
abstractHere we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits.
Reproduction assets foundThe paper's core asset is the 'Global Spectrum of Plant Form and Function Dataset' (species mean values for six plant traits plus categorical traits and references), explicitly deposited publicly under a CC-BY license in the TRY File Archive with DOI 10.17871/TRY.81. This is a paper-specific, publicly actionable trait/Dataset · publicThe dataset is available under a CC-BY license at the TRY File Archive (https://www.try-db.org/TryWeb/Data.php):
Díaz, S. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. TRY File
Archive https://doi.org/10.17871/TRY.81 (2022)244Open asset ↗TRY File Archive · 10.17871/TRY.81pdf-page:5 lines:1-62Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource-intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Here, we implement the popular self-supervised contrastive learning methods of NNCLR (Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum’s size and for classifying haploid and diploid kernels. We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.
Why it matches plant phenotyping methodsトウモロコシ種子胚の分類・セグメンテーションに対する自己教師あり画像解析手法を開発・比較検証しており、植物形質の取得・抽出が研究の中心です。
titleSelf-Supervised Maize Kernel Classification and Segmentation for Embryo Identification
Objectives A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms. Methods Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape. Results The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms. Conclusions Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.
Why it matches plant phenotyping methods植物葉画像を生成し、葉形状・葉脈・表面色を再現する画像生成手法を開発・評価しており、植物フェノタイピング関連の画像解析ワークフローが中心である。
abstractwe implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves
Reproduction assets foundThe paper explicitly states that the authors' code and data for the Leaf2Leaf generative leaf-image synthesis pipeline are publicly available on GitHub, which is a paper-specific, actionable asset.Code · publicData Availability: The code and data for reproducibility are available on GitHub ( https://github.com/AleBenfe/Leaf2Leaf ).Open asset ↗AleBenfe/Leaf2Leaflines:135-147Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract. The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot.ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species.iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.
Why it matches plant phenotyping methodsUAV画像・点群から森林の3D構造や個体樹木の高さ・樹冠径を扱う再利用可能なデータセットを提供し、抽出結果の検証や機械学習に用いるため、植物表現型データ基盤が中心です。
abstractThe SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes.
Reproduction assets foundThe paper is a data description paper for the SiDroForest collection; all four datasets (UAV-SfM point clouds/orthomosaics, individually labeled trees, synthetic tree-crown images, Sentinel-2 labeled patches) are published on PANGAEA with explicit public download availability.Dataset · publice future users time when attempting to classify
vegetation of central Siberian and eastern Siberian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et aOpen asset ↗PANGAEA · 10.1594/PANGAEA.933263lines:557-585Dataset · publicerian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. EverOpen asset ↗PANGAEA · 10.1594/PANGAEA.932821lines:557-585Dataset · publice PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biolOpen asset ↗PANGAEA · 10.1594/PANGAEA.932795lines:557-585Dataset · publicand orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biological and ecological applications is therefore quite rare and an important addition for scientific studiOpen asset ↗PANGAEA · 10.1594/PANGAEA.933268lines:557-585Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 8 Sept 2026
Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.
Why it matches plant phenotyping methods病斑領域という植物の疾病状態を蛍光画像から抽出する画像解析手法を開発し、実データで検証しているため、フェノタイピング手法が中心である。
abstractWe propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Interactive Annotation for object delineation can be considered as a semi-supervised few-shot learning problem where machine learning models learn from a small set of annotated pixels and generalize to the entire picture to extract the object of interest. One aim of interactive annotation is to reduce the effort of manually labeling data. Some existing works attempted to address this problem with deep metric learning so that the encoding layers in the network are able to extract features that boost discriminability among pixels belonging to different classes. To keep the data structure in the embedding space, metric loss with prototypes has been proposed. In our work, we improved the existing methods by developing a new objective function to update the network and prototypes simultaneously. The prototypes are optimized based on the loss that enhances their dissimilarity instead of clustering or sampling from the dataset. Moreover, we designed a GUI with the proposed method for interdisciplinary collaboration of image-support plant phenotyping studies.
Why it matches plant phenotyping methods植物画像のアノテーションと物体領域抽出を改善する学習法およびGUIを開発しており、植物フェノタイピング用の画像解析手法が中心です。
abstractIn our work, we improved the existing methods by developing a new objective function to update the network and prototypes simultaneously.
Background: Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data allows for highly accurate results with the availability of depth information. The goal of this study was to allow 3D annotation and apply 3D deep learning model using both point and voxel representations of the 3D data to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of data shows less time consumption and better segmentation performance than point-based networks. The segmented plants were postprocessed using correction algorithms for the main stem and branch. From the postprocessed results, seven architectural traits were extracted including main stem height, main stem diameter, number of branches, number of nodes, branch inclination angle, branch diameter and number of bolls. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 seconds were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R 2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits.
Why it matches plant phenotyping methods3D深層学習による綿花の部位分割と建築形質抽出が研究の中心であり、精度・推論時間・形質推定性能も検証しているため。
abstractThe goal of this study was to allow 3D annotation and apply 3D deep learning model using both point and voxel representations of the 3D data to segment cotton plant parts and derive important architectural traits.
RootAnnotation / quality controlSegmentationRoot system architecture
Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA's, and performs particularly well in the presence of non-root objects.
Why it matches plant phenotyping methods植物根系画像からRSAを抽出するセグメンテーション手法を開発し、データセット作成と他手法との検証・比較を行っており、植物フェノタイピング手法が中心です。
abstractAccurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems.
Reproduction assets foundThe paper's Data availability statement provides public GitHub repositories for the authors' ITErRoot training code and the Friendly Ground Truth annotation tool used to create the paper's root segmentation ground truth. Both are paper-specific, public, and actionable. No separate phenotype image dataset deposit URL isCode · publicada First Research Excellence Fund. https://www.cfref-apogee.gc.ca/program-programme/communication_guidelines-lignes_directrices-eng.aspx . This work was also supported by the Google Cloud Platform (GCP) Research Credits Program.
Data availability
The code used to train the neural networks in this study is available on Github ( https://github.com/p2irc/ITErRoot ). The annotation tool used to create ground truth segmentations for training is available on Github ( https://github.com/p2irc/friendly_ground_truth ).
Competing interests
The authors declare no competing interests.
References
1.
Clark RT
Three-dimensional root phenotyping with a novel imaging and software platform
Plant PhysiOpen asset ↗p2irc/ITErRootlines:1379-1497Code · publicby volunteer Computer Science students with experience with other annotation tools. Friendly Ground Truth was successfully employed to generate a dataset of root images that were used to train and evaluate the segmentation network structure proposed in this work. The annotation tool has been made publicly available on GitHub ( https://github.com/p2irc/friendly_ground_truth ) for use by the community to generate root segmentation datasets.
Iterative neural network architectureOpen asset ↗p2irc/friendly_ground_truthlines:70-78Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
A large number of apple seedlings are planted in orchards each year, where accurate and fast seedling grading to ensure their quality before planting has become a crucial problem. However, seedling grading by manual measurement of morphological indicators is laborious and inaccurate, and it’s thus highly desirable to be replaced by machine vision. Seedling segmentation is one of the key steps of measuring morphological indicators and grading by machine vision. Therefore, a segmentation method of apple seedlings based on BlendMask with ResNet-101 to do transfer learning was proposed. A total of 450 original images were captured with Azure Kinect DK sensor. Root, rootstock, graft union, and scion of apple seedlings were labeled using a novel labeling strategy, which probably affect segmentation of thin and long objects. Scion was labeled with three different strategies, namely whole labeling (WL), segmental labeling (SL), and segmental-end-merge labeling (SEML). Results showed that the most suitable strategy was the SL for scion, which obtained a mean average precision of 91.2 % and the highest mean intersection over union of 79.3 % in the three labeling strategies. The average precisions of root, rootstock, graft union, and scion with the SL were 98.9 %, 89.3 %, 90.6 %, and 85.6 %, respectively. Intersection over unions of root, rootstock, graft union, and scion by the SL were 87.2 %, 75.8 %, 69.3 %, and 84.9 %, respectively. And it cost about 285 ms on average to process an image with resolution 3840 × 2160 pixels. The above results illustrated that the SL strategy is conducive to improve segmentation precision of thin and long objects. Moreover, apple seedlings can be effectively segmented, which is beneficial for the machine vision to measure morphological indicators and grade.
Why it matches plant phenotyping methodsリンゴ苗の形態指標測定と等級付けに用いる画像セグメンテーション手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractTherefore, a segmentation method of apple seedlings based on BlendMask with ResNet-101 to do transfer learning was proposed.
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 confirmedEurope PMC · checked 8 Sept 2026
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. 3DCoordX enables rapid spatial annotation of cells even in highly curved biological shapes. Using 3DCoordX, we analyzed cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis (Arabidopsis thaliana). Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia (Marchantia polymorpha) and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia (Utricularia gibba). Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
Why it matches plant phenotyping methods植物器官の3Dデジタル表現から細胞位置を注釈し、細胞成長や形態特徴を解析する専用ツールを開発しており、表現型取得・抽出手法が研究の中心です。
abstractWe developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX.
Reproduction assets foundThe paper deposits its phenotyping datasets (raw cell boundaries, PlantSeg predictions, segmented cells, annotated 3D cell meshes, and csv attribute files) in the BioStudies repository under accession S-BSST734, making the paper-specific 3D plant organ data publicly available.Dataset · publicThe datasets of this study have been deposited with the BioStudies data repository ( https://www.ebi.ac.uk/biostudies ) under the accession S-BSST734. Example dataset contains raw cell boundaries, cell boundaries, predictions from PlantSeg, nuclei images, segmented cells as well as the annotated 3D cell meshes, and the associated attribute files in csv format. The 3D meshes used in different manuscript figures are also available for download from the repository.Open asset ↗BioStudies · S-BSST734lines:161-178Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Fluctuations in illumination are one of the major sources for SIF retrieval errors during temporal continuous field measurements. In this study, we propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR), which are acquired using a quantum sensor at a sampling frequency higher than that obtained using spectral measurements. The proposed method is based on the coefficient of variation (known as relative standard deviation) of the high-frequency PAR during a SIF measurement to determine the quality of the SIF value. To evaluate the method, spectral and PAR data of a healthy maize canopy were collected under various illumination conditions, including clear, cloudy, and rapidly fluctuating illumination. The SIF values were retrieved by 3FLD, SFM, and SVD. The results showed that SFM and 3FLD were sensitive to illumination stability. The determination coefficients (R2) between PAR and SIF extracted by SFM and 3FLD were higher than 0.8 on clear and cloudy days and only approximately 0.48 on the day with rapidly fluctuating illumination. By removing the unqualified data using the proposed method, the R2 values of SFM and 3FLD on the day of rapidly fluctuating illumination significantly increased to 0.72. SVD was insensitive to illumination stability. The R2 values of SVD on days with clear, cloudy, and rapidly fluctuating illumination were 0.73, 0.76, and 0.61, respectively. By removing the unqualified data, the R2 values of SVD were increased to 0.66 on the day with rapidly fluctuating illumination. The results indicated that the quality assessment method based on high-frequency PAR data can eliminate the incorrect SIFs due to unstable illumination. The method can be used to extract more accurate and reliable SIF datasets from long-term field observations for the study of the relationship between SIF and vegetation photosynthesis.
Why it matches plant phenotyping methods高頻度PARセンサーを用いてSIF測定値の品質を評価・除外する手法を提案し、異なる照明条件のトウモロコシ群落データで検証している。植物の生理状態（SIF）取得の技術的品質管理が中心である。
abstractwe propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR)
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 15 Sept 2026
Abstract The abilities of plant biologists and breeders to characterize the genetic basis of physio-logical traits is limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale at low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study in the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of humans unassisted to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.
Why it matches plant phenotyping methods植物フェノタイピング用のGUIと対話型画像セグメンテーション手法を開発・評価しており、表現型取得ワークフロー自体が中心である。
abstractWe propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS).
Abstract: Powdery mildew and other plant illnesses are a big problem in agriculture specially Wine. Every year farmers from all over the world loses many plants and money. Due to the climate change this will enlarge continuously. The fight against these diseases is expensive and time-consuming. In this paper I will talk about especially Wine related diseases like powdery mildew how it can be reduced by using Drone and machine learning as a helpful tool. Keywords: Machine learning, Drone, Tensor Flow, Annotation, AZURE, Powdery mildew, CNN (Convolution neural network).
Why it matches plant phenotyping methodsドローン画像と機械学習を用いて植物病害を解析する手法が論文の中心であり、植物の病害状態を観測する画像ベースのフェノタイピングに該当する。
abstracthow it can be reduced by using Drone and machine learning as a helpful tool
Corn seed materials of different quality were imaged, and a method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model. In this study, RGB and near-infrared (NIR) images were acquired with a multispectral camera to train the model, which was proved to be effective in identifying defective seeds and defect-free seeds, with an averaged accuracy of 95.63%, an averaged recall rate of 95.29%, and an F1 (harmonic average evaluation) of 95.46%. Our proposed method was superior to the traditional method that employs a one-pathway CNN with 3-channel RGB images. At the same time, the influence of different parameter settings on the model training was studied. Finally, the application of the object detection method in corn seed defect detection, which may provide an effective tool for high-throughput quality control of corn seeds, was discussed.
Why it matches plant phenotyping methodsトウモロコシ種子の欠陥状態を画像から検出する手法を開発・評価しており、表現型取得・抽出が研究の中心である。
abstracta method for defect detection was developed based on a watershed algorithm combined with a two-pathway convolutional neural network (CNN) model.
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 confirmedOpenAlex · Crossref · checked 15 Sept 2026
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
Why it matches plant phenotyping methods植物表現型データのデジタル記録・取得を目的とするオープンソースアプリの開発であり、表現型測定ワークフローが中心的です。
abstractTherefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses.
Reproduction assets foundThe paper describes PhenoApp, an open-source Android phenotyping app. Authors provide the app's source code (Gitea, archived on Zenodo) and underlying example input/output phenotype data files on Zenodo under CC0. The SHAPE II project website is a project page, not a paper-specific data deposit, and is excluded.Code · publice ‘in’ folder of the app main directory and no additional source data is required).
-
Output_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants fOpen asset ↗gitea.julius-kuehn.de · JKI/pheno-applines:333-433Code · publicput_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants from the German Federal Ministry of Education and Research to FS (SelWineQ, FKZ 031B0889Open asset ↗Zenodo · 10.5281/zenodo.5525779lines:333-433Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.
Why it matches plant phenotyping methods植物形質の高スループット取得を目的に、GUIと新規インタラクティブ画像セグメンテーション手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractWe propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS).
Genome-wide association studies (GWAS) have benefited from the advances of sequencing methods for the generation of high-density genomic data. By bridging genotype to phenotype, several genes have been associated with traits of agricultural interest. Despite this, there is still a gap between genotyping and phenotyping due to the large difference in throughput between the two disciplines. Although cutting-edge phenomics technologies are available to the community, their costs are still prohibitive at the small lab level. Semiautomated methods of investigation provide a valid alternative to generate large-scale phenotyping data able to deeply investigate the characteristics of different plant organs. Beyond automation, phenomics data management is another major constraint to consider; while bioinformatics pipelines are well-trained for releasing high-quality genomic data, fewer efforts have been done for phenotyping information. This chapter provides a guide for generating large-scale data related to the size and shape of fruits, leaves, seeds, and roots and for downstream analysis for curation and preparation of clean datasets, through removal of outliers and performing primary statistical analysis. Different steps to be carried out in the R environment will be shown for gathering the appropriate input information to use in GWAS avoiding any possible bias.
Why it matches plant phenotyping methods植物の果実・葉・種子・根のサイズと形状を大規模に取得し、Rでデータをキュレーション・前処理する半自動フェノタイピングパイプラインが中心であるため。
abstractThis chapter provides a guide for generating large-scale data related to the size and shape of fruits, leaves, seeds, and roots and for downstream analysis for curation and preparation of clean datasets
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 13 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldAnnotation / quality controlMorphology / geometry measurementSegmentationRoot system architecture
Root systems of crops play a significant role in agro-ecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes and a good soil structure. Minirhizotrons, consisting of transparent tubes that create windows into the soil, have shown to be effective to non-invasively investigate the root system. Root traits, like root length observed around the tubes of minirhizotron, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, are time consuming and labor intensive. Therefore, an objective method for high throughput image analysis that provides data for field root-phenotyping is necessary. In this study we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample is performed. Training and segmentation are done using “Root-Painter”. Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer”. To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 58,000 images. Mainly the results show a high correlation ( R =0.81) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1 - 99.6 %. Our pipeline,combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.
Why it matches plant phenotyping methods根系画像から根長を自動抽出する高スループット表現型解析パイプラインの開発と、手動測定との大規模比較検証が研究の中心である。
abstractTherefore, an objective method for high throughput image analysis that provides data for field root-phenotyping is necessary.
Deep learning (DL) methods have transformed the way we now extract plant traits – both under laboratory as well as field conditions. Evidence suggests that “well-trained” DL models can significantly simplify and accelerate trait extraction as well as diversify the type of traits that one can extract. Training a DL model typically requires the availability of copious amount of annotated data. Creating a (large) annotated dataset requires effort, patience, time, and resources. This has become a major bottleneck in deploying DL tools in practice. SSL methods can use unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data. They have demonstrated superior transfer learning performance on down-stream classification tasks. In this work, we investigated the application of self-supervised learning (SSL) methods for plant stress classification using few labels. Plant stress classification is fundamentally challenging problem in that (1) disease classification may depend on abnormalities in a small number of pixels, (2) high data imbalance across different classes, (3) there are far fewer unlabeled plant stress images than natural images. We compared four different types of self-supervised learning methods on two different plant stress datasets. We find that pre-training on unlabeled plant stress images significantly outperforms transfer learning methods using random initialization for plant stress classification. SSL based model initialization and data curation improves annotation efficiency for plant stress classification task.
Why it matches plant phenotyping methods植物ストレス画像からの状態分類を対象に、自己教師あり学習手法を比較・評価し、少数アノテーションでの形質・ストレス抽出効率を検証しており、フェノタイピング手法が中心である。
abstractDeep learning (DL) methods have transformed the way we now extract plant traits – both under laboratory as well as field conditions.
A fundamental question in biology concerns how molecular and cellular processes become integrated during morphogenesis. In plants, characterization of 3D digital representations of organs at single-cell resolution represents a promising approach to addressing this problem. A major challenge is to provide organ-centric spatial context to cells of an organ. We developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX. It enables rapid spatial annotation of cells even in highly curved biological shapes. With the help of 3DCoordX we obtained new insight by analyzing cellular growth patterns in organs of several species. For example, the data indicated the presence of a basal cell proliferation zone in the ovule primordium of Arabidopsis thaliana . Proof-of-concept analyses suggested a preferential increase in cell length associated with neck elongation in the archegonium of Marchantia polymorpha and variations in cell volume linked to central morphogenetic features of a trap of the carnivorous plant Utricularia gibba . Our work demonstrates the broad applicability of the developed strategies as they provide organ-centric spatial context to cellular features in plant organs of diverse shape complexity.
Why it matches plant phenotyping methods植物器官の3D細胞位置を注釈・解析する専用ツールを開発し、細胞成長や形態形成関連の形質抽出に用いており、方法が研究の中心です。
abstractWe developed several general rules for the annotation of cell position and embodied them in 3DCoordX, a user-interactive computer toolbox implemented in the open-source software MorphoGraphX.
Deep learning (DL) methods have transformed the way we extract plant traits – both under laboratory as well as field conditions. Evidence suggests that “well-trained” DL models can significantly simplify and accelerate trait extraction as well as expand the suite of extractable traits. Training a DL model typically requires the availability of copious amounts of annotated data; however, creating large-scale annotated dataset requires non-trivial efforts, time, and resources. This has become a major bottleneck in deploying DL tools in practice. Self-supervised learning (SSL) methods give exciting solution to this problem, as these methods use unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data, and have demonstrated superior transfer learning performance on down-stream classification tasks. We investigated the application of SSL methods for plant stress classification using few labels. Plant stress classification is a fundamentally challenging problem in that (1) disease classification may depend on abnormalities in a small number of pixels, (2) high data imbalance across different classes, and (3) there are fewer annotated and available plant stress images than in other domains. We compared four different types of SSL methods on two different plant stress datasets. We report that pre-training on unlabeled plant stress images significantly outperforms transfer learning methods using random initialization for plant stress classification. In summary, SSL based model initialization and data curation improves annotation efficiency for plant stress classification tasks.
Why it matches plant phenotyping methods植物ストレス画像からの分類を対象に、自己教師あり学習を用いた少量アノテーション環境での表現学習・モデル初期化を比較検証しており、植物状態の抽出手法が中心である。
abstractWe investigated the application of SSL methods for plant stress classification using few labels.
Deep learning (DL) methods have transformed the way we extract plant traits – both under laboratory as well as field conditions. Evidence suggests that “well-trained” DL models can significantly simplify and accelerate trait extraction as well as expand the suite of extractable traits. Training a DL model typically requires the availability of copious amounts of annotated data; however, creating large annotated dataset requires non-trivial efforts, time, and resources. This has become a major bottleneck in deploying DL tools in practice. Self-supervised learning (SSL) methods give exciting solution to this problem, as these methods use unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data, and have demonstrated superior transfer learning performance on down-stream classification tasks. We investigated the application of SSL methods for plant stress classification using few labels. Plant stress classification is a fundamentally challenging problem in that (1) disease classification may depend on abnormalities in a small number of pixels, (2) high data imbalance across different classes, and (3) there are fewer annotated and available plant stress images than in other domains. We compared four different types of SSL methods on two different plant stress datasets. We report that pre-training on unlabeled plant stress images significantly outperforms transfer learning methods using random initialization for plant stress classification. In summary, SSL based model initialization and data curation improves annotation efficiency for plant stress classification tasks.
Why it matches plant phenotyping methods植物ストレス画像からの分類手法に対する自己教師あり学習を比較・評価しており、植物表現型（病害・ストレス状態）の抽出手法が中心である。
abstractDeep learning (DL) methods have transformed the way we extract plant traits
Abstract In response to the actual needs of informatization of the collectors engaged in the whole process of field wheat production, this paper constructed a phenotyping system based on field wheat, which was of great significance to improve the efficiency of data collection and reduce the labor intensity of the collectors. Based on the collection and management of phenotyping traits data from sowing to harvesting, combined with barcode recognition, database technology and mobile terminal APP technology, a field wheat phenotyping system was designed and developed, and the data was compared with collection efficiency by manual collection methods. The system was composed of the phenotyping trait collection system APP and the phenotype trait management system Web. The APP development architecture used client/server (C/S), the development language used Java, and the phenotyping traits were collected. As the core, upload collected data to the Web to realize data exchange and sharing between APP and Web; Web development architecture used browser/server (B/S) to realize the distribution of test tasks in the wheat production process, data management, report generation center, and statistical analysis. Compared with the manual data collection method, the application of APP could improve the data collection efficiency by 72%. This system had been realized the rapid collection, efficient management and automatic analysis of wheat phenotyping traits, which ensured the standardization of data collection and management, and improved the efficiency of data collection and utilization. The system could support the phenotyping collection and management of other crops, and it is also suitable for field phenotyping surveys in various industries such as fruits, vegetables and so on.
Why it matches plant phenotyping methods圃場コムギの表現型形質を収集・管理・分析するAPP/Webシステムを開発し、手作業との効率比較も行っており、表現型取得ワークフローが研究の中心である。
abstractthis paper constructed a phenotyping system based on field wheat
During the past few years, milder autumn and winter seasons have caused severe problems to cauliflower harvest of Brittany region in France, mainly due to curd deformation. Consequently, cauliflower breeders are working on breeding new varieties that are more robust to climate change to stabilize the quality of cauliflower production. The aim of this study was to identify at which stage of the curd formation, significant difference can be detected between healthy and stressed cauliflower. A non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed. Plants exposed to vernalization stress were sampled at different times around primary meristem stage, then both MRI imaged and apex dissected. A work flow was developped to extract features from MRI images. A classification on phenotype was learned by LDA, QDA, PLSDA and CNN binary classification between two groups: healthy and stressed cauliflower. Promising F1 score and MCC up to 95% were achieved. Curd deformation is the main cause for cauliflower’s later physiological disorders when reaching maturity. Therefore, the cauliflowers with deformation could be removed at the earliest, e.g., screening for plant breeding. At the same time, the healthy cauliflowers are not destroyed and continue their life cycle.
Why it matches plant phenotyping methodsMRI画像からカリフラワーの健全・ストレス状態を分類する非侵襲的表現型解析ワークフローを開発し、複数の分類器で性能評価しており、表現型取得・抽出法が中心である。
abstractA non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step. Results We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset. Conclusion The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.
Why it matches plant phenotyping methods種子画像の個体分割、合成データ生成、Mask R-CNN転移学習を開発・検証し、種子長・幅などの形態形質を高 throughput に抽出する方法が研究の中心である。
abstractWe synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation.
Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4-10 minutes to segment a maize shoot and consumes 10-20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.
Why it matches plant phenotyping methodsトウモロコシの3D点群を器官レベルに分割・注釈するツールを開発し、植物形態の表現型推定と深層学習用データ構築を技術的に支援するため、フェノタイピング手法が中心です。
abstractWe propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots.
Reproduction assets foundThe paper's authors publicly released the Label3DMaize toolkit (MATLAB source code and executable) on GitHub, which implements the paper's point cloud segmentation/annotation analysis for maize shoots. A supporting data deposit (GigaScience Database, 10.5524/100884) is cited but its URL is not among the allowed URLs,soCode · publicgmented point clouds. The segmentation algorithm and this toolkit will be extended to other crops according to their morphological characteristics, which will promote the automatic 3D point cloud segmentation of plants.
Availability of Supporting Source Code and Requirements
Project name: Label3DMaize Toolkit
Project home page: https://github.com/syau-miao/Label3DMaize.git
Source code and executable program: [ 57 ]
Operating systems: Windows
Programming languages: MATLAB
License: GNU General Public License (GPL)
RRID:SCR_021029
biotools ID: label3dmaize
Data AvailabilityOpen asset ↗Label3DMaize · syau-miao/Label3DMaizelines:277-291Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.
Why it matches plant phenotyping methods農業圃場のマルチテンポラルUAV LiDARについて、品質管理、作物列・区画抽出、作物表現型解析への利用を中心に技術を開発・評価しているため、植物フェノタイピング手法として含める。
abstractThis study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
Research ConductedApple (Malus x domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker-assisted selection minimizes these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype-phytochemical associations in apples, we have developed a high-throughput integration strategy for genomic and multi-platform metabolomics data. Methods124 apple genotypes, including members of three pedigree-connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome-wide association studies (mGWAS) were conducted with 10,000 single nucleotide polymorphisms and phenotypic data acquired via LC-MS and 1H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree-based analyses (PBA). Key ResultsUsing our developed method, 519, 726, and 177 putative mQTL were detected in LC-MS positive and negative ionization modes and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two-step PBA, enabling discovery of novel candidate gene-metabolite relationships. Main ConclusionComplementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multi-omics integration to advance data-driven, phytochemicalbased plant breeding.
Why it matches plant phenotyping methodsゲノム情報とLC-MS／NMR代謝物プロファイルを統合する高スループット手法を開発し、植物の化学的形質としての代謝物QTL検出を検証・実証しているため、代謝解析が単なる生物学的実験の補助測定ではなく中心的な方法貢献である。
abstractwe have developed a high-throughput integration strategy for genomic and multi-platform metabolomics data
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
Why it matches plant phenotyping methods植物の根・シュート・葉の将来形態を時系列画像から予測し、セグメンテーションする深層学習手法の開発と公開データセットでのベンチマークが中心であるため、植物フェノタイピング方法研究に該当する。
abstractThis research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future.
Abstract Background:The use of 3D based high-throughput phenotyping improves theefficiency of crop management and monitoring practices. Thestructure-from-motion and multi-view stereo photogrammetry (SfM-MVS)technique, applicable to common RGB digital cameras, has been widely used forthis and can be implemented by many commercial and open-source tools. Byusing such tools, several outputs such as digital orthophoto map (DOM), digitalsurface model (DSM), and point cloud data (PCD) can be generated. However,there is a gap between these outputs and the final 3D plant phenotyping. Forexample, calculating plant height and canopy ground cover requires thesegmentation of each plot from the whole DOM, DSM, or original image. Theseintermediate processes are time-consuming, and to the best of our knowledge,there are no easy-to-use alternatives currently available. Results: In this study, a software package called EasyIDP (easy intermediatedata processor) was developed to link the products of SfM-MVS techniques with3D based plant phenotyping. A lotus (Nelumbo nucifera) breeding field was usedto demonstrate the following points: 1) clipping (segmenting) SfM-MVS productsaccording to a given plot boundary or region of interest (ROI); 2) transformingthe ROI of the SfM-MVS products into high-quality raw images to assist inobject detection; and 3) evaluating the accuracy of the previous transformationusing manual annotation. Conclusions: The proposed intermediate data processing tool showed anacceptable accuracy and potential to process the products from SfM-MVStechniques. By using the EasyIDP, a bridge between SfM-MVS products andplant phenotyping was conveniently achieved.
Why it matches plant phenotyping methodsEasyIDPはSfM-MVS生成物を植物表現型抽出へ接続する中間処理ソフトウェアとして開発・評価されており、表現型取得ワークフローが中心である。
abstractThe proposed intermediate data processing tool showed anacceptable accuracy and potential to process the products from SfM-MVStechniques.
Reproduction assets foundThe paper is a software article for EasyIDP, whose source code is publicly released on GitHub, and the authors explicitly state that the example data (UAV/SfM-MVS phenotyping case-study data) and Jupyter notebook analysis codes are available in a companion public repository (EasyIDP.paper). Both are paper-specific,公开,和Code · publica U19A2061.
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Ethics approval and consent to participate
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Not applicable.
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Consent for publication
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Not applicable.
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Availability of data and materials
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The download link of the example data, and Jupyter notebook codes for drawing all results figures, and the LaTeX
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codes of this manuscript, are available on https://github.com/HowcanoeWang/EasyIDP.paper.486
Competing interests
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The authors declare that they have no competing interests.
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Author details
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International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate
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School of Agricultural and Life Science, The University of Tokyo, 188-0002 Tokyo, Japan. 2
Key LaOpen asset ↗HowcanoeWang/EasyIDP.paperpdf-raw-page:19 lines:1-164Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Crop phenotypic data underpin many pre-breeding efforts to characterize variation within germplasm collections. Although there has been an increase in the global capacity for accumulating and comparing such data, a lack of consistency in the systematic description of metadata often limits integration and sharing. We therefore aimed to understand some of the challenges facing findable, accesible, interoperable and reusable (FAIR) curation and annotation of phenotypic data from minor and underutilized crops. We used bambara groundnut (Vigna subterranea) as an exemplar underutilized crop to assess the ability of the Crop Ontology system to facilitate curation of trait datasets, so that they are accessible for comparative analysis. This involved generating a controlled vocabulary Trait Dictionary of 134 terms. Systematic quantification of syntactic and semantic cohesiveness of the full set of 28 crop-specific COs identified inconsistencies between trait descriptor names, a relative lack of cross-referencing to other ontologies and a flat ontological structure for classifying traits. We also evaluated the Minimal Information About a Phenotyping Experiment and FAIR compliance of bambara trait datasets curated within the CropStoreDB schema. We discuss specifications for a more systematic and generic approach to trait controlled vocabularies, which would benefit from representation of terms that adhere to Open Biological and Biomedical Ontologies principles. In particular, we focus on the benefits of reuse of existing definitions within pre- and post-composed axioms from other domains in order to facilitate the curation and comparison of datasets from a wider range of crops. Database URL: https://www.cropstoredb.org/cs_bambara.html.
Why it matches plant phenotyping methods作物表現型データの標準化、Trait Dictionary、Crop Ontology、データキュレーションおよびFAIR準拠評価が研究の中心であり、再利用可能な表現型データ記述手法を扱うため。
abstractWe therefore aimed to understand some of the challenges facing findable, accesible, interoperable and reusable (FAIR) curation and annotation of phenotypic data from minor and underutilized crops.
Reproduction assets foundThe paper's bambara groundnut phenotype datasets and Trait Dictionary are publicly available: the curated bambara trait datasets are hosted in CropStoreDB, and the authors' Trait Dictionary (134 terms, crop code CO_366) is published on the Crop Ontology portal. Supplementary tables (S1–S7) contain the multi-species TD,Dataset · publicrepresentation of terms that adhere to Open Biological and Biomedical Ontologies principles. In particular, we focus on the benefits of reuse of existing definitions within pre- and post-composed axioms from other domains in order to facilitate the curation and comparison of datasets from a wider range of crops.
Database URL : https://www.cropstoredb.org/cs_bambara.html
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2020 Jun 27; Revised 2021 Apr 14; Accepted 2021 Apr 30; Collection date 2021.
Introduction
Technological advances in data acquisition have driven massive increases in the accumulation of crop tOpen asset ↗CropStoreDBlines:1-31Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Automated identification of plant diseases is very important for crop protection. Most automated approaches aim to build classification models based on leaf or fruit images. These approaches usually require the collection and annotation of many images, which is difficult and costly process especially in the case of new or rare diseases. Therefore, in this study, we developed and evaluated several methods for identifying plant diseases with little data. Convolutional Neural Networks (CNNs) are used due to their superior ability to transfer learning. Three CNN architectures (ResNet18, ResNet34, and ResNet50) were used to build two baseline models, a Triplet network and a deep adversarial Metric Learning (DAML) approach. These approaches were trained from a large source domain dataset and then tuned to identify new diseases from few images, ranging from 5 to 50 images per disease. The proposed approaches were also evaluated in the case of identifying the disease and plant species together or only if the disease was identified, regardless of the affected plant. The evaluation results demonstrated that a baseline model trained with a large set of source field images can be adapted to classify new diseases from a small number of images. It can also take advantage of the availability of a larger number of images. In addition, by comparing it with metric learning methods, we found that baseline model has better transferability when the source domain images differ from the target domain images significantly or are captured in different conditions. It achieved an accuracy of 99% when the shift from source domain to target domain was small and 81% when that shift was large and outperformed all other competitive approaches.
Why it matches plant phenotyping methods植物画像から病害状態を推定するCNN手法の開発と比較評価が研究の中心であり、植物病害フェノタイピング手法に該当する。
titleConvolutional Neural Network for Automatic Identification of Plant Diseases with Limited Data
Reproduction assets foundThe paper's few-shot plant disease classification uses two public image datasets, both explicitly linked in the Data Availability Statement: PlantVillage (source domain) and the coffee leaf dataset (target domain). No author code or models are shared.Dataset · publicThe PlantVillage dataset is available at https://github.com/spMohanty/PlantVillage-Dataset and the Coffee dataset at https://github.com/esgario/lara2018/ .Open asset ↗spMohanty/PlantVillage-Datasetlines:741-764Dataset · publicThe PlantVillage dataset is available at https://github.com/spMohanty/PlantVillage-Dataset and the Coffee dataset at https://github.com/esgario/lara2018/ .Open asset ↗esgario/lara2018lines:741-764Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.
Why it matches plant phenotyping methods深層学習による植物器官の画像検出モデルを開発し、注釈データで訓練・評価しており、植物形態の取得手法が研究の中心である。
abstractIn our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN.
Field / plotRootAnnotation / quality controlMorphology / geometry measurementSegmentationRoot system architecture
The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R 2 =0.99), profile wall (R 2 =0.76) and core-break (R 2 =0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.
Why it matches plant phenotyping methodsCNNソフトウェアを用いた根画像からの根量・根密度抽出法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use.
Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Networks Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.
Why it matches plant phenotyping methods個体樹冠の画像ベース delineation を評価する標準ベンチマークデータセットと評価用Rパッケージを構築しており、植物形態の抽出・比較手法が中心である。
abstractwe created a novel benchmark dataset to assess individual tree delineation methods.
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 · public375 developed an R package ( https://github.com/weecology/NeonTreeEvaluation_package) forOpen asset ↗weecology/NeonTreeEvaluation_packagepdf-page:21 lines:1-71Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o, toluidine blue-o, and lugol’s-iodine) images with three-fold annotation including physical, morphological, and tissue grading based on weight, different section area, and tissue zone respectively. In addition, we prepared ground truth segmentation labels for three different tuber weights. We have validated the pertinence of annotations by performing multi-label cell classification, employing convolutional neural network (CNN), VGG16, for unstained and stained images. The accuracy has been achieved up to 0.94, while, F2-score reaches to 0.92. Furthermore, the ground truth labels have been verified by semantic segmentation algorithm using UNet architecture which presents the mean intersection of union up to 0.70. Hence, the overall results show that the data are very much efficient and could enrich the domain of microscopy plant cell analysis for DL-framework.
Why it matches plant phenotyping methodsジャガイモ塊茎の細胞形態・組織特性を対象とする大規模画像データセットを構築し、分類・セグメンテーションで検証しており、植物フェノタイピング用データ資源が中心である。
titleA large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment
Reproduction assets foundThe paper's potato tuber microscopy image dataset (raw stained/unstained images plus ground truth segmentation labels) is publicly deposited on figshare by the authors.Dataset · publicThis dataset is publicly available on figshare47 (https://doi.org/10.6084/m9.figshare.c.4955669) which can be
downloaded as a zip file.Open asset ↗figshare · 10.6084/m9.figshare.c.4955669pdf-page:5 lines:1-35Plant phenotyping relevance match · UnverifiedCrossref · checked 9 Sept 2026
This work introduces a method that combines remote sensing and deep learning into a framework that is tailored for accurate, reliable and efficient counting and sizing of plants in aerial images. The investigated task focuses on two low-density crops, potato and lettuce. This double objective of counting and sizing is achieved through the detection and segmentation of individual plants by fine-tuning an existing deep learning architecture called Mask R-CNN. This paper includes a thorough discussion on the optimal parametrisation to adapt the Mask R-CNN architecture to this novel task. As we examine the correlation of the Mask R-CNN performance to the annotation volume and granularity (coarse or refined) of remotely sensed images of plants, we conclude that transfer learning can be effectively used to reduce the required amount of labelled data. Indeed, a previously trained Mask R-CNN on a low-density crop can improve performances after training on new crops. Once trained for a given crop, the Mask R-CNN solution is shown to outperform a manually-tuned computer vision algorithm. Model performances are assessed using intuitive metrics such as Mean Average Precision (mAP) from Intersection over Union (IoU) of the masks for individual plant segmentation and Multiple Object Tracking Accuracy (MOTA) for detection. The presented model reaches an mAP of 0.418 for potato plants and 0.660 for lettuces for the individual plant segmentation task. In detection, we obtain a MOTA of 0.781 for potato plants and 0.918 for lettuces.
Why it matches plant phenotyping methodsUAV画像から個体植物を検出・セグメンテーションし、個体数とサイズを推定するMask R-CNN手法の開発・最適化・性能評価が中心であり、植物フェノタイピング手法に該当する。
abstractThis work introduces a method that combines remote sensing and deep learning into a framework that is tailored for accurate, reliable and efficient counting and sizing of plants in aerial images.
NASA’s ICESat-2space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of bio-geoscience research applications. Given the novelty of elevation and the derived data products from the ICESat-2 mission, the research community needs software tools that can facilitate photon-level analyses to support product validation and development new analysis methods. Here, we describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03). Developed in MATLAB, the GUI facilitates the reading and display of ATL03 Hierarchical Data Format (HDF) files, the manual labeling of individual photons into target classes of choice using a number of point selections tools and enables eventual saving of labeled data in ASCII format. Other capabilities include saving and loading of labeling sessions to manage labeling tasks over time. We expect labeled data generated using the application to serve two main purposes. First, serve as ground truth for validating various products from ICESat-2 mission, especially for study sites around the world that do not have existing reference datasets such as airborne lidar. Second, serve as training and validation data in the development of new algorithms for generating various ICESat-2 data products. We demonstrate the first use case through a validation case study for the land and vegetation product (ATL08), which provides canopy and terrain height estimates, over two sites. For the first site, located in northwestern Zambia, we used ICESat-2 ATL03 data acquired at night and for our second site in Texas, US, we used ATL03 data acquired during the day. The PhotonLabeler application is freely available as a compiled MATLAB binary to enable free access and utilization by interested researchers.
Why it matches plant phenotyping methodsICESat-2光子データを手動ラベリングし、植生・樹冠高を含む植物関連プロダクトの検証用データを作成するソフトウェアであり、植物状態の取得・検証ワークフローが中心的です。
abstractwe describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03).
Reproduction assets foundThe paper's own PhotonLabeler software (a MATLAB GUI for labeling ICESat-2 ATL03 photon data, used to generate the validation measurements in the case study) is explicitly stated to be publicly available on the authors' GitHub with a compiled binary and user manual. The ATL03/ATL08 data are generic mission products, soCode · publicling sessions. A saved session file contains the state of the application at the time of saving and
253 stores input files path and parameters to enable one to pick up labeling from where they left.
254 2.2.5 Software availability
255 PhotonLabeler is available to interested scientists through our project website on GitHub
256 (https://github.com/Oht0nger/PhoLabeler/releases/tag/v1.0). The application is available as a compiled
257 binary, which one can install without a MATLAB license. The option requires a download of free
258 MATLAB runtime environment. On our GitHub page, we also provide a detailed user manual on how
259 to use the software.
260 2.3 Case study: Using manually labeled datOpen asset ↗Oht0nger/PhoLabeler · v1.0pdf-layout-page:9 lines:1-61Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.
Why it matches plant phenotyping methods植物表現型プラットフォーム向けのデータワークフロー、Pythonアプリケーション、データベース、品質検証を開発しており、表現型データ処理が中心的な方法論的貢献である。
abstracta data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database.
Many newly observed phenotypes are first described, then experimentally manipulated. These language-based descriptions appear in both the literature and in community datastores. To standardize phenotypic descriptions and enable simple data aggregation and analysis, controlled vocabularies and specific data architectures have been developed. Such simplified descriptions have several advantages over natural language: they can be rigorously defined for a particular context or problem, they can be assigned and interpreted programmatically, and they can be organized in a way that allows for semantic reasoning (inference of implicit facts). Because researchers generally report phenotypes in the literature using natural language, curators have been translating phenotypic descriptions into controlled vocabularies for decades to make the information computable. Unfortunately, this methodology is highly dependent on human curation, which does not scale to the scope of all publications available across all of plant biology. Simultaneously, researchers in other domains have been working to enable computation on natural language. This has resulted in new, automated methods for computing on language that are now available, with early analyses showing great promise. Natural language processing (NLP) coupled with machine learning (ML) allows for the use of unstructured language for direct analysis of phenotypic descriptions. Indeed, we have found that these automated methods can be used to create data structures that perform as well or better than those generated by human curators on tasks such as predicting gene function and biochemical pathway membership. Here, we describe current and ongoing efforts to provide tools for the plant phenomics community to explore novel predictions that can be generated using these techniques. We also describe how these methods could be used along with mobile speech-to-text tools to collect and analyze in-field spoken phenotypic descriptions for association genetics and breeding applications.
Why it matches plant phenotyping methods植物表現型記述を自然言語処理・機械学習で構造化し、遺伝子機能予測や育種・フェノミクスに利用する計算ツールと方法を扱っており、表現型データ処理が中心である。
abstractHere, we describe current and ongoing efforts to provide tools for the plant phenomics community to explore novel predictions that can be generated using these techniques.
RootAnnotation / quality controlRoot system architecture
Motivation Trait data are fundamental to quantitatively describe plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready-to-use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (i) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (ii) maximized species coverage through data standardization within and among traits, and (iii) implemented data quality checks. Main types of variables contained GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain Global coverage with data from arid, continental, polar, temperate, and tropical biomes. Data on root traits derived from experimental studies and field studies. Time period and grain Data recorded between 1911 and 2019 Major taxa and level of measurement GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with sub-species or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 sub-species or varieties, 6,214 species, 1,967 genera and 254 families. Due to variation in data sources, trait records in the database include both individual observations and mean values. Software format GRooT includes two csv file. A GitHub repository contains the csv files and a script in R to query the database.
Why it matches plant phenotyping methods植物の根形質を大規模に標準化・品質管理して提供する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である。
abstractwe created the Global Root Trait (GRooT) Database
Reproduction assets foundThe paper's core asset is the GRooT root trait database (two csv files) plus the authors' R script (GRooTExtraction) for querying/error-risk calculation, explicitly deposited in a public GitHub repository with a project website.Dataset · publicGRooT is public and will be maintained in a GitHub repository
(https://github.com/GRooT-Database/GRooT-Data).Open asset ↗GRooT-Database/GRooT-Datapdf-page:8 lines:1-48Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 9 Sept 2026
RootAnnotation / quality controlCountingMorphology / geometry measurementSegmentationRoot system architecture
We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.
Why it matches plant phenotyping methods植物画像の深層学習セグメンテーション用ソフトウェアを開発・評価し、根長、バイオポア数、根粒数という植物形態形質の抽出に用いているため、フェノタイピング手法が中心である。
abstractWe present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis.
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.
Why it matches plant phenotyping methods合成データとインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発し、実画像で性能検証しているため、方法が研究の中心である。
abstractan instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars
Reproduction assets foundThe authors publicly release both the synthetic and real-world seed image datasets and the analysis code (Mask R-CNN deployment and multivariate analysis notebooks) via their GitHub repository, explicitly stated in Data availability and Code availability sections.Dataset · publicSynthetically generated and real-world datasets can be obtained from the following GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Code · publicCode to reproduce the deployment of the trained Mask R-CNN and multivariate analysis is formatted as IPython notebooks and can also be obtained from the GitHub repository ( https://github.com/totti0223/crop_seed_instance_segmentation ).Open asset ↗https://github.com/totti0223/crop_seed_instance_segmentationlines:149-171Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
fAPAR is a radiometric quantity describing the fraction of photosynthetically active radiation (PAR) absorbed by a plant canopy. It is an important component of carbon cycle and energy balance models and has been named as one of the 50 Global Climate Observing System (GCOS) essential climate variables (ECVs). Space agencies such as the ESA and NASA produce satellite fAPAR products in order to address the need for spatially explicit global data to address environmental and climate change issues. Given the derived nature of satellite fAPAR products it is essential to independently verify the results they produce. In order to do this, validation sites (or networks of sites) are needed that directly correspond to the measurands. Further to this, in order to understand divergences between product and validation data, uncertainty information should be provided with all measurement results. The canopy radiative transfer models which are used in satellite-derived fAPAR products implement simplistic assumptions about the state of the plant canopy and illumination conditions in order to retrieve an fAPAR estimate in a computationally feasible time. This contribution assesses the impact of the assumptions made by the Sentinel-2 SNAP-derived fAPAR and includes it in a validation of the product over a field site (Wytham Woods, UK), which also has concurrent fAPAR measurements. This is achieved using a 3D model of Wytham Woods which is used to simulate biases associated with specific assumption types. These are used to convert the in situ measurements to the same quantity assumed by the satellite product. The measurement network which provides the fAPAR data is also traceable to SI through sensor calibrations and has associated uncertainty estimates. To our knowledge, these latter points have not been implemented in the biophysical product validation literature, which may explain some of the large discrepancies seen between validation and satellite-derived fAPAR data. The ultimate aim of this work is to demonstrate a validation framework for derived biophysical variables such as fAPAR which properly considers the quantity estimated by the satellite and that measured by the in situ sensors, whilst providing metrologically derived uncertainties on the in situ data. This will help to properly inform users as to the quality of the data and determine whether the GCOS requirements set for fAPAR are attainable, ultimately improving carbon cycle and energy balance estimates.
Why it matches plant phenotyping methods植物キャノピーのfAPARという生理・状態形質について、3Dモデル、現地センサー、校正、不確かさを組み合わせた衛星プロダクトの検証フレームワークが研究の中心である。
abstractThis contribution assesses the impact of the assumptions made by the Sentinel-2 SNAP-derived fAPAR and includes it in a validation of the product over a field site (Wytham Woods, UK), which also has concurrent fAPAR measurements.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
BACKGROUND: The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure. RESULTS: We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively. CONCLUSION: We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.
Why it matches plant phenotyping methods植物器官セグメンテーション手法の訓練・評価用データセットとベンチマークを提供しており、植物フェノタイピング手法が中心である。
abstractThe production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods.
Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using Multi-Task Learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification. We adopted a modified ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count Mean Squared Error (MSE) by more than 40 %, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75 % fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available.
Why it matches plant phenotyping methods植物画像から複数形質を同時推定するマルチタスク深層学習手法の開発・評価が中心であり、明確な植物フェノタイピング方法論研究である。
abstractWe present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification.
Reproduction assets foundThe paper's authors provide public analysis code (MTL phenotyping framework) on GitHub, and the study analyzes publicly available CVPPP plant image datasets (Ara2013, A1, A4) hosted on plant-phenotyping.org. Both are paper-specific, public, and actionable.Code · publicCode available at https://github.com/andobrescu/Multi_task_plant_phenotyping .Open asset ↗andobrescu/Multi_task_plant_phenotypinglines:224-295Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017-challenge .Open asset ↗lines:607-694Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 9 Sept 2026
Heat stress and resulting sunburn is a major abiotic stress in perineal specialty crops. For example, such stress to the maturing fruits on apple tree canopies can cause several physiological disorders that result in considerable crop losses and reduced marketability of the produce. Thus, there is a critical technological need to effectively monitor the abiotic stress under field conditions for timely actuation of remedial measures. Fruit surface temperature (FST) is one of the stress indicators that can reliably be used to predict apple fruit sunburn susceptibility. This study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring. The tool integrates a smartphone connected thermal-Red-Green-Blue (RGB) imaging sensor and a custom developed application ('AppSense 1.0') for apple fruit sunburn prediction. This tool is configured to acquire and analyze imagery data onboard the smartphone to estimate FST. The tool also utilizes geolocation-specific weather data to estimate weather-based FST using an energy balance modeling approach. The 'AppSense 1.0' application, developed to work in the Android operating system, allows visual display, annotation and real-time sharing of the imagery, weather data and pertinent FST estimates. The developed tool was evaluated in orchard conditions during the 2019 crop production season on the Gala, Fuji, Red delicious and Honeycrisp apple cultivars. Overall, results showed no significant difference (t 110 = 0.51, p = 0.6) between the mobile FST monitoring tool outputs, and ground truth FST data collected using a thermal probe which had accuracy of ±0.4 °C. Upon further refinements, such tool could aid growers in real-time apple fruit sunburn susceptibility prediction and assist in more effective actuation of apple fruit sunburn preventative measures. This tool also has the potential to be customized for in-field monitoring of the heat stressors in some of the sun-exposed perennial and annual specialty crops at produce maturation.
Why it matches plant phenotyping methodsリンゴ果実表面温度という植物ストレス状態を、スマートフォン接続型熱・RGB画像センサーと専用アプリで取得・推定する手法を開発し、圃場でプローブ測定と検証しているため、植物フェノタイピング手法が中心である。
abstractThis study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring.
Natural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These representations include the entity-quality (EQ) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically rich format, as well as numerical vector representations generated using natural language processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.
Why it matches plant phenotyping methods植物表現型記述をNLPで構造化・ベクトル化し、手動キュレーションとの性能比較で検証する計算手法が中心である。
abstractWe computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code on GitHub (irbraun/phenologs) and all files needed to reproduce the results (including the phenotype/EQ datasets used) on Zenodo (doi 10.5281/zenodo.3255020). These are paper-specific, public, and actionable assets for the phenotype-Code · publicThe code used to produce the results of this work is available at github.com/irbraun/phenologs . Files necessary to reproduce the discussed results, datasets used to generate figures presented in this work, and other supplemental files are available at doi.org/10.5281/zenodo.3255020 .Open asset ↗irbraun/phenologs · 10.5281/zenodo.3255020lines:792-814Dataset · publicFiles necessary to reproduce the discussed results, datasets used to generate figures presented in this work, and other supplemental files are available at doi.org/10.5281/zenodo.3255020 . This data repository also includes versions of the previously described datasets available as supplemental data of Oellrich, Walls et al. (2015) and Lloyd and Meinke (2012) , for the purpose of making this study reproducible without any additional external files.Open asset ↗10.5281/zenodo.3255020lines:792-814Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract High‐throughput phenotyping systems provide abundant data for statistical analysis through plant imaging. Before usable data can be obtained, image processing must take place. In this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods. Because obtaining accurate training data is a major obstacle to using supervised learning methods for segmentation, a novel approach to producing accurate labels was developed. We demonstrated that, with careful selection of training data through such an approach, supervised learning methods, and neural networks in particular, can outperform thresholding methods at segmentation.
Why it matches plant phenotyping methods植物画像から背景を分離するセグメンテーション手法を開発・比較し、教師データ生成法も提案しているため、表現型取得の技術が中心である。
abstractIn this study, we used supervised learning methods to segment plants from the background in such images and compared them with commonly used thresholding methods.
Reproduction assets foundThe paper's DATA AVAILABILITY statement points to the authors' public GitHub repository containing all segmentation analysis code and related data, and to CyVerse Data Commons hosting the raw maize image data used in the study.Code · publicdata were obtained for the rest of the plant. In more challeng-
ing cases of plant segmentation, such as the field environment,
our method serves as an excellent starting point, and its perfor-
mance could be improved by incorporating more training data.
DATA AVAILABILITY
All code along with related data are posted on Github at
https://github.com/jasonradams47/PlantSegmentationCode.The raw image data used in this study are hosted at CyVerse
(Liang & Schnable, 2017).
CONFLICT OF INTEREST
The authors have no competing financial interests.
ORCID
Jason Adams https://orcid.org/0000-0003-2085-4911
Yumou Qiu https://orcid.org/0000-0003-4846-1263
Yuhang Xu https://orcid.org/0000-0003-4351-4602
JamesOpen asset ↗jasonradams47/PlantSegmentationCodepdf-raw-page:10 lines:1-83Dataset · publicThe raw image data used in this study are hosted at CyVerse
(Liang & Schnable, 2017).Open asset ↗pdf-raw-page:10 lines:1-83Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization , where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.
Why it matches plant phenotyping methods合成画像で学習したインスタンスセグメンテーションによる種子形態フェノタイピング手法を開発・検証し、複数作物への適用性能も評価しているため、方法が中心的である。
abstractan instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset.
High-throughput phenotyping systems are becoming increasingly powerful, dramatically changing our ability to document, measure, and detect phenomena. Unfortunately, taking advantage of these trends can be difficult for scientists with few resources, particularly when studying nonstandard biological systems. Here, we describe a powerful, cost-effective combination of a custom-built imaging platform and open-source image processing pipeline. Our maize ear scanner was built with off-the-shelf parts for <$80. When combined with a cellphone or digital camera, videos of rotating maize ears were captured and digitally flattened into projections covering the entire surface of the ear. Segregating GFP and anthocyanin seed markers were clearly distinguishable in ear projections, allowing manual annotation using ImageJ. Using this method, statistically powerful transmission data can be collected for hundreds of maize ears, accelerating the phenotyping process.
Why it matches plant phenotyping methods低コストのトウモロコシ穂スキャナーと画像処理パイプラインを開発し、穂表面および種子マーカーを高スループットに測定する方法が研究の中心である。
abstractHere, we describe a powerful, cost-effective combination of a custom-built imaging platform and open-source image processing pipeline.
Field / plotLiDAR / point cloudAnnotation / quality controlArchitecture / morphology / geometry
Characteristics describing below canopy vegetation are important for a range of forest ecosystem applications including wildlife habitat, fuel hazard and fire behaviour modelling, understanding forest recovery after disturbance and competition dynamics. Such applications all rely on accurate measures of vegetation structure. Inherent in this is the assumption or ability to demonstrate measurement accuracy. 3D point clouds are being increasingly used to describe vegetated environments, however limited research has been conducted to validate the information content of terrestrial point clouds of understory vegetation. This paper describes the design and use of a field frame to co-register point intercept measurements with point cloud data to act as a validation source. Validation results show high correlation of point matching in forests with understory vegetation elements with large mass and/or surface area, typically consisting of broad leaves, twigs and bark 0.02 m diameter or greater in size (SfM, MCC 0.51–0.66; TLS, MCC 0.37–0.47). In contrast, complex environments with understory vegetation elements with low mass and low surface area showed lower correlations between validation measurements and point clouds (SfM, MCC 0.40 and 0.42; TLS, MCC 0.25 and 0.16). The results of this study demonstrate that the validation frame provides a suitable method for comparing the relative performance of different point cloud generation processes.
Why it matches plant phenotyping methods林床植生の構造を3D点群で測定する手法について、点群と点インターセプト測定を共登録する検証フレームを設計・評価しており、植物形質（植生構造）の取得精度検証が中心である。
abstractThis paper describes the design and use of a field frame to co-register point intercept measurements with point cloud data to act as a validation source.
Genebanks are valuable sources of genetic diversity, which can help to cope with future problems of global food security caused by a continuously growing population, stagnating yields and climate change. However, the scarcity of phenotypic and genotypic characterization of genebank accessions severely restricts their use in plant breeding. To warrant the seed integrity of individual accessions during periodical regeneration cycles in the field phenotypic characterizations are performed. This study provides non-orthogonal historical data of 12,754 spring and winter wheat accessions characterized for flowering time, plant height, and thousand grain weight during 70 years of seed regeneration at the German genebank. Supported by historical weather observations outliers were removed following a previously described quality assessment pipeline. In this way, ready-to-use processed phenotypic data across regeneration years were generated and further validated. We encourage international and national genebanks to increase their efforts to transform into bio-digital resource centers. A first important step could consist in unlocking their historical data treasures that allows an educated choice of accessions by scientists and breeders.
Why it matches plant phenotyping methods7 दशकにわたるコムギ表現型データを大規模に整理・品質評価・検証し、再利用可能な処理済みデータとして提供することが中心であり、植物フェノタイピングデータセットとして適格です。
abstractThis study provides non-orthogonal historical data of 12,754 spring and winter wheat accessions characterized for flowering time, plant height, and thousand grain weight during 70 years of seed regeneration at the German genebank.
Reproduction assets foundThe paper deposits its historical wheat phenotypic data (FT, PH, TGW for 12,754 accessions), outlier-corrected and BLUE-processed datasets, and example R analysis scripts in the e!DAL-PGP repository under DOI 10.5447/IPK/2019/11, which is an allowed URL and appears verbatim in the text.Dataset · publicPhilipp, N. et al. Historical phenotypic data from seven decades of seed regeneration in a wheat ex situ collection hosted at the
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). e!DAL - Plant Genomics and Phenomics Research Data Repository,
https://doi.org/10.5447/IPK/2019/11 (2019).Open asset ↗e!DAL - Plant Genomics and Phenomics Research Data Repository · 10.5447/IPK/2019/11pdf-page:9 lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · bioRxiv · checked 9 Sept 2026
I. A BSTRACT Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development – a process referred to as plant phenotyping – is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyse the data in images depicting plant growth. In this paper, a deep learning approach is developed to locate fluorescent markers in 3D timeseries microscopy images. The approach is not dependant on marker morphology; only simple 3D point location annotations are required for training. The approach is evaluated on an unseen timeseries comprising several volumes, capturing growth of plants. Results are encouraging, with an average recall of 0.97 and average F-score of 0.78, despite only a very limited number of simple training annotations. In addition, an in-depth analysis of appropriate loss functions is conducted. To accompany [the finally-published] paper we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (Fiji) software. Network models will be released online.
Why it matches plant phenotyping methods植物の成長を対象に、3D時系列顕微鏡画像から蛍光マーカーを自動検出・位置推定する深層学習手法を開発し、未知時系列で評価しているため、植物フェノタイピング手法が中心である。
abstractAutomated tools are required to analyse the data in images depicting plant growth.
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 · 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 confirmedOpenAlex · Crossref · Europe PMC · checked 10 Sept 2026
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.
Why it matches plant phenotyping methods植物フェノタイピング画像のタッセル分割について、クラウドソーシングによる教師データ生成の品質・速度・評価指標を検証しており、表現型取得ワークフローが中心です。
abstractHere we explore the use of crowdsourcing to generate a large number of training data of good quality.
Reproduction assets foundThe paper's crowdsourced corn tassel bounding-box annotations (phenotyping measurements) are publicly deposited on figshare, and the analysis software is on GitHub, both explicitly stated in the Data Availability statement and Methods.Dataset · publicData Availability: The software for this project is available from: https://github.com/ashleyzhou972/Crowdsource-Corn-Tassels . The data for this project are available from: https://doi.org/10.6084/m9.figshare.6360236.v2 .Open asset ↗figshare · 10.6084/m9.figshare.6360236.v2lines:201-240Code · publicThe software for this study is available from: https://github.com/ashleyzhou972/Crowdsource-Corn-TasselsOpen asset ↗github · ashleyzhou972/Crowdsource-Corn-Tasselslines:257-277Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · Crossref · checked 10 Sept 2026
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.\n\nAuthor SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, but they require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel - the male flower of the corn plant - from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.
Why it matches plant phenotyping methodsトウモロコシ雄穂画像のセグメンテーション用教師データをクラウドソーシングで生成・評価する方法を開発し、精度や品質指標を比較検証しており、植物表現型取得ワークフローが中心である。
abstractWe explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting.
Genebanks are a rich source of genetic variation. Most of this variation is absent in breeding programs but may be useful for further crop plant improvement. However, the lack of phenotypic information forms a major obstacle for the educated choice of genebank accessions for research and breeding. A promising approach to fill this information gap is to exploit historical information gathered routinely during seed regeneration cycles. Still, this data is characterized by a high non-orthogonality hampering their analysis. By examining historical data records for flowering time, plant height, and thousand grain weight collected during 70 years of regeneration of 6,207 winter wheat ( Triticum aestivum L.) accessions at the German Federal ex situ Genebank, we aimed to elaborate a strategy to analyze and validate non-orthogonal historical data in order to charge genebank information platforms with high quality ready-to-use phenotypic information. First, a three-step quality control assessment considering the plausibility of trait values and a standard as well as a weather parameter index based outlier detection was implemented, resulting in heritability estimates above 0.90 for all three traits. Then, the data was analyzed by estimating best linear unbiased estimations (BLUEs) applying a linear mixed-model approach. An in silico resampling study mimicking different missing data patterns revealed that accessions should be regenerated in a random fashion and not blocked by origin or acquisition date in order to minimize estimation biases in historical data sets. Validation data was obtained from multi-environmental orthogonal field trials considering a random subsample of 3,083 accessions. Correlations above 0.84 between BLUEs estimated for historical data and validation trials outperformed previous approaches and confirmed the robustness of our strategy as well as the high quality of the historical data. The results indicate that the IPK winter wheat collection reveals an extraordinary high phenotypic diversity compared to other collections. The quality checked ready-to-use phenotypic information resulting from this study is the first brick to extend traditional, conservation driven genebanks into bio-digital resource centers.
Why it matches plant phenotyping methods過去の再生記録から植物形質情報を抽出・品質管理し、統計推定と独立圃場試験で検証する分析戦略が研究の中心である。
abstractwe aimed to elaborate a strategy to analyze and validate non-orthogonal historical data in order to charge genebank information platforms with high quality ready-to-use phenotypic information.
BACKGROUND: Image-based plant phenotyping has become a powerful tool in unravelling genotype-environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a 'gold-standard' and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count. RESULTS: to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability. CONCLUSIONS: While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem.
Why it matches plant phenotyping methods画像ベース植物フェノタイピングにおける葉数アノテーションの観察者間・内変動を、専用ツール、市民参加型基盤、機械学習アルゴリズムと比較検証しており、測定手法の技術的妥当性評価が中心である。
abstractHere we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.
Reproduction assets foundThe article states that the Arabidopsis image dataset used for the leaf-counting observer-variability study is publicly available at the plant-phenotyping.org datasets page, and the citizen-science annotations were collected via the authors' public Zooniverse 'Leaf Targeting' project. No author analysis code or trainedDataset · publicAvailability of data and materials
The image dataset used in this article is available at http://www.plant-phenotyping.org/datasets .Open asset ↗plant-phenotyping.orglines:307-379Dataset · publicThe A data (RPi) were included as part of a larger citizen-powered study (“Leaf Targeting”, available at https://www.zooniverse.org/projects/venchen/leaf-targeting ) built on ZooniverseOpen asset ↗Zooniverse · venchen/leaf-targetinglines:132-149Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Published1 Jan 2018International Journal of Precision Agricultural AviationCited by 1 · OpenAlex ↗
The existing identification of wheat lodging based on unmanned aerial vehicle (UAV) is significantly dependent on the artificial ground annotation method, which exhibits low annotation accuracy and strong subjectivity, thus resulting in a low degree of separation for the annotated lodging area and the non-lodging area. To solve the problem of insufficient applicability of traditional annotation research to agricultural images, especially wheat field lodging images, a lodging annotation method in the study based on semi-automatic image segmentation algorithm was proposed. Firstly, a total of 101 farmlands with lodging occurred during the flowering, filling and mature period of wheat in 2019 and 2021 were segmented as the research objects. The above images were respectively changed into RGB and HSV color space and converted into four vegetation indexes, including excess-green (ExG), green leaf index (VEG), normalized green-red difference index (NGRDI), as well as red-green ratio index (GRRI). Secondly, lodging regions were extracted and modified from the image in accordance with color features. Lastly, the JM distance of lodging and non-lodging areas served as an index to examine the effect of image annotation for data analysis and evaluation of segmentation accuracy. The result of the experiment indicated that there was a very significant difference between the JM distance based on the annotation method proposed in this study and the result based on manual annotation. GRRI and ExG were the most suitable features for image annotation. The method proposed in this study had high generalization performance for the images captured in the three fertility periods in 2019 and 2021, and the images with poor image annotation results took up a small proportion. In brief, the lodging area annotation method proposed in this study increases the annotation accuracy by extracting lodging areas using a semi-automatic image segmentation algorithm. The proposed method outperforms the manual annotation method. Keywords: semi-automatic image annotation, wheat lodging, unmanned aerial vehicle, image processing, feature separability DOI: 10.33440/j.ijpaa.20220501.193 Citation: Zhang G, He F M, Yan H F, Xu H F, Pan Z G, Yang X Y, Zhang D Y, Li W F. Methodology of wheat lodging annotation based on semi-automatic image segmentation algorithm. Int J Precis Agric Aviat, 2022; 5(1): 47–53.
Why it matches plant phenotyping methods小麦の倒伏状態を対象に、UAV画像から倒伏領域を抽出・アノテーションする半自動画像セグメンテーション法を開発し、手動法との比較と精度評価を行っているため、植物フェノタイピング手法が中心である。
abstracta lodging annotation method in the study based on semi-automatic image segmentation algorithm was proposed
Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.
Why it matches plant phenotyping methods植物画像から穂・小穂の位置と個数を推定する深層学習手法を開発し、注釈付きデータセットと性能評価を提示しており、フェノタイピング手法が研究の中心です。
abstractWe present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 10 Sept 2026
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf count per plant. We argue that combining different datasets when training a deep neural network is beneficial and improves the results of the proposed approach. We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results show that the proposed method significantly outperforms the winner of the previous CVPPP challenge, improving the results by a minimum of 50% on each of the test datasets, and can achieve this performance without knowing the experimental origin of the data (i.e. \"in the wild\" setting of the challenge). We also compare the counting accuracy of our model with that of per leaf segmentation algorithms, achieving a 20% decrease in mean absolute difference in count (|DiC|).
Why it matches plant phenotyping methods植物画像から葉数という形態形質を推定する深層学習手法を開発し、既存手法・ベンチマークデータセットで性能評価しており、表現型取得・抽出法が研究の中心である。
abstractHere, we propose an automated, deep learning based approach for counting leaves in model rosette plants.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 10 Sept 2026
Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area.
Why it matches plant phenotyping methodsトマト植物の病害を画像から認識する深層学習手法を開発・比較し、データセット上で性能評価しており、植物状態の取得・推定が中心である。
abstractwe present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Field / plotWhole plant / canopy / plot / fieldAnnotation / quality controlVisualization / data management
Plant phenotyping has emerged as a comprehensive field of research as the result of significant advancements in the application of imaging sensors for high-throughput data collection. The flip side is the risk of drowning in the massive amounts of data generated by automated phenotyping systems. Currently, the major challenge lies in data management, on the level of data annotation and proper metadata collection, and in progressing towards synergism across data collection and analyses. Progress in data analyses includes efforts towards the integration of phenotypic and -omics data resources for bridging the phenotype-genotype gap and obtaining in-depth insights into fundamental plant processes.
Why it matches plant phenotyping methods植物フェノタイピングにおける画像センサー、高スループットデータ収集、データ管理・統合・解析を総括するレビューであり、方法論的内容が中心です。
abstractPlant phenotyping has emerged as a comprehensive field of research as the result of significant advancements in the application of imaging sensors for high-throughput data collection.
In studies on agricultural robot vision systems, data used to evaluate algorithm performance, such as successful recognition rates, vary because of various factors. If the variation is too large, representation of the actual performance of algorithms by the data is bound to be poor. Here we present a method for analysing the quality of data used to evaluate the performance of a recognition algorithm for occluded tomatoes based on measurement system analysis. The measurement system included a soft measurement tool (a counting method for the number of successful recognitions), appraisers, measured objects (recognition results of 300 occluded tomato images), the usage method for the soft measurement tool and measurement environments. The measurement system was analysed on the basis of its repeatability and reproducibility. Repeatability and reproducibility were both evaluated based on Fleiss's Kappa values, free-marginal multirater Kappa values and Kendall coefficients. Test results showed that repeatability was excellent or fair to good based on Fleiss's Kappa values and excellent based on free-marginal multirater Kappa values and Kendall coefficients for the three appraisers. Further improvement in the soft type of measurement tool is necessary. Reproducibility was fair to good with Fleiss's Kappa values and free-marginal multirater Kappa values, and good with Kendall coefficients. Large values of measured feature resulted in inferior repeatability and reproducibility.
Why it matches plant phenotyping methodsトマト画像の認識結果を対象に、認識性能評価データの測定システムの反復性・再現性を検証しており、植物器官の画像認識による表現型取得・評価手法が中心です。
abstractHere we present a method for analysing the quality of data used to evaluate the performance of a recognition algorithm for occluded tomatoes based on measurement system analysis.
Poster presented at PAGXXV Subtropical fruits, like avocado and cherimoya, are key crops for food security in a wide range of countries, with an increasing commercial importance worldwide. Even though their importance is starting to be recognized and high throughput sequencing approaches are currently being used to characterize genome-wide patterns from natural diversity populations and breeding stocks, currently ontological available information for these subtropical fruits crops is scarce and often not based in internationally standardized formats. Thus, the challenge to correlate the expanding molecular information data available with plant phenotype and crop traits remains an important issue in breeding programs for these crops. With the aim to facilitate future analyses we present a controlled vocabulary for harmonizing the annotation of phenotypic and genomic data for these crops. These new ontologies represent an extended ontology to fit avocado and cherimoya traits commonly used in variety descriptions, mainly established by Biodiversity International and the International Union for the Protection of New Varieties of Plants (UPOV), but also custom ad hoc descriptors. The developed ontology includes measurable or observable characteristics of plants as well as abiotic and biotic stress susceptibility. The resource is available in standard OBO formats ready to be used in GMOD and Tripal inspired biological databases to allow data sharing and reusability. The approach followed here can be of interest to other crops in which standardized ontologies are still missing
Why it matches plant phenotyping methodsアボカドとチェリモヤの植物形質を標準化・相互運用する表現型オントロジーを開発した研究であり、形質データ記述のための再利用可能な方法・リソースが中心である。
abstractwe present a controlled vocabulary for harmonizing the annotation of phenotypic and genomic data for these crops.
Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets.
Why it matches plant phenotyping methods植物フェノタイピング用の画像データセットとアノテーション、評価基準を整備したベンチマーク研究であり、方法開発を支えるデータ基盤が中心です。
abstractThis paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants.