Accurate litchi counting from whole-tree images is essential for yield estimation, orchard management, and plant phenotyping, but remains challenging in real orchards because fruits occur in dense, heavily occluded clusters and vary markedly in scale, illumination, and appearance across ripening stages, particularly when green fruits resemble surrounding foliage. Existing methods have shown promise, but their robustness in complex orchard environments remains limited. To address these challenges, we propose FG-LCNet, a two-stage foreground-guided litchi counting framework. In the first stage, an enhanced fruit-cluster detector improves the localization of small and ambiguous clusters under complex canopy backgrounds. In the second stage, the detected foreground regions are fed into a density-regression network with hybrid attention, while a consistency-based training strategy is introduced to improve robustness to appearance and illumination variations. To support this study, a large-scale litchi counting dataset was established, consisting of 1,126 whole-tree images collected from five orchards and spanning three ripening stages, with approximately 120,000 fruit-level dot annotations and more than 20,000 cluster-level bounding boxes. FG-LCNet achieved the best overall counting performance, with an MAE of 7.44 and an RMSE of 11.01. It showed clear advantages in high-density fruit-cluster scenarios and cross-orchard validation, while maintaining competitive results across orchard-region and maturity-stage subsets. The framework further retained inference efficiency suitable for practical deployment. These results indicate that FG-LCNet provides an effective solution for robust litchi counting and offers potential for other clustered fruit-counting tasks.
Why it matches plant phenotyping methods果実数という植物器官形質を whole-tree 画像から推定する二段階画像解析手法を開発し、データセット構築と交差果樹園検証まで行っており、表現型取得・抽出法が中心である。
abstractwe propose FG-LCNet, a two-stage foreground-guided litchi counting framework.
Reproduction assets foundThe paper's implementation code is explicitly stated as publicly available at the authors' GitHub repository (FG-LCNet). The litchi counting dataset (1,126 whole-tree images with ~120,000 dot annotations and 20,000+ bounding boxes) is not yet fully public: a ~100-image annotated subset is promised upon acceptance, and,Code · publicdustry Technology Research System (CARS-32-21), Hainan Modern Agricul-
655
tural Industry Technology System (HNARS-08-G02).
656
Conflicts of Interest
657
The authors declare that there is no conflict of interest regarding the publication of this article.
658
Data Availability
659
The implementation code is publicly available at https://github.com/johnhamtom/FG-LCNet .
660
Upon acceptance, a representative subset of approximately 100 annotated litchi images will be
661
released to support reproducibility and preliminary benchmarking. The full dataset is being further
662
organized for future release. Before full release, the complete dataset can be obtained from the
663
corresponding authorOpen asset ↗johnhamtom/FG-LCNetpdf-raw-page:28 lines:1-81Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Published4 Sept 2026Springer Science and Business Media LLC
Abstract Plant diseases substantially reduce global crop yields, and cotton production is particularly vulnerable to field-acquired variability in symptom appearance, background clutter, and illumination changes that limit the reliability and scalability of expert visual inspection. This study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition that is suitable for deployment on resource-constrained edge devices. Using the SAR-CLD-2024 dataset (322 RGB images captured under natural agricultural conditions across seven categories, including healthy and diseased leaves), images were preprocessed via resizing and normalization and augmented online in the training set (random rotations, flips, brightness/contrast adjustments, and random cropping). An EfficientNet-B3 backbone initialized with ImageNet-pretrained weights was fine-tuned using categorical cross-entropy loss and Adam optimization, with early stopping, checkpointing, regularization, and a fixed-seed 70/15/15 train–validation–test partition to enhance reproducibility and reduce leakage. Performance was evaluated on an independent test set using accuracy, precision, recall, F1-score, MCC, balanced accuracy, Cohen’s kappa, confusion matrix, multi-class ROC/AUC, and precision–recall analysis, alongside computational benchmarking (parameters, FLOPs, memory, and inference latency) and comparative experiments against contemporary CNN, lightweight, and transformer-based models. The model showed stable convergence over 30 epochs with a small training–validation gap, predominantly correct predictions with limited confusion among visually similar classes, consistently high precision–recall behavior under moderate class imbalance, and stable performance across repeated runs with low variability and a tight confidence interval. Grad-CAM heatmaps localized necrotic lesions, discoloration, and infected tissues while largely ignoring background, and failure cases were associated with early-stage symptoms, occlusion, shadows, and inter-class similarity. Overall, the framework provides a reproducible, interpretable, and efficient solution for cotton leaf disease classification with practical implications for trustworthy, low-latency, on-device decision support in precision agriculture.
Why it matches plant phenotyping methods綿葉の病徴を画像から分類する深層学習手法の開発が中心で、独立テスト、比較評価、計算性能評価、Grad-CAMによる病徴局在化を実施しているため、植物病害フェノタイピング手法に該当する。
abstractThis study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition
Reproduction assets foundThe paper's Data Availability statement explicitly names the SAR-CLD-2024 cotton leaf dataset used for all experiments as publicly available on Kaggle with a direct URL. No author analysis code or trained model checkpoint is deposited.Dataset · publicntribute to the development of fully automated, scalable, and real-time smart agriculture
systems.
Declaration
Funding
Datta Meghe Institute of Higher Education and Research Wardha, Maharashtra, India
Data Availability: The SAR-CLD-2024 cotton leaf dataset used in this study is publicly
available through the Kaggle platform at: https://www.kaggle.com/datasets/pantho12/sar-cld-2024-dataset-for-cotton
This dataset includes annotated images of various cotton leaf diseases collected under diverse
environmental conditions. All data utilized in this work are freely accessible, and the data
processing methodology has been described in detail to facilitate reproducibility.
Conflict of interest The aOpen asset ↗Kaggle · SAR-CLD-2024pdf-raw-page:32 lines:1-38Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Why it matches plant phenotyping methodsオリーブ遺伝資源の標準化フェノタイピングプロトコルを体系化し、複数機関で実装・検証して大規模データセットを生成した方法論中心の研究である。
abstractthe historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
Page 8 of 15
Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Crop phenotyping is crucial for advancing plant breeding, yet remains a significant challenge. Manual approaches are labor-intensive and do not scale to the analysis of large datasets, while computational methods like Vision-Language Models (VLMs) lack the adaptability for fine-scale spatial reasoning and diverse phenotyping scenarios. To bridge the gaps, we present iPheno, a fully open-source, domain-specialized multimodal VLM for fine-scale crop phenotyping. A key innovation of iPheno is its dual-aware architecture. First, a spatial-aware feature extractor samples mask regions into local K-Nearest Neighbors (KNN) graphs and enables fine-scale analysis of arbitrary-shaped regions; second, a task-aware Mixture-of-Experts (MoE) routing mechanism activates specialized modules for each phenotyping task. To train iPheno and achieve rigorous benchmarking, we constructed iPheno-120K, a large-scale high-precision dataset designed for multiple phenotyping tasks. Evaluations on iPheno-120k test set and other publicly available datasets showed that iPheno outperformed all fine-tuned baselines, by improving F1-score by 17.1% (LLaVA-1.6-13B) to 28.9% (MiniCPM-o-9B), while achieving the highest inference speed and memory efficiency. A web server (https://ipheno.ai4bread.com), a mobile application (www.ipheno.cn), and a stand-alone PC client (https://github.com/2997029323/iPheno-PC-Client) are available for iPheno.
Why it matches plant phenotyping methods植物表現型を対象とするVLMの開発、マルチタスク評価、専用データセット構築が研究の中心であり、明確な方法論的貢献がある。
abstractwe present iPheno, a fully open-source, domain-specialized multimodal VLM for fine-scale crop phenotyping.
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Technological advances have expanded the adoption of digital technologies in agriculture, helping to reduce labour effort, increase profitability, improve crop efficiency and productivity, enhance product quality, mitigate environmental impacts, and promote human health. This context also extends to soybean farming, a sector of major economic importance in Brazil. Most importantly, Brazil has the global leadership in soybean production with biological nitrogen fixation (BNF) replacing chemical fertilisers. The research and evaluation of BNF is limited by manual counting of nodules, a time-consuming procedure. This study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images. The dataset includes 1,701 images acquired under controlled conditions: 1,662 images of soybean roots with nodules and 39 images of isolated nodules without roots. A total of 49,210 nodule instances are manually annotated with bounding boxes. SoyNodules was designed to promote reuse and interoperability in alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable) and to support the development, training, and evaluation of computer vision and deep learning methods for precision agriculture.
Why it matches plant phenotyping methods大豆根粒を自動識別する画像データセットであり、手作業計数の代替となる植物器官形質の抽出・評価を支援する方法論的データセット。
abstractThis study presents SoyNodules, designed for the automatic identification of soybean nodules, consisting of a dataset of images.
Reproduction assets foundThe paper is a data descriptor for SoyNodules, an annotated dataset of 1,701 soybean root/nodule images with 49,210 bounding-box annotations, publicly deposited on Zenodo with a DOI. The same repository also hosts the authors' annotation-format conversion script (AnyLabeling to Pascal VOC/COCO), per the Code AvailabilDataset · publicThe SoyNodules dataset, released as version 1.0, is publicly available on Zenodo [28]
at https://doi.org/10.5281/zenodo.22081914.Open asset ↗Zenodo · 10.5281/zenodo.22081914pdf-page:9 lines:1-43Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background and aims Phytolith analysis is widely applied in palaeoecological and archaeological research, but its interpretive strength depends on the availability of robust modern reference collections. This study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms, including forbs, shrubs, trees, and C3 grasses. Methods Phytoliths were extracted from available plant parts, including leaves, stems, flowers, seeds, seed pods, cones, and roots, depending on sample availability. Morphotypes were identified following ICPN 2.0, with grass silica short cell phytoliths (GSSCPs) further classified by shape and size to examine subfamily-level patterns. Phytolith morphotype percentage data were analysed using Hellinger transformation, PerMANOVA, PCA, LDA, and hierarchical clustering to assess compositional differences among plant growth forms and grass subfamilies. Key results Phytolith production varied strongly among growth forms and plant parts. Grasses were abundant producers, whereas most forbs, shrubs, and trees were trace producers or non-producers. Leaves were the most consistent source of phytoliths, while seeds and seed pods were predominantly non-producers. Grass silica short-cell phytolith (GSSCP) morphotypes showed clear subfamily-level differentiation. Pooideae produced Rondel morphotypes. Danthonoideae produced Rondel as well as wide Bilobate types. Panicoideae and Oryzoideae exhibited a pronounced Bilobate signature, commonly associated with Polylobate and Cross forms. Non-grass taxa (woody, shrubs, and forbs) were dominated by Spheroids, Tracheary elements, Epidermal, and Polygonal sheets and other non-diagnostic forms. Phytolith assemblages differed significantly among plant families, with Poaceae uniquely producing GSSCPs, while non-grass families showed greater overlap in assemblage composition. Conclusion By expanding taxonomic and anatomical coverage, this study strengthens the capabilities of phytoliths in the reconstruction of grasslands and in general paleo vegetation in Australia, especially where other proxies such as pollen are limited.
Why it matches plant phenotyping methods植物部位の珪酸体を抽出・形態分類し、成長形態やイネ科亜科を識別する現代参照コレクションを構築しており、植物形質の取得・判別手法が研究の中心である。
abstractThis study expands the modern Australian phytolith reference collection by analysing 42 native plant species representing 24 families and 37 genera with emphasis on silicification patterns across major growth forms
Reproduction assets foundThe authors explicitly state that the R scripts used for data analysis and figure generation are publicly available on their GitHub repository, which directly reproduces this paper's phytolith statistical analyses (PCA, LDA, PerMANOVA, clustering, plots). Supplementary data files contain the paper's measurements but noCode · publicntification of all plant specimens collected for this
study.
A
14 FUNDING
M
15 Funding for this study was provided by ARC Discovery grant DP210100508 and a Ph.D.
D
16 fellowship (UQGSS) to MH.
TE
17 DATA AVAILABILITY
18 The R scripts used for data analysis and figure generation are publicly available on GitHub
EP
19 repository: https://github.com/Manoshi-sporo/Australian-Phytolith-Reference-Collection.
20 CONFLICTS OF INTEREST
CC
21 The authors declare no competing financial or commercial interests.
A
22 AUTHOR CONTRIBUTIONS
23 MH: writing original draft, conceptualization, software, investigation. AC: Supervision, Writing -
24 Review and editing, FM: Supervision, Writing-Review and editing.Open asset ↗Manoshi-sporo/Australian-Phytolith-Reference-Collectionpdf-layout-page:34 lines:1-87Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像の超解像法を比較・ベンチマークし、スペクトル一貫性や植生指数の信頼性を評価する研究であり、植物キャノピー形質の画像取得・抽出基盤が中心である。
abstractThis study benchmarked an SR evaluation framework for UAV-based five-band crop imagery
To address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR. First, a Lightweight Contour-Guided Aggregation Backbone (LCGA-Backbone) is constructed. In the PResNet residual blocks, an InceptionDWConv2d-based direction-aware depthwise separable spatial mixing strategy is introduced to capture local, horizontal, and vertical lesion texture patterns with low computational overhead. Meanwhile, a Contour-guided Efficient Global Aggregation Block (CEGA Block) is embedded after the outputs of the C3, C4, and C5 stages. Through contour-difference enhancement, channel shuffle, group-wise efficient global aggregation, and bottleneck channel mixing, the proposed block strengthens the representation of the boundaries of small lesions, weak textures, and contextual semantics. Second, a Multi-scale Evidence Interaction Fusion Neck (MEIF-Neck) is used to perform multi-scale evidence interaction and salient-region competition after cross-scale feature concatenation. A lightweight feature reconstruction process is further implemented using Spatial-Evidence RepNCSPELAN (SE-RepNCSPELAN), which is developed from a YOLO-style feature fusion structure. Finally, a Direction-Amplitude Decoupled Deformable Attention (DAD-DA) mechanism is introduced to decompose sampling offsets into direction rotation residuals and radius gains while incorporating an aspect-ratio-aware geometric compression-restoration strategy, thereby improving the geometric stability of sampling locations during the decoding stage. Experimental results on the constructed rice leaf disease dataset show that CED-RTDETR achieves AP, AP50, and AP75 values of 29.5%, 75.2%, and 17.6%, respectively, outperforming RT-DETRv2-R50 by 4.1, 4.9, and 3.8 percentage points. To further evaluate the model on an additional public benchmark dataset, experiments were also conducted on the public Rice Disease Dataset. On this dataset, CED-RTDETR achieves AP, AP50, and AP75 values of 34.1%, 77.1%, and 23.2%, respectively, improving upon RT-DETRv2-R50 by 4.3, 5.6, and 3.4 percentage points. These results indicate that the proposed method achieves consistent overall performance improvements on both the constructed dataset and the public benchmark dataset.
Why it matches plant phenotyping methodsイネ葉の病斑・病害を画像から検出する深層学習モデルを開発し、構築データセットと公開ベンチマークで性能検証しているため、植物病害状態のフェノタイピング手法が中心です。
abstractTo address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR.
Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.
Why it matches plant phenotyping methods3D LiDARによる植物構造計測と生理計測を統合したデータセットで、モデルの較正・ベンチマーク・評価を主目的としており、植物フェノタイピング手法と再利用可能なワークフローが中心である。
abstractthree-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds
Quantitative flowering phenotypes are needed to support breeding and harvest management in Hypericum perforatum L. (St. John’s wort), but manual flower assessment is slow and difficult to standardize under field conditions. UAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions. The images represent variable illumination, soil moisture, weed pressure, and developmental stages. The dataset provides 59,163 plant bounding boxes and 107,054 flower bounding boxes. As an application example, cascaded YOLOv8 plant and flower detectors achieved mAP@0.50:0.95 values of 0.977 and 0.950, respectively. UAV-Hyp supports scalable flower quantification and the development of time-series phenotyping methods for genotype comparison and quality-oriented medicinal-plant breeding.
Why it matches plant phenotyping methods植物の開花形質を定量化するUAV画像データセットを提供し、検出性能も評価しているため、フェノタイピング用データセット・解析手法が中心です。
abstractUAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions.
The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production.
Why it matches plant phenotyping methodsトウモロコシ幼苗の検出画像から株間という植物形態・配置形質を自動抽出するYOLOv8手法を開発し、データセット構築と性能検証まで行っており、フェノタイピング手法が中心である。
abstractAutomatic plant spacing calculation is realized based on detection outputs
Thermal imaging enables non-invasive assessment of canopy temperature, an essential indicator of plant stress, yet the lack of color cues and strong shadow interference make plant segmentation in thermal images difficult. Recent advances in foundation models have demonstrated improved performance and generalizability across applications, showing promise for domain-specific applications with limited annotated datasets such as plant segmentation in thermal images. This study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping. Building upon the SAM-CLIP framework, we design a unified pipeline spanning zero-shot inference, few-shot and low-shot fine-tuning, and active learning to maximize accuracy with minimal supervision. Evaluations on two thermal datasets, LadyBird Brassica and UGA Brassica, demonstrate robust performance after minimal adaptation across both datasets and superior performance compared with baselines, achieving mIoU D values of 97.54% on the LadyBird dataset and 76.94 % on the UGA dataset. We also release the resulting thermal segmentation annotations to support community benchmarking and reproducible research, highlighting the potential of LMMs to enable scalable, high-quality dataset construction for field phenotyping. The released datasets can be found at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89
Why it matches plant phenotyping methods熱画像から植物を分割する手法を開発・評価し、植物フェノタイピング用データセットとアノテーションも公開しているため、フェノタイピング手法が中心的である。
abstractThis study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping.
Reproduction assets foundThe authors publicly released the paper-specific thermal segmentation annotations (20,538 LadyBird masks and 37,790 UGA masks) via a Cornell Box link stated in the abstract, results, and data availability statement. No author analysis code or trained model checkpoints are explicitly released; the mmsegmentation GitHub/Dataset · publicwe generated and publicly released segmentation annotations for the complete LadyBird and UGA thermal image datasets using the best-performing SAM-CLIP model. Specifically, the final model obtained through the multi-round training process was used to generate 20,538 masks for the LadyBird dataset and 37,790 masks for the UGA dataset. Details of the generated annotations are provided in Supplementary Fig. S1 , and both annotated datasets are publicly available at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89Open asset ↗lines:220-232Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis. To address this, we present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making. It is compiled from 45 open-source datasets, including the widely used PlantVillage corpus, and comprises 765,186 high-quality question-answer (QA) pairs grounded over 150,841 images spanning 38 crop species and 89 disease conditions. Questions are organized into 3 levels of cognitive complexity and 9 distinct categories. Each was phrased following expert guidance and generated via an automated two-stage pipeline: template-based QA synthesis from image metadata, followed by multi-stage linguistic re-engineering. The dataset was iteratively reviewed by domain experts for scientific accuracy and relevance. We find that current frontier vision-language models, including recent open-source instruction-tuned multimodal LLMs, perform poorly on PlantExpertVQA. However, parameter-efficient fine-tuning of a compact 2B-parameter model on a small fraction of the dataset yields substantial improvements across all question categories, demonstrating its effectiveness for domain adaptation.
Why it matches plant phenotyping methods植物病害画像を対象とする大規模VQAデータセットの構築・ベンチマークが研究の中心であり、植物の病害状態を画像から評価する再利用可能なデータセットです。
abstractwe present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making.
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 code for the programmatic QA generation pipeline, the data-refinement and template-paraphrasing steps, the automated outlier-detection pipeline, and the parameter-efficient fine-tuning experiments reported in this work is publicly available at https://github.com/syed-nazmus-sakib/PlantExpertVQA.Open asset ↗syed-nazmus-sakib/PlantExpertVQAhtml-lines:578-597Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Background Accurate and efficient detection of citrus leaf diseases is important for orchard monitoring, early intervention, and intelligent disease management. However, practical application in natural orchard environments remains challenging because of complex backgrounds, large variation in symptom scale, strong interclass similarity, and limited edge computing resources. Methods In this study, a lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes. A self-built dataset was constructed from public images and field images, containing 6414 images across six classes. StarNet-S050 was introduced as the backbone, C3k2-Star was designed to enhance feature fusion across scales, and a lightweight shared detection head, MNS-Head, was constructed to reduce prediction redundancy. Results Compared with YOLOv11n, SKM-YOLOv11 increased Precision, Recall, and mAP @0.5 by 2.92, 1.47, and 0.95 percentage points, respectively. Meanwhile, FLOPs, parameter count, and model size were reduced by 31.7%, 32.7%, and 34.55%, respectively. Edge deployment on Jetson Orin NX Super achieved an inference speed of 72.45 frames per second. Conclusion The proposed model has strong potential for real-time citrus disease screening on resource-constrained devices and provides a feasible solution for edge-based intelligent disease management in natural orchards.
Why it matches plant phenotyping methods柑橘葉の病害状態を画像から認識する軽量物体検出モデルを開発し、データセット、精度比較、エッジ実装性能まで評価しており、植物表現型（病害状態）の取得・推定手法が中心である。
abstracta lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes.
Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods, this study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging. As part of our contributions, a synthetic–real coupled multimodal dataset was constructed by integrating Blender virtual agricultural scenes (Blender VAS) with real field images. Building upon the existing Depth Anything V2 foundation model, we developed a novel module called Transfer-based Agricultural Metric Depth Anything V2 (TAM-Depth V2) for absolute metric depth estimation through parameter-efficient fine-tuning, depth decoder reconstruction, and joint loss optimization. Furthermore, we designed a novel multi-source prompt-based segmentation framework, MSP-SAM2, to generate positive and negative prompts for zero-shot crop instance segmentation. Finally, a new inverse physical plant height estimation algorithm, RANSAC-Per, was introduced to estimate plant height by combining truncated percentile statistics with local RANSAC micro-plane fitting, thereby reducing the effects of depth noise and field microtopographic variation. The result showed that TAM-Depth V2 achieved stable absolute depth estimation, with an RMSE of 0.1162 m and an AbsRel of 4.25%. Compared to the original box-prompted SAM 2, MSP-SAM2 achieved a 4.4% improvement in mIoU, reaching 91.6% and a recall of 93.2%. On a 350-plant multi-crop test set, Depth4PH achieved R 2 = 0.948, RMSE = 12.23 cm, and MAE = 8.82 cm, and MAPE =10.15%, with crop-specific RMSEs ranging from 3.99 cm (cucumber) to 20.73 cm (maize), significantly outperforming the traditional Global-MinMax baseline (which had an RMSE of 22.62 cm). These results indicate that Depth4PH provides a promising foundational pathway for high-throughput crop phenotyping. With future optimization for edge deployment, it holds significant potential to support high-throughput monitoring in precision agriculture.
Why it matches plant phenotyping methods植物高の画像取得・深度推定・セグメンテーション・高さ抽出アルゴリズムを一体化した植物表現型計測フレームワークの開発と検証が中心であり、データセット構築と性能評価も含む。
abstractthis study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging.
Accurate monitoring of agronomic phenology is essential for yield estimation and food security assessment. However, currently available maize phenology datasets usually represent only a limited number of growth stages, restricting their application in process-based crop modeling and stage-specific agricultural management. Here, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution. By coupling MODIS spectral information with meteorological drivers in an energy-driven XGBoost framework, we retrieved eight key agronomic stages: Emergence, Three-leaf, Seven-leaf, Jointing, Flowering, Silking, Milking, and Maturity. Validation against observations from 91 agrometeorological stations during 2009-2024 demonstrates robust performance, with an overall RMSE of less than 5 days and R² values greater than 0.63 across all stages. Beyond overall accuracy, the dataset shows strong spatial consistency and temporal stability, preserves coherent regional phenological gradients, and captures interannual variations over the 24-year period. This long-term, multi-stage dataset provides a valuable benchmark for crop model calibration, climate change impact assessment, and the development of adaptive agricultural strategies in one of the world's major maize-producing regions.
Why it matches plant phenotyping methodsMODISスペクトル情報と気象データ、XGBoostを組み合わせてトウモロコシの8つの生育段階を推定し、観測データで検証した高解像度フェノロジーデータセットであり、植物形質の取得・抽出手法とベンチマークが中心です。
abstractHere, we present a high-resolution maize phenology dataset for Northeast China spanning 2001-2024 at 500-m spatial resolution and daily temporal resolution.
Early and accurate identification of plant diseases is essential for improving crop productivity and ensuring food security. Many existing deep learning-based plant disease classification methods rely solely on leaf images collected from a controlled environment, which limits their applicability in real-world agricultural conditions where symptoms may be visually unclear and influenced by environmental factors. To address these challenges, this study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification. The proposed architecture employs EfficientNet-B0 for visual feature extraction, BERT for semantic representation of symptom descriptions, and a lightweight multilayer perceptron for modeling environmental factors such as temperature, humidity, rainfall, and soil moisture. Features from all three modalities are fused into a unified representation to train the CNN model. The model is trained and tested upon the Context-Aware Multimodal Augmented PlantVillage dataset covering 38 plant diseases and healthy classes. Experimental results show that AgriFusionNet gives an overall accuracy of 98.94% on the dataset Context-Aware Multimodal Augmented PlantVillage, with competitive precision and recall and F1-score. The multimodal framework facilitates the co-learning of visual, semantic, and contextual environmental representations and the analyses of the confusion matrix and feature interactions give insights into cross-modal relationships. The proposed approach aims to explore context-aware multimodal representation learning for agricultural AI applications, with emphasis on integrating complementary visual, semantic, and contextual information.
Why it matches plant phenotyping methods葉画像を中心に、症状記述と環境情報を統合して植物病害状態を分類する手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractthis study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification.
Reproduction assets foundThe paper's data availability statement points to the Context-Aware Multimodal Augmented PlantVillage dataset (leaf images, symptom text, environmental data used for the phenotyping/classification analysis) deposited publicly on IEEE Dataport with a DOI matching an allowed URL.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Dataset. IEEE Dataport. https://dx.doi.org/10.21227/9jat-r836 [Accessed on August 2025].Open asset ↗IEEE Dataport · 10.21227/9jat-r836lines:1029-1047Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Chili is an important economic and nutritional crop with a relatively limited availability of different disease-resistant varieties. Leaf diseases, including those caused by fungi, bacteria, viruses, pests, or nutritional deficiencies, significantly compromise production and crop quality. Early detection of these diseases is key to reducing yield loss; however, traditional visual examinations are limited by time constraints, human subjectivity, and low detection sensitivity at early stages of infection. To overcome these challenges, this study proposes a deep learning–based framework for early multi-class detection of chili leaf diseases using convolutional neural networks (CNNs). A real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh. The dataset was preprocessed, augmented, and split into training and validation sets using an 80:20 ratio. Five pre-trained CNN architectures—DenseNet121, EfficientNetB3, MobileNetV2, ResNet50, and InceptionV3 were evaluated using a transfer learning strategy. Experimental results demonstrate that MobileNetV2 achieved the best performance, attaining an overall classification accuracy of 96%. The results indicate that the proposed system demonstrates strong generalization capability and effectively discriminates visually similar chili leaf diseases. This work can be considered a valuable application in precision agriculture, providing an efficient, automated, and practical approach for in situ early diagnosis of chili leaf diseases through smart farm management.
Why it matches plant phenotyping methods唐辛子葉の病害状態を画像から分類するCNN手法の開発・比較評価とデータセット構築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。
abstractA real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh.
This dataset contains images of Manalagi apple diseases from Indonesia. Data collection was conducted from August 2024 to June 2026. Data were collected in apple orchards. All images were captured under natural environmental conditions. A total of 1168 unique Manalagi apple specimens were successfully documented. The specimens consisted of both healthy and diseased fruit. This dataset comprises four classes: Healthy, Anthracnose, Black Pox, and Powdery Mildew. Each specimen was observed and photographed directly. The documentation process yielded approximately 5100 raw images. The images were captured using various smartphone cameras and DSLR cameras. Each device has different camera specifications. The image size depends on the device used. Images that passed quality inspection were selected for the next stage. Each fruit specimen is cropped from the selected raw image. Each image was then labeled according to its disease class. The image size was standardized to 1024 × 1024 pixels. All images were saved in JPEG format. The curation process yielded 482 images. Each image represents a distinct fruit specimen.
Why it matches plant phenotyping methodsリンゴ果実の健全・病害状態を画像で記録し、分類用データセットとして構築・キュレーションした研究であり、植物病害表現型の取得方法と再利用可能なデータセットが中心です。
titleImage dataset of manalagi apple fruits for multi-class disease classification using deep learning.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository of Manalagi apple fruit disease images (raw, curated, and augmented), directly usable for plant disease phenotyping/classification.Dataset · publicRepository name: Mendeley Data
Data identification number: DOI: 10.17632/9zgkwwv9j8.6
Direct URL to data: https://data.mendeley.com/datasets/9zgkwwv9j8/6Open asset ↗Mendeley Data · 10.17632/9zgkwwv9j8.6html-lines:97-124Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published24 Jul 2026International Journal of Computer Information Systems and Industrial Management ApplicationsCited by 0 · OpenAlex ↗
In India, to increase the wheat yield production and support sustainable agricultural practices, timely and correct identification of wheat crop diseases such as yellow rust, brown rust, etc., is very much essential. Further, the majority of deep learning algorithms show high efficiency in plant disease detection and recognition, but these studies rely on handcrafted datasets captured under controlled laboratory conditions and accordingly will not perform well in the real world domain. Furthermore, there is a lack of real field wheat crop disease datasets collected in India, particularly from Haryana state, being the largest producer of wheat crops. Therefore, to address the issue of lack of region specific crop diseases dataset, which are region-specific, the wheat crop disease data set is curated, consisting of 2672 images of healthy and diseased leaves of wheat crops collected from the real field of CCS Haryana Agricultural University, Hisar. The dataset includes typically three classes of leaves of wheat crop, i.e., Yellow Rust (910), Brown Rust (922) and Healthy leaves (840) collected using both a DSLR camera and different smartphone cameras under natural lighting and field conditions. All images were validated and labeled by taking the expertise of wheat pathologists to ensure reliability on the dataset. The study also provides a comprehensive, systematic workflow for transforming raw data into a high-quality benchmark dataset for training using image preprocessing and augmentation techniques. Besides this, a comparative benchmarking analysis is performed under identical experimental conditions using three widely adopted deep learning architectures, which includes ResNet50, MobileNetV2 and EfficientNet-B0. The experimental results show that ResNet50 and EfficientNet-B0 achieve similar performance i.e. approximately 91% classification accuracy on real-field data. However, MobileNetV2 offers a lightweight alternative suitable for mobile and edge deployment. Furthermore, Grad-CAM based explainability analysis was performed to validate model predictions and highlight disease specific regions in wheat leaves. Therefore, this study contributes a practical region specific WheatDisease-HRY dataset and baseline benchmarking framework for developing robust AI based wheat disease diagnosis tools for real world agricultural applications in India.
Why it matches plant phenotyping methodsコムギ葉の病徴を画像から判定するデータセットを構築し、前処理・拡張、深層学習ベンチマーク、説明可能性解析までを中心的に扱うため、植物病害状態の画像ベース表現型手法として採用。
abstractThe study also provides a comprehensive, systematic workflow for transforming raw data into a high-quality benchmark dataset for training using image preprocessing and augmentation techniques.
Precision agriculture increasingly relies on detailed structural information, such as canopy height and canopy volume, to enhance crop health monitoring and operational safety. However, existing methods based on costly LiDAR or RGB-D sensors are often impractical for large-scale deployment in dynamic and unstructured horticultural environments. Furthermore, conventional 2D segmentation and SLAM-based pipelines typically generate sparse, geometrically inconsistent semantic maps which are insufficient for actionable structural analysis in agricultural applications. To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion. At inference, the proposed model takes a single RGB image as input and predicts voxel-wise geometry and semantics, from which task-oriented structural maps, including canopy height, canopy volume, and obstacle-aware traversability layers, are derived. Specifically, we first introduce a Depth-Aware Decoder Module that explicitly recovers depth in the spatial domain and fuses 2D-to-3D features, thereby mitigating depth ambiguity and reducing reliance on accurate pose. Second, an NCS-Guided Geometry Encoder is designed to inject normalized depth into voxel positional embeddings, enabling self-attention to perform global relational modeling within a depth-aware geometric coordinate system. In addition, a Global Encoder is utilized to refine local structural details, while an occupancy head produces the final 3D semantic completion outputs. We construct a horticultural 3D semantic scene dataset using an RGB-D sensor, which serves as a benchmark for evaluating our method, while the deployed model remains RGB-only. Extensive quantitative and qualitative experiments are conducted on both the Semantic-KITTI dataset and our dataset. On our dataset, the method achieves 82.31% occupancy IoU, 84.26% mIoU, and 86.25% precision. Beyond voxel-level evaluation, manual field measurements further show canopy height MAE values of 0.019-0.026 m and canopy volume proxy relative errors of 8.4%-11.4%. These results demonstrate the effectiveness of our approach in real-world agricultural scenarios, providing actionable structural insights for crop monitoring and autonomous robotic operations.
Why it matches plant phenotyping methods単眼RGB画像から植物の樹冠高・樹冠体積などの構造形質を推定する3Dフェノタイピング手法を開発し、データセット構築と実測検証も行っているため、方法が研究の中心である。
abstractwe propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion.
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 · checked 5 Sept 2026
Accurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping. However, unmanned aerial vehicle (UAV)-based panicle detection remains challenging because flight-altitude variation produces large target-scale changes. Flooded paddy backgrounds, leaf occlusion, and illumination fluctuations further obscure small panicle targets. To address these challenges, we constructed a composite multi-altitude dataset covering UAV imagery acquired from 3 to 20 m under varying field conditions. We then propose Panicle-DETR, a lightweight detection and counting framework based on a frequency-aware Cross Stage Partial (CSP) backbone. Rather than treating the Fast Fourier Transform (FFT) as a filter by itself, the proposed FasterFD module uses frequency-domain representations with learnable frequency-response reweighting to enhance panicle-related texture cues and reduce redundant background responses. A Lossless Feature Encoder is designed to preserve fine spatial information for small targets across altitude-induced scale changes, while a composite metric loss based on Normalized Gaussian Wasserstein Distance (NWD) and Inner-IoU improves localization for adherent and overlapping panicle clusters. On the composite dataset, Panicle-DETR achieved a Precision of 90.97%, a Mean Absolute Error of 4.28, and an R2 of 0.957 for single-frame panicle counting. With 13.78 M parameters and 53.0 GFLOPs, the framework achieved 16.9 FPS with 1.96 GB peak GPU memory in a battery-powered notebook benchmark, supporting its potential for resource-constrained field-side UAV image analysis.
Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質推定を中心に、マルチ高度データセットと専用解析フレームワークを開発・評価しているため。
abstractAccurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping.
Grapevine yellows diseases, including Flavescence Dorée (FD) and Bois Noir (BN), severely affect vineyards, making reliable detection methods essential. Detecting symptoms, particularly in Chardonnay variety, remains challenging due to year-to-year vine variability and the presence of symptoms that can be misleading. We present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024). The dataset includes healthy leaves, grapevine yellows - overwhelmingly BN, and three other diseases (Esca, Discoloration, and Leafroll) with visually similar symptoms particularly for Leafroll, providing a diverse benchmark for disease recognition. Ground-truth labels were assigned based on field inspections conducted by Comité Champagne expert viticulturists who selected the vines before acquisitions and analyzed the images after acquisition. Images were acquired under laboratory conditions using a DJI Phantom 4 Multispectral camera from leaves collected in the Plumecoq Comité Champagne experimental vineyards. Leaves were collected on designated vines and placed on polystyrene boards on a 2x2 grid. One RGB image and five multispectral images corresponding to the Blue (B - 450 nm ± 16 nm), Green (G - 560 nm ± 16 nm), Red (R - 650 nm ± 16 nm), Red-Edge (RE - 730 nm ± 16 nm), and Near-Infrared (NIR - 840 nm ± 26 nm) bands were captured using the same acquisition system (camera-to-board distance of approximately 90 cm) over the four years.. For the vast majority, ambient light was used; for some acquisitions, indirect halogen spotlights were used. The 512x512 leaf images were manually cropped from the acquired original images with the same offsets between the bands; offsets were already present due to the parallax phenomenon. The dataset is organized into six folders (B, G, R, RE, NIR, and RGB). Each leaf is represented by one RGB image and five corresponding multispectral band images, enabling multimodal analysis and machine learning for grapevine disease detection. Each image filename encodes acquisition and annotation information as follows: Characters 1–2: acquisition year (21 = 2021, 22 = 2022, 23 = 2023, 24 = 2024). Character 3: image type/band (0 = RGB, 1 = B, 2 = G, 3 = R, 4 = RE, 5 = NIR). Character 4: class (J = Grapevine Yellows, T = Healthy, S = Esca, E = Leafroll, D = Discoloration). Character 5: illumination condition (0 = ambient light, G = additional left halogen spotlight, D = additional right halogen spotlight, 2 = additional both left and right halogen spotlightsl). Characters 6–9: image identifier, numbered sequentially from 0000. Character 10: relabelling status. After image acquisition, all leaves were independently reviewed by expert viticulturists on the recorded RGB images. A value of 1 indicates that the expert confirmed the original field label, 0 indicates that the original label was considered incorrect based on the information visible in the RGB image, and D indicates that the sample was reclassified as Discoloration. This dataset complements the "Multi-annual spectral data of Chardonnay grapevine leaves" dataset published on Recherche Data Gouv. While the previous dataset contains spectral measurements, the present dataset provides multispectral images acquired from the same grapevine leaves, enabling multimodal analyses that combine spectral signatures with image-based information. Note, however, that there is no bijection between the respective files.
Why it matches plant phenotyping methodsブドウ葉の病徴をマルチスペクトル画像で取得し、専門家ラベル付きの疾病認識用データセット／ベンチマークとして提供しており、植物状態の画像ベース表現型計測が中心である。
abstractWe present a dataset of multispectral images of grapevine leaves collected over four years (2021–2024).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Robust validation of both research data and its accompanying metadata is essential for ensuring adherence to FAIR principles. Current approaches often handle these aspects separately, hindering a holistic quality assessment. Building upon previous BioHackathon work establishing ARCs (Annotated Research Context) as RO-Crates (ARC RO-Crate), we aim to develop and demonstrate an integrated validation strategy for FAIR digital objects. It distinguishes between validating the metadata descriptor and the payload data files.For the metadata descriptor, validation will ensure structural and semantic compliance to the base RO-Crate specification and the ARC-ISA family of RO-Crate profiles, using and extending the RO-Crate validator tool.For the payload data files, validation targets the actual content, since data files often require domain-specific structural and value constraints, which requires explicit schema definitions. For this, we will integrate Frictionless for checking data content against community standards (e.g. MIAPPE, as demonstrated in the HORIZON project AGENT). Crucially, this project will also explore mechanisms for specifying expected data structures’ requirements within the ARC RO-Crate itself. This aims to provide a more self-contained description of data, investigating how such internal requirements can be linked to data validation frameworks, complementing the crate’s metadata validation.The overall goal is to provide a powerful, holistic validation mechanism for ARC RO-Crates, enhancing their reliability, trustworthiness, and FAIRness. A MIAPPE-compliant plant phenomics dataset will serve as a use case. This integrated validation approach aims to streamline quality control for researchers and will be packaged as a deployable microservice, offering broad applicability across diverse research workflows.
Why it matches plant phenotyping methodsARC RO-Crateのデータ・メタデータ検証サービスを開発する研究で、MIAPPE準拠の植物フェノミクスデータセットを具体的ユースケースとして扱う。植物表現型データの再利用可能な検証ツール／ワークフローが中心である。
abstractA MIAPPE-compliant plant phenomics dataset will serve as a use case.
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:664-703Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Plant diseases affecting leaves and fruits cause substantial yield and economic losses worldwide, particularly in horticultural crops cultivated under diverse agro-climatic conditions. Early and accurate disease diagnosis is essential for effective crop management; however, manual inspection is time-consuming, subjective, and often infeasible at large scale. In this work, we present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition. The dataset comprises labeled RGB images of healthy and diseased leaves and fruits from three economically important crops—tomato, chilli, and papaya—captured under real-field and semi-controlled environments, reflecting significant variability in illumination, background complexity, and disease severity. To demonstrate the applicability of the dataset, several commonly used convolutional neural network (CNN) architectures, including VGG, ResNet, DenseNet, MobileNet, and EfficientNet models, were trained and evaluated on the TCP dataset using transfer learning. Experimental results show that deep CNN models can effectively learn discriminative visual features corresponding to disease-specific patterns such as leaf spots, lesions, discoloration, curling, and fruit surface abnormalities. Lightweight models such as MobileNet achieve competitive performance with reduced computational cost, while deeper architectures provide improved accuracy at the expense of higher complexity. The results highlight the importance of dataset diversity for robust model generalization across multiple crops and plant organs. The TCP dataset provides a challenging benchmark for single-crop and multi-crop disease classification and supports the development of advanced deep learning, attention-based, and explainable AI models for precision agriculture. By enabling reproducible research and realistic performance evaluation, this dataset contributes toward scalable and practical AI-driven plant disease diagnosis systems aimed at reducing yield losses and supporting sustainable agriculture.
Why it matches plant phenotyping methods植物の葉・果実の病徴を画像から評価する大規模データセットとベンチマークを中心に扱っており、植物病害状態の画像ベース表現型解析に該当する。
abstractwe present the Tomato–Chilli–Papaya (TCP) Fruit and Leaf Disease Dataset, a comprehensive multi-crop image dataset designed to support deep learning-based plant disease recognition.
Reproduction assets foundThe paper introduces the TCP (Tomato-Chilli-Papaya) fruit and leaf disease image dataset and reports CNN experiments on it. The dataset is publicly deposited on Mendeley Data, and the authors state that analysis code is available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:27 lines:1-51Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:27 lines:1-51Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Plant varieties are essential for the survival of human beings and animals, as they act as an alternative source of food, fiber, fodder, and other raw materials for domestic needs and industries in any society. Early identification of plant leaf diseases is very important for keeping the health of crops intact. Crops being infected can affect the overall yield of crops, which may be detrimental to the earnings of farmers. With the emergence of artificial intelligence technology, it has become possible to deploy systems for quicker identification of illnesses. This work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves. For this purpose, the dataset has been created after retrieving data from the PlantVillage dataset. The clinical reliability of different deep learning models of various representational capacities has been tested while using ImageNet pre-trained parameters. The experimental results show that MobileNetV2 achieves the highest accuracy of 96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%). The test accuracy and class-wise F1-scores for the CNN are observed to be substantially very high. The generalization ability and result of DCNN and MCNN are moderate and poor, respectively, as observed. Additionally, the proposed CNN only uses the disease-affected areas on the leaf, thus making the result more interpretable
Why it matches plant phenotyping methods葉の画像から病徴を推定する深層学習手法を開発・比較し、モデル性能と解釈性を評価しているため、植物表現型取得が中心である。
abstractThis work has been carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves.
Rice seedling salt-tolerance evaluation commonly relies on visual scoring or destructive assays, which are subjective, labor-intensive, and difficult to standardize for population-level analysis. This study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings. The framework integrates controlled hydroponic cultivation, RGB imaging, RicePhenoSeg-assisted annotation and trait extraction, ELMERF-based semantic segmentation, and image-derived quantification of salt-induced shoot injury. Using this framework, we constructed the Rice Seedling-Salt RGB Dataset (RSSD), which contains green shoot tissues, yellow shoot tissues, roots, and background from hydroponically grown rice seedlings. Based on RSSD, ELMERF achieved a mean Intersection over Union of 51.4% and a mean Accuracy of 89.5%, outperforming nine representative segmentation models. We further defined shoot yellowing rate (SYR) as an image-derived quantitative trait describing visible salt-induced shoot injury. The framework was applied to 261 re-sequenced rice accessions for population-level phenotyping and genome-wide association analysis. Compared with standard evaluation score and seedling death rate, SYR showed a more continuous phenotypic distribution and detected 36 significant SNPs, including a major signal near the Saltol/OsHKT1; 5 region. Notably, 34 SYR-associated SNPs were not detected by conventional visual scores. Overall, this study provides a targeted hydroponic RGB phenotyping framework for standardized rice seedling salt-stress evaluation and genetic analysis.
Why it matches plant phenotyping methods深層学習によるRGB画像セグメンテーション、形質抽出、データセット構築、性能比較を中核とし、画像由来の塩ストレス傷害形質を定量化する植物フェノタイピング手法である。
abstractThis study developed a new deep-learning-assisted hydroponic RGB phenotyping framework for standardized salt-stress evaluation and genetic mapping in rice seedlings.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits datasets, source code, and supporting data in a public GitHub repository (ELMERF), which covers the RSSD RGB image dataset, segmentation code, and phenotyping/GWAS analysis assets. RiceVarMap is a cited external SNP database, not a paper-specific asset.Code · publicThe datasets, source code, and other supporting data are openly available on the ELMERF repository (https://github.com/PhenoCodexh/ELMERF).Open asset ↗PhenoCodexh/ELMERFhtml-lines:446-478Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semantic gap, we propose a framework for constructing a multimodal granular Knowledge Graph (KG) to monitor genotype-phenotype interactions across time and experiments. In this work, we focus on wheat Triticum aestivum as a representative target crop to validate our methodology across complex canopy environments. Our pipeline first distills noisy field notes to extract entities and relations, dynamically constructing the KG by converting unique instances into hierarchical class entities via RDF-typing. These graph nodes are then aligned with standardized ontologies (PO, RO, WTO) using PlantDeBERTa. To visually ground the constructed graph, a Vision-Language Model paired with a wheat-segmentation ViT generates attention-based softmaps, linking specific KG entities directly to image pixels. We introduce a central observation node Plant_Obs_Id to connect these multimodal subgraphs temporally. Evaluated on 500 curated WisWheat samples using Pointing Game accuracy, Visual Word Sense Disambiguation (VWSD), and rank-based metrics, our neuro-symbolic approach successfully maps complex field observations to a structured graph. This enables automated field note auditing, temporal stress monitoring, and precise spatial trait localization for wheat breeders.
Why it matches plant phenotyping methods植物のマルチモーダル表現型データを知識グラフと画像に統合し、画像画素への形質局在化を行う中核的な計算フレームワークを提案・評価しているため。
abstractwe propose a framework for constructing a multimodal granular Knowledge Graph (KG) to monitor genotype-phenotype interactions across time and experiments
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion. In this work, we show that the conventional 3D plant phenotyping pipeline could be streamlined and significantly accelerated with 3D Foundation Models (3DFMs), and particularly, present one of the first cross-crop 3D phenotyping frameworks powered by 3DFMs. The framework replaces COLMAP-style sparse initialization with 3DFM-based feed-forward geometric recovery, combines geometry-constrained 3D Gaussian Splatting for dense reconstruction, enables few-view reconstruction through iterative view synthesis and refinement, and converts reconstructed geometry into measurable organs through 2D-to-3D semantic transfer, metric scale recovery, and organ instance separation. We further construct a cross-crop dataset with smartphone-based image acquisition, diverse plant morphologies, and manual annotations for segmentation and phenotypic evaluation. Experiments across 26 plant sequences show that 3D Foundation Models reduce the average reconstruction time from 6.52 minutes to 1.58 seconds while maintaining high reconstruction quality and phenotyping accuracy. These results suggest a fresh technical route for high-throughput 3D plant phenotyping, from low-cost image acquisition to fast reconstruction, perception, scale recovery, and phenotypic measurement.
Why it matches plant phenotyping methods3D画像再構成から器官分離・形質測定までを統合した高速植物フェノタイピング手法を開発し、複数作物・データセットで性能評価しているため。
abstractpresent one of the first cross-crop 3D phenotyping frameworks powered by 3DFMs
Brinjal (eggplant) is a critical crop in South Asia, especially in Bangladesh, but its production is drastically affected by numerous diseases that inhibit yield and quality. Manual diagnosis of disease is time-consuming, subjective, and prone to errors, necessitating automated, scalable technology. To address these issues, this paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification using Simple Vision Transformer (ViT) with Patch Dropout and Contrastive Captioner (CoCa) methods. One new dataset of 1,823 field-harvested brinjal images encompassing five disease classes including Phomopsis Blight, Fruit and Shoot Borer, Fruit Cracking, Wet Rot, and Healthy samples were prepared through real-world agricultural data collection from Bangladesh. The approach includes extensive preprocessing, class balancing (under-sampling/oversampling), and resilient augmentation methods. The PD-ViCo model significantly improves classification performance under data imbalance with patch dropout regularization and CoCa-style aggregation, resulting in better generalization and robustness. On a range of imbalanced, under-sampled, and oversampled datasets, PD-ViCo achieved a classification accuracy of 99.12% and F1-score of 97.76%, outperforming both ViT and Swin Transformer across all key evaluation metrics. Explainability was also applied using Grad-CAM and Grad-CAM + + , generating visual explanations of model decisions and maintaining conformity to disease-affected regions in the images. These visualizations ensure the credibility of the model and its usability for real agricultural conditions. This study demonstrates that PD-ViCo is a highly accurate, interpretable, and lightweight model for multi-class brinjal disease diagnosis. Not only does it advance state-of-the-art in agricultural AI, but it also provides a valuable dataset and an understandable decision-making protocol that can be applied directly by farmers, agronomists, and agricultural technologists.
Why it matches plant phenotyping methods植物画像から病害状態を分類するモデル、データセット、説明可能性評価を中心に開発・検証しており、植物フェノタイピング手法として適格。
abstractthis paper proposes PD-ViCo, a lightweight, efficient transformer-based model for brinjal fruit disease classification
Reproduction assets foundThe paper's own field-harvested brinjal disease image dataset (1,823 images, five classes) is publicly deposited on Mendeley Data, with an explicit availability statement and URL matching an allowed entry. No code or model checkpoint deposit is stated.Dataset · publicThe data utilized in this study is publicly accessible on Mendeley Data Repository at the following link: [ https://data.mendeley.com/datasets/ngc58fsxgd/1 ].Open asset ↗Mendeley Data · ngc58fsxgd/1lines:226-251Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Plant diseases remain a major challenge to global food production, and timely, accurate, and scalable detection of plant stress is critical to reducing these losses. Recent advances in digital imaging and artificial intelligence offer unprecedented opportunities for precision crop disease detection and management. Yet, existing plant disease datasets remain often fragmented across crop and disease systems, and are largely dominated by controlled-environment imagery. The lack of standardized, interoperable, and representative datasets limits reproducibility, transferability, and scalability of AI systems, thereby constraining their deployment in operational agricultural applications. Here we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource that includes LeafNet 2.0, a large-scale multimodal digital image dataset comprising 255,855 image–text pairs across 37 crop species, 197 crop–disease classes, and 9 geographic regions spanning tropical, subtropical, and temperate agricultural systems. Unlike conventional datasets, LeafNet 2.0 integrates biologically grounded symptom descriptions with image-level annotations of early and late disease stages, enabling symptom-aware analysis of disease progression under realistic field conditions. We further introduce LeafBench 2.0 as part of LeafMD, a visual-question answering benchmark covering nine fine-grained plant pathology tasks, including pathogen classification, lesion characterization, symptom interpretation, and disease severity assessment. Evaluation across 16 vision–language models revealed substantial performance gaps between coarse disease recognition and fine-grained pathological reasoning, while agriculture-adapted models consistently outperformed several larger general-domain architectures on symptom-oriented tasks. Together, LeafNet 2.0 and LeafBench 2.0 establish LeafMD as a multimodal resource for developing disease-aware agricultural foundation models and studying fine-grained pathological reasoning in real-world environments.
Why it matches plant phenotyping methods植物病害の画像・症状記述データセットとベンチマークを構築し、病徴解釈・病変特徴・病害重症度評価を対象にモデル性能を評価しており、植物状態の取得・評価手法が中心である。
abstractHere we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource
Rice leaf diseases pose a major challenge to crop health and agricultural productivity, particularly when timely and accurate diagnosis is required under natural field conditions. The development of automated disease recognition systems depends heavily on the availability of large, well-annotated image datasets. However, many existing rice leaf disease datasets are limited in terms of environmental variability, disease representation, and real-field imaging conditions. To address this gap, this paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors from the experimental fields of the Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh, between July 2023 and July 2024. The dataset contains 4152 images belonging to five classes: Bacterial Leaf Blight, Bacterial Leaf Streak, Sheath Blight, Leaf Blast, and Healthy Leaf. The images were acquired from two rice varieties, BR11 and BRRI dhan34, under natural field conditions across varying illumination environments in order to reflect practical disease recognition scenarios. All images were manually annotated by trained annotators under expert supervision. The dataset is systematically organized and publicly released to support reproducible research in rice disease classification. In addition to dataset presentation, benchmark experiments using Xception, NASNetMobile, and InceptionV3 are provided to demonstrate its applicability for deep learning-based disease recognition. BanglaRiceLeaf is expected to serve as a useful resource for plant disease analysis, comparative model evaluation, and future research in precision agriculture and agricultural computer vision.
Why it matches plant phenotyping methodsイネ葉の病徴・健全状態を画像で分類する公開ベンチマークデータセットであり、データ収集・注釈・ベンチマーク評価が中心です。
abstractthis paper presents BanglaRiceLeaf, an original rice leaf image dataset collected and curated by the authors
Reproduction assets foundThe paper's core asset is the BanglaRiceLeaf rice leaf disease image dataset (4152 field images, five classes), publicly released on Harvard Dataverse with DOI 10.7910/DVN/XAOBYW. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicData Identification Number: https://doi.org/10.7910/DVN/XAOBYW
Direct URL to Data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XAOBYW
Access Instructions: This dataset is publicly available on the Harvard Dataverse repository and can be accessed for academic, research, and instructional purposes.Open asset ↗Harvard Dataverse · doi:10.7910/DVN/XAOBYWhtml-lines:100-131Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published1 Jul 20262026 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)Cited by 0 · OpenAlex ↗
Plant diseases pose a serious threat to global food production, leading to yield losses, increased production costs, and environmental damage. Plant pests and diseases have widespread negative impacts on economic, ecological, environmental, and human health, and calculating the true cost of these damages is extremely difficult. Traditional diagnostic methods also present significant limitations in terms of time and cost. This study adopts an image processing-based approach to this problem. Using the Crop Disease Detection dataset, plant disease detection was performed using CNN, DNN, K-NN, SVM, XGBoost, and Random Forest algorithms, employing both deep learning and machine learning methods. The study demonstrates that CNN architectures designed from scratch, without resorting to pre-trained models such as ResNet and MobileNet, can also exhibit high performance. The highest accuracy rate was obtained with the CNN model at 94.08%. In machine learning models, grid search was used for hyperparameter optimization, and the best results were achieved through this method.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する画像処理・機械学習手法を比較評価しており、病害フェノタイピング手法が研究の中心である。
abstractThis study adopts an image processing-based approach to this problem.
Abstract Tomato cultivation represents a critical component of nutrition, economic development, and public health, yet it is increasingly compromised by foliar diseases that diminish yield and intensify dependency on hazardous agrochemicals. Although deep learning models have demonstrated strong capabilities for automated disease recognition, existing benchmark datasets exhibit limited real-world utility, primarily due to the absence of in-field imagery and precise annotations of diseased regions. A novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting. Furthermore, this study introduces SLIF-Tomato , the S ri L ankan I n- F ield Tomato leaf disease dataset, which is the first complete in-field dataset comprising class labels and bounding box annotations collected under diverse real-world conditions. The proposed approach achieved 99.66% and 99.91% accuracy rates on two curated versions of the SLIF-Tomato dataset. Subsequently, the YOLOv12-large model is employed to detect diseased regions, which obtained an average precision score of 88.5%. These contributions advance the development of accurate, efficient and field-adaptable diagnostic systems for tomato leaf disease management in precision agriculture.
Why it matches plant phenotyping methodsトマト葉の病害領域を画像から検出・認識する手法を開発し、実圃場データセットも構築・評価しており、植物病害状態の表現型取得が中心的です。
abstractA novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting.
Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.
Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの性能を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
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 · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background Seed maturation is a critical developmental phase during which seeds acquire traits essential for nutritional value, desiccation tolerance, and long-term survival. Abscisic acid (ABA) signalling is a key regulator of this process, coordinating gene expression programs underlying the acquisition of seed quality traits. However, the molecular regulation of many of these traits remains poorly understood. To address this, we performed a comprehensive analysis of seed maturation in Arabidopsis thaliana, combining physiological and transcriptomic approaches across wild-type plants and mutants affected in ABA biosynthesis, signalling, and catabolism. Results We generated a high-resolution transcriptome dataset covering seed development from 12 days after pollination to the dry seed stage in wild-type and ten mutant lines. In parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance. Integration of these datasets using weighted gene co-expression network analysis (WGCNA) identified gene modules associated with specific trait acquisition patterns. This approach enabled the identification of coordinated transcriptional programs linked to distinct seed quality traits, extending beyond individual gene-level analyses. Notably, modules associated with desiccation tolerance and longevity were enriched for genes involved in stress responses and ABA-regulated pathways, highlighting the complex and multifactorial regulation of these traits. Conclusions This study provides a comprehensive physiological and transcriptomic framework for understanding seed maturation and the acquisition of key seed quality traits in Arabidopsis thaliana. By linking gene expression dynamics to trait development, our work offers new insights into the regulatory networks underlying seed resilience and storage capacity. The dataset is made accessible through SeedMatExplorer (https://www.bioinformatics.nl/SeedMatExplorer), an open-access web platform that enables interactive exploration and supports hypothesis generation. Together, this resource represents a valuable tool for advancing research on seed biology and improving seed performance in agricultural contexts.
Why it matches plant phenotyping methods種子成熟に伴う複数の植物形質を体系的に取得し、トランスクリプトームと統合した再利用可能なデータセットおよび探索プラットフォームを提供しており、単なる生物学的実験の routine 測定を超える。
abstractIn parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance.
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research.
Why it matches plant phenotyping methodsヒマワリ頭花の発達・不稔種子数という植物形質を、画像取得とYOLOによる推定パイプラインで定量化する手法を開発・評価しており、方法が研究の中心である。
abstractwe developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads.
Reproduction assets foundThe authors state the source code is available on GitHub and the CVAT-annotated dataset is available via a public share link; the GitHub repository URL is explicitly provided and matches an allowed URL. The dataset link itself is not given, so only the code/checkpoint repository qualifies as an actionable public asset.Code · publicThe developed system is available as a Telegram bot [ 19 ] and the source code is available on GitHub [ 20 ]. The CVAT annotated dataset is available via a public share link.Open asset ↗lines:84-103Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The robust Dutch rose, also known as the Rosa hybrida is distinguished by its vibrant colors, superior product quality, and extended vase life. These rose varieties, originating from Netherlands, have proven highly successful in Indian agricultural conditions and the international export industry. The dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals. The primary purpose of this dataset is to support machine learning activities in agriculture and specifically for tasks such as automatic petal health evaluation and rose variety categorization. Although the rose flower is scientifically rich and has a wide range of industrial uses, it has not been given much attention in machine learning, especially when compared to other plant-based datasets. This study adds to the accuracy of quality assessment through the use of modern computer vision and machine learning methods, thus helping the agriculture sector, rose-based edible product making, and flavor development industries.
Why it matches plant phenotyping methodsバラ花弁画像データセットの構築と、花弁の健康状態・色分類による植物状態評価が研究の中心であり、画像ベースの表現型計測データセットに該当する。
abstractThe dataset consists of a total of 1,995 high resolution petal image collected during this research, encompassing petal color categories, such as red, yellow, white, pink, purple, orange, bi-color, and multi-color, as well as health statuses including fresh, dry, and diseased petals.
Reproduction assets foundThe paper's own rose petal image dataset is publicly deposited on Mendeley Data, and the authors' validation/metadata scripts are publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicThe RoseVisuals dataset is publicly available on Mendeley Data at Direct URL to data: https://data.mendeley.com/datasets/f44jwtbfjg/5. Data Identification Number: 10.17632/f44jwtbfjg.5. Repository Name: RoseVisuals.Open asset ↗Mendeley Data · 10.17632/f44jwtbfjg.5html-lines:246-284Code · publicThe RoseVisuals codebase, comprising all validation scripts, is publicly available on GitHub Repository at https://github.com/Arya-S14/RoseVisuals-Validation-Doc.Open asset ↗GitHubhtml-lines:246-284Plant 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
Abstract Functional traits are critical for understanding species interactions within ecosystems and their responses to environmental changes. Yet, traits related to fruits and seeds are still underrepresented, especially in tropical ecosystems where mutualisms between fruits and fruit-eating animals are prominent. Here, we introduce AnnonFruitTraits 1.0, a comprehensive dataset of 34,772 records encompassing 26 frugivory-related traits for 2,266 species (ca. 90% of total species) of the pantropical plant family Annonaceae (Magnoliales). This dataset includes trait definitions and their significance for frugivory, as well as a description of our workflow from data acquisition to visualization. To facilitate data accessibility and reproducibility, we provide an accompanying R package (AnnonTraits) that enables users to explore, summarise, and visualise the dataset. By assessing species and trait coverage across genera and regions, we identified major data gaps in the Asia-Pacific region and in several Annonaceae genera (e.g., Artabotrys , Miliusa , Orophea, Polyalthia , and Uvaria ). Our findings show the importance of expanding trait data collection and taxonomic efforts, particularly in underrepresented regions and lineages. AnnonFruitTraits is a valuable resource for advancing research on seed dispersal, plant–animal interactions, and tropical forest conservation.
Why it matches plant phenotyping methods植物の果実・種子形質を大規模に整理した再利用可能なデータセットであり、データ取得から可視化までのワークフローと探索・要約・可視化用Rパッケージを提供しているため、形質データ資源として中心的です。
abstractHere, we introduce AnnonFruitTraits 1.0, a comprehensive dataset of 34,772 records encompassing 26 frugivory-related traits for 2,266 species
Sapodilla (Manilkara zapota), or chickoo, is a key tropical fruit, very popular in India, Mexico, and Thailand, as it is nutritionally and economically valuable. Nonetheless, the production of sapodillas is often affected by several diseases, which reduce fruit quality and quantity. The dataset used in this paper is a sapodilla fruit image dataset, comprising 1,518 images, gathered in the field under the practicing conditions on 18 February 2025, 22 February 2025, in Rahu village, Pune district, Maharashtra, India, with the use of smartphone cameras. The data is sorted into four categories, namely: Anthracnose, Bacterial rot, Healthy, and Sap bleeding. The photographs were taken in different backgrounds and in different lighting conditions to represent real-life cultivation conditions. The data is expected to be useful in machine learning-based plant disease detection, classification, and analysis, and spur the creation of intelligent and sustainable agricultural systems.
Why it matches plant phenotyping methodsサポディラ果実の病害・健全状態を画像で記録したデータセット自体が中心で、植物病害の画像ベース表現型解析に利用できる。
titleA curated image dataset for sapodilla fruit (Manilkara zapota) disease and fruit quality analysis.
Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.
Why it matches plant phenotyping methodsメロンの気孔形質を画像から自動抽出するデータセット、改良Mask R-CNN、インスタンスセグメンテーション、楕円フィッティングを開発・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 established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images.
Reproduction assets foundThe paper's authors explicitly state that the source code for model training and inference (including the key modules: DCMHA, eSE-enhanced FPN, Mask R-CNN/ViT pipeline) is publicly available at a GitHub repository, which matches an allowed URL. The melon stomatal image dataset (8,154 images) is described in detail but,Code · publicCode Availability
The source code for model training and inference, including the implementation of the
key modules, is publicly available at:
https://github.com/huangyao110/qk_maskrcnn_trsv2.gitOpen asset ↗huangyao110/qk_maskrcnn_trsv2pdf-page:22 lines:1-322Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
The Vines-DB dataset contains 1,218 original high-resolution RGB images of seven ornamental vine species collected under field conditions at the Utah Agricultural Experiment Station's Greenville Research Farm in Logan, Utah, USA. The dataset was generated from 168 individual vine plants that were transplanted in 2022 and photographed repeatedly across multiple months during the 2023 and 2024 growing seasons (July-October). Images were captured with an iPhone 16 Pro equipped with a 48 MP camera between 10:00 AM and 12:00 PM under daylight. Vines were grown on 1.2m x 2.4m trellises and photographed from a distance of 1m against black or white Styrofoam backdrops to improve contrast and reduce background noise. The dataset includes Akebia quinata, Campsis radicans, Hydrangea anomala petiolaris, Lonicera x heckrottii, Campsis x tagliabuana 'Madame Galen', Parthenocissus quinquefolia, and Wisteria floribunda. All original images were manually annotated in Roboflow by trained annotators to produce polygon-based instance segmentation masks for eight classes, including seven species and background. After preprocessing and data augmentation, the working dataset was expanded to 2,307 images for model development and evaluation. The augmented dataset was divided into 2,019 training images, 192 validation images, and 96 test images using stratified sampling to maintain balanced representation. Vines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology. The dataset enables applications such as automated canopy cover estimation, species identification, and scalable field phenotyping. In addition, repeated monthly imaging of the plants captures temporal variation in canopy development and plant appearance, increasing the dataset's utility for segmentation benchmarking under realistic field conditions.
Why it matches plant phenotyping methods植物のRGB画像とポリゴン注釈から成るデータセットを構築し、セグメンテーション評価およびキャノピー被覆推定などの植物フェノタイピングを支援することが中心であるため。
abstractVines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology.
Reproduction assets foundThe paper's core asset is the Vines-DB RGB image dataset with instance segmentation annotations, publicly deposited on OSF with an explicit DOI and URL matching an allowed URL.Dataset · publicData accessibility Repository name: Vines-DB
Data identification number: 10.17605/OSF.IO/YJHCK
Direct URL to data: https://osf.io/yjhck/overviewOpen asset ↗OSF · 10.17605/OSF.IO/YJHCKpdf-page:2 lines:1-49Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
In-season estimation of wheat grain yield potential is critical for crop management and advancing breeding efforts. Spike and spikelet counts serve as key indicators directly linked to yield potential, yet their assessment still relies on manual counting which is both labor-intensive and error-prone. High-resolution digital (RGB) imagery combined with deep learning-based object detection methods has substantially advanced automatic wheat spike detection and counting. However, precise spikelet-level phenotyping remains largely underexplored. This study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations. For spike detection, the pre-trained YOLOv11 achieved superior accuracy (mAP@0.5 = 95.8%, Pearson r = 0.993) with shorter training and inference times compared to YOLOv12. For spikelet detection, the non-pretrained YOLOv11 demonstrated higher accuracy (mAP@0.5 = 99.0%), while counting performance was comparable across models. These results establish OBB-based YOLO detection as a robust and scalable approach for AI-driven wheat phenotyping.
Why it matches plant phenotyping methods小麦の穂・小穂という収量関連形質の画像ベース検出・計数手法を比較評価し、大規模ベンチマークデータセットも構築しているため、フェノタイピング手法が中心である。
abstractThis study evaluates two recent YOLO variants, YOLOv11 and YOLOv12, for wheat spike and spikelet detection and counting using oriented bounding boxes (OBB), and introduces a new large-scale benchmark dataset comprising 48,521 spike and 60,404 spikelet instances with OBB annotations.
Reproduction assets foundThe paper openly states its supporting data (spike/spikelet imagery with OBB annotations) is available on Zenodo, and the underlying models are deployed on the authors' public WheatAI cloud platform.Dataset · publicData availability
The data supporting the findings of this study are openly available at: https://doi.org/10.5281/zenodo.20215489 .Open asset ↗zenodo · 10.5281/zenodo.20215489lines:219-266Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.
Why it matches plant phenotyping methodsUAV画像と深層学習により、イネの分げつ角度・株元幅という植物形態形質を自動推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.
Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 × 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.
Why it matches plant phenotyping methodsリンゴ果実の硬度とBrixという植物器官形質を、ハイパースペクトル画像と深層学習で非破壊推定するデータセット・モデル・汎化性能評価が研究の中心である。
abstractThis study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness.
Reproduction assets foundThe paper explicitly states that the hyperspectral apple dataset (5756 apples, firmness/Brix/starch measurements) is deposited in the University of Essex research data repository and that the data cleaning, model training, and analysis code is on GitHub, both with public URLs.Dataset · publicThe datasets generated during and analysed during the current study are available in the University of Essex repository ( https://researchdata.essex.ac.uk/228/ )Open asset ↗researchdata.essex.ac.uk · 228lines:192-220Code · publicthe code used for data cleaning, model training and analysis are available on GitHub: ( https://github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaging.git )Open asset ↗github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaginglines:192-220Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published8 Jun 2026International Journal of Engineering and ManufacturingCited by 0 · OpenAlex ↗
Plant disease detection is vital for agricultural sustainability and food security. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have achieved high accuracy in this domain, CNNs often require millions of parameters and substantial computation. ViTs suffer from the quadratic time and space complexity of self-attention (SA), limiting their use on resource-constrained devices. Although SA is capable of modelling long-range dependencies when symptoms are dispersed, many plant diseases exhibit small, localized lesions or texture changes; therefore, Neighborhood Attention (NA) offers a more efficient and targeted alternative by focusing on nearby regions rather than the entire image. This work proposes a custom Localized NA block implemented in TensorFlow/Keras that operates directly on CNN feature maps, bypassing patch embedding and transformer modules. A lightweight CNN is then developed by combining depth-wise separable convolutions with the proposed localized NA block. In addition, a 100-category plant disease dataset covering 16 crops is presented. The dataset is curated, class-balanced, and made publicly available to support reproducibility and encourage further research. The proposed 9-layer CNN, with just 1.7M parameters and a size of 6.74 MB, achieved a favorable balance between accuracy, model size, and computational efficiency, compared with MobileNetV1, MobileNetV2, DenseNet121, InceptionV3, MobileViT-XXS, and EfficientViT-M0, achieving 98.97%± 0.33% accuracy on PlantVillage and 93.36%± 0.28% on the proposed dataset. The ablation study showed that the NA block improved test accuracy by approximately 2–3%, while Grad-CAM visualizations indicated more precise targeting of diseased areas in the leaf image.
Why it matches plant phenotyping methods植物葉画像から病徴を推定する軽量CNNと注意機構を開発し、複数データセットで比較評価・アブレーションを行い、さらに100カテゴリの公開データセットを提示しているため、植物フェノタイピング手法が中心である。
abstractThis work proposes a custom Localized NA block implemented in TensorFlow/Keras that operates directly on CNN feature maps
Reproduction assets foundThe paper's authors curated a 100-category plant disease dataset and explicitly state it is publicly available on Kaggle in both augmented-train and raw split forms. These are paper-specific, public, actionable phenotype image datasets. The PlantVillage benchmark is a third-party dataset, not a paper-specific asset, soDataset · publicrs declare no conflict of interest
Funding Declaration
This research work was supported by KLE Technological University, Hubbali, India under the Ph.D. Fellowship
Program.
Data Availability Statement
The newly curated 100-category Plant Disease Dataset used in this study is publicly available on Kaggle.
Augmented Train Dataset: https://www.kaggle.com/datasets/rithambararajput/augmented-train
Raw Dataset: https://www.kaggle.com/datasets/rithambararajput/100-class-split-raw-dataset
The Plant Village dataset, used as a benchmark for comparative evaluation, is also publicly accessible at:
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.Ethical Declarations
This study does noOpen asset ↗Kaggle · rithambararajput/augmented-trainpdf-raw-page:19 lines:1-51Dataset · publicsupported by KLE Technological University, Hubbali, India under the Ph.D. Fellowship
Program.
Data Availability Statement
The newly curated 100-category Plant Disease Dataset used in this study is publicly available on Kaggle.
Augmented Train Dataset: https://www.kaggle.com/datasets/rithambararajput/augmented-train
Raw Dataset: https://www.kaggle.com/datasets/rithambararajput/100-class-split-raw-dataset
The Plant Village dataset, used as a benchmark for comparative evaluation, is also publicly accessible at:
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.Ethical Declarations
This study does not involve human participants or animals. Therefore, ethical approval was not rOpen asset ↗Kaggle · rithambararajput/100-class-split-raw-datasetpdf-raw-page:19 lines:1-51Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In this context, this study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata. Soybean plants were grown under water deficit and well-watered conditions, generating significant variations in stomatal structural opening and associated gas exchange traits. To capture stomata variations, epidermal printing techniques were employed, with images obtained by microscopy. The dataset was labeled using the intelligent polygon tool of the Roboflow application, with 269 images of the adaxial and abaxial surfaces of leaves annotated in two categories: open and closed stomata. The images underwent geometric transformations to facilitate model training. The results demonstrated that the YOLOV8 neural network achieved precision recall and mAP greater than 90%, highlighting its effectiveness in detecting and classifying stomata. By integrating automated classification of aperture states (open and closed) with a defined physiological stress context in soybean, this work establishes a dataset specifically designed for functional analysis. This approach extends the applicability of deep learning toward stress-oriented plant physiology studies, offering a robust tool for evaluating crop adaptation under climate change scenarios.
Why it matches plant phenotyping methodsヨロウ豆の気孔開閉状態を画像から自動検出・分類するYOLOv8手法とデータセットを開発・評価しており、植物表現型取得が研究の中心である。
abstractthis study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata.
Plant leaf disease classification and severity grading are essential for precision agriculture, enabling timely intervention and optimized management. Existing models often fail to recognize previously unseen disease categories due to rigid label spaces and limited representation of plant phenotypes. To address these challenges, we propose a knowledge-driven unified framework for plant disease classification and severity grading. A Common Knowledge Learner consolidates fundamental features of plant species, disease categories, and severity levels from labeled data, forming a transferable representation space. It employs a multi-level contrastive learning strategy to capture both global semantic representations and fine-grained lesion patterns. Building on these representations, a Cross-Domain Adaptation module leverages a teacher-student framework with Low-Rank Adaptation (LoRA) bridges in-domain and out-of-domain feature spaces using large-scale unlabeled data. Meanwhile, a contrastive feature library enables similarity-based reasoning and supports flexible label space expansion during inference without retraining. We evaluate our approach on Leaf-CG, a large-scale dataset comprising 441,448 images from 59 plant species, 373 disease categories, and four severity levels. Experiments demonstrate that our framework outperforms existing baselines, achieving 94.9% disease classification accuracy and 90.6% severity grading accuracy in-domain. Under out-of-domain conditions, the method achieves 82.1% true positive rate (TPR) in open-set settings, highlighting its strong generalization ability and potential for practical plant disease management. Code and dataset are available at https://www.uniplantcg.samlab.cn.
Why it matches plant phenotyping methods植物画像から病害分類と病害重症度を推定する計算・画像ベース手法を開発し、大規模データセットで評価しており、フェノタイピング手法が中心である。
abstractwe propose a knowledge-driven unified framework for plant disease classification and severity grading.
Reproduction assets foundThe paper's Leaf-CG dataset (test subset publicly available), analysis code, and trained model weights (plant.pth, disease.pth, severity.pth) are explicitly released at the authors' site https://www.uniplantcg.samlab.cn. Cited datasets (AI Challenger 2018, PlantVillage, etc.) are prior work, not paper-specific assets.Code · publicving 94.9% disease classification accuracy and 90.6% severity grading accuracy in-domain. Under out-of-domain conditions, the method achieves 82.1% true positive rate (TPR) in open-set settings, highlighting its strong generalization ability and potential for practical plant disease management. Code and dataset are available at https://www.uniplantcg.samlab.cn .
Keywords
Plant disease diagnosis Knowledge-driven learning Domain adaptation pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-onlyOpen asset ↗uniplantcg.samlab.cnlines:1-29Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Southern leaf blight (SLB) is a foliar disease of maize (Zea mays L.) caused by the necrotrophic fungal pathogen Cochliobolus heterostrophus. Genetic resistance is the most effective control method for SLB. Developing disease resistant maize lines requires field trials during which disease phenotypes must be visually assessed. Remote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity that is otherwise labor-intensive and subjective. This project used a deep learning approach to estimate SLB disease severity of single-row maize plots from drone imagery. Over 26,000 plot-level images produced from flights conducted across three growing seasons were labeled with in-field visual scores taken contemporaneously by expert raters. Variation in environmental conditions contributed to a labeled image dataset that reflects the complexity of agronomic field experiments. We assessed the ability of nine deep learning models from three architectural families to estimate disease severity. The best-performing model, EVA-02-B, achieved strong cross year generalization (R 2 = 0.697). Error analysis found that performance was more strongly associated with seasonal disease progression and flight-score time offset than with image-level noise. UAV-based deep learning estimated SLB severity with comparable precision to expert raters. This study lays the groundwork for integrating automated phenotypes into genetic studies of disease resistance. PLAIN LANGUAGE SUMMARY Southern leaf blight (SLB) of maize is a disease that causes yield loss worldwide and developing resistant varieties offers the best hope for controlling the disease. Studying SLB resistance requires plant pathologists to visually score severity in the field, a labor-intensive method that requires expertise. To address these challenges, we asked whether SLB severity scoring could be automated using drone images and artificial intelligence (AI). We trained AI models using three years of image and score data then compared the results to visual scores taken by five plant pathologists. The best performing AI model showed a similar level of consistency to the experts and proved capable of scoring severity despite unpredictable and uncontrollable conditions that affect field imaging experiments such as weeds or shadows. These findings provide a validated method that improves the efficiency of maize disease research, a critical area of study for agricultural sustainability and productivity.
Why it matches plant phenotyping methodsドローン画像と深層学習により、トウモロコシの葉病害重症度という植物状態を推定し、複数年データで性能と汎化性を評価した手法研究である。
abstractRemote sensing using drones is an emerging technology that can be leveraged for high-throughput phenotyping of disease severity
Manual inspection of landscape pathology and textile fiber defects suffers from inherent subjective bias and suboptimal throughput. To bypass these bottlenecks, we propose the Ghost-Convolution Enlightened Vision Transformer (GeT). We constructed a novel hybrid neural network architecture, the Ghost-Convolution Enlightened Vision Transformer (GeT), which synergistically integrates the lightweight local feature extraction proficiency of Convolutional Neural Networks (CNN) with the global semantic modeling capabilities of Vision Transformers (ViT). Utilizing a newly established standard dataset (GLDP15k) comprising 15, 000 heterogeneous field images, the model was subjected to rigorous hyperparameter optimization and ablation studies. Optimization on the GLDP15k dataset yielded a peak accuracy of 96.8% across 12 target classes, maintaining a Kappa-coefficient of 0.941. Constrained to 1.16 M parameters, the architecture executes at 5.5 ms per image (180 FPS) on edge hardware. A 6-month application at Yuexiu Park demonstrated a 3.2-fold improvement in detection efficiency and a 35% reduction in pesticide usage compared to manual inspections. This study not only elucidates the interpretability of hybrid attention mechanisms in phytopathology but also adapts these vision-based paradigms to the detection of microscopic anomalies in textile weaving patterns, providing a scalable and computationally efficient solution for both precision plant protection and industrial fabric defect inspection.
Why it matches plant phenotyping methods植物病害を画像から認識する深層学習モデルを開発し、専用データセットで検証・応用しており、植物の病害状態の取得方法が中心的な研究貢献である。
abstractUtilizing a newly established standard dataset (GLDP15k) comprising 15, 000 heterogeneous field images, the model was subjected to rigorous hyperparameter optimization and ablation studies.
ToBRFV is a major threat to tomato and pepper crops because it spreads quickly and survives for a long time in the environment. Since there are few ways to control it after infection, early detection before symptoms are visible is crucial. Yet, only limited public datasets are available for this research. We present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection. In this study, two tomato cultivars and two pepper cultivars, all of which are commercially important and widely cultivated in greenhouses, were selected. Using these plants ensures that the dataset reflects real-world agricultural practices and captures variability across commercially grown types. Both healthy and ToBRFV-inoculated plants from each cultivar were included in the imaging process. All plants were cultivated under fully controlled greenhouse conditions in Adana Province, Türkiye. Healthy and infected tomato plants were grown in two separate greenhouses to prevent cross-contamination. Imaging was conducted over a 29-day period using Red-Green-Blue (RGB) and Visible Near Infrared (VNIR) cameras, including narrowband captures at 800 nm and 1000 nm, from multiple viewing angles. Infection status was confirmed via Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) analysis at multiple time points. The dataset is organized into four clean, labelled subsets and released under a CC BY 4.0 license. This resource provides unique opportunities for developing and benchmarking computer vision and machine learning approaches for pre-symptomatic plant disease detection, spectral feature analysis, and integration into precision agriculture systems. By combining controlled experimental design, spectral diversity, and open access, it establishes a robust foundation for cross-disciplinary research in plant pathology, agricultural engineering, and artificial intelligence.
Why it matches plant phenotyping methods植物病害状態を対象にした縦断マルチスペクトル画像データセットであり、公開データセットとして開発・ベンチマーク利用を目的とするため、表現型取得が中心です。
abstractWe present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection.
Reproduction assets foundThe article is a Data in Brief describing the authors' own openly released longitudinal multispectral plant image dataset (ToBRFV-LMID) for tomato and pepper disease detection, deposited on Zenodo under CC BY 4.0 with a direct DOI URL. This is a paper-specific, public, directly actionable phenotype/image asset. No codeDataset · publicData accessibility
Repository name: ZENODO
Data identification number: 10.5281/zenodo.17244968
Direct URL to data: https://doi.org/10.5281/zenodo.17244968Open asset ↗ZENODO · 10.5281/zenodo.17244968html-lines:98-126Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology. Whereas the PlantVillage and Hermos datasets, for instance, contain mainly scanned or laboratory-acquired leaves, GVLiD features vineyard in situ images along with detailed metadata (GPS, lighting, weather, and device model) and expert-verified annotations. To measure the reliability of the annotation, label consistency was very high (κ = 0.86-0.92; 95% CI) as assessed by inter- and intra-rater agreement. Besides Indian viticulture, the dataset also aims to support the field of foliar disease detection in precision agriculture and ML benchmarking, which face significant challenges due to variable illumination and natural leaf backgrounds under field conditions. GVLiD is intended to enable worldwide, reproducible, real-world testing of AI systems for crop disease monitoring.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像で注釈化したデータセットであり、植物病害状態の画像ベース表現型測定と再現可能なベンチマークが中心です。
abstractWe introduce the Grapevine (Vitis vinifera) Leaf Image Dataset (GVLiD), a carefully curated set of 3,477 annotated images of Grapevine (Vitis vinifera) leaves to catalyze research in computer vision and plant pathology.
Reproduction assets foundThe paper's grapevine leaf image dataset (GVLiD) is deposited on Mendeley Data, but that URL is not among the allowed URLs. The authors' validation/analysis code (image-quality metrics, metadata-completeness checks, annotation-reliability calculations) is publicly available on GitHub at the allowed URL, with explicit 'Code · publicAll validation scripts (image-quality metrics, metadata-completeness checks, and annotation-reliability calculations) are publicly available in the GVLiD GitHub repository. This ensures full reproducibility of all validation results reported here.
git clone https://github.com/MilindGayakwad/DNNOpen asset ↗https://github.com/MilindGayakwad/DNNhtml-lines:255-349Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Accurate detection of tobacco plants in complex field environments is critical for precision agriculture, crop monitoring, and yield estimation. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to environmental and subjective factors. In this study, we propose an improved YOLO11-based framework for automated tobacco plant detection, specifically designed to address challenges such as scale variation, dense distribution, and background interference. The framework integrates four key modules: the Edge-Enhanced Feature Stem (EEFS) to strengthen low-level feature extraction, the Multi-Scale Kernel Interaction (MSKI) to capture multi-scale contextual information, the Adaptive Weighted Feature Fusion (AWFF) to optimize feature aggregation, and the Global–Local Synergistic Attention (GLSA) to enhance feature discrimination by jointly modeling local details and global context. A comprehensive UAV-based tobacco dataset was constructed, encompassing multiple lighting conditions, collection heights, and observation angles. Experimental results demonstrate that the proposed method significantly outperforms the YOLO11 baseline and achieves superior performance compared to mainstream YOLO variants. Ablation studies and heatmap visualizations confirm the effectiveness of each module. Furthermore, the model exhibits robust performance under multi-dimensional environmental perturbations, including varying illumination, scale, and camera angles. The proposed framework provides a practical and efficient solution for automated tobacco plant counting, offering potential applications in UAV-based precision agriculture and large-scale crop monitoring.
Why it matches plant phenotyping methodsUAV画像からタバコ個体を自動検出・計数する手法を開発し、専用データセット、比較実験、アブレーション、頑健性評価まで行っており、植物個体数の取得方法が中心です。
abstractwe propose an improved YOLO11-based framework for automated tobacco plant detection
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Why it matches plant phenotyping methods植物の3D形態表現型抽出を目的としたデータセットで、画像・点群・器官アノテーション・グラフ・形質値を提供し、再現可能なフェノタイピング手法の開発と評価を直接支援している。
abstractThis article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下小麦の単視点3D再構成とアモーダル補完を開発し、データセット構築、複数手法の系統評価、器官形質推定誤差の検証まで行っており、植物フェノタイピング手法が中心である。
abstractthis study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
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 codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB). HLB, also known as citrus greening disease, is a major pathology caused by the bacterial pathogen Candidatus Liberibacter asiaticus , particularly in species of the citrus genus. The dataset is constituted of terrestrial images acquired in a commercial sweet orange orchard of the variety Pera Rio ( Citrus sinensis (L.) Osbeck). The images describe large portions of canopy, with healthy leaves and sections infected by HLB as well as some confounding factors naturally present in orchards. Multispectral images were acquired with a multi-lens camera within the visible-near-infrared domain, resulting in 14 narrow spectral bands. The image acquisition was conducted during two field campaigns in 2023 and 2024. In total, the dataset contains 2,978 images divided into two classes HLB (1,681) and non-HLB (1,297). Originally, data are stored in TIFF format as 14 monochromatic images, organised by spectra band. Additionally, an HDF5-format version is provided, where images are stored as 3D arrays with spectral bands in ascending order. This format is compatible with various programming languages, enables efficient data handling, and is optimised for machine learning and image processing applications, supporting reproducible and portable analysis. This dataset is a valuable resource for the development and benchmarking of classification models, including deep learning approaches, aimed at the detection of HLB. Phytopathology imaging datasets are scarce yet essential for advancing digital agriculture and the development of robust tools for crop disease detection worldwide.
Why it matches plant phenotyping methods柑橘葉・樹冠のマルチスペクトル画像からHLB感染状態を推定するデータセットであり、植物病害状態の表現型取得と機械学習ベンチマークを中心とする。
abstractThis article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB).
Reproduction assets foundThe article is a Data in Brief describing a public multispectral HLB citrus image dataset deposited on Data INRAE (Recherche Data Gouv, DOI 10.57745/054NAB), plus an authors' GitHub repository with preprocessing, registration, and model training scripts. Both are paper-specific, public, and directly actionable.Dataset · publicData accessibility
Repository name: Data INRAE
Data access link: https://doi.org/10.57745/054NABOpen asset ↗Data INRAE · 10.57745/054NABhtml-lines:89-123Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen’s κ = 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP 50 and 81.8% mAP 50:95 on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component’s non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP 50 at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.
Why it matches plant phenotyping methods植物病害の症状を画像から検出・分類する視覚モデルを開発し、注釈付き大規模データセットで検証しているため、植物状態の画像ベース表現型計測が中心である。
abstractWe constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops
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
PollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks, whose annotation was fully verified by an expert palynologist to guarantee the taxonomic reliability of every published instance. The dataset is designed to close a concrete gap in existing palynological datasets, which typically combine low taxonomic diversity, few samples per class, and low-resolution crops restricted to bounding boxes. PollenBB16 contains 16,198 brightfield optical microscopy images at the native resolution of 3088 × 2064 pixels and 36,383 pixel-accurate polygons across 16 species from the Biobío Region, spanning endemic, native and exotic species of high ecological and melliferous value such as Eucryphia glutinosa and Quillaja saponaria (endemic), Gevuina avellana and Aristotelia chilensis (native), and Medicago sativa and Brassica rapa (introduced). Each spatial position is recorded at three focal planes. The displacement along the z axis reveals features of the exine together with information on the internal structure of the grain that remain inaccessible on a single plane. From this multifocal information, more robust convolutional networks can be trained with more accurate classification. The operational quality of the dataset is backed by a leakage-safe partition that keeps the three focal planes of the same position in the same subset to avoid metric inflation, complemented by a YOLO11n-seg baseline trained for 50 epochs that reaches 0.985 mask mAP@50 on the validation set, establishing a reproducible reference point. Beyond deep learning, PollenBB16 enables interdisciplinary applications in aerobiology, biodiversity monitoring under climate change, ecological restoration of the South American temperate forest, and botanical-origin authentication of Chilean monofloral honeys.
Why it matches plant phenotyping methods植物由来の花粉粒を対象とした高解像度画像データセットとセグメンテーション基準を構築し、深層学習による画像解析を再現可能な形で検証しているため、植物フェノタイピング手法・データセットとして中心的です。
abstractPollenBB16 is an RGB pollen image dataset of Chilean flora with pixel-accurate instance segmentation masks
Reproduction assets foundThe paper's PollenBB16 pollen image dataset (16,198 multifocal RGB microscopy images with pixel-accurate instance segmentation masks) and the accompanying authors' script polygons_to_bboxes.py are publicly deposited on Zenodo (10.5281/zenodo.19830051), per the Data Availability Statement.Code · publicThe only custom code distributed with this Data Descriptor is the Python script polygons_to_bboxes.py, which regenerates the YOLO bounding-box labels in labels_bb/ from the polygon labels in labels/. The script is packaged inside the scripts/ folder of the same Zenodo repository that hosts the dataset (10.5281/zenodo.19830051) and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, with no restrictions on access.Open asset ↗Zenodo · 10.5281/zenodo.19830051html-lines:731-797Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., " Parsha (Scab) ," " Brown spotting ," " White-Gray spotting ," and " Rust ," which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management.
Why it matches plant phenotyping methodsポプラ葉の病徴を画像から検出・分類する手法と公開データセットが研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採択。
abstractThis study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea
Abstract Artificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge, especially in smart farming. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability. However, agricultural data are highly diverse and heterogeneous, complicating model development. This study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources (including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery) covering the period 2016–2022. It supports both physical and Machine Learning-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for other regions or crops, advancing smart farming through scalable, data-driven solutions. The open publication of the code further supports this objective. We further anticipate its potential contribution to developing foundation models as well as to the creation of new knowledge in biology and agronomy.
Why it matches plant phenotyping methodsブドウの9段階のフェノロジーを対象に、圃場観測と衛星画像などを統合した再利用可能な地理参照データセットを構築しており、植物形質取得・モデル化の方法論が中心である。
abstractThis study presents a proposed georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Aragón, Spain.
Accurate detection of citrus leaf and fruit diseases is important for precision orchard management. However, real orchard images often contain small disease symptoms, leaf and fruit overlap, illumination variation, and cluttered backgrounds, making reliable detection challenging. This study proposes LDTC-YOLO, a lightweight YOLOv8n-based detection model for typical citrus leaf and fruit diseases in real orchard environments. To improve detection accuracy and model compactness, LDTC-YOLO integrates an Adaptive Feature Pyramid Network (AFPN) for cross-level feature fusion, Coordinate Attention (CA) for disease-region feature enhancement, a Lightweight Shared Convolutional Detection (LSCD) head for reducing parameter redundancy, and Wise-IoU (WIoU) for bounding-box regression optimization. In addition, a self-collected handheld citrus disease dataset, HOCD-4, was constructed using close-range smartphone images captured in real orchards. The dataset covers leaf and fruit symptoms of four typical citrus diseases: Huanglongbing/citrus greening (HLB), black spot, canker, and melanose. Experimental results show that LDTC-YOLO achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 0.915, 0.843, 0.894, and 0.648, respectively. Compared with YOLOv8n, LDTC-YOLO reduced parameters, GFLOPs, and model size from 3.006 M to 1.887 M, 8.1 to 7.4, and 5.97 MB to 3.83 MB, while increasing inference speed from 43.14 FPS to 47.45 FPS. These results indicate that LDTC-YOLO improves detection performance while maintaining a compact and efficient model profile, providing a potential reference for citrus disease detection under real orchard imaging conditions.
Why it matches plant phenotyping methods柑橘葉・果実の病徴を画像から検出する軽量モデルと実圃場データセットを開発・評価しており、植物の病害状態を推定するフェノタイピング手法が中心である。
abstractThis study proposes LDTC-YOLO, a lightweight YOLOv8n-based detection model for typical citrus leaf and fruit diseases in real orchard environments.
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationCountingGrowth / development / phenology
Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画データセットであり、自動フェノロジー検出と空間検証のためのベンチマーク基盤が中心である。
abstractThe Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions.
Reproduction assets foundThe paper describes a public multi-modal Actinidia chinensis phenology dataset (annotated images, georeferenced videos, ground-truth counts) deposited on Zenodo, plus authors' MIT-licensed preprocessing scripts on GitHub. CVAT and FiftyOne are generic third-party tools and excluded.Dataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:12 lines:1-92Code · publicCustom scripts for dataset preparation are publicly available under the MIT License at https://github.com/Open asset ↗GitHubpdf-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
AppleField / plotRGB / grayscaleFruitClassificationObject detectionSegmentationGrowth / development / phenology
This article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red). The dataset serves as a resource for detecting and segmenting apples across different developmental phases. Each image includes pixel-level instance segmentation masks annotated in JSON format using the VGG Image Annotator (VIA), ensuring compatibility with deep learning frameworks. The dataset's real-world variability-spanning lighting conditions, occlusions, and clustered fruit arrangements-enhances its utility for training generalizable computer vision models in precision agriculture. It supports tasks such as fruit detection, segmentation and growth-stage classification, addressing the scarcity of annotated data for transitional maturity phases. With 2574 annotated apple instances, this dataset facilitates research on maturity grading and transfer learning for agricultural robotics. By standardizing annotations and incorporating diverse field conditions, this dataset reduces preprocessing overhead and accelerates the development of deployable AI solutions for orchard management. It is particularly valuable for improving model robustness in heterogeneous environments, thereby advancing data-driven horticultural practices.
Why it matches plant phenotyping methodsリンゴ果実の発育段階・成熟度という植物器官の状態を対象に、画素単位アノテーション付き画像データセットを構築しており、観測・抽出手法の再利用可能な基盤が中心である。
abstractThis article presents a comprehensive dataset of 1406 RGB images of apples ( Malus domestica ), covering three key growth stages-immature (green), semi-mature (color transition), and mature (red).
Reproduction assets foundThe paper is a data descriptor for a public apple image dataset (1406 RGB images, pixel-level instance segmentation masks in JSON) deposited on Mendeley Data with a direct URL and DOI, matching the allowed URL exactly.Dataset · publicRepository name: Wang, Dandan; Wang, Bo (2026), “A Multi-Stage, Pixel-Level Annotated Apple Dataset for Precision Agriculture Research”, Mendeley Data, V4
Data identification number: 10.17632/gfcmdbvw65.4
Direct URL to data:https://data.mendeley.com/datasets/gfcmdbvw65/4Open asset ↗Mendeley Data · 10.17632/gfcmdbvw65.4html-lines:1-97Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle ( Sonchus oleraceus ) and little mallow ( Malva parviflora ). Hyperspectral imaging provides rich spectral-spatial data cubes that can support the development of deep learning models and autonomous technology for precision weed management. Plants were grown in a greenhouse under five conditions: standard, drought, overwatering, excess fertilizer, and no fertilizer. Custom MATLAB scripts were utilized for preprocessing, including k-means clustering to define regions of interest (ROIs), and extraction of spectral metrics. Data visualization was performed using Wolfram language and MATLAB. The dataset includes both raw and ENVI-formatted hyperspectral cubes and pre-processed MATLAB outputs, supporting spectral feature engineering, benchmark development, and exploratory machine learning workflows for controlled environment stress classification.
Why it matches plant phenotyping methods植物のストレス状態を対象とするハイパースペクトル画像データセットで、ROI抽出・スペクトル指標化と機械学習ベンチマークを中心的に提供しているため、植物フェノタイピング手法・データセットとして適格。
abstractThis work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species
Reproduction assets foundThe paper is a Data in Brief article describing its own hyperspectral imaging dataset of annual sowthistle and little mallow under five abiotic stress treatments, deposited publicly on Zenodo (record 17398082). The dataset includes raw ENVI-format hyperspectral cubes, preprocessed MATLAB outputs (ROI masks, extracted植被Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: zenodo.17398082
Direct URL to data: https://doi.org/10.5281/zenodo.17398082Open asset ↗Zenodo · zenodo.17398082html-lines:92-120Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Tea (Camellia sinensis) is the world's second most consumed beverage, enjoyed daily by more than two billion people. In Bangladesh, it serves as a cornerstone agricultural export and a major sector of the domestic economy. However, commercial tea cultivation remains highly vulnerable to fungal and pest-related diseases such as Blight, Red Rust, and Helopeltis which severely reduce crop yield and compromise leaf quality. While early detection is critical to preventing widespread outbreaks, traditional manual inspection is slow, subjective, and highly error-prone. Deep learning provides a scalable alternative, yet single-branch networks often struggle to capture both minute disease lesions and broader structural degradation simultaneously. To address this, we propose a Hybrid Feature Fusion architecture that runs two highly efficient feature extractors in parallel: EfficientNetV2-Small to isolate fine-grained local textures, and MobileNetV3-Small to capture the global structural context of the leaf. The models were trained and evaluated on a real-world dataset of 2,000 annotated images, evenly distributed across the four target classes (Blight, Red Rust, Helopeltis, and Healthy). Before training, the images underwent a standardized preprocessing pipeline including resizing to 224 × 224 pixels and normalization, supplemented by a dynamic augmentation strategy featuring random rotations, horizontal flips, and brightness adjustments to improve model robustness. The proposed hybrid framework achieved an outstanding peak classification accuracy of 96.80% alongside a macro Area Under the Curve (AUC) of 0.9980. To rigorously validate its performance, the hybrid model was benchmarked against six diverse architectures: a Vision Transformer (ViT-B16 at 76.40%), a Custom CNN (89.60%), MobileNetV3 (94.40%), ResNet50 (95.60%), DenseNet121 (96.40%), and EfficientNetV2-B3 (97.60%). Although EfficientNetV2-B3 achieved a marginally higher raw accuracy, the proposed dual-branch framework delivered a superior precision-recall balance and faster convergence stability. These findings demonstrate that the proposed hybrid methodology is highly reliable and computationally balanced, making it an ideal candidate for integration into Internet of Things (IoT) edge devices for real-time disease monitoring in precision agriculture.
Why it matches plant phenotyping methods茶葉の病徴を画像から分類する深層学習手法の開発と、注釈付きデータセットおよび複数モデルとのベンチマーク検証が中心であり、植物病害状態の表現型推定に該当する。
abstractwe propose a Hybrid Feature Fusion architecture
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the curated 2000-image tea leaf dataset on Mendeley Data and the analysis code on GitHub, both with public URLs matching allowed_urls.Dataset · publicThe dataset comprising 2000 annotated tea leaf images was curated under real-world field conditions. It has been made available at https://data.mendeley.com/datasets/3x42rbj8yv/1.Open asset ↗3x42rbj8yv/1html-lines:465-480Code · publicThe computational code supporting the findings of this study is publicly accessible on GitHub: https://github.com/rayhankhan2192/Tea_Leaf_Disease_Model.Open asset ↗GitHub · rayhankhan2192/Tea_Leaf_Disease_Modelhtml-lines:465-480Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized 'Yotsuboshi' strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model's classifications of 'NormalN' and 'LowN' statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions.
Why it matches plant phenotyping methods植物の窒素状態をRGB画像から推定する深層学習法、標準化撮像用デバイス、アノテーション基準、データセットを開発し、NDVIおよび圃場条件で検証しており、表現型取得・推定手法が中心である。
abstractthis study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images.
Common beanRiceWheatLeafClassificationDisease symptoms / severity
In Bangladesh, crop leaf diseases create a serious risk to food security and production from agriculture. Timely identification of leaf diseases in rice, wheat, and bean crops is considered crucial for the implementation of effective disease detection and classification strategies. To address this challenge, a MobilenetV2-based disease identification and classification system is proposed in this research. Previous studies focus on classifying diseases of a single species, leaving the need to train models separately for each species. This research focuses on forming a single standard model to perform leaf disease classification for multiple crop species including rice, wheat, and beans. The approach makes use of transfer learning with the MobilenetV2 model, which is fine-tuned using a dataset of annotated crop leaf images specific to Bangladesh. Following a comprehensive evaluation, an overall accuracy of 97.87% was achieved in the classification of crop leaf diseases, which surpasses the accuracy of a number of previous studies focusing on leaf disease detection of a single crop. The system demonstrates the capability to rapidly diagnose diseases in real time by enabling the users to prompt intervention to mitigate potential crop losses, ultimately leading to amplified crop yield and food security. Overall, the research highlights the promise of AI-powered solutions in tackling crop leaf disease detection, which in turn encourages greater research and technology adoption to support sustainable farming methods especially in the crop disease classification domain in Bangladesh and throughout the world. Received: 24 May 2025 | Revised: 9 March 2026 | Accepted: 14 April 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Bangladeshi Crops Disease Dataset at https://www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset. Author Contribution Statement Md. Mahmudul Hasan: Conceptualization, Methodology, Visualization, Supervision. Md. Omar Faruq: Software, Validation, Writing – original draft. Mahadi Hasan Musa: Formal analysis, Investigation. Mohammad Mamunur Rashid: Resources, Data curation, Writing – review & editing. Khandaker Mohammad Mohi Uddin: Writing – review & editing, Project administration, Supervision.
Why it matches plant phenotyping methods葉画像から作物の病害状態を推定する深層学習手法を開発・評価しており、植物病害フェノタイピングが中心的な技術貢献である。
abstracta MobilenetV2-based disease identification and classification system is proposed in this research.
Reproduction assets foundThe paper's Data Availability Statement openly provides the Bean Disease Dataset on Kaggle, which is one of the two public image datasets used to train the multi-crop leaf disease classification model. The Bangladeshi Crops Disease Dataset URL is not among the allowed URLs, so only the bean dataset is reported. No codeDataset · publict
The authors declare that they have no conflicts of interest to
this work.
Data Availability Statement
The data that support the findings of this study are openly
available in the Bangladeshi Crops Disease Dataset at https://
www.kaggle.com/datasets/nafishamoin/bangladeshi-crops-disease-
dataset and the Bean Disease Dataset at https://www.kaggle.com/datasets/therealoise/bean-disease-dataset.Author Contribution Statement
Md. Mahmudul Hasan: Conceptualization, Methodology,
Visualization, Supervision. Md. Omar Faruq: Software, Valida-
tion, Writing – original draft. Mahadi Hasan Musa: Formal
analysis, Investigation. Mohammad Mamunur Rashid: Resources,
Data curation, Writing – review & editing.Open asset ↗Kaggle · therealoise/bean-disease-datasetpdf-raw-page:11 lines:1-83Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control. A deep learning approach is used as the core method to identify diseases in rice leaves. This methodology employs a range of advanced deep learning architectures to achieve top-tier feature extraction and classification. The publicly available rice leaf disease dataset on Zenodo supports research reproducibility and data transparency. We systematically process a balanced dataset of 1914 image samples using Python with TensorFlow and a GPU to enable high-speed computation for large-scale image processing. This study conducts a systematic comparative evaluation of five deep transfer learning architectures (InceptionV3, DenseNet201, ResNet152V2, EfficientNetV2L and MobileNetV2) trained independently. The base backbone models are then integrated with guided GrabCut segmentation with contour-detection method for interpretable disease localization. In this work, the methods of segmentation by GrabCut and contour detection are introduced to make the results of the study easier to interpret and explain the disease areas, but the final classification outcomes are obtained only on the basis of the underlying deep transfer learning models. As a result, infected leaf areas can be identified more effectively, allowing for better understanding and explainable of the disease.To enhance interpretability, GrabCut segmentation and contour detection are applied as post-hoc visualization techniques to highlight diseased regions corresponding to CNN predictions. These techniques do not influence the classification training process. All five models InceptionV3, DenseNet201,ResNet152V2,EfficientNetV2L and MobileNetV2 demonstrated their effectiveness in detecting rice diseases during training, validation, and testing phases, with models trained over 30 epochs. The training methods and accuracy rates of the models were compared during validation and final testing. InceptionV3 demonstrated the most moderate performance of 98.80% training, 98.44% validation, and 98.43% test accuracy, which means that it has strong generalization and consistent learning behavior. The performance of very high-density networks such as DenseNet201 (98.72% train, 98.43% val, 98.43% test), ResNet152V2 (99.02% train, 99.22% val, 97.39% test), EfficientNetV2L model accuracies (39.01% train, 48.70% val, 44.50% test) also showed competitive results, which validated the effectiveness of deep transfer learning in the classification of rice leaf disease, while MobileNetV2 model accuracies (98.09% train, 98.18% val, 96.87% test) indicate that a lightweight model can still achieve reliable classification performance with lower computational complexity. In general, the comparative analysis defines InceptionV3 as the most stable and efficient model in the framework proposed. These results illustrate InceptionV3 superior generalization ability, supported by explainable methods for improved feature localization, confirming the viability of transfer learning for accurate and practical rice disease detection using GrabCut segmentation and contour detection technique. The complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.
Why it matches plant phenotyping methodsイネ葉の病徴領域を画像から分類・局在化する深層学習ワークフローが研究の中心であり、GrabCut・輪郭検出と複数モデルの比較評価を含むため、植物病害状態の画像ベース表現型計測として採用。
abstractA deep learning approach is used as the core method to identify diseases in rice leaves.
Reproduction assets foundThe paper explicitly states that the complete implementation code and the rice leaf disease image dataset (1914 samples) used in this study are publicly available: code on the authors' GitHub repository and the dataset on Zenodo (DOI 10.5281/zenodo.15817084). Both are paper-specific, public, and actionable.Code · publicThe complete implementation code and data used for the research experimentation is publicly available at https://github.com/ummershakeel03/Rice-Leaf-Diseases-Classification for reproducibility and reuse.Open asset ↗ummershakeel03/Rice-Leaf-Diseases-Classificationhtml-lines:1357-1368Dataset · publicThe dataset for this research study is available at: https://doi.org/10.5281/zenodo.15817084.Open asset ↗10.5281/zenodo.15817084html-lines:1357-1368Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Image analysis of pits and grains provide alternative routes for overcoming the invasive approach of genomic tools in the investigation of archaeological or modern plant material, which is only seldom a viable option due to the complex and laborious methodologies required. Nevertheless, any investigation of pit morphology and cultivar interpretation requires a high quality, comprehensive dataset for comparison. Such a benchmark dataset for the morphology of olive (Olea europaea) pits is presented in this paper, designed to facilitate similar research and establish a base for future investigations. The dataset was established by image analysis of pits of 18 olive cultivars that were photographed in both lateral and dorsal positions. A dedicated MATLAB® code was developed to extract the silhouettes of each pit and to calculate 16 morphometric traits of each view of the pit. Altogether, a total of 1008 photos of 504 pits of the 18 cultivars, together with their detailed morphometric description and statistical analysis are available here. These were used to test the accuracy of the dataset and the new approach in representing the different cultivars.
Why it matches plant phenotyping methodsオリーブ核の画像から形態形質を抽出する専用コードと、検証用ベンチマークデータセットを開発・提示しており、植物形質取得法が中心である。
abstractSuch a benchmark dataset for the morphology of olive (Olea europaea) pits is presented in this paper, designed to facilitate similar research and establish a base for future investigations.
Reproduction assets foundThe paper's olive pit images (1008 photos of 504 pits) and morphometric trait data (16 parameters per view) are openly deposited on Zenodo, along with the authors' MATLAB 'PitAnalyzer' software used for silhouette extraction and trait calculation. Both are paper-specific, public, and directly actionable via the Zenodo.Dataset · publicAll the images are available on a dedicated Zenodo repository17. The file name of each image comprises an abbreviation of the cultivar name (Table 1), tree number (a, b or c), pit number (1–30) and the pit position (VD VL for dorsal and lateral, respectively).Open asset ↗Zenodohtml-lines:220-292Code · publicThe code that was used in this work is compiled as a stand-alone software based on MATLAB “PitAnalyzer”. The software is available to download at the following repository, where any use of it should be attributed appropriately to this publication (https://zenodo.org/records/18789307).Open asset ↗Zenodohtml-lines:381-404Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published6 May 2026International Journal of Science, Strategic Management and TechnologyCited by 0 · OpenAlex ↗
Agriculture is the primary source of livelihood for majority of India’s population, with paddy serving as a staple food for a large segment of people. However, paddy cultivation is affected by several challenges that vary with climate, location, and farming practices. Among these, nutrient deficiencies in paddy leaves significantly impact crop yield and quality, making early detection crucial for effective farm management. The following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves . A diverse dataset of paddy leaf images showing different types and severity levels of nutrient deficiencies is collected, and a Convolutional Neural Network (CNN) is used in order for image classification. The model is trained and tested on diverse dataset, demonstrating strong performance in accurately detecting nutrient deficiencies in paddy leaves.
Why it matches plant phenotyping methodsイネ葉画像から栄養欠乏という植物状態をCNNで分類する手法とデータセットが研究の中心であり、植物フェノタイピング手法に該当する。
abstractThe following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves .
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized 'Yotsuboshi' strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model's classifications of 'NormalN' and 'LowN' statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions.
Why it matches plant phenotyping methodsイチゴ葉の窒素状態という植物状態を、RGB画像・深層学習・低コスト撮影筒で非破壊推定する手法を開発し、データセット作成と複数条件で検証しており、表現型取得・抽出法が研究の中心である。
abstractthis study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Tiller production is a critical determinant of turfgrass canopy density and plant performance, yet manual tiller counting is too labor-intensive for large breeding programs. Methods: To address this limitation, we evaluated 770 plants from an interspecific bentgrass hybrid population and developed three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8. Using a large annotated image dataset, we assessed each method's accuracy, robustness under occlusion, and computational efficiency. Results: Although two-stage detectors are often expected to provide superior precision for complex plant structures, the one-stage YOLOv8 model achieved the highest accuracy (R² = 0.97) and processed images substantially faster than Faster R-CNN, while both the edge-based method and Faster R-CNN showed reduced performance in dense canopies. Discussion: These findings demonstrate that recall-oriented one-stage detection can outperform more complex two-stage models for phenotyping tasks involving fine, highly occluded structures. The resulting workflow provides a reliable, high-throughput solution for generating biologically meaningful tiller counts and offers a transferable framework for integrating image-derived phenotypes into genetic analyses and breeding pipelines across grass species.
Why it matches plant phenotyping methodsイネ科植物の分げつ数を画像から自動抽出する複数手法を開発・比較検証しており、植物表現型取得法が研究の中心である。
abstractdeveloped three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Context Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification. Techniques Three deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes-angular leaf spot, bean rust, and healthy leaves-was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance. Outcomes ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis. In conclusion The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.
Why it matches plant phenotyping methods豆葉の病徴を画像から認識する深層学習手法を比較・ベンチマークしており、植物病害状態の取得手法が研究の中心である。
abstractThree deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification.
Reproduction assets foundThe paper's data availability statement points to the Augmented iBean dataset on IEEE DataPort, the public bean leaf image dataset used for all phenotyping/classification experiments in this study. No author analysis code or trained model checkpoints are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/bean-leaf-disease-augmented-ibean-dataset.Open asset ↗ieee-dataport · bean-leaf-disease-augmented-ibean-datasethtml-lines:446-496Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. Its rapid and destructive spread has turned it into a major global threat, causing widespread damage that continues to grow with time. Countries like South Africa, the United States, and Australia have implemented extensive measures to control the spread of PSHB, including the establishment of specialized agricultural support centers for early detection. However, there is still a strong need to make PSHB detection more accessible, allowing even non-experts to easily identify infections at an early stage. Artificial Intelligence (AI) has shown great promise in plant disease detection, but a major challenge in the case of PSHB was the lack of a suitable dataset for training AI models. In the proposed work, we first created a dedicated dataset by collecting images of trees infected with PSHB. We applied a range of preprocessing techniques to refine the dataset and prepare it for AI applications. Building on this, we developed a novel AI-based method, where we trained a deep learning model using a multi-convolutional layer network combined with a Fourier transformation layer. Additionally, an attention mechanism and advanced feature extraction techniques were incorporated to further boost model performance. As a result, the proposed approach achieved an impressive top accuracy of 92.3% in detecting PSHB infections, showing the potential of AI to offer a simple, efficient, and highly accurate solution for early disease detection.
Why it matches plant phenotyping methods植物画像から感染状態を推定するデータセットと深層学習手法を開発し、性能評価まで行っており、病害フェノタイピング手法が中心である。
abstractwe first created a dedicated dataset by collecting images of trees infected with PSHB.
Plant stress monitoring is invaluable in realizing sustainable agriculture because it enables the people practicing it to take early measures to counteract losses in yield caused by environmental stressors like drought and nutrient deficiencies, as well as caused by pathogen infections. The proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level. In order to support this methodology and further studies, we are now publicly releasing a new collection of synchronized thermo-RGB image pairs of stressed and healthy plants, collected both in controlled settings and in the field. The data is labeled to differentiate various stress phenotype and contains over 4286 of images, and hence forms a substantial platform to evaluate multimodal plant phenotyping methods. Empirical evaluations indicate that MMViT model achieves a general classification of 94.3% when using the two modalities, which is better than the single-modality ViT used on the thermal images (85.5%) and the RGB images (93.3%). These experimental results emphasize the performance of multimodal fusion whereby the other spectral cues are used to complement a stress classification. The described framework, together with the useful dataset, will contribute to the advancement of precision agriculture as it is an open and data-driven instrument to monitor plant health automatically.
Why it matches plant phenotyping methods熱画像とRGB画像を統合して植物ストレス表現型を分類するモデルを開発し、公開データセットと性能評価も提示しており、植物フェノタイピング手法が中心である。
abstractThe proposed study presents a new Multi-Modal Vision Transformer (MMViT) architecture that is designed to combine both thermal and RGB imagery to take detection to the next level.
ABSTRACT Smart agriculture based on the use of Artificial Intelligence for crop disease detection to ensure food security. Fungal disease is one of the major causes that affects the quality of the vegetables. Convolutional neural networks (CNNs) and vision transformers (ViTs) enable the detection of crop diseases at an early stage, allowing farmers to take preventive measures and minimize further losses. The proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset. An interactive cross attention module (ICAM) facilitates bidirectional information exchange between CNN and transformer token representations, while an attention guided gated fusion (AGGF) mechanism adaptively combines complementary features. The Indian Crop Visual Disease Dataset (ICVDD‐5) has been developed in a real field for the proposed work with the help of domain experts. The dataset contains 880 diverse images depicting both healthy and diseased specimens of five vegetable crops. The crops selected for this research initiative include Brinjal, Cabbage, Chili, Okra, and Tomato. These five crops are examined for about 21 distinct disease classes. Comprehensive ablation studies are conducted to prove the contributions of each architectural component, including CNN‐only, ICAM‐disabled, and AGGF‐disabled configurations. Experimental results demonstrate that the proposed ICAG‐Net achieves a test accuracy of approximately 70%–73% with improved macro‐F1 score compared to baseline CNN models under identical training settings. The novelty of this work lies in an extensible solution for real world crop disease diagnosis systems and offers insights into hybrid CNN–transformer architectures for small scale agricultural datasets.
Why it matches plant phenotyping methodsCNN・Transformerによる植物病害症状の画像検出手法を開発し、実圃場画像データセットの構築、アブレーション、ベースライン比較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 proposed method introduces an ensemble of Custom CNN to understand multilevel local features and Pretrained ViT to capture global dependencies as well as contextual information from the dataset.
Abstract 3D Gaussian Splatting (3DGS) [KKLD23] has transformed novel‐view synthesis from RGB images, yet remains restricted to the visible spectrum. Many applications, including agricultural monitoring, rely on multi‐spectral imaging, where spectral camera alignment and scalability pose major challenges. We present MS‐Splatting—a multi‐spectral 3DGS framework enabling unified multi‐view consistent reconstruction and rendering across both visible and invisible spectra. Our key component is a neural color representation that encodes per‐primitive features shared across spectral bands, decoded through a shallow multi‐layer perceptron into spectrum‐specific radiance. By leveraging inter‐band correlations, this formulation enhances detail while reducing memory consumption compared to independent band modeling via per‐channel modeling with spherical harmonics. Our method enables accurate parallax‐free novel‐view vegetation index rendering for plant monitoring and enhances RGB novel view synthesis quality by exploiting details revealed through multi‐spectral bands. Our evaluation demonstrates that MS‐Splatting exceeds the current leading methods in both categories. In addition, we introduce a multi‐spectral dataset from aerial captures covering outdoor environments, specifically designed for evaluating these applications. We will release our code and dataset to facilitate further research. The project page is located at: https://meyerls.github.io/ms_splatting
Why it matches plant phenotyping methodsマルチスペクトル3D再構成法を開発し、植物モニタリング用の植生指数レンダリングを実現することが中心で、評価用データセットも提供している。
abstractWe present MS‐Splatting—a multi‐spectral 3DGS framework enabling unified multi‐view consistent reconstruction and rendering across both visible and invisible spectra.
Plant disease diagnosis based on visual symptoms is crucial for preventing yield loss; however, deployment in practical settings remains challenging due to inter-class similarity, background noise, and limited computational resources. This study presents a plant disease classification framework evaluated on a curated multi-crop dataset aggregated from multiple publicly available repositories, comprising 51 disease and healthy classes. The dataset includes approximately 45,000 original images that were expanded through controlled augmentation during training to improve generalization. We benchmark eight ImageNet-pretrained tiny vision transformer architectures trained for up to 50 epochs. Among these, CAFormer-s18 achieved strong validation performance but with increased computational overhead. To enable efficient and computationally lightweight solutions, we design two fully customized convolutional neural networks: PlantaNetLite (1.28M parameters) and PlantaNet (2.58M parameters). After hyperparameter optimization and full 100-epoch training, PlantaNet achieved 99.37% validation accuracy and 99.66% test accuracy with a compact model size (9.85 MB) and moderate computational cost, while PlantaNetLite achieved a best validation accuracy of 99.22% under further parameter reduction. Qualitative Grad-CAM and Grad-CAM++ analyses provide insight into the regions influencing model predictions. Overall, the proposed models demonstrate competitive accuracy while maintaining computational efficiency, highlighting their potential suitability for resource-constrained deployment scenarios.
Why it matches plant phenotyping methods植物の視覚症状から病害状態を推定する画像ベースの表現型解析手法を開発・比較し、データセット上で性能評価しているため、方法が中心的である。
abstractThis study presents a plant disease classification framework evaluated on a curated multi-crop dataset aggregated from multiple publicly available repositories
Reproduction assets foundThe paper's Data Availability Statement explicitly states the curated multi-crop plant disease image dataset used for all classification experiments is publicly available on Kaggle at the authors' URL. No author analysis code, trained model checkpoints, or code repository is disclosed in the supplied blocks.Dataset · publicThe dataset used in this study is publicly available at https://www.kaggle.com/datasets/alimransonet/plant-disease-dataset.Open asset ↗Kaggle · alimransonet/plant-disease-datasethtml-lines:727-758Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published22 Apr 2026International Journal of Agriculture and Animal Production
Grain diseases lead to losses of 20-40% of the harvests each year, representing a threat to the food security of the world. Accurate and automated diagnosis of disease from remote picture taking would be key to prompt and directed interventions. Most current deep-learning approaches, however, are based on controlled lab images, on a single crop and ignore the assessment of disease severity. CropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation. CropDisease-12 is a benchmark dataset of 43200 images belonging to 12 classes representing four major crops (tomato, wheat, rice, and cotton) from PlantVillage and its own disease dataset collected in Yavatmal, Maharashtra, India. When evaluated on the CropDisease-12 test split, CropHybrid-Net outperforms all baselines tested such as standalone Swin-T (94.5%), ViT-B/16 (93.9%) and EfficientNet-B4 (93.7%), with the highest accuracy of 97.8%, and macro f1 score of 97.3%. The average value of AUC for the 12 classes is 0.995. In addition, a comprehensive literature review has been conducted, comprising of 62 papers (2015-2024), and grouped into five research streams: conventional machine learning, CNN-based methods, transfer learning, transformer-based methods, and multi-task severity approaches. The Grad-CAM visualizations are in line with the locations of biologically meaningful lesions. The framework proposed is deployed on common precision agriculture-edge of-use devices and achieves the goal of 39.3 ms per image, being relevant to smart precision agriculture applications.
Why it matches plant phenotyping methods植物病害の画像から病害状態と重症度を推定するCNN・Transformer手法を開発し、ベンチマークデータセット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。
abstractCropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation.
Rice ( Oryza sativa ) production underpins food security in many rice-consuming nations. As a critical developmental transition that directly determines yield and grain quality, flowering dates and timing are genetically complex and highly sensitive to environmental fluctuations. This complexity requires new methods to quantify diurnal floral characteristics, which are essential to hybrid breeding in cereals. Here, we present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice. After monitoring 172 rice accessions selected from the Chinese Rice Mini-Core Collection using cost-effective time-lapse imaging platforms for 16 days, we acquired over 530,000 accession-level images and established the Open Rice Flowering Training (ORFT) dataset, with over 39,000 panicles and 350,000 anthers annotated. Next, a two-stage customised DL model (i.e. YOLACT-Panicle for panicle segmentation and UNet-Anther for anther identification) was trained using the ORFT set, enabling ultra-high-frequency measures of anther extrusion at the minute level. Based on trait analysis, we further fitted curves to dynamically identify diurnal flowering patterns, including key timepoints such as the initial flowering timepoint ( T Ini. ), quickest flowering timepoint ( T Qck. ), and peak flowering time ( T Peak ), and novel traits such as the duration of rapid flowering phase ( P Rpd. ) and flowering density across key phases. After validating BloomSight-derived traits against manual observations, we classified the japonica and indica accessions into three patterns: Slow, Moderate, and Fast, all of which had distinct flowering windows. These analyses helped us integrate phenotypic variations into a genome-wide association study (GWAS), revealing many significant single nucleotide polymorphisms (SNPs) associated with known (e.g. EMF1 , OsMYB8 , and PME42 ) and several repeatedly identified unknown loci (one of these loci has been recently verified by other groups), demonstrating the value of the BloomSight framework. Taken together, we believe that BloomSight provides an ultra-high-frequency framework for diurnal flowering phenotyping, enabling the measurement of biological meaningful floral traits with minute-level resolution that can enable flowering-related developmental studies and hybrid-breeding applications in rice and more broadly benefit the plant and crop research community.
Why it matches plant phenotyping methodsイネの開花動態を高頻度画像と深層学習で抽出するフェノタイピング基盤を開発し、データセット構築と手動観測による検証も行っているため、方法が研究の中心である。
abstractwe present BloomSight, an ultra-high-frequency and deep-learning (DL) powered framework for phenotyping and measuring minute-level flowering dynamics in japonica and indica rice
Reproduction assets foundThe paper's Data and code availability statement explicitly provides public access to the ORFT annotated image dataset (BioStudies S-BSST2157), Python source code for floral trait analysis (GitHub The-Zhou-Lab/BloomSight), and trained DL models (GitHub releases). SRA accessions are molecular sequencing data, not phenotCode · publicPython-based source codes for automating floral trait analysis using the above data are accessible via our GitHub repository ( https://github.com/The-Zhou-Lab/BloomSight ).Open asset ↗The-Zhou-Lab/BloomSightlines:336-349Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Plant diseases are the cause of heavy losses of crop production and, therefore, a big contributor to food shortages. Identifying these diseases as early as possible is important to limit the negative effects that these diseases have on the yields, as slow response time will lead to the spread of diseases and further loss. Traditionally, trained staff will go into the fields, multiple times during the growth period, and inspect the plants in samples through field disease monitoring. These traditional processes are time-consuming and costly, and can be error-prone, if the staff is not properly educated or if the staff simply makes mistakes due to oversight, for example. To aid farmers with the process of correctly identifying diseases, artificial intelligence deep learning methods have been employed in recent years. However, to train such deep learning models, one needs to obtain sufficiently large and high-quality datasets and a model architecture that is capable of extracting relevant features to accurately classify the plant leaves. Datasets are still a limitation in the field of plant leaf disease classification. As such, domain adaptation methods such as transfer learning are often employed to overcome this data shortage. However, in current research, these domain adaptation methods almost exclusively rely on ImageNet as the pretraining dataset, a dataset that is domain unrelated to plant leaf disease detection, and models are often left unmodified and un-optimized as a result. In this work, we propose the pretraining of an improved attention-based and SiLU-activated DenseNet201 architecture called PLDC-Net that is pretrained on a large-scale plant leaf disease dataset constructed by the authors to create a domain-specific base model for better domain adaptation to new plants and diseases, validating the improved results through transfer learning, fine-tuning, one-shot learning, and few-shot learning. PLDC-Net has managed up to just over 24% improvements in F1-Score over the baseline in domain adaptation results.
Why it matches plant phenotyping methods植物葉の病害状態を画像分類するモデルと専用データセットを開発し、転移学習等で性能検証しており、表現型取得・推定手法が研究の中心である。
abstractwe propose the pretraining of an improved attention-based and SiLU-activated DenseNet201 architecture called PLDC-Net that is pretrained on a large-scale plant leaf disease dataset constructed by the authors to create a domain-specific base model
The application of computer vision in precision agriculture has demonstrated considerable promise in automated plant disease detection. However, the effectiveness of such approaches is strongly dependent on the availability of high-quality, domain-specific datasets, particularly for deployment on resource-constrained edge devices. This paper introduces CocoaDetectDB, a publicly available image dataset developed for the detection of cocoa plant diseases under Tiny Machine Learning (TinyML) constraints. The dataset comprises images of healthy cocoa pods and three major cocoa diseases—Cocoa Black Pod Disease (CBD), Cocoa Swollen Shoot Virus Disease (CSSVD), and Frosty Pod Rot (FPR)—captured under real-world field conditions and supplemented with openly accessible public data. Images were curated, cleaned, and resized to a uniform resolution of 112 × 112 pixels to support low-memory and low-power inference. To validate the suitability of the dataset for automated disease classification, baseline experiments were conducted using MobileNetV2 and a lightweight quantized TensorFlow Lite model. Experimental results demonstrate classification accuracies of 99.13% and 93.75%, respectively, indicating that CocoaDetectDB contains sufficiently discriminative features for both conventional lightweight models and TinyML deployment. The dataset is intended to support future research in cocoa disease detection, edge AI, and resource-efficient agricultural monitoring systems.
Why it matches plant phenotyping methodsココア植物の病害状態を画像で判定する公開データセットを構築し、軽量モデルで適合性を検証しており、画像ベースの植物表現型取得・分類が中心である。
abstractThis paper introduces CocoaDetectDB, a publicly available image dataset developed for the detection of cocoa plant diseases under Tiny Machine Learning (TinyML) constraints.
Accurate identification of plant leaf diseases is essential for modern agriculture. This paper presents SynerFANet, a hybrid deep learning framework designed to improve disease detection in sugarcane leaves. SynerFANet integrates two core modules: AdaptiveMBNet and WiseAttentionNet for comprehensive feature extraction and processing. AdaptiveMBNet combines MBConv layers with attention mechanisms to improve feature quality and reduce computation, enabling more accurate disease detection. WiseAttentionNet incorporates attention mechanisms into depthwise and expansion layers to enhance feature recalibration. The combination of the two cores inside SynerFANet can improve the representation capacity and robustness of the overall model. We evaluate the model using two datasets: a new proposed field-collected SugarLeaf-IDN dataset and the publicly available PlantVillage dataset. SynerFANet achieves superior accuracy with a moderate parameter size and GFLOPs, providing a balanced trade-off between predictive performance and computational cost, and exhibiting stable convergence during training. SynerFANet achieves 95.81% validation accuracy on our challenging real-world SugarLeaf-IDN dataset and 99.85% (SOTA) on the controlled PlantVillage benchmark.
Why it matches plant phenotyping methodsサトウキビ葉の病徴を画像から検出する深層学習モデルを開発し、実フィールドおよび公開データセットで性能評価しており、植物表現型取得・判定手法が中心である。
abstractThis paper presents SynerFANet, a hybrid deep learning framework designed to improve disease detection in sugarcane leaves.
RGB / grayscaleFlowerClassificationGrowth / development / phenology
This study presents a comprehensive BDFlower growth stage dataset designed to support research in precision agriculture and floriculture. The dataset encompasses eight common flower species found in Bangladesh: Bush Allamanda, Red Hibiscus, Yellow Bell, Pinwheel Flower, Pink Periwinkle, White Madagascar Periwinkle, Marvel of Peru, and White Hibiscus. Each species is represented across three growth stages-Early, Mid, and Full-resulting in 24 distinct classes. A total of 23,334 colour images are included, comprising 3889 original photographs and 19,445 augmented samples generated with five augmentation techniques. Bush Allamanda contains 499 images, Red Hibiscus contains 489 images, Yellow Bell contains 483 images, Pinwheel Flower contains 497 images, Pink Periwinkle contains 452 images, White Madagascar Periwinkle contains 472 images, Marvel of Peru contains 468 images and White Hibiscus contains 529 images. Each image was collected using smartphone camera at three-time intervals per day, spaced eight hours apart, to capture natural variations in lighting and appearance. The dataset is further organized into training, validation, and testing splits, enabling direct application to machine learning workflows. This is a publicly available dataset specifically curated for flower growth stage classification. In addition to dataset collection, we also conducted a simple experiment using a CNN model to evaluate its performance on this dataset. It is intended to facilitate the development of robust computer vision models that can monitor flower development, with potential applications in automated plant phenotyping, crop monitoring, and digital floriculture systems.
Why it matches plant phenotyping methods花の生育段階を画像で分類する公開データセットを構築し、CNN評価も行っており、植物表現型取得・解析が研究の中心である。
abstractThis is a publicly available dataset specifically curated for flower growth stage classification.
Reproduction assets foundThe paper's own flower growth-stage image dataset (BDFlower) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, directly reproducing the paper's phenotyping (flower growth stage) image measurements. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicRepository name: Data Mendeley
Data identification number: 10.17632/m8g2wynwyr.2
Direct URL to data: https://data.mendeley.com/datasets/m8g2wynwyr/2Open asset ↗10.17632/m8g2wynwyr.2html-lines:94-129Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
We introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties (Sethy, Prabira; Pamerelli, Ranjith, 2026; Mendeley Data, V1, doi:10.17632/khfd7pzskd.1) and present an identification approach based on deep residual transfer learning. Using a transfer-learned ResNet-50 with image augmentation and an 80/10/10 train/validation/test split, the model attains 100.0% validation accuracy and 98.74% accuracy on the held-out test set. Per-class one-vs-rest AUCs on validation are 1.000 for all 22 classes; test AUCs range from 0.9924 to 1.000 (mean ≈ 0.999), with separate confusion matrices and ROC curves provided for validation and test partitions. These results demonstrate that deep residual transfer learning can robustly discriminate closely related panicle morphotypes when trained on a carefully curated dataset. We release the dataset to support reproducible research in germplasm identification, varietal purity assessment, and automated phenotyping.
Why it matches plant phenotyping methodsイネ穂画像のベンチマークデータセットと、深層学習による穂形態の自動識別手法が研究の中心であり、再現可能な植物表現型解析基盤として明示されている。
abstractWe introduce ``Primitive Indian Paddy Panicle Images,'' a benchmark image dataset of 22 primitive Indian rice panicle varieties
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 5 Sept 2026
To improve crop genetics, high-throughput, effective and comprehensive phenotyping is a critical prerequisite. While such tasks were traditionally performed manually, recent advances in multimodal foundation models, especially in vision-language models (VLMs), have enabled more automated and robust phenotypic analysis. However, plant science remains a particularly challenging domain for foundation models because it requires domain-specific knowledge, fine-grained visual interpretation, and complex biological and agronomic reasoning. To address this gap, we develop PlantXpert, an evidence-grounded multimodal reasoning benchmark for soybean and cotton phenotyping. Our benchmark provides a structured and reproducible framework for agronomic adaptation of VLMs, and enables controlled comparison between base models and their domain-adapted counterparts. We constructed a dataset comprising 385 digital images and more than 3,000 benchmark samples spanning key plant science domains including disease, pest control, weed management, and yield. The benchmark can assess diverse capabilities including visual expertise, quantitative reasoning, and multi-step agronomic reasoning. A total of 11 state-of-the-art VLMs were evaluated. The results indicate that task-specific fine-tuning leads to substantial improvement in accuracy, with models such as Qwen3-VL-4B and Qwen3-VL-30B achieving up to 78%. At the same time, gains from model scaling diminish beyond a certain capacity, generalization across soybean and cotton remains uneven, and quantitative as well as biologically grounded reasoning continue to pose substantial challenges. These findings suggest that PlantXpert can serve as a foundation for assessing evidence-grounded agronomic reasoning and for advancing multimodal model development in plant science.
Why it matches plant phenotyping methodsPlantXpertは作物フェノタイピング向けの画像ベンチマークとVLM評価基盤を構築しており、表現型解析手法・データセットの開発が研究の中心である。
abstractwe develop PlantXpert, an evidence-grounded multimodal reasoning benchmark for soybean and cotton phenotyping.
Plant diseases have a significant impact on global food security, especially in staple crops like maize (Zea mays). Traditional disease detection systems depend on professional visual inspection, which is labor-intensive, time-consuming, and not scalable for large agricultural areas. Convolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically. A curated dataset of approximately 7,000 high-resolution maize leaf photos was created, representing four classes: healthy, Common Rust (Puccinia sorghi), Northern Leaf Blight (Exserohilum turcicum), and Gray Leaf Spot (Cercospora zeae-maydis). Data were sourced from the Plant Village dataset, real-world field collections from Indian farms, and supplemented synthetically to simulate varied climatic circumstances. Advanced methods including as adaptive learning rate scheduling, gradient clipping, and significant data augmentation were used to train a bespoke CNN model that was improved by transfer learning with ResNet50 and VGG16 backbones. The model attained a test accuracy of 98.2%, beating classic machine learning algorithms like SVM (88.5%) and Random Forest (84.3%). Visualization approaches such as feature maps, Grad-CAM, and LIME improved interpretability and showed the model's capacity to locate disease-relevant features. Web-based user engagement is made possible by deployment-ready implementation, which enables farmers to upload leaf photos for immediate diagnosis. With the potential to cut maize crop losses by 20–30%, this research offers a scalable and affordable alternative to early disease detection in precision agriculture. Future research will investigate autonomous farm management with drone-based real-time surveillance and IoT system integration.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から推定するCNN手法を開発し、データセット、比較評価、精度検証、解釈性分析まで行っており、植物表現型取得が中心である。
abstractConvolutional Neural Networks (CNNs) are used in this study's deep learning (DL) architecture to detect maize leaf diseases accurately and automatically.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
It has been repeatedly shown that spike productivity is the main component of wheat yield. The main spike parameters related to productivity are size, the number of grains and spikelets per spike, and the presence or absence of awns. In modern genetic research, morphometric analysis of hundreds and thousands of spikes is required to determine the loci that control spike productivity traits. On the other hand, thousands of accessions in modern collections of wheat genetic resources need detailed description. These considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods. These methods allow for automated acquisition of trait values that can serve as the basis for digital plant collections. Here we propose an extended set of spike characteristics obtained both manually and through digital image analysis and present plant characterization. These data form the basis of the updated version of the SpikeDroidDB database (http://spikedroid.biores.cytogen.ru/). The digital description of the spike consists of two blocks. The block of uploaded data includes a description of the plant and contains five tables: collection; variety sample (year of cultivation (vegetation), sowing identifier, taxonomic information, etc.), planting site, and characteristics of the spike determined manually (length, width of frontal and lateral views, type and color of the spike, etc.) The block of extracted features includes spike characteristics obtained by digital phenotyping and contains six tables: characteristics of the spike outline in the image; characteristics of the quadrangle model, values of the color components of the spike, dominant colors of the spike, and texture characteristics of the spike in the image. The most illustrative and significant features of the spike have been identified, allowing for the formation of the spike digital certificate, which includes size, shape, and color features derived from the digital images. The features forming the digital certificate have been compared between two wheat species, T. aethiopicum and T. carthlicum. It is shown that the features of the digital certificate allow for a clear representation of the spike model and the identification of distinct parameters: colors of the spike and awns and roundness of the frontal view of the spike. The database interface has been supplemented with the ability to upload data on plant and spike characteristics, as well as their images, in the batch mode.
Why it matches plant phenotyping methods小麦穂の画像解析による形態形質の自動取得、デジタル表現、データベース基盤を中心に開発・提示しており、植物フェノタイピング手法が研究の中核である。
abstractThese considerations motivate the development of digital technologies for describing spike traits in wheat, which can be achieved through image analysis methods.
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Abstract— Plant diseases remain a persistent threat to global agricultural output, annually destroying an estimated 20–40% of total crop yields worldwide. Prompt and reliable identification of infection at early stages is indispensable for guiding timely intervention and protecting food security. Conventional diagnostic workflows, which depend on field visits by trained agronomists, are inherently subjective, time-consuming, and impractical at the scale of modern large-area farming. To address these operational gaps, this study proposes a fully automated deep learning pipeline tailored for classifying plant diseases from photographs collected under uncontrolled, realistic field conditions. The core of the architecture is a hybrid model that couples EfficientNet-B4 [3] with a Convolutional Block Attention Module (CBAM) [4], equipping the network with the capacity to localize and emphasize abnormal leaf tissue while filtering out irrelevant scene elements. The system is developed and benchmarked on a purpose-built dataset of 54,306 labeled images representing 26 disease classes across 14 crop species. A structured preprocessing workflow—encompassing Contrast Limited Adaptive Histogram Equalization (CLAHE), mosaic-based class balancing, and mixup regularization [10]—is incorporated to enhance the model's tolerance to lighting inconsistencies and skewed class distributions. On the held-out test partition, the proposed model attains a top-1 accuracy of 96.7%, outperforming six well-established CNN baselines. Gradient-weighted Class Activation Mapping (Grad-CAM) [11] visualizations further confirm that the model directs its attention toward pathologically relevant regions, lending credibility to its predictions. These properties collectively position the framework as a strong foundation for lightweight, smartphone-deployable disease advisory tools for smallholder farmers. Keywords—Plant Disease Detection, Deep Learning, EfficientNet, CBAM Attention, Grad-CAM, Real-Field Image Dataset, Convolutional Neural Networks, Precision Agriculture
Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習パイプラインを開発し、専用データセットとベースライン比較で検証しており、病害表現型の取得・推定が中心である。
abstractthis study proposes a fully automated deep learning pipeline tailored for classifying plant diseases from photographs collected under uncontrolled, realistic field conditions.
This dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision. The images were collected in situ from experimental and commercial vineyards in the north of Portugal, covering different vineyard conditions and management practices. The dataset includes healthy leaves from three grapevine Portuguese cultivars Loureiro, Viosinho and Malvasia Fina, photographed under natural lighting conditions without artificial adjustments. It is organized into five categories: healthy leaves and leaves showing symptoms of downy mildew ( Plasmopara viticola ), powdery mildew ( Erysiphe necator ), Esca complex and Erineum Mite ( Colomerus vitis ). Images are provided in JPEG format with a resolution of 3000 × 3000 pixels and 1024 × 1024 pixels and arranged in folders by health status and disease type. This dataset can be used for machine learning and deep learning applications in disease detection/classification, cultivar identification, and can support other precision agriculture applications, as well as being used for agricultural robotics and educational purposes. An evaluation on three deep learning architectures demonstrated the suitability of the dataset into separating the five classes.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像化した再利用可能なデータセットで、植物の健康状態・病害状態の画像ベース推定を支えることが中心です。深層学習による5クラス分類評価も記載されています。
abstractThis dataset consists of a collection of high-resolution RGB images of grapevine leaves, designed to support research in plant pathology, precision viticulture, and computer vision.
Reproduction assets foundThe paper is a Data in Brief article describing a public Zenodo repository of RGB grapevine leaf images (healthy plus downy mildew, powdery mildew, Esca complex, erineum mite) collected for plant disease/phenotyping research, with explicit data accessibility details. No author analysis code or trained model checkpointsDataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.17343473
Direct URL to data: https://zenodo.org/records/17343473Open asset ↗Zenodo · 10.5281/zenodo.17343473html-lines:93-144Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Accurate detection of wheat seedlings is crucial for monitoring early population establishment and evaluating sowing quality. However, detection in real field environments remains challenging due to diverse seedling morphology, varying planting densities, occlusion, and complex background interference. Although deep learning has promoted the development of agricultural vision systems, existing wheat seedling detection methods still suffer from two key limitations: (1) insufficient modeling of spatial contextual relationships, leading to degraded accuracy under dense planting and complex field conditions; and (2) difficulty in balancing detection performance and computational efficiency, restricting real-time deployment on resource-limited agricultural devices. To address these issues, this study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules: (1) the Depthwise-Transformer-Vision (DTV) module integrates Depthwise Separable Convolutions (DSC), Vision Transformer, and multi-scale spatial pooling to efficiently represent local structures, spatial context, and global patterns of wheat seedlings; (2) the Feature Enhancement Module(FEM) incorporates coordinate attention to enhance seedling-related features while suppressing background interference; and (3) the Feature Coordination Module (FCM) performs multi-scale feature interaction with reduced computational cost. These components jointly improve robustness under dense planting and complex field conditions while maintaining lightweight deployment characteristics. Furthermore, we construct the Wheat Seedling Dataset (WSD), covering multiple planting densities, varieties, and field environments across two growing seasons. Experimental results show that TCE-YOLO outperforms mainstream detectors while maintaining high efficiency, providing a deployable solution for wheat seedling detection under real field conditions.
Why it matches plant phenotyping methods小麦苗の画像検出手法を開発し、複数条件・作期を含むデータセットを構築して性能比較しているため、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes Transformer-Coordinate Attention-Efficient YOLO (TCE-YOLO), a detection framework designed with three key modules
Centralizing valuable community data and resources into a user-friendly interface and accessible repository has become essential for agricultural science; embracing Findable Accessible, Interoperable, and Reusable (FAIR) principles is now standard for effective databases. SorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community. The SorghumBase team curates genomic, transcriptomic, variation, and phenotypic information and aggregates community events, providing rich visualizations and bulk data access. The modular framework of the database is built with open-access software to yield a robust, modifiable, and sustainable data infrastructure. Release 9 of SorghumBase includes: (i) 88 sorghum reference genomes and an updated pan-gene index, (ii) over 100 million variants have been mapped onto the 2 genomes, BTx623 and Tx2783, (iii) assignment of 41 million Reference Cluster SNP identifiers (rsIDs) from BTx623 across the pan-genome, (iv) updated gene search homology, gene expression, and germplasm visualizations and features, (v) added and standardized 234 phenotypic data from 40 community-generated GWAS studies and 148 traits from the Sorghum QTL Atlas (Oz Sorghum), (vi) improved news, funding, and a research content management system for community access and interaction, (vii) outreach materials including training documents and videos, and (viii) community engagement initiatives through training and working groups. SorghumBase serves as a hub for sorghum data and stakeholder engagement while promoting community standards to drive research and multi-omics breeding approaches.
Why it matches plant phenotyping methodsソルガムの表現型データを標準化・統合し、可視化とアクセスを提供する研究基盤であり、表現型情報のデータ基盤として中心的です。
abstractSorghumBase (https://www.sorghumbase.org) is a knowledgebase designed for the sorghum research community.
Abstract Abstract. Accurate and timely identi cation of plant diseases is essential for improving crop productivity and ensuring sustainable agricultural practices. This paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops: Apple, Banana, Citrus, Guava, Mango, and Papaya. The dataset comprises high-quality RGB images representing both healthy and diseased samples, with disease symptoms including spots, lesions, discoloration, blight, rot, and fungal and bacterial infections captured under diverse real-world conditions. Variations in illumination, background complexity, viewing angles, growth stages, and symptom severity are intentionally included to enhance the robustness and generalizability of learning models developed using this data. The dataset is structured in a class-wise manner and preprocessed to support direct integration with deep learning frameworks. It is extensively used to train, validate, and evaluate deep learning based plant disease classi cation models, enabling automatic feature learning from raw images without manual intervention. Experimental usage demonstrates that the dataset is well suited for convolutional neural networks and attentionbased architectures, facilitating e ective discrimination between multiple disease categories across di erent crops and plant organs. By providing a uni ed multi-crop, multi-disease benchmark, this dataset aims to accelerate research in automated crop disease diagnosis, precision agriculture, and intelligent decision-support systems for sustainable farming.
Why it matches plant phenotyping methods植物の葉・果実の病徴画像を収録したデータセット／ベンチマークであり、病害状態の画像ベース推定を中心的に扱うため。
abstractThis paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops
Reproduction assets foundThe paper's core asset is the ABCGMP fruit and leaf disease image dataset, publicly deposited on Mendeley Data, with author analysis code also stated to be available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:33 lines:1-56Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:33 lines:1-56Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 5 Sept 2026
Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, organs, growth stages, and field conditions. General-purpose vision foundation models offer a natural route to label efficiency, but their web-scale pretraining objectives transfer weakly to agricultural imagery, where semantics are often determined by fine organ geometry inside repetitive, texture-dominated scenes. We introduce SPROUT, a diffusion foundation model for multi-crop plant phenotyping. SPROUT learns from 2.6 million unlabeled open-field images (MCD-2.6M) using a pixel-space Diffusion Transformer, and selects transferable features with a label-free effective-rank criterion over denoising timesteps. This design shifts pretraining from crop-based invariance to structure-preserving denoising, making the representation better aligned with dense phenotyping tasks. We evaluate SPROUT across dense phenotyping tasks, including organ segmentation, crop-weed parsing, depth estimation, and counting. SPROUT consistently improves over strong web-pretrained baselines, with the largest gains on dense structural prediction, and shows favorable label and compute efficiency compared with general-purpose and crop-specific foundation models. The source code and MCD-2.6M dataset are publicly available.
Why it matches plant phenotyping methods植物フェノタイピング向けの拡散基盤モデルを開発し、複数の作物・器官に対する画像ベースの構造推定タスクで評価しているため、表現学習法とデータセットが中心的な方法論的貢献である。
abstractWe introduce SPROUT, a diffusion foundation model for multi-crop plant phenotyping.
Accurate fruit detection in citrus orchards is essential for yield estimation, precision harvesting, and automated orchard monitoring. Although UAV-based imaging has become a powerful tool in precision agriculture, publicly available datasets for orange fruit detection remain scarce, particularly those integrating multispectral data under real field conditions. This lack of open resources limits the development and benchmarking of robust deep-learning models for cross-spectral and illumination-invariant detection. We present CampanetaOrangeFruit, a dataset acquired with a DJI Mavic 3 Multispectral UAV flying at 14 m above ground level over a commercial citrus orchard in Corbera, Valencia, Spain. The dataset comprises 550 synchronized captures (RGB + four multispectral bands: R, G, RE, NIR) for a total of 2750 images and 301,232 annotated orange instances. Each image includes YOLOv5-format annotations generated through a homography-based reprojection process, ensuring geometric consistency across spectral modalities. CampanetaOrangeFruit uniquely provides pixel-aligned, cross-spectral UAV imagery with fine-grained fruit-level annotations, enabling research on fruit detection, yield estimation, and domain adaptation in real-world orchard environments. It represents a valuable benchmark for advancing deep-learning approaches in precision agriculture and sustainable citrus production.
Why it matches plant phenotyping methods柑橘果実を対象としたUAV画像データセットとアノテーションを提供し、果実検出・収量推定モデルの開発およびベンチマークを中心課題とするため、植物フェノタイピング用データセットとして採用する。
abstractpublicly available datasets for orange fruit detection remain scarce
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Seed varietal purity and physiological viability are critical determinants of crop yield and quality. However, non-destructive assessment faces significant challenges in fine-grained variety discrimination and the perception of internal defects. This study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation. We introduce a Knowledge–Vision Alignment (KVA) module that incorporates encyclopedic morphological descriptions to guide feature learning, significantly enhancing few-shot generalization. Complementarily, a Dual-Spectral Fusion (DSF) module combines high-resolution RGB textures with penetrative Short-Wave Infrared (SWIR) sensing to jointly characterize external and internal traits. Experimental results on a custom multimodal dataset of 6000 samples across 12 crop categories demonstrate that S3-Net achieves 96.9% accuracy for species identification and 95.8% for viability detection. Notably, S3-Net outperforms ResNet-50 by 40.3% in extreme 1-shot scenarios. With a stable inference throughput of 95 fps, the system meets the high-throughput demands of industrial-scale applications, providing a robust and efficient solution for intelligent agricultural phenotyping.
Why it matches plant phenotyping methods種子の生理的状態（viability）をRGB・SWIR融合で非破壊推定するセンシング／AI手法が研究の中心であり、データセットと性能評価も提示しているため。種識別のみなら対象外だが、viability検出は植物状態のフェノタイピングに該当する。
abstractThis study proposes S3-Net, an AI-driven multimodal sensing framework that integrates vision–language alignment with dual-spectral sensor fusion for autonomous seed quality evaluation.
Introduction Intelligent rice disease prevention and control are crucial components in the development of smart agriculture. In recent years, with the rapid advancement of computer vision technologies, a variety of deep learning-based methods for rice disease identification have been proposed, and some models have already surpassed the diagnostic performance of agricultural technicians. However, the existing models generally suffer from high computational complexity and limited generalization capabilities, rendering them difficult to deploy on edge devices for real-time and accurate disease recognition under offline field conditions. Methods To promote engineering applications of related technologies, this study investigated leaf disease identification methods for mountain-grown rice oriented toward edge intelligence. Based on a self-constructed image dataset of mountain rice leaf diseases and following the design principles of lightweight convolutional neural networks, a novel lightweight mountain rice disease recognition model architecture suitable for edge intelligent devices was constructed. Furthermore, a mountain rice leaf disease recognition application was developed for smartphones on the Android platform. Results Field validation experiments demonstrated that the application achieves an average accuracy of 92.41% across multiple disease categories and an average inference speed of approximately 22.47 frames per second on various smartphone models, indicating high real-time performance and recognition accuracy. Discussion The research outcomes will provide a reliable theoretical foundation and technical support for the intelligent prevention and control of mountain rice diseases.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から認識する軽量CNN、データセット、スマートフォン実装を開発し、精度と推論速度を検証しており、植物病害表現型の取得・抽出が中心である。
abstracta novel lightweight mountain rice disease recognition model architecture suitable for edge intelligent devices was constructed
Grapevine Trunk diseases (GTDs) represent a major threat for the wine industry. Despite several break-through, their etiology remains unclear and no curative treatment is currently available. Wood anatomy and water transport contribute to the symptoms of young plant decline. This study investigates wood anatomical alterations in two Alsatian grapevine cultivars presenting different susceptibility to GTDs, focusing on wood structure over six months of vegetative growth and in response to infection. Using a validated FasGa staining protocol, wood sections from transverse, tangential, and radial directions were stained to differentiate lignified and cellulosic tissues. Microscopic analysis was performed at x4, x10, and x40 magnifications, yielding a dataset of 4771 images. To support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits. Pre-established woody tissues presented higher xylem vessels diameter in Gewurztraminer than Riesling, with a dorsoventral arrangement whereas the number of vessels remained the same all over the cross section. No significant anatomical changes were observed in established woody tissues, whereas newly formed xylem anatomy showed a possible rearrangement during infection, especially in Gewurztraminer cultivar. Furthermore, colorimetric analysis quantified the lignification of woody tissues in response to wounding damage compared to un-treated plants. While definitive conclusions remain limited due to the experimental timeframe and sample variability, the findings highlight the need for longer-term studies and broader cultivar evaluation. Code and microscopy images have been made publicly available, providing a scalable digital tool for future research in plant vascular systems.
Why it matches plant phenotyping methods植物組織画像から木部解剖形質と木化を定量する計算モデルを開発・検証し、大規模画像データセットと公開コードを提供しており、表現型取得手法が研究の中心である。
abstractTo support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits.
Reproduction assets foundThe paper's microscopy image dataset (4771 grapevine wood images) is publicly deposited on Zenodo with an explicit DOI matching an allowed URL. The authors also state their Python analysis pipeline is available at github.com/courbot/vineside, but that URL is not among the allowed URLs, so only the Zenodo image dataset,Dataset · publicThis database can benefit the research community, and is publicly available
online at https://doi.org/10.5281/zenodo.18850060 [35].Open asset ↗Zenodo · 10.5281/zenodo.18850060pdf-page:4 lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The accuracy of papaya leaf disease classification is of highest priority in early-stage plant health surveillance and green farming. This paper presents HASPNet, a hierarchically attentive signal-preserving network specially designed for fine-grained papaya leaf disease classification from the newly proposed BDPapayaLeaf Dataset of 2,159 high-resolution images of five pathological classes. The network introduces a coordinated hierarchical attention framework; by integrating residual feature fusion with sequential SE and CBAM modules, HASPNet synchronizes multi-scale signal preservation with dual-stage recalibration, allowing the model to isolate subtle pathological signatures while maintaining global structural integrity. The architecture is additionally optimized using Swish activation, depthwise separable convolutions, and a cosine warm-up learning rate schedule to produce efficient gradient flow and convergence stability. Exhaustive ablation experiments validate the critical contribution of each architectural block, and the complete HASPNet obtains an accuracy of 93.87% (corresponding to a 6.13% error rate), an F1-score of 94%, and a reduced inference time of 21.33 ms, by a large margin surpassing top state-of-the-art backbones like MobileNetV2, DenseNet121, Inception-V3, Xception, and ResNet50 in both performance and computational efficiency. Additionally, activation function experiments validate Swish as the optimal non-linearity for this task. Interpretability is enhanced using Grad-CAM visualizations, which validate the model's attention on disease-specific leaf regions. Given the lack of existing benchmarks for the BDPapayaLeaf Dataset, HASPNet is evaluated against standard CNN backbones (MobileNetV2, ResNet50, etc.) to establish a performance-complexity baseline, justifying its selection for resource-constrained agricultural environments. The results validate the model's domain adaptability, and it is a strong candidate for real-world agricultural diagnostic systems and a valuable addition to vision-based plant pathology.
Why it matches plant phenotyping methodsパパイヤ葉の病徴画像から病害状態を分類する深層学習手法を開発し、アブレーション、既存CNNとの比較、Grad-CAMで技術検証しているため、植物フェノタイピング手法が中心です。
abstractThis paper presents HASPNet, a hierarchically attentive signal-preserving network specially designed for fine-grained papaya leaf disease classification from the newly proposed BDPapayaLeaf Dataset of 2,159 high-resolution images of five pathological classes.
Existing tomato datasets often focus on short-term experiments or lack integrated environmental and agronomic data. We present Horti-M3-Tomato, a comprehensive three-year dataset collected in Northeast China's greenhouse, including high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices. Spanning three growing seasons (2023-2025), the dataset integrates temporal imaging, environmental monitoring, soil data, and manual phenotypic and yield records. Horti-M3-Tomato supports research on growth dynamics, genotype-environment interactions, and provides a benchmark for AI-based phenotyping and precision horticulture. The dataset is openly available for further research in controlled-environment agriculture.
Why it matches plant phenotyping methodsトマトの画像・環境センサーデータ・手動表現型記録を統合したデータセットであり、AIベースの表現型解析のベンチマークとして明示されているため、表現型データ基盤が中心です。
abstractincluding high-resolution RGB images, environmental sensor data (recorded every 30 minutes), soil conditions, and detailed agronomic records such as yield data and management practices.
Seed selection constitutes the initial and one of the most critical steps in agricultural productivity. The identification of high-quality seeds is a labor-intensive and costly process that requires considerable expertise. Within the scope of smart farming applications, this study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images. The proposed model determines whether a seed will germinate using RGB images and morphological traits automatically extracted from these images. The dataset used in this study comprises a total of 3,645 images belonging to three different seed types. For each seed type (okra, eggplant, and tomato), 405 seed images were acquired from three distinct imaging sources (digital microscope, camera, and scanner), labeled, and subsequently sown in seed trays. The germination status of each sown seed was systematically monitored and matched with its corresponding image data. The dataset was partitioned into 80% training and 20% testing subsets. Following 5-fold stratified cross-validation, the proposed model achieved an average weighted F1-score of 0.95 on the training set and 0.93 on the testing set for germination capacity prediction. The performance of the proposed model was further compared with widely used deep convolutional neural network architectures, including VGG19, ResNet50, and EfficientNetB5. Comparative results demonstrate that the proposed model provides competitive and robust performance for seed germination prediction. Overall, the findings indicate that the proposed approach can effectively be utilized for automated seed germination prediction. Future research should evaluate the generalizability of the model by conducting performance assessments on additional seed types.
Why it matches plant phenotyping methodsRGB画像から種子の形態形質を自動抽出し、発芽状態を予測する深層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含める。
abstractthis study proposes a deep learning–based model designed to automate the seed selection process by accurately predicting seed germination capacity from seed images.
Doubled haploid (DH) technology can fast-track crop breeding. Haploid induction yields haploids with only one set of genomes, which are usually sterile. Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines. Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent. While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF. Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification. AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers. This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications. We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds. Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation. We further demonstrate AutoSiQ's utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant). A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering. By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis .
Why it matches plant phenotyping methodsハプロイド稔性を画像から定量するデータセットと深層学習パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.
Reproduction assets foundThe paper's AutoSiQ dataset (annotated scanned Arabidopsis inflorescence images with seven-class silique ontology and manual fertility counts) is publicly deposited on Zenodo per the data availability statement. The YOLOv5 GitHub repository is a generic third-party library, not an authors' code asset.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://zenodo.org/records/17905566 .Open asset ↗zenodo · 17905566lines:367-402Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.
Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。
abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Early diagnosis of plant diseases is critical for global food safety, yet most AI solutions lack the generalization required for real-world agricultural diversity. These models are typically constrained to specific species, failing to perform accurately across the broad spectrum of cultivated plants. To address this gap, we first introduce the FloraSyntropy Archive, a large-scale dataset of 178,922 images across 35 plant species, annotated with 97 distinct disease classes. We establish a benchmark by evaluating numerous existing models on this archive, revealing a significant performance gap. We then propose FloraSyntropy-Net, a novel federated learning framework (FL) that integrates a Memetic Algorithm (MAO) for optimal base model selection (DenseNet201), a novel Deep Block for enhanced feature representation, and a client-cloning strategy for scalable, privacy-preserving training. FloraSyntropy-Net achieves a state-of-the-art accuracy of 96.38% on the FloraSyntropy benchmark. Crucially, to validate its generalization capability, we test the model on the unrelated multiclass Pest dataset, where it demonstrates exceptional adaptability, achieving 99.84% accuracy. This work provides not only a valuable new resource but also a robust and highly generalizable framework that advances the field towards practical, large-scale agricultural AI applications.
Why it matches plant phenotyping methods植物病害画像の大規模データセットと診断モデルを開発・ベンチマークしており、植物の病害状態を画像から推定する方法が中心である。
abstractWe establish a benchmark by evaluating numerous existing models on this archive
Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.
Why it matches plant phenotyping methods樹木の枝・樹冠形状を対象とするステレオ深度推定データセットを開発し、画素単位の視差ラベルと実画像との比較検証を提供しており、植物構造の取得方法が中心である。
abstractWe present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5).
Field / plotMultimodalWhole plant / canopy / plot / fieldClassificationGrowth / development / phenology
Abstract Phenological monitoring of Actinidia chinensis is critical for optimising operational costs and yield prediction. However, current manual assessment methods are time-consuming, making them impractical for large-scale precision agriculture applications. Most existing phenological datasets focus exclusively on image data without spatial validation. The Multi-Modal Actinidia chinensis Phenology Dataset is composed of (i) 1 665 annotated images of phenological stages from bud to fruit set and (ii) georeferenced videos with systematic manual ground truth of spatial stage distributions. The dataset employs an adapted 17-class BBCH system that consolidates visually similar stages, excludes problematic categories, and introduces generic structural classes to address practical annotation difficulties. Additionally, the data is organised hierarchically across various plant structures, genders, and phenological stages. The annotated images offer versatility for a range of applications, including training data for computer vision models to detect phenological stages. Furthermore, the georeferenced videos facilitate the validation of automated counting algorithms. This combined approach enables plant-level detection accuracy and provides an illustrative methodology for spatial validation that users can extend to additional orchards, promoting the development and benchmarking of automated phenological monitoring systems for precision agriculture applications in kiwifruit production.
Why it matches plant phenotyping methodsキウイフルーツの生育段階を対象とした注釈画像・地理参照動画のデータセットで、植物フェノロジー自動検出の訓練、検証、ベンチマークを目的とする方法論的成果である。
titleA Multi-Modal Dataset for Automated Phenological Stage Mapping in Actinidia chinensis
Reproduction assets foundThe paper is a Data Note describing the Multi-Modal Actinidia chinensis Phenology Dataset, which is explicitly stated to be publicly available on Zenodo with a DOI matching an allowed URL. The dataset contains the paper's own phenotyping assets: 1,665 annotated images with bounding-box phenological labels, georeferenedDataset · publicThe Multi-Modal Actinidia chinensis Phenology Dataset described in this Data Descriptor is publicly available
at Zenodo: https://doi.org/10.5281/zenodo.17371025. This dataset comprises two components: (1) 1 665
JPEG images (1 024 × 1 024 pixels) with corresponding Pascal VOC XML annotation files containing bounding
box coordinates and phenological class labels, and (2) 24 MP4 video files (3 840 × 2 160 pixels) with corresponding
GPX coordinate files and Excel validation files containing manual ground truth counts.Open asset ↗Zenodo · 10.5281/zenodo.17371025pdf-page:13 lines:1-62Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
None
1.
Value of the Data
•
The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rapid and accurate identification of DUS (Distinctness, Uniformity, and Stability) test traits in lettuce leaves is essential for advancing multi-omics-driven intelligent breeding. It also plays a critical role in germplasm protection and enhancing agricultural competitiveness. However, the phenotypic traits of lettuce leaves are highly diverse and complex due to both genotypic variation and environmental influences, posing significant challenges for precise DUS trait quantification. To address these challenges, we propose a high-precision phenotypic trait extraction pipeline and introduce an interpretable phenotypic fingerprinting framework for lettuce subgroup identification. First, a lightweight semantic segmentation network guided by group attention is developed to extract leaf components. Then, shape, color, and texture traits are comprehensively quantified. Following UPOV (International Union for the Protection of New Varieties of Plants) guidelines, we establish quantitative methods for seven DUS test traits: leaf shape, leaf tip shape, leaf margin shape, leaf vein shape, color hue, brightness, and anthocyanin coloration. Finally, PCA (Principal component analysis) was used to select 13 key traits, capturing over 95.82% of the total variance, for constructing "phenotypic ID" of lettuce varieties. Experiments conducted on 709 lettuce leaf image datasets showed that the accuracy of subgroup identification based on phenotypic fingerprints reached 98.59%. This study offers a scalable approach for automated DUS test trait evaluation and intelligent crop variety identification, providing a novel paradigm with strong potential for application in precision breeding and germplasm resource management.
Why it matches plant phenotyping methodsレタス葉画像からDUS形質を抽出・定量化する画像解析パイプラインを開発し、709画像で評価しており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a high-precision phenotypic trait extraction pipeline and introduce an interpretable phenotypic fingerprinting framework for lettuce subgroup identification.
Reproduction assets foundThe article provides a public GitHub repository containing the authors' source code for the lettuce phenotypic fingerprint pipeline. The 709-image dataset and annotations are only available upon request, so they do not qualify as public assets.Code · publicThe data used to support the findings of this study are available upon request from the corresponding author, and the source code is accessible at https://github.com/qiuguangjie87/PP_Phenotypic_Fingerprint .Open asset ↗PP_Phenotypic_Fingerprintlines:263-278Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Abstract Cherry is widely cultivated but remains challenging to harvest due to economic and ecological constraints, especially in developing countries such as Pakistan. Climate change, limited use of technology, and foliar diseases worsened by pesticide use further reduce productivity, particularly during fruiting. Conventional disease assessment depends on expert observation and grower experience, making it subjective and time-consuming. A comprehensive evaluation was conducted on the PlantCity dataset, which contains 5,714 high-density, full-color RGB images collected under challenging conditions and categorized into 5 classes. We compared three approaches: deep learning pre-trained, transfer learning, and a machine learning pipeline. Models were evaluated by accuracy, precision, recall, F1-score, Cohen’s Kappa, inference time, FLOPs, and throughput. Grad-CAM was used to improve interpretability. Transfer learning using DenseNet169 achieved the highest performance, with 99.80% accuracy, 99.80% precision, 99.80% recall, and a Cohen’s Kappa of 99.74%. These results were significantly higher than those obtained by other deep learning architectures and handcrafted baselines. Grad-CAM heatmaps confirmed that the models focused their attention on pathological areas. The proposed transfer-learning-based framework, particularly DenseNet169, demonstrates state-of-the-art diagnostic accuracy and features a modular structure. This design enables deployment on both high-performance servers and resource-constrained embedded devices, thereby facilitating early disease detection in precision agriculture.
Why it matches plant phenotyping methods植物画像から病害状態を分類する画像ベース表現型推定が研究の中心であり、複数モデルの比較・性能評価とGrad-CAMによる検証を行っているため。
abstractConventional disease assessment depends on expert observation and grower experience, making it subjective and time-consuming.
Intelligent forest tree breeding has advanced plant phenotyping, yet existing research largely focuses on large-leaf agricultural crops, with limited attention to fine-grained leaf analysis of sapling trees in open-field environments. Natural scenes introduce challenges including scale variation, illumination changes, and irregular leaf morphology. To address these issues, we collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances. To our knowledge, this is the first instance segmentation dataset specifically designed for forestry leaves in open-field conditions. We propose LeafInst, a novel segmentation framework tailored for irregular and multi-scale leaf structures. The model integrates an Asymptotic Feature Pyramid Network (AFPN) for multi-scale perception, a Dynamic Asymmetric Spatial Perception (DASP) module for irregular shape modeling, and a dual-residual Dynamic Anomalous Regression Head (DARH) with Top-down Concatenation decoder Feature Fusion (TCFU) to improve detection and segmentation performance. On Poplar-leaf, LeafInst achieves 68.4 mAP, outperforming YOLOv11 by 7.1 percent and MaskDINO by 6.5 percent. On the public PhenoBench benchmark, it reaches 52.7 box mAP, exceeding MaskDINO by 3.4 percent. Additional experiments demonstrate strong generalization and practical utility for large-scale leaf phenotyping.
Why it matches plant phenotyping methods森林葉の個体分割と表現型解析のためのUAV画像データセットおよび新規セグメンテーション手法を開発・評価しており、植物表現型取得が中心である。
abstractwe collected UAV RGB imagery of field-grown saplings and constructed the Poplar-leaf dataset, containing 1,202 branches and 19,876 pixel-level annotated leaf instances.
Reliable aboveground biomass (AGB) estimates for woody crops are required for carbon accounting and MRV; however, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed orchards. We benchmarked four LiDAR modalities across three Mediterranean woody-crop sites in Córdoba (Spain), IFAPA, Doña María, and Villaseca using open national airborne laser scanning (PNOA/ALS), Riegl ALS, unmanned laser scanning (ULS), and mobile laser scanning (MLS). The field inventory used 58 fixed-area plots (20×50 m; 0.1 ha) collected in December 2024-January 2025 (1,867 trees) and species-specific allometries based on D2r to derive tree and plot AGB; carbon was computed using wood carbon fractions (0.445 olive; 0.457 almond) and CO2e via IPCC conversion. Plot-level LiDAR metrics (e.g., mean height, p95, maximum height, and cover proxies) were extracted from normalized point clouds and modeled with Random Forest, XGBoost, and an ensemble under an 80/20 train-test split. Mean field AGB differed among sites (33.89, 30.94 and 12.76 Mg ha−1 for Villaseca, Doña María, and IFAPA). In the provided summaries, XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). The results support cross-platform LiDAR for orchard AGB mapping and identify conditions under which open national LiDAR can enable scalable MRV. In addition, we evaluated TreeQSM-based quantitative structure models (QSMs) as an independent tree-level 3D reconstruction approach and examined their site-dependent agreement with field inventory estimates.
Why it matches plant phenotyping methods複数のLiDARモダリティと解析手法を比較・ベンチマークし、樹木およびプロットの地上部バイオマスを推定する技術評価が研究の中心であるため。
abstractWe benchmarked four LiDAR modalities across three Mediterranean woody-crop sites
Field / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldClassificationPhysiological trait estimationBiomass / plant weightWater status / transpiration
Burullus Lagoon, situated in the Nile Delta of Egypt, is a Ramsar-listed wetland of high ecological importance, particularly in relation to its floristic diversity. This study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring. Hyperspectral reflectance measurements were obtained for 41 plant species selected from a total of 63 floristically identified taxa, based on ecological dominance, spatial recurrence across sampling sites (≥3 stands), and suitability for reliable field spectral acquisition. Spectroscopic data were collected from 44 stands representing lagoon shores, islets, and open-water habitats using an ASD FieldSpec spectroradiometer covering the 350–2500 nm spectral range. A set of vegetation indices was applied to evaluate key biophysical and biochemical properties associated with plant vitality, water status, and biomass. The results indicate that the red and near-infrared regions provide the highest discriminatory capability among species, whereas the shortwave infrared region exhibits more limited discriminatory capability. Dominant taxa, including Phragmites australis and Atriplex halimus , displayed elevated near-infrared reflectance, consistent with differences in canopy structure and biochemical composition. Most species showed vegetation index responses broadly indicative of healthy physiological conditions, although interspecific variability suggests contrasting stress responses among taxa. Overall, the study demonstrates the applicability of field-based hyperspectral data for species-level discrimination in wetland environments and delivers a curated spectral library to support biodiversity conservation and long-term ecosystem management at Burullus Lagoon.
Why it matches plant phenotyping methods野外ハイパースペクトル計測とスペクトルライブラリ構築が研究の中心で、植物の活力、水分状態、バイオマスなどの状態推定に用いているため、単なる生態調査ではなく植物表現型計測への実質的応用に該当する。
abstractThis study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring.
Accurate flower-load assessment is critical for informed thinning strategies in orchard management. UAV-based deep learning automated counting offers efficiency advantages, yet precise counting is heavily dependent on abundant annotated data, which is scarce and costly to obtain in agricultural settings. While semi-supervised learning alleviates dependency on manual annotation, its application to UAV-based orchard imagery faces challenges: complex backgrounds and small target sizes, which undermine pseudo-label reliability. To address these challenges, this study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages. First, a color-SAM flower extractor (CSAM-FE) is proposed to preprocess images using a strategy combining color thresholding with the Segment Anything Model (SAM), suppressing background noise and extracting high-quality flower clusters, thereby providing purified inputs for the subsequent counting network. Second, an uncertainty-guided semi-supervised flower counting network (USCount-Net) is proposed for accurate stage-specific flower counting with limited labeled data. The USCount-Net incorporates two key components: an adaptive pseudo-label filtering (PLF) mechanism based on frequent forward uncertainty estimation (FFUE) is designed to dynamically suppress noisy gradient backpropagation, mitigating error propagation from unreliable pseudo-labels; and a noise-sensitive adaptive gated fusion (AGF) module is introduced to fuse cross-scale features without redundancy, addressing significant scale variations across phenological stages and observation angles. Comparative experiments on a self-built apple flower counting dataset demonstrate that USCount-Net achieves lower MAE and RMSE than state-of-the-art methods at 10%, 30%, and 50% labeling ratios. The results demonstrate that the proposed methodology serves as methodological support for rapid and precise apple flower counting in low-annotation agricultural scenarios.
Why it matches plant phenotyping methodsリンゴ花の画像抽出・計数手法と半教師あり解析ネットワークを開発し、データセット上で比較評価しているため、植物表現型取得が中心である。
abstractthis study proposes a two-stage framework to achieve separate counting of apple flowers at different phenological stages.
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' USCount-Net source code on GitHub and the self-built apple flower counting dataset (UAV images, annotations, flower cluster images) on Google Drive.Code · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗USCount-Netlines:681-780Dataset · publicThe source code is publicly available at https://github.com/haohuihui5019/USCount-Net . And the source dataset can be accessed at https://drive.google.com/drive/folders/1KP8H0qIuct56hWre5GV6ZJnzwOpen asset ↗lines:681-780Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
NeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldOrgan identification2D/3D reconstructionSegmentation
Organ segmentation of plant point clouds is a prerequisite for the high-resolution and accurate extraction of organ-level phenotypic traits. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, the existing techniques for organ segmentation still face limitations in resolution, segmentation accuracy, and generalizability across various plant species. In this study, we proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species. PlantSegNeRF performed two-dimensional (2D) instance segmentation on the multi-view images to generate instance masks for each organ with a corresponding instance identification (ID). The multi-view instance IDs corresponding to the same plant organ were then matched and refined using a specially designed instance matching (IM) module. The instance NeRF was developed to render an implicit scene containing color, density, semantic and instance information, which was ultimately converted into high-precision plant instance point clouds based on volume density. The results proved that in semantic segmentation of point clouds, PlantSegNeRF outperformed the commonly used methods, demonstrating an average improvement of 16.1 %, 18.3 %, 17.8 %, and 24.2 % in precision, recall, F1-score, and intersection over union (IoU) compared to the second-best results on structurally complex datasets. More importantly, PlantSegNeRF exhibited significant advantages in instance segmentation. Across all plant datasets, it achieved average improvements of 11.7 %, 38.2 %, 32.2 % and 25.3 % in mean precision (mPrec), mean recall (mRec), mean coverage (mCov), and mean weighted coverage (mWCov), respectively. Furthermore, PlantSegNeRF demonstrates superior few-shot, cross-species performance, requiring only multi-view images of few plants to train models applicable to specific or similar varieties. This study extends organ-level plant phenotyping and provides a high-throughput way to supply high-quality 3D data for developing large-scale artificial intelligence (AI) models in plant science. • A comprehensive dataset of well-labeled two-dimensional (2D) images and point clouds dataset of plants was established, including various varieties and growth stages. 50 plant samples were collected for each type. • A novel multi-view image instance matching (IM) module was proposed to align plant organ instance identifications (IDs) across different viewpoints, serving as the foundation for organ-level instance segmentation. • A multi-channel instance neural radiance fields (NeRF) module with encoding color, semantic, and instance information was developed to achieve high-precision mapping of 2D image colors, semantics, and aligned instances into 3D space, enabling point cloud background removal and fine-grained segmentation of plant organs.
Why it matches plant phenotyping methods植物器官の3D点群再構成・インスタンス分割を開発し、セグメンテーション性能を検証する手法研究であり、器官レベル表現型抽出を直接支援するため。
abstractwe proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species.
Maize phenotyping remains a major bottleneck in genetic analysis and breeding. Despite advances in drones, field robots, and gantry phenotyping systems, ultra-affordable, high-throughput, field-based maize phenotyping at single-plant resolution is still lacking, largely due to the high cost, complex deployment, and limited flexibility of existing platforms under heterogeneous field conditions. To address these challenges, we propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe ( G iraffe + Li zard M aize P henotyping S yst e m), including two end-to-end phenotyping modules for maize plant architecture (the Giraffe module) and leaf traits (the Lizard module). The imaging device in the Giraffe module are built from modular electronics and 3D-printed parts from local retailers to achieve high-quality image acquisition. The Giraffe and the Lizard modules operate at speeds of 15 seconds and 8 seconds per sample, with costs of $379.1 and $241.1, respectively. Both modules feature fine-tuned YOLOv11x segmentation models for reliable and robust target segmentation, followed by customized Python-based analytical pipelines that enable precise extraction and quantification of phenotypic traits. This methodology achieves high accuracies ( R² ) for five key traits, including plant height (0.928), heights of above-ear leaves (0.87∼0.958), ear height (0.925), above-ear leaf number (0.837), and leaf width (0.937). To enhance accessibility, we developed user-friendly graphical interfaces and publicly released manually annotated datasets and source code to support broader adoption and further innovation. This work provides a practical and accessible solution for high-throughput field phenotyping and offers new opportunities for democratizing crop phenomics through affordable, open-source technologies.
Why it matches plant phenotyping methods低コストな撮像装置、コンピュータビジョン解析、形質抽出パイプライン、GUI、データセットとコードを統合したトウモロコシ表現型測定システムの開発・検証が研究の中心である。
abstractwe propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe
Nodule formation and their involvement in biological nitrogen fixation are critical features of leguminous plants, with phenotypic characteristics closely linked to plant growth and nitrogen fixation efficiency. However, the phenotypic analysis of root nodules remains technically challenging due to their small size, weak texture, dense clustering, and occlusion. To address these challenges, this study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions. In addition, A hybrid small-object detection method, SCO-YOLOv8s, was proposed, integrating Swin Transformer and CBAM attention mechanisms into the YOLOv8s framework to enhance global and local feature representation. Furthermore, an Otsu segmentation-based post-processing module was incorporated to validate and refine detection results based on geometric features, boundary sharpness, and image entropy, effectively reducing false positives and enhancing robustness in complex scenes. Using this integrated approach, over 3375 nodules were identified from a single plant sample in under 1 min, with extracted phenotypic features such as diameter, color, and texture. A total of 10,879 high-quality annotated images were collected from 39 peanut varieties across 14 provinces and 31 soybean varieties across 12 provinces in China, addressing the current lack of large-scale datasets for legume root nodules. The SCO-YOLOv8s model achieved a precision of 97.29 %, a mAP of 98.23 %, and an overall identification accuracy of 95.83 %. This integrated approach provides a practical and scalable solution for high-throughput nodule phenotyping, and may contribute to a deeper understanding of nitrogen fixation mechanisms.
Why it matches plant phenotyping methods根粒の画像取得・検出・セグメンテーション・形質抽出を統合した高スループット表現型解析手法を開発し、精度評価と大規模データセット構築も行っているため、方法が研究の中心である。
abstractthis study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions.
Robotic apple harvesting requires precise instance segmentation and 3D localization, especially for multi-colored apples under complex orchard conditions with occlusions and variable lighting. Current deep learning methods lack robustness and accuracy for such scenarios, limiting automation. This study proposes improved YOLOv8-based models to enhance segmentation of multi-colored apples, combined with a high-precision 3D localization pipeline to advance practical robotic harvesting. To address these issues, this study collected apple images in three colors from two locations, creating a dataset of 5171 images. Four enhanced YOLOv8-based models—RA-YOLO, GA-YOLO, YA-YOLO, and MCA-YOLO—were proposed for segmenting red, green, yellow, and mixed multi-colored apples. RA-YOLO integrates the GD mechanism and EMBConv structure based on EfficientNet's MBConv. GA-YOLO replaces standard convolutions with dynamic serpentine convolution and adds the P6 layer for large object detection. YA-YOLO utilizes deformable convolution (DCNv2) and introduces the new attention mechanism MPCA. MCA-YOLO combines the P6 layer, DCNv2, and EMBConv structure, merging the strengths of other models. RA-YOLO, GA-YOLO, and YA-YOLO achieved mAP values of 95.2 %, 96.4 %, and 95.4 %, respectively, for single-colored apple instance segmentation, surpassing baseline models and those in existing literature. MCA-YOLO achieved mAP values of 95.6 %, 96.6 %, and 94.6 % for single-colored apples and 95.6 % for mixed multi-colored apples. Ablation experiments validated the necessity of each module. Finally, a high-precision 3D localization and shaping pipeline was developed, achieving an average localization error of 2.636 mm and a shaping error of 0.768 mm, enabling millimeter-level localization and sub-millimeter-level shaping for apple harvesting optimization.
Why it matches plant phenotyping methodsリンゴ果実のインスタンス分割、3D位置推定、形状推定を開発・検証しており、収穫対象の単なる検出を超えて果実形状という植物器官形質を定量化する手法が中心である。
abstractThis study proposes improved YOLOv8-based models to enhance segmentation of multi-colored apples, combined with a high-precision 3D localization pipeline
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract In machine learning–driven plant phenotyping, well-annotated image datasets are essential for developing robust models capable of capturing phenological variability across environments. Here, we introduce DeepPhenoTree – Apple Edition, a multi-site, multi-variety RGB image dataset dedicated to the detection of key phenological stages in apple trees. The dataset comprises 48,320 time-stamped RGB images acquired across four European orchards of the Apple REFPOP consortium under contrasting climatic conditions. From this large corpus, a carefully curated subset of 808 representative images was manually annotated. It includes 241,600 expert annotations covering developmental stages from dormant bud to fruit maturity. Images were acquired using a standardized tractor-mounted phenotyping platform equipped with active flash illumination, ensuring consistent lighting conditions across sites and acquisition dates. Phenological structures were annotated following the BBCH scale, with bounding boxes adapted to organ visibility and developmental stage. In addition to the dataset, we provide baseline deep learning experiments to illustrate detection performance and assess model generalization across locations.
Why it matches plant phenotyping methodsリンゴの生育ステージを検出する注釈付き画像データセットと、標準化された撮像プラットフォームおよびベースラインモデルを提供しており、植物フェノタイピング手法・再利用可能データが研究の中心である。
abstractHere, we introduce DeepPhenoTree – Apple Edition, a multi-site, multi-variety RGB image dataset dedicated to the detection of key phenological stages in apple trees.
Accurate in-season prediction of seed yield and seed composition traits such as oil and protein are useful for gaining accuracy and efficiency in soybean breeding. These predictions can also inform farmers, enabling them to improve their field management practices, and guide their market decisions. We report a Transformer-based deep learning framework built on 30 years of multi-environment performance data from the Northern and Southern Uniform Soybean Tests (UST) across North America. Unlike earlier studies on seed yield, oil and protein prediction that focus on limited years, regions, single modalities, we utilized a comprehensive dataset that includes weather, genotype, and management factors, ensuring a more holistic approach to soybean yield, oil, and protein prediction. Our model integrates multivariate time-series weather data with genotypic relationship information, maturity group, and geographic location, to predict variety performance in diverse environments. Our model captures complex temporal patterns associated with trait variability; showing high predictive accuracy (R2) of 77.6 ± 0.2%, 63.9 ± 4.7%, and 79.3 ± 2.3% for seed yield, oil, and protein, respectively. Additionally, for seed yield, we also evaluated multiple interpretability methods to assess feature importance for predictor variables and critical growing timepoints, and solar radiation and temperature were noted as the key predictors. Overall, these results demonstrate the usefulness of a Transformer-based model in trait predictions, and the utility of large cooperative datasets from breeding programs.
Why it matches plant phenotyping methodsTransformerによるダイズ収量・油・タンパク質形質の予測フレームワークが研究の中心であり、植物形質の計算的推定手法を開発・評価している。
abstractWe report a Transformer-based deep learning framework built on 30 years of multi-environment performance data
Forest inventories play an essential role in managing and protecting forest resources as well as quantifying carbon stocks. Recent advances in Uncrewed Aerial Vehicles (UAVs) have enhanced capabilities for efficiently monitoring forest dynamics across large geographic areas. RGB cameras are typically preferred for rapid and scalable forest inventory missions owing to three distinct advantages, including low cost, ease of use, and high resolution. However, compared with multispectral or hyperspectral sensors, the limited spectral signals of RGB cameras pose challenges for tree crown detection and classification. The ability of deep learning methods to capture structural and contextual cues from imagery helps alleviate some of the limitations of RGB data. In this study, we propose an Individual Tree Crown (ITC)-based framework leveraging UAV data and advanced deep learning models for inventories of individual trees in dense and natural forests. First, we develop the ITC-based Multi-Task Convolutional Neural Network (ITCMNet), which incorporates multi-scale contexts to simultaneously and accurately identify individual tree crowns, discriminate tree species, and assess tree vitality. Second, structural parameters for each individual crown are extracted to estimate forest carbon storage using species-specific allometric models. Unlike conventional pixel-based methods, our proposed ITCMNet enables precise forest investigations at the ITC level, enhancing both performance and interpretability. We collected a comprehensive dataset consisting of 2456 ultra-high resolution (1.6 cm) UAV RGB images and 27,160 labeled trees across 105 plots distributed in three dense forests and one city park in Germany to evaluate our framework. The ITCMNet demonstrated robust tree crown delineation performance, achieving an F1 score of 0.81. Additionally, our method attained an F1 score in species classification (i.e., 0.54 for angiosperms and 0.76 for gymnosperms) and vitality identification (0.66). Utilizing precise tree parameters, species information, and species-specific allometric models, our carbon storage estimation surpassed current satellite-based carbon products. The carbon stock estimation achieved an R 2 of 0.83 and the carbon storage range in the Bamberg forests is approximately 50 to 110 Mg C/ha. These results show that our proposed framework provides detailed, cost-effective forest inventories, highlighting its potential to support various downstream forestry applications. The dataset and source code are available ( https://www.dlr.de/en/eoc/about-us/remote-sensing-technology-institute/photogrammetry-and-image-analysis/public-datasets/bamforests ; https://github.com/WendyFan52/ITCMNet ).
Why it matches plant phenotyping methodsUAV画像と深層学習により個体樹冠を検出し、樹冠構造、樹種、樹勢などの植物状態を抽出する枠組みを開発・評価しており、植物フェノタイピング手法が中心である。
abstractwe propose an Individual Tree Crown (ITC)-based framework leveraging UAV data and advanced deep learning models for inventories of individual trees in dense and natural forests.
Maize, a critical staple crop in Zambia, faces persistent threats from foliar diseases such as Gray Leaf Spot, Northern Corn Leaf Blight, and Maize Streak Virus, significantly affecting smallholder productivity. Limited access to expert diagnostics, coupled with complex field conditions including occlusions and variable lighting, necessitates accessible, real-time disease detection systems tailored to local environments. To address this gap, this study first developed a novel field-captured dataset of Zambian maize leaf images, annotated with bounding boxes for disease lesions and labeled by disease type and severity to reflect real-world agri-ecological variability. Building on this dataset, we propose ZamYOLO-Maize, a multi-stage automated diagnostic framework integrating lesion detection, hierarchical disease classification, and severity assessment. A comparative evaluation was conducted using four state-of-the-art object detection models: YOLOv5n, YOLOv8s, YOLOv10s, and YOLOv8n, with performance assessed using precision, recall, F1-score, and inference speed. Experimental results demonstrate that YOLOv10s achieved the highest predictive performance (Precision = 0.997, Recall = 0.999, F1-score = 0.999), while YOLOv8n provided the optimal trade-off for edge deployment, achieving the fastest inference speed (4.65 ms/image) with a competitive F1-score of 0.995. The framework exhibited strong robustness under field variability, confirming its practical applicability. By integrating a locally representative dataset with an efficient deep learning pipeline, this study establishes a scalable foundation for mobile-based maize disease diagnostics, contributing to precision agriculture and supporting food security initiatives in Zambia and comparable agricultural regions.
Why it matches plant phenotyping methodsトウモロコシ葉画像から病斑、病害種、重症度を推定するデータセットと深層学習フレームワークを開発・比較評価しており、植物病害表現型の取得・抽出が中心です。
abstractthis study first developed a novel field-captured dataset of Zambian maize leaf images, annotated with bounding boxes for disease lesions and labeled by disease type and severity
Grapevines ( Vitis vinifera L.) undergo structural and physiological changes throughout the growing season, progressing through distinct phenological stages that require regular monitoring. This dataset consists of high-resolution point cloud data acquired with a stationary terrestrial laser scanner (TLS) to document grapevine development from early leaf development to dormancy. Georeferenced point clouds were generated from 15 TLS scans along two vineyard rows at nine phenological stages. The dataset also includes multispectral and RGB photogrammetric point clouds and orthorectified raster products from an unmanned aerial vehicle survey conducted before harvest. Ground-truth measurements leaf area index, grape production, and pruning wood biomass were collected for each monitored grapevine. As a result, the dataset provides multi-temporal TLS observations that support grapevine structural analysis and development, phenological monitoring, and can be used for the development of AI-based models for precision viticulture.
Why it matches plant phenotyping methodsブドウの生育・構造・フェノロジーを対象とするTLS点群および関連画像データセットであり、植物フェノタイピング用の再利用可能なデータ基盤として中心的です。
titleTLS-grapevine2024: A terrestrial laser scanner point cloud dataset of grapevines at different phenological stages.
Reproduction assets foundThe paper is a Data in Brief article describing the TLS-grapevine2024 dataset itself, publicly deposited on Zenodo with DOI 10.5281/zenodo.16751663. This is a paper-specific, openly available asset containing the TLS point clouds, UAV imagery/rasters, and ground-truth agronomic measurements (LAI, grape production, prunDataset · publicditions: clear sky.
Data source location
Institution: University of Trás-os-Montes e Alto Douro
City/Town/Region: Arroios, Vila Real, Norte
Country: Portugal
Coordinates: 41°17′28.83″N 7°43′17.90″W,
Altitude: 435 m
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.16751663
Direct URL to data: https://doi.org/10.5281/zenodo.16751663
Related research article
None
1.
Value of the Data
•
This dataset covers nine phenological stages of grapevine growth from April 2024 to January 2025, providing multi-temporal terrestrial laser scanner (TLS) observations for structural and phenological analysis.
•
It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This data article describes a curated RGB-Depth image dataset captured using an Intel RealSense D435 stereo depth camera mounted on an autonomous mobile platform during field deployments at commercial baby broccoli farms in Victoria, Australia. The dataset comprises 1759 paired RGB images (640 × 480 pixels) and corresponding 16-bit depth frames acquired under both daytime (natural sunlight) and night-time (LED illumination) conditions, designed to support research in agricultural computer vision and robotic harvesting. Images were selected from 39,765 raw acquisitions through a reproducible Python curation pipeline applying quality filtering (blur detection, brightness thresholds, corruption detection), perceptual hash-based duplicate removal, and manual review. The final dataset includes 924 daytime and 835 night-time image pairs containing baby broccoli plants at various growth stages. The dataset provides RGB camera intrinsic parameters and pixel-aligned depth maps to enable 3D point cloud reconstruction. Potential applications include developing deep learning models for crop detection and segmentation, validating depth-based size estimation methods, and benchmarking illumination-robust vision systems. All data and curation code are publicly available under a CC BY 4.0 license.
Why it matches plant phenotyping methodsRGB-Depth画像データセットの構築と再現可能なキュレーションを中心とし、作物検出に加えてサイズ推定という植物形質の評価・ベンチマークに利用できるため。
titleA field-acquired RGB-Depth image dataset for computer vision-based baby broccoli detection and size estimation under varying illumination conditions.
Reproduction assets foundThe paper is a data article describing a public Mendeley Data repository containing the authors' field-acquired RGB-D baby broccoli image dataset (1759 image pairs, ground truth diameter annotations, camera intrinsics, and curation/annotation code), directly reproducing the paper's phenotyping measurements and analysisDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/px5p6zdk6k.3
Direct URL to data: https://data.mendeley.com/datasets/px5p6zdk6k/3Open asset ↗Mendeley Data · 10.17632/px5p6zdk6k.3html-lines:95-155Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 15 Sept 2026
Precise localization and recognition of flowers are crucial for advancing automated agriculture, particularly in plant phenotyping, crop estimation, and yield monitoring. This paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m, and YOLOv12n for flower object detection under two annotation regimes: single-image single-bounding box (SISBB) and single-image multiple-bounding box (SIMBB). The FloralSix dataset, comprising 2,816 high-resolution photos of six different flower species, is also introduced. It is annotated for both dense (clustered) and sparse (isolated) scenarios. The models were evaluated using Precision, Recall, and Mean Average Precision (mAP) at IoU thresholds of 0.5 (mAP@0.5) and 0.5-0.95 (mAP@0.5:0.95). In SISBB, YOLOv8m (SGD) achieved the best results with Precision 0.956, Recall 0.951, mAP@0.5 0.978, and mAP@0.5:0.95 0.865, illustrating strong accuracy in detecting isolated flowers. With mAP@0.5 0.934 and mAP@0.5:0.95 0.752, YOLOv12n (SGD) outperformed the more complicated SIMBB scenario, proving robustness in dense, multi-object detection. Results show how annotation density, IoU thresholds, and model size interact: recall-optimized models perform better in crowded environments, whereas precision-oriented models perform best in sparse scenarios. In both cases, the Stochastic Gradient Descent (SGD) optimizer consistently performed better than alternatives. These density-sensitive sensors are helpful for non-destructive crop analysis, growth tracking, robotic pollination, and stress evaluation.
Why it matches plant phenotyping methods花の物体検出モデルを比較評価し、注釈付きFloralSixデータセットを導入することが中心で、花の認識・局在化を植物フェノタイピングへ応用する技術研究である。
abstractThis paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m, and YOLOv12n for flower object detection under two annotation regimes: single-image single-bounding box (SISBB) and single-image multiple-bounding box (SIMBB).
Abstract Agricultural research increasingly relies on data-driven approaches for crop yield prediction that complement more established crop growth models, including machine learning techniques. However, these approaches rely on large training datasets. Here, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England on which a variety of crops are grown. In addition, the data also contains satellite-derived remote sensing data, weather data, and data on soil type, all aligned at a grid resolution of 10 km. Weather data is available at a daily temporal resolution, satellite data at 5-day resolution, while crop yield data is available at yearly resolution. This effort has been made possible through careful anonymisation of the yield data while preserving the alignment with remote sensing, weather, and soil data. This data will be useful both to train machine learning models of yield prediction as well as to parameterize mechanistic crop growth models. Furthermore, the anonymisation procedure itself will be of interest to the research community, as it represents a solution to a common problem on the interface of agricultural research and farming practice.
Why it matches plant phenotyping methods圃場単位の作物収量という植物形質を、衛星・気象・土壌情報と整合した再利用可能な大規模データセットとして構築しており、収量予測モデルの訓練・評価用データ基盤が中心です。
abstractHere, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England
Reproduction assets foundThe paper's authors provide public R code for merging/aligning climate, soil, and Sentinel-1 data and anonymising yield data in a GitHub repository. The CYCLeSS dataset itself is on figshare, but that URL is not in the allowed list, so only the code asset is reported.Code · publicnts of this repository. Researchers who are further interested in the underlying data should contact the authors affiliated with UKCEH.
Code availability
R code used to merge and align available UK climate, soil, and Sentinel-1 synthetic aperture radar data to the same 1 km 2 grid is provided in the following GitHub repository: https://github.com/alan-turing-institute/CYCLeSS-dataset-code . Dummy data and code needed to replicate the final process of merging climate, soil, and satellite data with UKCEH precision yield data and anonymisation of field locations is contained within the ‘CLYCESS_anonymisation.zip’ folder shared as part of this repository. R version 4.2.3 was used for the creatioOpen asset ↗https://github.com/alan-turing-institute/CYCLeSS-dataset-codelines:200-271Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model applicability, low accuracy, and limited datasets make this research essential. Existing models face issues with large parameters, complex computations, and insufficient semantic information capture. This paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges. The model integrates a brand-new lightweight detection head LE-Head to reduce the parameter quantity and computational complexity. To boost accuracy, the model integrates the newest FCA attention mechanism into its high-level semantic processing component, strengthening its capacity to interpret complex semantic data. Testing on a custom rice disease dataset comprising 54,240 images, the model attained 94.3% accuracy with a parameter count of 2.32 M. The enhanced model achieves a 0.4% increase in accuracy while reducing parameters by 10% relative to the baseline YOLOv11. The detection model is more lightweight, can adapt to the computing power of field detection equipment, is more suitable for practical scenario applications, and provides technical support for the development of smart agriculture.
Why it matches plant phenotyping methodsイネ病害を画像から検出するモデルと大規模データセットの開発・評価が中心で、植物の病害状態を推定する画像ベース表現型手法に該当する。
abstractThis paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges.
Plant diseases continue to pose a serious challenge to agriculture, leading to substantial yield losses and posing a threat to global food security. Accurate and early identification of plant diseases is crucial for effective crop management. However, traditional manual inspection methods are time-consuming, subjective, and heavily dependent on expert knowledge. With recent advances in artificial intelligence and computer vision, automated plant disease detection systems have emerged as a reliable alternative. These systems depend strongly on large, well-annotated image datasets for training and evaluation. Among publicly available resources, the PlantVillage dataset is one of the most widely used benchmarks for plant disease research. It contains over 50,000 high-resolution RGB leaf images captured under controlled conditions and covers 14 crop species, including tomato, potato, and bell pepper, along with multiple other plants. The dataset represents 38 distinct classes encompassing both healthy and diseased leaf categories. All images are collected against uniform backgrounds, providing visual consistency and making the dataset suitable for benchmarking machine learning and deep learning–based plant disease classification models. This survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection. It traces the progression from traditional handcrafted feature–based classifiers to convolutional neural networks, transfer learning approaches, and recent transformer-based architectures. Various methods are compared in terms of classification accuracy, generalization ability, computational efficiency, and robustness. In the survey we have identified that Transfer Learning model showed 99.75% accuracy. The survey also discusses key limitations of the dataset, particularly its controlled imaging conditions and challenges related to real-field deployment. Finally, future research directions are highlighted, including domain adaptation, explainable artificial intelligence, multi-disease recognition, and real-world agricultural applications. This work aims to offer researchers a structured understanding of the PlantVillage dataset and support the development of next-generation intelligent crop disease diagnostic systems.
Why it matches plant phenotyping methodsPlantVillage画像を用いた植物病害状態の画像ベース推定手法とデータセットを中心に、複数の機械学習手法を比較・レビューしているため、植物フェノタイピング方法論のレビューとして含める。
abstractThis survey provides a comprehensive review of the PlantVillage dataset and its contribution to the advancement of automated plant disease detection.
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高スループット画像フェノタイピングによる大規模データセットを提示し、画像取得基盤と作物成長動態の解析を中心に扱っているため、方法論文として適格です。
titleFIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper's canopy cover analysis code is publicly available on the authors' ETH GitLab repository. The FIP 1.0 soybean image/trait dataset itself is deposited in the ETH Research Collection and Hugging Face, but those URLs are not among the allowed URLs, so only the code asset qualifies.Code · publicCode availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.Open asset ↗gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycoverhtml-lines:207-226Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Leaves are central indicators of photosynthesis and plant growth status, and their precise monitoring is crucial for smart agriculture. Dense leaf detection, as a foundation for leaf morphology analysis, must address challenges such as occlusion and overlap, directly enabling key tasks including phenotypic trait extraction, disease identification, and yield estimation. Leaves are the most important plant organs, and monitoring leaves is a crucial aspect of crop surveillance. Dense leaf detection plays an important role as a fundamental technology for leaf monitoring. Existing dense leaf detection methods rely on traditional modular detectors and generic feature extraction, lacking designs tailored to real-world dense leaf scenarios. The methods for dense leaf detection generally use traditional modular detectors and general feature extraction techniques, without designing methods specifically for dense leaves in reality. In detail, in complex field scenarios, it still faces challenges like incomplete individual feature extraction due to high leaf overlap and difficult network convergence caused by excessive leaf density. To this end, we propose the Leaf-DETR framework, which effectively addresses these challenges through the Progressive Feature Fusion Pyramid Network (P-FPN) and the Crowded Query Refinement Strategy (CQR). First, we construct the largest dense leaf detection dataset to date, containing 1696 images and 85,375 annotation boxes. Second, P-FPN alleviates the feature confusion problem of overlapping leaves through the multi-stage fusion of features and the Adaptive Feature Aggregation module (AFA), enhancing the interaction between low-level details and high-level semantics. Third, the CQR strategy significantly reduces the matching cost of crowded candidate boxes and improves the network convergence efficiency by culling a crowded query method and introducing a one-to-many matching mechanism. Finally, experimental results show that Leaf-DETR improves mAP@50 by 1% and AR@300 by 1.4% over the baseline model on our self-constructed dataset, outperforming existing detection methods. Furthermore, the model exhibits extremely fast training convergence and demonstrates strong generalization capability on both field-collected monitoring images and other staple crops, fully highlighting its practical value in complex agricultural scenarios. Finally, experiments show that Leaf-DETR outperforms existing detection methods on the self-built dataset and demonstrates good performance generalization in monitoring collected images, as well as for other staple food crops, which verifies its practicality in complex agricultural scenarios. The code and detailed information are available at http://leafdetr.samlab.cn.
Why it matches plant phenotyping methods葉の密集検出モデルとデータセットを開発・評価し、葉形態などの表現型抽出を可能にする画像ベース手法が研究の中心であるため。
abstractDense leaf detection, as a foundation for leaf morphology analysis, must address challenges such as occlusion and overlap, directly enabling key tasks including phenotypic trait extraction, disease identification, and yield estimation.
Reproduction assets foundThe paper's data availability statement explicitly points to an authors' public site (http://leafdetr.samlab.cn) hosting the Leaf-DETR code and detailed information, qualifying as a paper-specific public code asset. The self-constructed KiwiFruitLeaf dataset (1696 images, 85,375 annotation boxes) is described but its公开Code · publicThe code and detailed information are available at http://leafdetr.samlab.cn . For testing purposes, detailed instructions for running the model can be found in the repository's README file.Open asset ↗lines:504-529Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Abstract Crop diseases remain a significant threat to agricultural productivity and fruit quality, particularly for high-value crops such as strawberries and grapes. Early and reliable detection of these diseases under real-world conditions is essential but remains challenging due to variations in environment, illumination, and imaging perspectives. Leveraging recent advances in deep learning and computer vision, this study presents a robust framework for the automated detection and classification of strawberry and grape diseases using convolutional neural network (CNN) models, i.e., VGG16, ResNet101v2, InceptionV3, and DenseNet121. Unlike many existing works that rely solely on controlled or publicly available datasets, we constructed two specialized datasets by combining field-captured images under diverse environmental conditions with online sources, thereby enhancing robustness and ecological validity. The strawberry dataset includes six disease classes, while the grape dataset encompasses seven classes, covering economically significant pathologies such as anthracnose, black rot, gray mold, powdery mildew, sour rot, and leaf scorch. Extensive experiments were conducted using state-of-the-art CNN architectures, including VGG16, ResNet101v2, InceptionV3, and DenseNet121. On strawberries, DenseNet121 and InceptionV3 achieved accuracies of 94% (training) and 95% (testing), respectively, while VGG16 delivered superior performance on grapes, achieving 95% (training) and 92% (testing). Beyond technical accuracy, the proposed models were explicitly designed for applicability in actual field conditions, ensuring that the system can be directly adapted for use by farmers and plant pathologists as a practical decision-support tool. The findings provide a foundation for scalable, automated, and field-ready disease detection systems, contributing to more sustainable, data-driven crop management practices.
Why it matches plant phenotyping methods植物画像からイチゴ・ブドウ病害を自動検出・分類する深層学習手法を開発し、実圃場画像を含むデータセットで性能評価しているため、植物病害表現型の取得・推定が中心である。
abstractthis study presents a robust framework for the automated detection and classification of strawberry and grape diseases using convolutional neural network (CNN) models
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Kale (Brassica oleracea var. acephala) is a high value leafy vegetable with an extensive domestication history and germplasm diversity, making it an ideal target for genetic improvement. To meet growing food security needs particularly with controlled environment agriculture (CEA) systems, specialized breeding strategies are required. The goal of this study was to survey the phenotypic architecture of a global kale germplasm collection under commercial CEA conditions. This study establishes a phenotypic baseline and serves as a hypothesis generating resource for future genetic and physiological studies in kale and other leafy vegetables grown under CEA. RESULTS: A total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods. Significant differentiation was observed across all traits, with coefficient of variation ranging from 2.5% to 180.7%, confirming broad genetic variability among accessions. Trait correlation networks and hierarchical clustering grouped phenotypes into seven biologically corresponding modules including leaf, stem and root morphology, plant architecture, hyperspectral indices, and seedling growth. These modules highlight coordinated phenotypic patterns among traits. Integrative yield analyses combining partial least squares variable importance in projection with differential trait analysis identified 28 phenotypes most strongly associated with total aboveground fresh weight, a robust proxy for CEA vegetative yield. Principal component analysis further distilled these traits into three orthogonal components explaining 87.1% of total yield variation. These components represented modules related to plant organ size, canopy structure, and density, emphasizing their biological contribution to harvestable biomass. CONCLUSIONS: This study generates a foundational phenomics resource and comprehensive dissection of kale’s yield architecture under CEA conditions. The composition of traits identified constitutes a targeted set of breeding traits to be further validated for improved leafy vegetable yield. By integrating large-scale germplasm resources with phenomics, this work establishes the utility of a high-throughput phenotypic analysis for further leafy crop research and improvement.
Why it matches plant phenotyping methods大規模なハイスループット植物表現型解析を中核とし、113形質の取得、統合解析、再利用可能なフェノミクス資源の構築を行っているため。
abstractA total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods.
Pepper / chilliField / plotLiDAR / point cloudWhole plant / canopy / plot / fieldClassificationObject detectionSegmentationTrackingGrowth / development / phenology
Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants.
Why it matches plant phenotyping methods植物の器官レベル表現型解析を支援する4D点群データセットを構築し、成長状態分類・器官分割・追跡などを可能にする研究であり、フェノタイピング用データ基盤が中心です。
abstractPepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages.
Reproduction assets foundThe authors publicly release the Pepper-4D spatiotemporal 3D pepper point cloud dataset (with plant- and organ-level annotations) and associated code via a GitHub repository stated in the Data Availability Statement. CloudCompare is a generic third-party tool, not a paper-specific asset.Dataset · public.J.; writing—original draft preparation, F.A.; writing—review and editing, D.L.; visualization, F.A. and D.L.; supervision, D.L.; project administration, H.Y.; funding acquisition, D.L. and H.Y. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This work was supported in part by the Shanghai Sailing Program under Grant 24YF2701200, in part by the Fundamental Research Funds for the Central Universities under Grant 2232025D-50, and in part by Donghua UnOpen asset ↗foysalahmed10/Pepper-4Dlines:238-264Code · publicang Q., Zeng Y., Hou J., Zhe X. WarpingGAN: Warping multiple uniform priors for adversarial 3D point cloud generation; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; New Orleans, LA, USA. 18–24 June 2022; pp. 6397–6405.
Associated Data
Data Availability Statement
Data and code can be found at https://github.com/foysalahmed10/Pepper-4D (accessed on 9 February 2026).Open asset ↗foysalahmed10/Pepper-4Dlines:308-314Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleLeafStem / branchWhole plant / canopy / plot / fieldClassificationSegmentation
Abstract Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species. Data were acquired using Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS), include field-measured attributes such as species identity and Diameter at Breast Height (DBH), and terrestrial and aerial RGB imagery. TLS point clouds were georeferenced and co-registered with centimetre-level accuracy, enabling precise integration with ALS data. The dataset includes segmented individual trees and wood–leaf classifications, suitable for applications such as tree morphology analysis, biomass estimation, and species classification. To support benchmarking, outputs from established classification algorithms (LeWoS, TLSeparation, CANUPO, and Random Forest) are included. As one of the first open-access LiDAR datasets from Indian tropical forests, it provides critical reference data for developing and validating forest structure models. It can also aid biomass mapping efforts in support of large-scale missions such as NASA-ISRO’s NISAR and ESA’s BIOMASS.
Why it matches plant phenotyping methods個体樹木のLiDAR点群・RGB画像と樹木セグメンテーションを含む公開データセットで、樹形解析や森林構造モデルの開発・検証、分類アルゴリズムのベンチマークを目的としており、植物形質取得が中心です。
abstractThis dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species.
Reproduction assets foundThe paper's authors explicitly state that all code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset, which is an allowed URL. The paper's core LiDAR dataset is deposited on Zenodo (10.5281/zenodo.153Code · publicAll code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset .Open asset ↗https://github.com/moonis-ali/Datasetlines:479-553Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotSegmentationStress / disease detectionDisease symptoms / severity
Plant diseases pose significant threats to agriculture, making proper diagnosis and effective treatment crucial for protecting crop yields. In automatic diagnosis processing, image segmentation helps to identify and localize diseases. Developing robust image segmentation models for detecting plant diseases requires high-quality annotations. Unfortunately, existing datasets rarely include segmentation labels and are typically confined to controlled laboratory settings, which fail to capture the complexity of images taken in the wild. Motivated by these, we established a large-scale segmentation dataset for plant diseases, dubbed PlantSeg. In particular, PlantSeg is distinct from existing datasets in three key aspects: (1) Annotation types: PlantSeg includes detailed and high-quality disease area masks. (2) Image sources: PlantSeg primarily comprises in-the-wild plant disease images rather than laboratory images provided in existing datasets. (3) Scale: PlantSeg contains the largest number of in-the-wild plant disease images, including 7,774 diseased images with corresponding segmentation masks. This dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.
Why it matches plant phenotyping methods植物病害領域の画像セグメンテーション用データセットを構築し、病害領域マスクとベンチマーク基盤を提供することが中心で、植物の病害状態を直接推定する方法論的貢献である。
abstractThis dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.
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 codes for the baseline reproduction are presented in https://github.com/tqwei05/PlantSeg.Open asset ↗https://github.com/tqwei05/PlantSeghtml-lines:720-764Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
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 · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
•
VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
•
VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
RiceRootMorphology / geometry measurementSegmentationRoot system architecture
Root phenotypic traits such as length and number are critical indicators of plant growth and productivity. However, accurate extraction of these traits remains challenging due to the slender morphology, dense overlap, and frequent occlusion within root systems. Traditional digital image processing methods suffer from low throughput and limited robustness, while most deep learning-based approaches rely on semantic segmentation, which fails to distinguish individual roots and therefore limits their applicability in instance-level phenotypic analysis.To address these limitations, we propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping, with rice roots as a representative case. Building upon the MP-Former framework, our model introduces two key components: the Guided-Enhancement Pixel Decoder (GEPD) and the Asymmetric Dual-Query Decoder (ADQD). The GEPD enhances multi-scale feature representations via Hybrid Convolution Aggregator, Semantic-Guided Fusion Module and Frequency-Guided Feature Enhancement Module, effectively capturing fine root structures and low-contrast regions. ADQD employs asymmetric interaction between semantic and instance queries to improve long-range dependency modeling and instance separation in occluded scenarios.Additionally, we present the Rice Root Segmentation Dataset (RRSD), comprising of 343 high-resolution images with instance-level annotations. Experimental results show that DGA-MP-Former achieves state-of-the-art performance on RRSD, with 57.2% AP 0.5:0.95 and 87.4% AP 0.5 . Importantly, the accurate instance segmentation results enable reliable computation of instance-level geometric traits, such as root perimeter and area. To quantitatively assess phenotypic measurement accuracy, Relative Area Error (RAE) and Relative Perimeter Error (RPE) are further introduced, achieving 26.4% and 20.2%, respectively. These results demonstrate that the proposed method effectively bridges instance segmentation accuracy and phenotypic quantification reliability, supporting high-throughput and precise root phenotyping.
Why it matches plant phenotyping methodsイネ根の個体別セグメンテーションモデルを開発し、データセット提供、性能評価、および根の形態形質推定まで行っており、植物フェノタイピング手法が研究の中心である。
abstractwe propose Dual-Guided Asymmetric MP-Former (DGA-MP-Former), a novel instance segmentation model tailored for root phenotyping
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 Rice Root Segmentation Dataset is open sourced for the research community at ”https://github.com/Run-19/DGA-mpformer”.Open asset ↗Run-19/DGA-mpformerhtml-lines:442-469Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection in the following ways: (1) by creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods; (2) by introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments.
Why it matches plant phenotyping methodsUAV画像・3D点群から個体樹冠を抽出する新規手法を開発し、複数データセットで精度・頑健性を評価しているため、植物形態の取得・抽出が中心である。
abstractThis work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization.
Reproduction assets foundThe authors state that the MATLAB code implementing the DTCD/DTCD-PC method, together with the datasets (Acacia-6, AgiosNikolaos-3) and results, is publicly available at their project page. Since the article is published (accepted), this is an actionable public asset containing the paper's tree-detection analysis code,Code · public.P.; All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The code implementing the proposed method together with our
results, and the links to the datasets are publicly available after paper acceptance at the following
linkhttps://sites.google.com/site/costaspanagiotakis/research/tree-detection-dtcd, accessed on 30
January 2026.
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AIC Akaike Information Criterion
AMS3D Adaptive Mean Shift 3D
CHM Canopy Height Model
CHT Circular Hough Transform
CSP ComOpen asset ↗pdf-layout-page:24 lines:1-62Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Soybean ( Glycine max L. ) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.
Why it matches plant phenotyping methods大豆種子画像を標準化して公開するデータセット研究であり、種子形質の自動画像解析・セグメンテーションを支援する方法論的資源が中心です。
titleAn open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications.
Reproduction assets foundThe paper is a data descriptor for a public Mendeley Data repository containing the authors' own soybean seed image dataset (raw scans and segmented seed images) used for seed phenotyping, with an explicit direct URL and DOI.Dataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c733bjz4m3.3
Direct URL to data: https://data.mendeley.com/datasets/c733bjz4m3/3Open asset ↗Mendeley Data · 10.17632/c733bjz4m3.3html-lines:115-142Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Food security continues to be a significant challenge the world over, with crop production becoming increasingly threatened by crop diseases and pest infestations. In the case of chili production, farmers often suffer significant yield loss and economic insecurity due to the unpredictable nature of both of these problems. Current pest control options (agrochemical and organic methods alike) have not reliably been enough for timely and effective control and demonstrate the importance of early and effective pest and disease identification processes. To solve this problem, the present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves. The present work trains and evaluates three models (YOLOv5, YOLOv7, and YOLOv8) on a newly created and balanced dataset of over 28,800 images combining 20 total classes of pest and leaf diseases. The dataset was supplemented by preprocessing the images and conducting an augmentation process to create a total of 32,000 images for training to generate reliable models. The results from the experiments found that YOLOv8 provided the best baseline performance of 95.1% mean Average Precision (mAP), while YOLOv5 had an mAP of 86.1%, and YOLOv7 had an mAP of 67.5%. An additional enhancement in the construction of a Modified YOLOv8 hybrid model—reflecting all advantages of YOLOv5, YOLOv7, and YOLOv8—achieved a highest mAP of 99.5% to be the most effective model in this study, the results suggest that the newly proposed Modified YOLOv8 framework, is highly accurate and reliable for the early detection of pests and diseases in chili, and is helpful to improve sustainable agricultural practices, mitigate crop losses, and increase global food security.
Why it matches plant phenotyping methodsチリ葉の病害を画像から検出・分類する深層学習手法の開発、比較評価、データセット構築が研究の中心であり、植物の病害状態を直接推定するため対象範囲に該当する。
abstractthe present work describes a deep learning implementation that leverages advanced YOLO-based architectures for the detection and classification of leaf diseases and pest on chili plant leaves.
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement
Pollen and spore morphology provides essential taxonomic reference data for floristic and environmental studies in tropical regions, where modern comparative datasets remain limited. This study documents the morphological characteristics of pollen and spores recovered from a shallow soil profile in a degraded mixed deciduous forest within Sri Nan National Park, northern Thailand. Using a non-acetolysis extraction protocol and systematic sub-sampling of a 30-cm profile, pollen and spores representing 37 plant families were identified, including lycophytes, bryophytes, monilophytes, gymnosperms, and angiosperms. Spore-producing taxa, particularly monilophytes, dominate the assemblage, while angiosperm pollen includes both arboreal and non-arboreal elements. More than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions. The resulting dataset expands the regional palynological reference framework for northern Thailand and tropical Southeast Asia and supports consistent taxonomic identification in palynological, floristic, and comparative paleoecological studies, particularly in human-impacted forest-agriculture mosaics.
Why it matches plant phenotyping methods植物の花粉・胞子形態を標準化して記載し、高解像度画像を含む再利用可能な地域参照データセットを構築しており、形態取得・記述が研究の中心です。
abstractMore than 100 morphotypes are described based on aperture type, exine ornamentation, size, and symmetry, supported by high-resolution photomicrographs and standardized morphotype descriptions.
This study addresses the challenge of organ-level instance segmentation in cotton point clouds, which arises from significant morphological variations and leaf occlusion across growth stages. To achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed. A dataset containing 1,065 cotton plants from seedling to boll-opening stages was constructed via multi-view image reconstruction and augmented through random rotation and scaling. Methodologically, a two-stage pipeline was designed: semantic segmentation was first performed using the PointNeXt network, where its residual MLP blocks enhanced edge and local feature learning; instance segmentation was then conducted by applying density-adaptive DBSCAN clustering to the semantic results, effectively mitigating over-segmentation in emerging leaves. Experimental results indicate that the semantic segmentation achieved an mIoU of 0.9846, representing a 7.2% improvement over PointNet++. The subsequent instance segmentation attained an ARI of 0.983, reduced the over-segmentation rate by 63%, and maintained an error below 3% for leaves smaller than 5 cm 2 . The framework provides reliable technical support for the automated extraction of key phenotypic traits such as leaf area index and leaf inclination distribution.
Why it matches plant phenotyping methods綿花葉の点群から器官レベルの葉を自動抽出するセグメンテーション手法を開発・評価し、葉面積指数や葉傾斜分布などの表現型形質への応用を示しているため、方法が中心的である。
abstractTo achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed.
This article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis in four economically vital agricultural crops: Bottle Gourd ( Lagenaria siceraria ), Zucchini ( Cucurbita pepo ), Papaya (Carica papaya), and Tomato ( Solanum lycopersicum ). Addressing the scarcity of region-specific agricultural data, a total of 5266 original images were acquired directly from diverse agricultural fields in Bangladesh using a SONY ALPHA 7 II full-frame camera under natural lighting conditions. The dataset encompasses 28 distinct classes, covering a wide spectrum of biotic stressors including viral (Mosaic Virus, Leaf Curl), fungal (Downy Mildew, Anthracnose, Alternaria Blight), bacterial (Bacterial Blight, Xanthomonas), and pest-induced damage (Insect Hole, White Spot), alongside Healthy samples. To ensure scientific reliability, each image underwent a rigorous two-stage validation process by senior agronomists. To tackle class imbalance and facilitate the training of data-intensive Deep Learning models, the dataset was expanded using a Python-based augmentation pipeline incorporating geometric transformations (rotation, flipping) and photometric adjustments (noise, brightness) resulting in a final repository of 28,000 images (5266 original and 22,734 augmented). All files are standardized to 512×512 pixels in JPG format. This expert-validated resource serves as a critical benchmark for developing robust computer vision algorithms (e.g., CNNs, Vision Transformers) for precision agriculture, enabling research into fine-grained classification, object detection, and cross-crop transfer learning in subtropical farming environments.
Why it matches plant phenotyping methods植物病害症状を画像で分類するための専門家検証済みデータセットを構築し、再利用可能なベンチマークとして提供しているため、植物表現型取得法が中心です。
abstractThis article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis
Reproduction assets foundThe paper is a Data in Brief article describing the Agri-Vision Bangladesh multi-crop leaf disease image dataset, publicly deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/8t6k37ztxc.2). This is a paper-specific public asset containing the original and augmented plant images used in the study.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/8t6k37ztxc.2
Direct URL to data: https://data.mendeley.com/preview/8t6k37ztxc?a=a88a48f1-a9b0-4354-a081-cc8f1e936364Open asset ↗Mendeley Data · 10.17632/8t6k37ztxc.2html-lines:93-117Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Time-resolved phenotyping of disease symptoms enables dissection of resistance mechanisms and improves diagnosis, but acquiring phenotypic data at satisfactory scale remains challenging. Advances in imaging and image processing have improved measurement precision, robustness, and throughput, but further improvements are needed for practical application. We present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms. All images are geometrically aligned with a median precision of 0.16 mm (≈5 pixels). The dataset includes transformation matrices, symptom segmentation masks, metadata on treatments, weather, crop phenology, and disease occurrence, and a lightweight Python toolkit for loading, aligning, inspecting, and editing image sequences. These resources enable detailed investigation of leaf-level disease dynamics such as lesion, pustule, and fruiting body emergence rates, lesion growth, and dynamic interactions of disease development with spatial and environmental contexts. They offer a broad basis for developing improved methods for image alignment and symptom detection, segmentation, and tracking, possibly by tackling these connected challenges within a single end-to-end framework.
Why it matches plant phenotyping methods葉の病徴を対象とした高解像度時系列画像データセットで、幾何位置合わせ、病徴セグメンテーション、追跡用ツールを提供しており、植物病害表現型の取得・解析基盤が中心である。
abstractWe present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms.
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 · publicWe provide a lightweight Python toolkit to facilitate loading, inspection, and curation of the image sequences and their associated processing products in the associated Git repository (https://github.com/and-jonas/sympathique-wheat).Open asset ↗github.com/and-jonas/sympathique-wheathtml-lines:317-337Plant phenotyping relevance match · UnverifiedbioRxiv · checked 6 Sept 2026
Flow cytometry provides a reliable and fast method for estimating genome size and ploidy levels in plants. Until recently, most studies employed fresh tissues, which limits the use of the method with samples from remote areas or when an extremely high number of samples needs to be processed in a short time. Although there is growing evidence that silica-dried material can be used for ploidy estimation in some taxa, no flora-wide study has been available so far. Here, we provide methodological aspects of an unprecedented study exploring ploidy variation of non-apomictic angiosperms in the Eastern Alps. We have analysed ca. 45,000 silica-dried samples of 1135 species using flow cytometry with DAPI as stain. We were able to obtain ploidy level information from 1104 (97%) of species. The unsuccessful species included succulent plants of the family Crassulaceae (genera Jovibarba, Rhodiola, Sedum, Sempervivum), the achlorophyllous parasitic or mycoheterotrophic genera Orobanche and Hypopitis, and a handful of others. About 80% of samples were successfully analysed using a single universal protocol and leaf tissue, while in the remaining species the use of alternative tissues (such as petioles or flowers) and/or protocol modifications were needed (targeting composition of buffers, duration of fixation or staining time or use of alternative buffers). A total of 377 species (34%) included polyploid cytotypes and 179 (16%) species were ploidy-variable. As a community resource, we provide relative genome sizes and ploidy assignments of 1332 cytotypes retrieved from 1104 species along with methodological details (e.g. buffers, standards, analysed plant organs, histogram quality). We believe that this dataset will facilitate future research in particular species as well as in flora-wide investigations of ploidy level variation of the Central European flora in general. We are confident that novel cytotypes of many species will be discovered in other geographic areas, and we would be delighted if the present dataset could serve the botanical community for comparison.
Why it matches plant phenotyping methods植物のゲノムサイズ・倍数性を推定するフローサイトメトリー法の大規模な適用と、組織・バッファー・固定・染色条件の改良を中心に扱い、再利用可能なデータセットも提供しているため。
abstractFlow cytometry provides a reliable and fast method for estimating genome size and ploidy levels in plants.
Introduction: Accurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding. However, cotton seedling segmentation remains challenging due to fine-scale and complex organ morphology, uneven point density with noise, and the lack of high-quality annotated datasets. Methods: To address these issues, we propose DCSFormer, a tailored extension of Point Transformer V3 designed for cotton seedling point cloud segmentation. The model introduces the DCS Block, which leverages dynamic sparse expert routing and dual-channel attention to adaptively capture global semantic dependencies and subtle local geometric variations, thereby improving stem-leaf boundary discrimination. In addition, the proposed CLFSkip replaces traditional skip connections with a cross-layer fusion strategy, effectively integrating multi-scale features while preserving organ-level details. We also constructed an annotated cotton seedling dataset to support training and evaluation. Results and Discussion: Experimental results show that DCSFormer achieves 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1, outperforming multiple comparison models. Furthermore, when evaluated against baseline models on two public datasets, Crops3D and Pheno4D, DCSFormer exceeds the baseline across all four metrics, further validating its effectiveness and generalizability. This work provides an effective solution for precise cotton seedling organ segmentation.
Why it matches plant phenotyping methods綿花幼苗の3D点群から器官を抽出する手法を開発し、アノテーション済みデータセットの構築と複数データセットでの性能検証を行っており、植物表現型取得が中心である。
abstractAccurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding.
Reproduction assets foundThe authors constructed an annotated cotton seedling point cloud dataset (100 samples with semantic/instance organ labels and ground-truth traits) used for training and evaluating DCSFormer, and the data availability statement points to a public Kaggle repository containing it. No author analysis code or trained model/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://www.kaggle.com/datasets/tengfeiliu333/dcsformer-cotton/croissant/download .Open asset ↗Kaggle · tengfeiliu333/dcsformer-cottonlines:957-1015Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Brinjal (Solanum melongena) or eggplant is one of the four most essential vegetable crops that are grown in Bangladesh and contribute significantly to the agricultural industry of the country. Brinjal supports the livelihood of numerous small farmers; however, brinjal is severely susceptible to various fruit diseases, which have serious impacts on yield quality and may cause considerable economic losses. While most existing plant disease datasets primarily focus on leaf-related disorders, only a limited number include fruit-related diseases and even those contain very few classes. This gap is significant because fruit diseases directly affect crop quality, market value, and overall yield. This is why we present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases. This data set consists of 1823 high-quality, labelled images, across five distinct classes: Phomopsis Blight, Shoot and Fruit Borer, Fruit Cracking, Wet Rot, and Healthy Fruit. The images were collected from real farm conditions in numerous areas of Bangladesh to ensure a robust sample of varied environmental and farming practices impacting the growth of diseases. This dataset is designed with the unique aim to support plant disease research and enhance training of deep learning models for autonomous disease detection. Lastly, the dataset will allow early disease detection, enhancing crop management practice, reduction of losses, and increasing farmers' economic returns. The release of this dataset will encourage agricultural research as well as practical use in precision agriculture.
Why it matches plant phenotyping methodsナス果実の病徴を対象とした画像データセットの構築・公開が中心であり、植物の病害状態を画像から識別する再利用可能な表現型データ資源に該当する。
abstractwe present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases.
Reproduction assets foundThe paper's brinjal fruit disease image dataset (1823 labeled images, five classes) is publicly deposited on Mendeley Data, and the authors' model training/augmentation code is publicly available on GitHub.Code · publicThe complete code, along with augmentation scripts and model development, is publicly available in our GitHub repository [12].Open asset ↗GitHubhtml-lines:299-357Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Image segmentation is a fundamental component of vision-based agricultural robotics, enabling accurate fruit localization, disease detection, and automated harvesting. However, real-world strawberry fields present significant challenges due to irregular fruit morphology, dense foliage occlusions, variable ripeness, and strong illumination variability. Moreover, segmentation models trained on a single dataset often fail to generalize across domains, limiting their practical deployment. This paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets: Db1 (instance segmentation), Db2 (lesion segmentation), and Db3 (semantic segmentation). A unified preprocessing and evaluation framework is adopted to ensure fair comparison using standard metrics, including Intersection-over-Union (IoU), Dice coefficient, Precision, and Recall. Extensive in-domain experiments demonstrate that deep learning models significantly outperform classical approaches, with U-Net and SegFormer achieving IoU values above 0.95 on Db1 and up to 0.83 on Db3. Cross-domain zero-shot evaluations reveal a substantial generalization gap, with U-Net suffering IoU drops of up to 100\%, while SegFormer consistently exhibits improved robustness and reduced cross-domain degradation across most transfer scenarios. To our knowledge, these results establish the first systematic multi-dataset benchmark for strawberry segmentation under domain shift, highlighting the importance of transformer-based architectures for robust agricultural perception and providing practical insights for real-world robotic deployment.
Why it matches plant phenotyping methodsイチゴの病斑・果実を画像から分割する手法を複数データセットで比較・ベンチマークし、ドメインシフト下の性能を評価しているため、植物の病害状態・器官形態の取得が中心である。
abstractThis paper presents a comprehensive benchmark of classical computer vision methods, convolutional neural networks, instance-based models, and transformer-based architectures across three heterogeneous public strawberry datasets
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
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 · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification. A curated dataset of Malabar spinach images collected from Habiganj Agricultural University and supplemented with public samples was categorized into three classes: Alternaria, straw mite, and healthy leaves. A lightweight SpinachCNN established a strong baseline, while Spinach-ResSENet, enhanced with squeeze-and-excitation modules, improved channel-wise attention and feature discrimination. A customized Vision Transformer (SpinachViT) and SwinV2-Base were further investigated to assess the benefits of transformer-based architectures under limited data. To mitigate annotation scarcity, we employed SimSiam-based self-supervised pretraining on unlabeled images, followed by supervised fine-tuning with cross-entropy or a hybrid objective combining cross-entropy and supervised contrastive loss. The best-performing domain-optimized model, SimSiam-CBAM-ResNet-50, incorporated Convolutional Block Attention Modules and achieved 97.31% test accuracy, 0.9983 macro ROC-AUC, and low calibration error, while maintaining robustness to Gaussian and salt-and-pepper noise. Although a SwinV2-Base benchmark pretrained on ImageNet-22k reached slightly higher accuracy (97.98%, 98.99% with test-time augmentation), its 86.9M parameters and reliance on large-scale pretraining reduce feasibility for edge deployment. In contrast, the SimSiam-CBAM model offers a more parameter-efficient and deployment-friendly solution for real-world agricultural applications. Model decisions are interpretable via Grad-CAM, Grad-CAM++, and LayerCAM, which consistently highlight biologically relevant lesion regions. The spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.
Why it matches plant phenotyping methods葉画像から病害状態を分類する深層学習手法を開発・比較し、公開データセットと解釈可能性・頑健性も評価しており、植物表現型取得が中心である。
abstractThis work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification.
Reproduction assets foundThe paper's Malabar spinach leaf disease image dataset (the phenotyping input used for all measurements) is explicitly stated as publicly available on Hugging Face, with the URL given in the abstract and Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.Open asset ↗huggingface · saifullah03/malabar_spinach_leaf_disease_datasethtml-lines:585-614Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 13 Sept 2026
Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R2 = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding.
Why it matches plant phenotyping methods3D画像再構成と深層学習によってイネの害虫被害症状・被害重症度を定量化する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractthis study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation.
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 13 Sept 2026
High-resolution UAV photogrammetry has become a key technology for precision agriculture, enabling centimeter-level crop monitoring and point-level plant localization. However, point-level maize localization in UAV imagery remains challenging due to (1) extremely small object-to-pixel ratios, typically less than 0.1%, (2) prohibitive computational costs of quadratic attention on ultra-high-resolution images larger than 3000 x 4000 pixels, and (3) agricultural scene-specific complexities such as sparse object distribution and environmental variability that are poorly handled by general-purpose vision models. To address these challenges, we propose the Additive Kolmogorov-Arnold Transformer (AKT), which replaces conventional multilayer perceptrons with Pade Kolmogorov-Arnold Network (PKAN) modules to enhance functional expressivity for small-object feature extraction, and introduces PKAN Additive Attention (PAA) to model multiscale spatial dependencies with reduced computational complexity. In addition, we present the Point-based Maize Localization (PML) dataset, consisting of 1,928 high-resolution UAV images with approximately 501,000 point annotations collected under real field conditions. Extensive experiments show that AKT achieves an average F1-score of 62.8%, outperforming state-of-the-art methods by 4.2%, while reducing FLOPs by 12.6% and improving inference throughput by 20.7%. For downstream tasks, AKT attains a mean absolute error of 7.1 in stand counting and a root mean square error of 1.95-1.97 cm in interplant spacing estimation. These results demonstrate that integrating Kolmogorov-Arnold representation theory with efficient attention mechanisms offers an effective framework for high-resolution agricultural remote sensing.
Why it matches plant phenotyping methodsUAV画像から個体位置を抽出する手法を開発し、個体数と株間距離という植物群落形質を推定しており、データセット構築と技術評価も中心的である。
abstractTo address these challenges, we propose the Additive Kolmogorov-Arnold Transformer (AKT)
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.
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.
Why it matches plant phenotyping methods複数センサー・ロボットプラットフォームによる圃場フェノタイピング用データセットを構築・提供し、形質推定法の開発、センサー比較、汎化評価を可能にすることが中心的な貢献である。
abstractwe present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset
Reproduction assets foundThe paper's MuST-C multi-sensor, multi-temporal crop phenotyping dataset (RGB/multispectral images, LiDAR point clouds, LAI and biomass reference measurements) is publicly available via the authors' project webpage, and the authors' custom Python processing/loading code is publicly available on GitHub.Dataset · publicThe MuST-C dataset is available via our project webpage https://www.ipb.uni-bonn.de/data/MuST-C/or directly via the bonndata public access repository 10.60507/FK2/OX9XTM34Open asset ↗html-lines:421-440Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.
Why it matches plant phenotyping methods植物の複雑形質を予測するゲノム・フェノミック選抜用Rパッケージを開発し、データ準備からモデル評価までの再利用可能なワークフローを提供・検証しているため、フェノタイピング関連ソフトウェアとして中心的です。
abstractThis study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction.
Reproduction assets foundThe paper's authors publicly released the MTMEGPS R package (analysis code/workflow) on GitHub, and the independent multi-environment maize validation dataset (phenotypes and genotypes) is publicly available via the Genomes to Fields initiative DOI. Both are paper-specific, public, and actionable.Dataset · publicnal phenotypic information.
2.2
Independent multi-environment maize validation dataset
The datasets analyzed in this study were obtained from the Genomes to Fields (G2F) initiative ( www.genomes2fields.org ). The dataset comprises 135 unique maize hybrids evaluated across nine experimental sites during the 2018 growing season ( https://doi.org/10.25739/anqq-sg86 ). Phenotypic measurements were collected following standardized protocols provided by the G2F consortium, as detailed in the accompanying documentation available on the project website.
The traits evaluated in this study included plant height (distance from the plant base to the ligule of the flag leaf), ear height (distance fOpen asset ↗10.25739/anqq-sg86lines:51-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km² with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m²/m²) and digital hemispherical photography (DHP) images (RMSE = 0.46 m²/m²) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R² = 0.70, RMSE = 0.86 m²/m²). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.
Why it matches plant phenotyping methods森林の植物面積密度を推定する3D再構成ワークフローを開発し、実測LAI等で検証した大規模フェノタイピング製品・データセットであり、植物形質取得が中心である。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
Field / plotRGB / grayscaleLeafMorphology / geometry measurementLeaf traits
Abstract Despite similar universal primary physiological functions, plant leaves exhibit myriad shapes and sizes. Understanding this morphological variation is invaluable in plant taxonomy, ecology, evolution, and biomimetics. Achieving a comprehensive understanding of eco-evo-devo research requires diverse leaf-image datasets collected across regions and over time. While many datasets support morphometric studies using advanced imaging and machine learning, few provide standardised leaf images that enable uniform interspecific comparisons. We present a dataset of 161 high-quality RGB images of leaves of wild and cultivated tree species from Kerala, India, collected between 2020 and 2023. All leaves, including their petioles, were scanned using a digital scanner (Epson L360), centrally framed on a white background, and uniformly scaled to 1024 × 1024 pixels. In addition, the dataset comprises codes to compute the leaf morphometry using two novel objective morphometric measures: Segmental fractal complexity ( D ΣS ) and Geometric entropy ( S L ). These metrics were validated against the leaf dataset, showing strong correlations between D ΣS and leaf dissection index ( LDI ) ( ρ = 0.94) and between S L and D ΣS ( ρ = 0.94), confirming the relationship between leaf patterns and leaf lobiness, pinnation, and serration. D ΣS surpasses LDI by incorporating spatial positioning of leaflets, lobes and fine serration features. Both D ΣS and S L outperform geometric morphometric techniques, which are limited to intraspecific comparisons. Their objectivity, ease of use, and lack of statistical preprocessing make D ΣS and S L reliable metrics for interspecific leaf comparisons. We encourage researchers to expand or replicate our analysis using codes and leaf datasets from diverse locations. This dataset supports the development and validation of future leaf morphometric techniques. Despite limitations in high-resolution imaging and intraspecific variability, it remains valuable for advancing research and fostering collaboration across taxonomy, ecology, and computer vision.
Why it matches plant phenotyping methods葉画像データセットの提供に加え、葉形態を定量化する新規指標とコードを提示・検証しており、植物表現型の取得・抽出手法が中心である。
abstractWe present a dataset of 161 high-quality RGB images of leaves of wild and cultivated tree species from Kerala, India, collected between 2020 and 2023.
Abstract Ensuring plant sustainability is critically important across numerous domains. Specifically, the detection and segmentation of leaves are essential for tasks such as identifying plant diseases, monitoring plant growth, and determining plant phenotypes. However, the limited diversity in both data and species within existing datasets prepared for instance segmentation tasks often leads to the development of models with poor generalization capabilities and significant biases. This study introduces GenYOLO-Leaf, a data-centric, open-source framework designed to address these limitations. GenYOLO-Leaf facilitates instance-based leaf segmentation with improved generalization capabilities using different data sets with enriched label information by extracting approximately 145K leaf instances. It can also serve as a valuable resource for transfer learning across various segmentation tasks. The developed framework underwent zero-shot evaluation using a total of eight distinct datasets: four for instance segmentation and four for semantic segmentation. Experimental results indicate that the framework achieved mAP scores ranging from 62 to 84% on instance segmentation datasets, while producing mean IoU scores between 86 and 99% on semantic segmentation datasets. The GenYOLO-Leaf framework that includes model weights for YOLOv11 and YOLOv8 is publicly available at https://github.com/aaslihanyildirim/GenYOLO-Leaf .
Why it matches plant phenotyping methods葉のインスタンスセグメンテーションを汎用的な植物形質抽出に用いるオープンソース枠組みを開発し、複数データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis study introduces GenYOLO-Leaf, a data-centric, open-source framework designed to address these limitations.
Accurate detection of tomato ripeness and size is critical for robotic thinning and harvesting but remains challenged by performance degradation in adverse weather, imprecise size estimation, and computational constraints on edge devices. To bridge this gap, we introduced (1) the TIDAW dataset (Tomato Images in Diverse Adverse Weather), synthetically generated via a physically-grounded atmospheric scattering model to simulate realistic rain and fog; and (2) Edge-YOLO-Tomato, a novel YOLOv8-based architecture, featuring four key innovations: a physics-aware scattering module that unifies multi-particle light transport theory with dual-attention mechanisms to explicitly model wavelength-dependent scattering for robust feature disentanglement; dilated convolutions enhancing receptive fields; a prior-embedded Wise-IoU loss incorporating botanical size distribution priors to rectify bounding box bias; and a compression framework that combines magnitude pruning and layer-wise pruning using neural architecture search. Extensive evaluations demonstrate leading performance: Edge-YOLO-Tomato achieves 93.3% mAP 50 and 74.3% mAP 50:95 on TIDAW, surpassing YOLOv8, YOLOv11, Faster R-CNN, and RT-DETR etc. by 1.1%-26.3% and 0.2%-2.2%, respectively. The compressed model attains a 4.7373 MB footprint (20.58% size reduction) with ≦ 0.5% accuracy loss and delivers 50% latency reduction on CPU. This work establishes a new paradigm for vision-based precision agriculture by unifying physical data synthesis, physics-aware modeling, and compression framework, enabling real-time robust fruit detection in uncontrolled environments. The codes are available at https://github.com/YLu567/Edge-YOLO-Tomato.
Why it matches plant phenotyping methodsトマト果実の成熟度・サイズを画像から推定するデータセットとエッジ向けモデルを開発・評価しており、果実形質の取得手法が中心的な貢献である。
abstractAccurate detection of tomato ripeness and size is critical for robotic thinning and harvesting
RaspberryField / plotWhole plant / canopy / plot / field
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsラズベリーの生育・フェノロジー段階を対象とする画像データセットとYOLOv8ベンチマークであり、植物状態の取得・推定手法が中心です。
titleRaspberryJalisco: A field-collected multi-class dataset of Raspberry (Rubus idaeus) phenological stages from Mexican commercial orchards with YOLOv8 benchmarks
Tomato, as a globally important economic crop, requires precise and timely disease management to secure yield and quality. Yet segmentation robustness is often limited by weak semantic understanding from single-modality images, narrow receptive fields of convolutional structures, and discontinuous boundary predictions. To address these issues, we propose the Multi-scale Linear Cross-modal Fusion Architecture for Tomato Leaf Disease Segmentation (MS-LCFNet). We construct a real-world field dataset covering five major tomato leaf diseases, annotated by experts with detailed textual descriptions to enable multimodal learning. MS-LCFNet strengthens semantic representation via cross-modal fusion, captures local and global context through an Adaptive Long-short Distance Perception module, and improves boundary continuity with a Physics-informed Smoothness-constrained Loss. Experiments show that MS-LCFNet achieves 87.13 % mIoU on our dataset and 90.78 % on PlantVillage, improving over previous state-of-the-art methods by + 4.62 % and + 4.48 %, respectively, and demonstrating superior accuracy and robustness in complex agricultural scenarios.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像からセグメンテーションする手法を開発し、独自データセットで性能評価しており、植物病害表現型の取得・抽出が中心である。
titleA multi-scale linear cross-modal fusion architecture for tomato leaf disease segmentation
Accurate crop yield mapping is essential for assessing climate change impacts on agriculture and identifying yield gaps. While high spatiotemporal resolution satellite products such as Planet Fusion (PF) with daily 3 m resolution imagery, offer new opportunities for detailed crop monitoring, the impact of the spatiotemporal resolution of satellite data on crop yield estimation remains underexplored. In this study, we create a benchmark dataset consisting of a 3 m resolution rice yield map for a heterogeneous paddy landscape with different cultivars, using PF-based accumulated near-infrared radiation from vegetation (NIRvPₐccᵤₘ) between heading and harvest stages. Comparisons against plot-level rice yield measurements yield an R² of 0.76. We cross-compare yield estimates from other satellite products—MODIS, Sentinel-2, Landsat 8, and a spatial-temporal Savitzky-Golay product—against the PF-based benchmark yield data resampled to relevant coarser spatiotemporal scales. We find that, compared to PF-based yield estimation, lower spatiotemporal resolution leads to higher yield underestimation. Additionally, the downsampled PF data exhibit patterns similar to those observed in the coarser-resolution products. High-spatiotemporal resolution PF data captures peak growth stages more accurately, alleviating the mixed-pixel problem and mitigating nonlinear effects where reflectance-yield relationships deviate from linear scaling. In contrast, coarser-spatiotemporal resolution products, such as Landsat 8 has longer revisit intervals, often miss critical phenological phase transitions (e.g., peak growing season), resulting in substantial yield underestimations compared to PF. Notably, we find that yield underestimations caused by lower spatiotemporal resolutions can surpass inter-annual yield variations. These findings underscore the importance of using satellite imagery with both high spatial resolution and frequent revisits to achieve sufficiently accurate yield estimates in smallholder-dominated, heterogeneous landscapes. By highlighting the trade-offs associated with different satellite-based spatiotemporal resolutions, the study underscores the importance of considering resolution impacts on yield estimation, offering insights for optimizing Earth observation-based agricultural management, particularly in smallholder farming settings.
Why it matches plant phenotyping methods衛星データによるイネ収量推定を中心に、ベンチマークデータセットの作成、異なる衛星時空間解像度の比較、圃場収量との検証を行っており、植物形質取得法が中核である。
abstractwe create a benchmark dataset consisting of a 3 m resolution rice yield map
Monitoring wheat growth, as one of the most important food grain sources for human nutrition, and forecasting yields are done through different phenological phases. Reliable estimates on yields play a crucial role in securing sufficient food supplies for the world's growing population. Currently, farmers estimate a wheat yield during the later stages of growth and are often biased in this process. Plant breeding scientists use a more accurate approach that collects data on the number of wheat ears manually counted at various locations throughout the field. A sufficiently precise count of wheat ears is one of the most important parameters for reliable early-stage prediction of wheat yield. To support the development of an affordable and trustworthy automated wheat ear detection approach, this work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties. Additionally, it evaluates six deep learning models for wheat ear detection. Among the F-RCNN-based models, RetinaNet, YOLOv8, and a Vision Transformer-based detector, RT-DETR, achieved the highest mean Average Precision (mAP@50) of 91%, with significantly higher computational complexity. BioS-Wheat complements Global Wheat Head Detection datasets, introducing a meaningful shift in data complexity with high sowing density and minimal row spacing, emphasizing the impact of agronomic diversity on model performance by an increased object occlusion and dense spatial arrangements. Enriched and agronomically diverse datasets support model robustness at different varieties, growth stages, and locations. This work offers a good baseline for establishing the procedure for image crowdsourcing, further dataset expansions, and model improvements.
Why it matches plant phenotyping methods小麦穂の画像検出による個体群形質推定を対象とし、注釈付きデータセットの構築と複数モデルの評価が中心であるため、表現型計測手法として収載する。
abstractthis work introduces a novel high-quality RGB smartphone image dataset, BioS-Wheat, comprising 5,696 annotated images across four wheat varieties.
This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection by (1) creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods, and (2) introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments.
Why it matches plant phenotyping methodsUAV画像・3D点群から個体樹冠を抽出・ delineateする新規手法を開発し、複数データセットで性能評価しているため、植物の樹冠形態・個体構造の画像ベース計測として中心的です。
abstractThis work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization.
High-precision plant phenotyping requires efficient 3D reconstruction with high fidelity, yet existing methods such as MVS and NeRF all have problems of feature dependence and error accumulation during 3D reconstruction, which leads to geometric distortion in reconstruction and restricts the reconstruction efficiency. To address this bottleneck, this study first determined the multi-view image acquisition strategy. Further, based on the self-built multi-view dataset of chili peppers, it proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm. Experimental results indicate that the Peak Signal-to-Noise Ratio ( PSNR ) improves by 52.0 %, 26.4 %, and 4.2 % compared to NeRF, Instant-NGP, and 3D Gaussian Splatting respectively. Additionally, the Structural Similarity Index Measure ( SSIM ) increases by 22.9 %, 12.8 %, and 4.3 % respectively over above methods. The point cloud data reconstructed based on this algorithm also has advantages in the measurement of phenotypic parameters. Compared with the actual measured values, the R² of the phenotypic parameters such as pepper plant height, canopy width, and leafstalk angle obtained in this study are 0.997, 0.954 and 0.978 respectively, and the RMSE are 0.236 cm, 1.082 cm and 2.344° respectively, and the MAE are 0.209 cm, 0.880 cm and 1.965° respectively. The accuracy was significantly better than that of the existing phenotypic calculation methods. Verification across different growth stages of wheat and maize was performed universally, with all errors remaining below 1.1 %, providing new ideas and technologies for high-precision, low-cost, and high-throughput crop phenotypic research.
Why it matches plant phenotyping methods植物の多視点画像から3D再構成し、草丈・群落幅・葉柄角などの形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心的である。
abstractit proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm.
Today, the agricultural sector faces significant challenges due to population growth and limited resources. Enhancing productivity and minimizing losses is of great importance for the sustainability of agriculture. Therefore, leveraging technological advancements plays a critical role, particularly in the development of sustainable farming practices. Among these advancements, artificial intelligence (AI) stands out with its potential to contribute significantly to agricultural production. The primary objective of this study is to provide farmers with fast and accurate information regarding plant health, thereby preventing the spread of diseases and optimizing agricultural output. In line with this goal, AI-based image processing techniques were employed. Specifically, this study focuses on detecting grapevine leaf diseases namely powdery mildew ($Erysiphe$ $necator$), downy mildew ($Plasmopara$ $viticola$), and grapevine rust mite ($Eriophyes$ $vitis$) using AI. Disease detection was carried out using leaf images, which were then used for classification. A hybrid dataset was constructed using a combination of publicly available images and manually collected samples captured via smartphone cameras in vineyards, fields, and gardens. This diverse and balanced dataset was used to train several CNN-based transfer learning models, including AlexNet, DarkNet53, Inception-ResNet-V2, Inception-V3, MobileNet-V3, ResNet50, ResNet101, VGG16, and VGG19 architectures. Among these, Inception-ResNet-V2 achieved the best performance with an accuracy of 97.45%, a training loss of 8.19%, a test accuracy of 93.00%, and a test loss of 20.60%. These results demonstrate that the model performs well in detecting diseases from grapevine leaves during both training and testing phases.
Why it matches plant phenotyping methodsブドウ葉の画像から病害状態を推定する画像解析・転移学習手法が研究の中心であり、データセット構築と複数モデルの性能評価も行っているため含める。
abstractAI-based image processing techniques were employed.
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Callus induction is a complex procedure in plant organ, cell, and tissue culture that underpins processes such as metabolite production, regeneration, and genetic transformation. It is important to monitor callus formation alongside subjective evaluations, which require labor-intensive care. In this research, the first curated lentil (Lens culinaris) callus dataset for instance segmentation was experimentally generated using three genotypes as one data set: Firat-87, Cagil, and Tigris. Leaf explants were cultured on MS medium fortified with different concentrations of gross regulators of BA and NAA to induce callus formation. Three biologically relevant stages, the leaf stage, the green callus, and the necrosis callus, were produced. During this process, 122 high-resolution images were obtained, resulting in 1185 total annotations across them. The dataset was evaluated across four successive generations (v5/7/8/11) of YOLO deep learning models under identical conditions using mAP, Dice coefficient, Precision, Recall, and IoU, together with efficiency metrics including parameter counts, FLOPs, and inference speed. The results show that anchor-based variants (YOLOv5/7) relied on predefined priors and showed limited boundary precision, whereas anchor-free designs (YOLOv8/11) used decoupled heads and direct center/boundary regression that provided clear advantages for callus structures. YOLOv8 reached the highest instance segmentation precision with mAP50@0.855, while it matched the accuracy with greater efficiency and achieved real-time inference with 166 FPS.
Why it matches plant phenotyping methods植物組織培養におけるカルスの形成段階・壊死状態を画像からインスタンスセグメンテーションする手法、データセット、モデル比較を中心に扱っており、植物状態の取得・定量化が本研究の主要な技術貢献である。
titleReal-Time Callus Instance Segmentation in Plant Tissue Culture Using Successive Generations of YOLO Architectures
Reproduction assets foundThe paper's lentil callus image dataset with annotations (122 images, 1185 annotations) is publicly available on Roboflow Universe per the Data Availability Statement. The YOLOv5 GitHub link and Ultralytics docs are generic third-party libraries, not authors' analysis code, and the FAO link is a cited reference, so allDataset · publicThe dataset used in this study, including annotated images for callus detection, is publicly available and can be accessed at Roboflow Universe: https://universe.roboflow.com/yunus-7v2b5/callus-hug7d , accessed on 13 September 2025. This repository contains all images and annotations generated and analyzed during the current study.Open asset ↗Roboflow Universe · callus-hug7dlines:314-345Code / 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
454
approval for publication.
455
456
Data Availability Statement
457
The ensemble data models and a corresponding JSON file with model metadata data are
458
housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
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
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
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,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
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
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
Additional Supporting Information may be found online in the Supporting Information section at
466
the end of the article.
467
Appendix S1. List of difficult-to-annotate genera and families removed from training and
468
downstream data.
469
Appendix S2. Table S1. Validation results for held-oOpen asset ↗Zenodo · 10.5281/zenodo.17675089pdf-raw-page:19 lines:1-55Code · publiclity Statement
457
The ensemble data models and a corresponding JSON file with model metadata data are
458
housed on Zenodo (https://doi.org/10.5281/zenodo.17079402). Images used in training,
459
validation, and testing are located here: https://zenodo.org/records/17675089. Code used for
460
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
462
(https://doi.org/10.5281/zenodo.17675089).463
464
Supporting Information
465
Additional Supporting Information may be found online in the Supporting Information section at
466
the end of the article.
467
Appendix S1. List of difficult-to-annotaOpen asset ↗GitHub · rafelafrance/phenobasepdf-raw-page:19 lines:1-55Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoGreenhouseMultimodalRGB / grayscaleMultispectral / hyperspectralFruitClassificationSegmentationGrowth / development / phenology
Computer vision and multispectral imaging have increasingly become essential tools in modern precision agriculture. Accurate ripeness assessment is critical for yield optimization, reducing post-harvest losses, and enabling automated harvesting systems. However, traditional RGB-based approaches struggle to differentiate subtle maturity changes, and existing solutions often fail under varying lighting, occlusion, or cultivar-specific conditions. To address these challenges, this study focuses on the integration of complementary spectral cues for reliable tomato ripeness evaluation. The work utilizes a curated RGB-NIR tomato dataset comprising 224 hyperspectral samples, processed into aligned multimodal image pairs with balanced ripeness categories.The proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues. The novelty of the methodology lies in the dynamic cross-attention mechanism, which learns inter-modal dependencies between RGB and NIR signals for enhanced ripeness interpretation. Performance metrics including accuracy, precision, recall, F1-score, mIoU, and AUC were used to comprehensively evaluate the system. Experimental results demonstrate that TomatoRipen-MMT significantly outperforms all baseline RGB-only, NIR-only, and fusion methods, achieving 94.8% classification accuracy and 82.6% mIoU. These findings establish the effectiveness of multimodal Transformers for robust, high-precision fruit maturity assessment in controlled and greenhouse environments.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を、RGB・NIR画像融合とTransformerで推定する手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThe proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues.
Reproduction assets foundThe paper's phenotyping analysis is built on a publicly available USDA/NAL hyperspectral tomato dataset, explicitly linked in the Data Availability statement with an exact URL match. No author code or model checkpoints are disclosed.Dataset · publicThe dataset analyzed in this study is publicly available at the https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_b_Hyperspectral_Imaging_Analysis_for_Early_Detection_of_Tomato_Bacterial_Leaf_Spot_Disease_b_/26046328.Open asset ↗26046328html-lines:1038-1053Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Clustering algorithms are widely used for phenotypic characterization and germplasm management, particularly in data-scarce crops such as neglected and underutilized species (NUS) that lack genomic resources. However, their performance under biologically realistic conditions remains poorly understood. Standard clustering methods commonly applied in crop research often assume distinct, isotropic, and homogeneous clusters, assumptions rarely satisfied in real-world phenotypic datasets. We developed a flexible and empirically calibrated simulation framework, using phenotypic data from West African fonio (Digitaria exilis), to benchmark the performance of eleven clustering algorithms under both idealized and realistic scenarios. Our simulations integrated heterogeneous trait distributions (normal, gamma), strong inter-trait correlations (up to r = -0.84), heteroscedasticity, and moderate population structure (mean Pst = 0.16 ± 0.001, achieved through iterative calibration). Each scenario was replicated 100 times, with clustering accuracy evaluated using external (ARI, NMI) and internal (Silhouette, Davies-Bouldin) validation metrics under standardized conditions. The results revealed consistently poor algorithm performance under realistic conditions (e.g., ARI < 0.07), including for widely used methods in Neglected and Underutilized Species (NUS) research such as K-means, GMM, and PAM. Notably, conventional validation metrics failed to detect biologically meaningful structure revealed by geometric diagnostics, highlighting a critical methodological limitation. Performance markedly improved under idealized conditions, validating our simulation framework. These findings highlight the risk of overinterpreting clustering outputs from weakly structured phenotypic datasets and expose key limitations in current biodiversity analysis practices, particularly those guiding plant genetic resource conservation programs. We provide an open-source R-based diagnostic tool, with parameter specifications to assist practitioners in selecting reproducible and interpretable clustering approaches for germplasm management and biodiversity assessment in data-scarce crops.
Why it matches plant phenotyping methods植物の表現型データを対象に、クラスタリング手法を現実的な条件でベンチマークするシミュレーション枠組みとR診断ツールを開発しており、表現型解析手法が研究の中心である。
abstractWe developed a flexible and empirically calibrated simulation framework, using phenotypic data from West African fonio (Digitaria exilis), to benchmark the performance of eleven clustering algorithms under both idealized and realistic scenarios.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete R simulation/clustering/evaluation script on Zenodo (DOI 10.5281/zenodo.15877863), a paper-specific, publicly actionable code asset. The empirical fonio trait data belong to a prior cited study (Bio et al.), not this paper, and supporting files/DOCode · publicthe complete R script used to simulate phenotypic datasets, apply clustering algorithms, and compute evaluation metrics is publicly available on Zenodo: https://doi.org/10.5281/zenodo.15877863Open asset ↗Zenodo · 10.5281/zenodo.15877863lines:107-122Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
Above-ground vertical structure is a critical variable for ecosystem monitoring, carbon accounting, and land management. However, the high cost and limited coverage of airborne lidar hinder its widespread application. To address this, we developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery. Unlike forestry-specific models that exclude human-made features, NAIP-CHM characterizes the full vertical structure of the landscape including vegetation, buildings, and infrastructure. We utilized a U-Net convolutional neural network with attention mechanisms and environmental conditioning, training and validating the model with a peer-reviewed, publicly available dataset of 22.8 million co-registered NAIP imagery and lidar-derived CHM pairs, with stratified sampling to ensure robustness in open-canopy ecosystems. The model achieved a pixel-wise root mean square error (RMSE) of 2.28 meters and an r2 of 0.87. Forested sites alone produced an r2 of 0.82 and RMSE of 3.82 meters. We provide the dataset, source code, and cloud-based tools to enable broad application without requiring specialized computational resources.
Why it matches plant phenotyping methods植生を含む景観の樹冠高・構造を航空画像から推定するモデルを開発し、公開データセットで検証している。植物キャノピーの明示的な構造形質推定が中心だが、建造物等も含むため植物以外の構造も対象とする点には留意が必要。
abstractwe developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery.
Reproduction assets foundThe paper's NAIP-CHM canopy height model, its CONUS 0.6 m dataset, trained weights, and full training/inference code are all publicly released with explicit availability statements and author-hosted URLs (Rangeland Analysis Platform server, GitHub, Zenodo, Colab notebook, Earth Engine app).Dataset · publicFor bulk download, COGs and associated index files are available via HTTP from the Rangeland Analysis Platform server ( http://rangeland.ntsg.umt.edu/data/naip-chm/ ).Open asset ↗Rangeland Analysis Platform serverlines:76-83Model / weights · publicThe source code, trained model weights, validation data, and auxiliary datasets required to reproduce the results are permanently archived in a Zenodo repository 31 .Open asset ↗Zenodolines:89-134Code / dataset availability confirmedOpenAlex · arXiv · checked 6 Sept 2026
High resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the full potential of accurate leaf tracking over time remains largely unexplored due to the absence of robust tracking methods-particularly for structurally complex crops such as canola. Existing plant-specific tracking methods are typically limited to small-scale species or rely on constrained imaging conditions. In contrast, generic multi-object tracking (MOT) methods are not designed for dynamic biological scenes. Progress in the development of accurate leaf tracking models has also been hindered by a lack of large-scale datasets captured under realistic conditions. In this work, we introduce CanolaTrack, a new benchmark dataset comprising 5,704 RGB images with 31,840 annotated leaf instances spanning the early growth stages of 184 canola plants. To enable accurate leaf tracking over time, we introduce LeafTrackNet, an efficient framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network. During inference, leaf identities are maintained over time through an embedding-based memory association strategy. LeafTrackNet outperforms both plant-specific trackers and state-of-the-art MOT baselines, achieving a 9% HOTA improvement on CanolaTrack. With our work we provide a new standard for leaf-level tracking under realistic conditions and we provide CanolaTrack - the largest dataset for leaf tracking in agriculture crops, which will contribute to future research in plant phenotyping. Our code and dataset are publicly available at https://github.com/shl-shawn/LeafTrackNet.
Why it matches plant phenotyping methods葉レベルの時系列追跡という植物表現型取得手法を開発し、専用ベンチマークデータセットで評価しているため、方法が中心である。
abstractTo enable accurate leaf tracking over time, we introduce LeafTrackNet, an efficient framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network.
Reproduction assets foundThe authors explicitly state that the CanolaTrack dataset (5,704 annotated RGB images of 184 canola plants), the LeafTrackNet code, and trained model weights are publicly available at their GitHub repository.Code · publicOur code and dataset are publicly
available at https://github.com/shl-shawn/LeafTrackNet.Open asset ↗shl-shawn/LeafTrackNet · LeafTrackNetpdf-page:1 lines:1-53Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset includes 1045 high-quality point clouds of field-grown maize collected using a terrestrial laser scanner (TLS). Point clouds of 520 plants from this dataset were segmented and annotated using a graph-based segmentation method to isolate individual leaves and stalks, ensuring consistent labeling across all samples. This labeled data was then used for fitting procedural models that provide a structured parametric representation of the maize plants. The leaves of the maize plants in the procedural models are represented using Non-Uniform Rational B-Spline (NURBS) surfaces that were generated using a two-step optimization process combining gradient-free and gradient-based methods. We conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset also includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled point cloud data (100k, 50k, 10k points), which can be readily used for different downstream computational tasks. MaizeField3D will serve as a comprehensive foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.
Why it matches plant phenotyping methods3D点群の収集・分割・注釈・手続き型モデル化を中核とする、植物表現型解析向けの再利用可能なデータセットである。
abstractwe present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research.
Reproduction assets foundThe paper's own MaizeField3D dataset (1045 TLS point clouds, 520 segmented/annotated plants, metadata, STL/DAT procedural model outputs) is publicly available on Hugging Face, with a project website and public GitHub code for the procedural NURBS surface generation used in the analysis.Dataset · publicThe MaizeField3D dataset is publicly available on the Hugging Face Datasets platform at https://huggingface.co/datasets/BGLab/MaizeField3D. It includes high-resolution point clouds, segmented plant models, metadata, and reconstructed outputs in STL and DAT formats.Open asset ↗BGLab/MaizeField3Dhtml-lines:343-354Code · publicThe code for procedural NURBS surface generation used in this work is available at https://github.com/baskargroup/ProceduralMaize3D.Open asset ↗baskargroup/ProceduralMaize3Dhtml-lines:343-354Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In this study, we present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf. The dataset consists of high-resolution images of leaves from both species, captured using a SONY α7 II DSLR camera and a OnePlus 7T lubricant Tea Leaf dataset includes images categorized into five disease classes: Algal Leaf Spot, Brown Blight, Grey Blight, Red Leaf Spot, and Healthy, while the Hibiscus Leaf dataset includes images labeled across eight conditions, including citrus spot, fungal infection, mild edge damage, and healthy foliage. To ensure balanced representation and address class imbalances, extensive data augmentation techniques-such as flipping, rotation, zooming, shifting, noise addition, and brightness adjustment-were applied, resulting in a total of 1,413 combined original images and 13,000 augmented images. The ConvNextTiny deep learning model was fine-tuned on this combined dataset to classify the various leaf conditions, achieving an overall accuracy of 96%. This demonstrates the model's robust performance and high discriminatory power across the diverse set of leaf diseases and conditions. This experiment highlights the utility of combining multiple plant species into a single dataset and utilizing a lightweight yet effective model like ConvNextTiny for plant disease classification. The resulting dataset, along with the model and training scripts, is publicly available to facilitate further research in plant pathology, computer vision, and smart farming applications, enabling more accurate and efficient early-stage disease detection for both Hibiscus and Tea plants.
Why it matches plant phenotyping methods植物葉の病害・健全状態を画像から分類するデータセットを構築し、分類モデルで性能評価しているため、植物フェノタイピング手法・ベンチマークが中心です。
abstractwe present a combined image dataset created from two distinct plant species: Hibiscus and Tea leaf
Reproduction assets foundThe paper's combined Hibiscus and Tea leaf disease image dataset is publicly deposited on Mendeley Data (DOI 10.17632/5bzy89brkv.4), and the authors' augmentation/training scripts are on a public GitHub repository; both are paper-specific, public, and directly actionable.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/5bzy89brkv.4
Direct URL to data: https://data.mendeley.com/datasets/5bzy89brkv/4Open asset ↗Mendeley Data · 10.17632/5bzy89brkv.4lines:1-46Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.
Why it matches plant phenotyping methodsリンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。
abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
Reproduction assets foundThe authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.Dataset · publicData accessibility
Repository name: Github
Data identification number: DOI 10.5281/zenodo.15600557
Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.Open asset ↗GitHub · DOI 10.5281/zenodo.15600557html-lines:84-123Code · publicAll data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].Open asset ↗GitHubhtml-lines:1-83Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Artificial Intelligence in Agriculture
CottonRiceRootMorphology / geometry measurementSegmentationRoot system architecture
Beyond its fundamental roles in nutrient uptake and plant anchorage, the root system critically influences crop development and stress tolerance. Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil, serving as a cost-effective root imaging method. However, the opacity of the soil often results in intermittent gaps in the root images, which reduces the accuracy of the root phenotype calculations. We present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture In addition, we built a hybrid root inpainting dataset (HRID) that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps. Compared with computer simulation root images, our dataset provides real root system architecture (RSA) and root texture information. Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology. The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels. Additionally, Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally, we remove the skip connections in U-Net and introduce an edge attention module (EAM) to capture more detailed information. Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. The codes for the EU-GAN and the 8922 labeled images are open-access, which could be reused by researchers in other AI-related work. This method establishes a robust solution for root phenotyping, thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.
Why it matches plant phenotyping methods根画像の欠損を補完するGAN手法と再利用可能なデータセットを開発・評価し、根形態形質の推定誤差改善を実証しており、植物フェノタイピング手法が中心である。
abstractWe present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture
Hyperspectral imaging (HSI) has recently emerged as a valuable tool for various agricultural applications. However, the widespread adoption of hyperspectral imaging is hindered due to the high cost and complexity of collecting and processing hyperspectral images. To address this gap, we introduce Agro-HSR,¹1Link to dataset: Agro-HSR. a large-scale RGB to hyperspectral image reconstruction dataset of sweet potatoes, specifically curated to promote easy access to hyperspectral images for the agricultural community. Agro-HSR comprises 1322 pairs of RGB and hyperspectral image cubes from 790 samples across three sweet potato varieties. For 141 of these samples, the agro-product quality attributes are included in the dataset. Each hyperspectral image cube covers 31 evenly spaced bands within the wavelength range of 400–1000 nm. Benchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR. These benchmarks evaluated the ability to predict critical quality parameters in sweet potatoes, including Brix, dry matter, and firmness, from reconstructed hyperspectral images. Agro-HSR enhances the accessibility of hyperspectral images and promotes opportunities for cross-domain research in deep learning and agricultural science, addressing critical challenges in assessing the quality of agro-products.
Why it matches plant phenotyping methodsサツマイモのハイパースペクトル画像再構成データセットを構築し、再構成画像から品質形質を推定するベンチマークを実施しており、植物形質取得・推定手法が中心である。
abstractBenchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR.
Better matching of the timing and amount of fertilizer inputs to plant requirements will improve nutrient use efficiency and crop yields and could reduce negative environmental impacts. Deep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies. A drone-based RGB image dataset was generated together with ground truthing data in winter wheat (2020) and in winter rye (2021) during tillering and booting in the long-term fertilizer experiment (LTFE) Dikopshof. In this LTFE, the crops were fertilized with the same amounts for decades. The selected treatments included full fertilization including manure (NPKCa+m+s), mineral fertilization (NPKCa), mineral fertilization but no nitrogen (N) application (_PKCa), no phosphorus (P) application (N_KCa), no potassium (K) application (NP_Ca), or no liming (Ca) (NPK_), as well as an unfertilized treatment. The image dataset consisting of more than 3600 UAV-based RGB images was used to train and evaluate in total of eight CNN-based and transformer-based models as baselines within each crop-year and across the two crop-year combinations, aiming to detect the specific fertilizer treatments, including the specific nutrient deficiencies. The field observations showed a strong biomass decline in the case of N omission and no fertilization, though the effects were lower in the case of P, K, and lime omission. The mean detection accuracy within one year was 75% (winter wheat) and 81% (winter rye) across models and treatments. Hereby, the detection accuracy for winter wheat was highest for the NPKCa+m+s (100%) and the unfertilized (96%) treatments as well as the _PKCa treatment (92%), whereas for treatments N_KCa and NPKCa the accuracy was lowest (about 50%). The results were similar for winter rye. In the cross-year and cross-cereal species transfer (training on winter wheat, application on winter rye, and vice versa), the mean accuracy was about 18%. The results highlight the potential of deep learning as a digital tool for decision-making in smart farming but also the difficulties of transferring models across years and crops.
Why it matches plant phenotyping methodsUAV RGB画像から作物の栄養欠乏・施肥状態を推定するデータセットと深層学習モデルを構築・評価しており、植物状態の取得・推定手法が研究の中心である。
abstractDeep learning can be a powerful digital tool for on-site, real-time, non-invasive diagnosis of crop nutrient deficiencies.
Early blight, caused by the fungus Alternaria solani, is a prevalent disease in potato crops that severely impacts yield and quality. Traditional detection methods are time-consuming, require expert knowledge, and depend on laboratory facilities. This study aims to develop an efficient and automated approach for detecting early blight in potato leaves using deep learning techniques. A deep learning-based software solution was created, utilizing a convolutional neural network (CNN) trained on a large, annotated dataset of potato leaf images showing various disease symptoms. Five widely used CNN architectures (ResNet, NasNet, MobileNet, VGG16, InceptionNet) were implemented and compared within a consistent MATLAB environment. The comparative analysis revealed differences in model performance, offering valuable insights into the suitability of each architecture for real-time disease detection on different devices. The study demonstrates that CNN-based models can effectively and automatically detect early blight in potato leaves, with certain architectures offering better adaptability and accuracy for practical, field-level applications.
Why it matches plant phenotyping methodsジャガイモ葉の病徴を画像から検出するCNNソフトウェアを開発し、複数モデルを比較評価しており、植物病害状態の画像ベース表現型取得が中心である。
abstractThis study aims to develop an efficient and automated approach for detecting early blight in potato leaves using deep learning techniques.
Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m⁻² sr⁻¹ nm⁻¹ year⁻¹). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R² ranging from 0.324 (p < 0.01) to 0.947 (p < 0.001). A strong correlation with tower-based GPP (R² = 0.55, p < 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF’s superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at https://doi.org/10.6084/m9.figshare.27075145.
Why it matches plant phenotyping methods高解像度SIFの再構成手法と公開データセットを開発し、タワー観測およびGPPで検証しており、植生の光合成活動という生理状態の推定が中心である。
abstractwe present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data.
Field / plotLeafClassificationDisease symptoms / severity
This Data Descriptor presents the Jute Diseases Image Dataset; a curated collection of 1390 high-resolution images aimed at supporting the development of machine learning models for timely identification and accurate diagnosis of jute (Corchorus) plant diseases. The dataset is categorized into five classes: Dieback (300), Holed (300), Mosaic (240), Stem Soft Rot (270), and Fresh (280) representing healthy leaves. Images were captured under varied natural lighting and directional conditions across diverse jute cultivation areas to enhance model generalizability. A rigorous pre-processing pipeline was applied, including uniform resizing to 1024 × 1024 pixels and removal of duplicate images to ensure data integrity. The dataset is organized into two components: a raw, pre-processed set and an augmented train-test split version, enabling immediate use in machine learning workflows. Additionally, Grad-CAM and Guided Grad-CAM techniques were applied to sample images to visualize and validate model attention on disease-relevant regions. This resource addresses the lack of labelled jute disease imagery and supports timely disease management, particularly for stakeholders in Bangladesh and other major jute-producing regions.
Why it matches plant phenotyping methods植物病害症状を画像として収集・ラベル化したデータセットであり、病害状態の画像ベース表現型判定を支援することが中心です。
abstractThis Data Descriptor presents the Jute Diseases Image Dataset; a curated collection of 1390 high-resolution images aimed at supporting the development of machine learning models for timely identification and accurate diagnosis of jute (Corchorus) plant diseases.
Reproduction assets foundThe paper's own jute disease image dataset (1390 labeled images, raw and augmented train/test splits) is publicly deposited in Harvard Dataverse with an explicit DOI and direct URL, matching an allowed URL. No separate analysis code or trained model checkpoint is publicly released.Dataset · publicData accessibility
Repository name: Harvard Dataverse
Data identification number: https://doi.org/10.7910/DVN/FJ1DM1
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FJ1DM1Open asset ↗Harvard Dataverse · doi:10.7910/DVN/FJ1DM1html-lines:1-91Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We present a meticulously curated, long-term (1981-2024) dataset documenting maize phenology dynamics across Northeast China, the nation's most critical commercial grain base. Derived from 61 national agrometeorological stations, it captures the timing of 10 pivotal phenological stages (sowing, emergence, three-leaf, seven-leaf, jointing, tasseling, flowering, silking, milking, maturity) and derives the durations of 4 growth period lengths (sowing-jointing, jointing-silking, silking-maturity, sowing-maturity). The dataset underwent a rigorous, multi-tiered quality control protocol, including automated checks for internal consistency and expert arbitration for ambiguous records, ensuring high integrity. Subsequent analysis employed kernel density estimation to characterize the probability distribution of phenological events and univariate linear regression to quantify decadal trends. The resulting repository is substantial, comprising 976 georeferenced diagnostic plots in JPEG format and two primary data tables in XLSX format, with a total volume of 601.04 MB. Systematically organized by province and station, this dataset serves as a foundational empirical resource for quantifying climate-driven shifts in crop development, enhancing the parameterization and validation of process-based crop models, and informing the development of optimized cultivation practices and regional climate adaptation frameworks.
Why it matches plant phenotyping methodsトウモロコシの複数の生育ステージと生育期間を長期・広域に収録し、品質管理済みデータセットとして構築しているため、植物フェノタイピングデータセットが研究の中心です。
abstractit captures the timing of 10 pivotal phenological stages
Reproduction assets foundThe paper is a data descriptor whose maize phenology dataset (1981–2024, 61 stations, 10 phenological stages, diagnostic plots and XLSX tables) is openly deposited in Science Data Bank. No custom code was created.Dataset · publich stage timing and duration, it empowers farmers and
307
agricultural planners to optimize production systems in response to evolving climatic
308
conditions, thereby enhancing regional food security resilience.
309
Data Availability
310
The dataset generated during this study is openly available in the Science Data Bank at
311
https://doi.org/10.57760/sciencedb.28709 or https://cstr.cn/31253.11.sciencedb.28709.312
Code availability
313
No custom code was created for the production of this dataset.
314
References
315
1.Cai C, Ding T, Chen W. (2024). Potential yield of world maize under global warming based on ARIMA-TR model.
316
Journal of Agrometeorology, 2024, 26(1).
317
2.Li M. RetrospectOpen asset ↗Science Data Bank · 10.57760/sciencedb.28709pdf-raw-page:15 lines:1-94Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Existing disease discovery in papaya leaves is most significant in achieving yield and profitability stability in the tropics but has proven difficult in the presence of deficiencies in manual exploration and tailored crop models in crop-AI systems. Therefore, this study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves. For real-world deployment in scarce-resource farming contexts, PapayaNet adopts batch norm and hierarchical attention steps in five convolution stages and accelerates both computational celerity and discriminability. Trained on 6618 manually annotated orchard images sourced from orchards in Bangladesh at a very high resolution, it has a 98.79% classification accuracy, all of which was realized using 483,926 parameters and an average infer time of 0.01 s, which is significantly better when evaluated using EfficientNetB6, DenseNet121, and VGG16. XAI methods, including Grad-CAM and LIME, showed model decisions towards the biologically informative parts of the leaf, thus boosting interpretability and user confidence. Systematic ablation analysis also confirmed the importance of distributed attention in ensuring robust generalization towards visually similar disease classes. An in-browser diagnostic portal deployed using Gradio provides intra-browser predictive deployment and interpretability overlay in real time, thus inviting field practicability. Given its low-latency inference and minimal computational footprint, PapayaNet is well-suited for integration into edge devices and drone platforms, offering a scalable solution for real-time in-situ crop health monitoring. This study advances the field of precision agriculture by delivering a crop-specialized, explainable, and deployable AI system for sustainable management of papaya diseases.
Why it matches plant phenotyping methodsパパイヤ葉の病害・健全状態を画像から分類するCNN手法を開発し、データセット、比較評価、アブレーション、実運用ポータルまで中心的に扱っているため、植物病害フェノタイピング手法に該当する。
abstractthis study introduces PapayaNet, a lightweight attention-guided convolutional network specifically structured for the automated classification of six papaya leaf states, including major diseases and healthy leaves.
Reproduction assets foundThe paper's papaya leaf image dataset is publicly deposited on Mendeley Data with an explicit availability statement and authors' URL; no code or model checkpoint availability is stated.Dataset · publiccript.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The dataset analysed of this study, titled ”Healthy and Unhealthy Papaya Leaf Images from Bangladeshi Orchards,” is publicly available in the Mendeley Data repository at ( https://data.mendeley.com/datasets/44p8v6ywsm/1 ).
Competing interests
The authors declare no competing interests.
References
1. Sandhu, G. K. & Kaur, R. Plant disease detection techniques: A review. In 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019 34–38 (2019). 10.1109/ICACTM.2019.8776827
2.
Ngugi LC Abelwahab M AboOpen asset ↗Mendeley Datalines:622-669Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Why it matches plant phenotyping methods植物の生育段階・生理・構造特性を対象に、標準化されたマルチスペクトル画像とフィールド分光データを収集・前処理した再利用可能なデータセットであり、ベンチマークやアルゴリズム検証を目的とするため、フェノタイピング手法が中心です。
abstractThis data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo)
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
Related research article
None
1.
Value of the Data
•
These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km 2 with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m 2 /m 2 ) and digital hemispherical photography (DHP) images (RMSE = 0.46 m 2 /m 2 ) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R 2 = 0.70, RMSE = 0.86 m 2 /m 2 ). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales. • We developed a large-scale ALS-data-driven 3D forest reconstruction workflow. • FoScenes product consists of 40 various forest scenes derived from NASA G-LiHT data. • The estimated leaf/plant area index strongly aligns with field data and EOS products. • FoScenes captures temporal structure variation by multi-dimensional characterization. • FoScenes can be integrated into DART for realistic simulations at varied scales.
Why it matches plant phenotyping methods森林の植物面積密度・葉面積指数を推定するALSベースの3D再構成ワークフローを開発し、実測LAI等で検証した方法・データセット研究であり、植物形態の取得が中心です。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
Field / plotFruitClassificationStress / disease detectionDisease symptoms / severity
Timely and accurate detection of pomegranate fruit diseases is critical for minimizing crop losses, preserving fruit quality, and supporting sustainable agricultural practices. This study introduces the Halabja Pomegranate Fruit Disease Image Dataset, a systematically compiled collection of images from orchards in one of Iraq's major pomegranate-producing regions. The dataset comprises 2178 original images and 28,314 augmented images, categorized into four specific classes: ectomyelois ceratoniae, colletotrichum spp., sunburn, and healthy fruit samples. To create an ecological setting and ensure significant class variation, images were captured in natural outdoor environments. A standard preprocessing step was applied, which involved resizing all images to 512×512 pixels and using several image augmentation techniques to improve the flexibility and robustness of machine learning models. The unique characteristics of this dataset make it highly suitable for developing machine learning and deep learning models aimed at plant disease detection and other computer vision tasks in precision agriculture. Its contextual relevance and content diversity make it valuable for building an effective diagnostic tool capable of functioning in real field conditions.
Why it matches plant phenotyping methods植物病害状態を画像で分類するデータセットの構築が中心で、再利用可能な植物表現型データとして適格です。
abstractThis study introduces the Halabja Pomegranate Fruit Disease Image Dataset
Reproduction assets foundThe paper is a data descriptor for the authors' own Halabja Pomegranate Fruit Disease Image Dataset (2178 original + 28,314 augmented images), publicly deposited on Zenodo with an explicit direct URL matching an allowed URL. This is a paper-specific public plant-image/phenotyping asset.Dataset · publicasses: Colletotrichum spp. (anthracnose), Ectomyelois ceratoniae (fruit borer), sunburn, and healthy fruit.
Data source location
Pomegranate orchards in Halabja city, Kurdistan region, Iraq (location code: 46,018).
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.15856012
Direct URL to data: https://zenodo.org/records/15856012
Halabja Pomegranate Fruit Disease Image Dataset. Zenodo [ 1 ].
Related research article
None
1.
Value of the Data
•
Regional Uniqueness: This dataset is the first publicly available collection of pomegranate fruit disease images from Halabja, in the Kurdistan Region of Iraq, an area renowned for its high-quality pomegranate proOpen asset ↗Zenodo · 10.5281/zenodo.15856012lines:1-52Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 8 Sept 2026
Published20 Nov 2025Iowa State University Digital Repository (Iowa State University)Cited by 0 · OpenAlex ↗
Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heterogeneous data across multiple platforms, with Unmanned Aerial Vehicles (UAV) seeing significant usage due to their high performance in crop monitoring, forecasting, and prediction tasks. Similarly, satellite missions have been shown to be effective for agriculturally relevant tasks. In contrast to UAVs, such missions are bound to the limitation of spatial resolution, which hinders their effectiveness for modern farming systems focused on micro-plot management. In this work, we propose a cross modal learning strategy that enriches high-resolution satellite imagery with UAV level visual detail for crop canopy trait estimation. Using a dataset of approximately co registered satellite UAV image pairs collected from replicated plots of 84 hybrid maize varieties across five distinct locations in the U.S. Corn Belt, we train a model that learns fine grained spectral spatial correspondences between sensing modalities. Results show that the generated UAV-like representations from satellite inputs consistently outperform real satellite imagery on multiple downstream tasks, including yield and nitrogen prediction, demonstrating the potential of cross-modal correspondence learning to bridge the gap between satellite and UAV sensing in agricultural monitoring.
Why it matches plant phenotyping methods衛星画像とUAV画像を統合し、作物キャノピー形質を推定するクロスモーダル学習法の開発が中心であり、植物形質推定用データセットと性能評価も含む。
titleCrossmodal learning for Crop Canopy Trait Estimation
Field / plotClassificationDisease symptoms / severity
The accurate identification of crop pests and diseases is critical for global food security, yet the development of robust deep learning models is hindered by the limitations of existing datasets. To address this gap, we introduce DLCPD-25, a new large-scale, diverse, and publicly available benchmark dataset. We constructed DLCPD-25 by integrating 221,943 images from both online sources and extensive field collections, covering 23 crop types and 203 distinct classes of pests, diseases, and healthy states. A key feature of this dataset is its realistic complexity, including images from uncontrolled field environments and a natural long-tail class distribution, which contrasts with many existing datasets collected under controlled conditions. To validate its utility, we pre-trained several state-of-the-art self-supervised learning models (MAE, SimCLR v2, MoCo v3) on DLCPD-25. The learned representations, evaluated via linear probing, demonstrated strong performance, with the SimCLR v2 framework achieving a top accuracy of 72.1% and an F1 score (Macro F1) of 71.3% on a downstream classification task. Our results confirm that DLCPD-25 provides a valuable and challenging resource that can effectively support the training of generalizable models, paving the way for the development of comprehensive, real-world agricultural diagnostic systems.
Why it matches plant phenotyping methods作物の病害・健全状態を画像で認識する大規模公開ベンチマークデータセットを構築・評価しており、植物状態の画像ベース表現型解析基盤が中心です。害虫認識も含まれますが、病害・健全状態の評価は植物フェノタイピングに該当します。
abstractwe introduce DLCPD-25, a new large-scale, diverse, and publicly available benchmark dataset.
Reproduction assets foundThe paper introduces DLCPD-25, a public crop pest/disease image dataset (221,943 images, 203 classes), with an explicit Data Availability Statement pointing to the authors' GitHub repository containing all image data and documentation.Dataset · publicThe DLCPD-25 dataset introduced and analyzed in this study is publicly available at: https://github.com/hwzhanng/DLCPD-25-Dataset (accessed on 20 October 2025). The repository provides access to all image data, and relevant documentation used in this research.Open asset ↗https://github.com/hwzhanng/DLCPD-25-Dataset · DLCPD-25lines:141-207Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
In facility horticultural production, intelligent disease recognition and precise intervention are vital for crop health and economic efficiency. We construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples. Handheld images capture fine lesion texture for close-up diagnosis common in greenhouses; drone images provide canopy-scale patterns and spatial context suited to open-field management; laboratory images offer controlled illumination and background for stable supervision and cross-crop feature learning. Our objective is robust cross-crop, cross-regional diagnosis and economically rational control. To this end, a model named CCGD-Net is proposed. It is designed as a multi-task framework. The framework incorporates a multi-scale perception module (MSFE) to produce hierarchical representations. It includes a cross-domain alignment module (CDAM) that reduces distribution shifts between greenhouse and open-field environments. The training follows an unsupervised domain adaptation setting that uses unlabeled target-region images. When such images are not available, the model functions in a pure generalization mode. The framework also integrates a regional economic strategy module (RESM) that transforms recognition outputs and local cost information into optimized intervention intensity. Experiments show an accuracy of 91.6%, an F1-score of 89.8%, and an mAP of 88.9%, outperforming Swin Transformer and ConvNeXt; removing RESM reduces F1 to 87.2%. In cross-regional testing (Weifang training → Honghe testing), the model attains an F1 of 88.0% and mAP of 86.5%. These results indicate that integrating complementary imaging modalities with domain alignment and economic optimization provides an effective solution for disease diagnosis across greenhouse and field systems.
Why it matches plant phenotyping methods植物病斑・冠層画像から病害状態を推定するマルチモーダル深層学習法を開発し、データセット、ドメイン適応、交差地域検証を含むため、植物フェノタイピング手法が中心である。
abstractWe construct a multi-source dataset from Bayan Nur, Weifang, and Honghe that integrates handheld camera photos, drone field images, and laboratory-controlled samples.
Methods, algorithms, and models for the creation and practical application of digital twins (3D models) of agricultural crops are presented, illustrating their condition under different levels of atmospheric CO2 concentration, soil, and meteorological conditions. An algorithm for digital phenotyping using machine learning methods with the U2-Net architecture are proposed for segmenting plants into elements and assessing their condition. To obtain a dataset and conduct verification experiments, a prototype of a software and hardware complex has been developed that implements the process of cultivation and digital phenotyping without disturbing the microclimate inside the chamber and eliminating the subjectivity of measurements. In order to identify new data and confirm the data published in open scientific sources on the effects of CO2 on crop growth and development, plants (ten species) were grown at different CO2 concentrations (0.015–0.03% and 0.07–0.09%) with a 10-fold repetition. A model has been built and trained to distinguish between cases when plant segments need to be combined because they belong to the same leaf (p-value = 0.05), and when they belong to a separate leaf (p-value = 0.03). A knowledge base has been formed, including: 790 3D models of plants and data on their physiological characteristics.
Why it matches plant phenotyping methods植物のデジタル表現型取得を中心に、U2-Netによる分割・状態評価アルゴリズム、ソフトウェア／ハードウェア複合体、検証実験、3Dモデルデータベースを開発しているため。
abstractAn algorithm for digital phenotyping using machine learning methods with the U2-Net architecture are proposed for segmenting plants into elements and assessing their condition.
Abstract This study introduces a robust early disease detection system for peach crops, leveraging computer vision and artificial intelligence to address significant economic losses caused by Brown Rot (Monilinia spp.) and Leaf Curl (Taphrina deformans). The methodology comprises a structured approach, starting with the collection of a high-quality dataset of 640 images captured under real-world field conditions. These images, representing healthy and diseased fruits and leaves, underwent a rigorous preprocessing pipeline that included background removal, color space conversion, resizing, and contour detection to optimize them for model training. A Convolutional Neural Network (CNN) was developed and validated using k-fold cross-validation, achieving an outstanding accuracy of 90.28\% for fruit disease detection and 96.43\% for leaf disease detection during the validation phase. The model's final performance, evaluated with a confusion matrix, demonstrated a remarkable 100\% precision for Brown Rot in fruits and 96.4\% precision for Leaf Curl in leaves. These results confirm the system's reliability and its potential for practical application in precision agriculture. The project culminates in a functional web application, showcasing the viability of deploying deep learning solutions as accessible tools for farmers to facilitate timely and proactive crop management.
Why it matches plant phenotyping methods桃の果実・葉の病徴を画像から検出・分類するコンピュータビジョン手法を開発し、データセット、前処理、CNN、交差検証、性能評価まで中心的に扱っているため、植物病害フェノタイピング手法に該当する。
abstractThis study introduces a robust early disease detection system for peach crops, leveraging computer vision and artificial intelligence
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:1-58Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Plant development depends on coordinated growth at cellular and organ scales, yet comparative analyses are hindered by inconsistent reporting of growth across studies. We conducted a meta-analysis of Arabidopsis thaliana growth dynamics, integrating data from 176 studies to create the first multiscale atlas of plant growth. We developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels. Analyses revealed both organ-specific and general growth strategies linked to size control. In the meristem, a conserved offset in cell expansion between central and peripheral zones was observed. Root elongation was driven mainly by cell expansion and differentiation in the elongation zone, rather than meristem activity. Hypocotyl and leaf growth showed unexpected parallels: early exponential elongation resembled primary morphogenesis, while later linear growth matched secondary morphogenesis. Comparing dark- vs light-grown hypocotyls and juvenile vs transition leaves showed that organ size was modulated by a trade-off between growth rate and duration of the scaling phase. Cellular-scale growth during early development was shown to influence final organ size, underscoring the need for early-stage measurements. This growth atlas provides benchmark values and a reference framework for interpreting mutant phenotypes, guiding experimental design, and advancing our understanding of growth regulation across plant organs.
Why it matches plant phenotyping methods植物の成長形質を統合・比較する数学的フレームワークと成長アトラスを構築しており、再利用可能な形質標準化・ベンチマークが研究の中心である。
abstractWe developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels.
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Worldwide food shortage has put the plant phenotyping research to the spotlight because phenotyping enhances crop yield under limited land use by accelerating the cycle of modern breeding. The prerequisite of organ-level phenotyping is the accurate segmentation of organs from crop point clouds. Though mainstream deep networks have reported satisfactory results on certain species, they usually require sampling each input point cloud to a fixed number before network learning. Most existing networks also recommend each input to contain less than 10,000 points, which may smooth the structural details and lead to deterioration of segmentation of small organs. Moreover, these methods are still struggling to face challenges such as segmenting crops with a large number of organs and scalability to multiple species. In this paper, we propose Organ3DNet—a novel deep-learning-based architecture tailored for organ segmentation on high-precision plant 3D data. By integrating a Sparse 3D Convolutional Network Backbone (S3DCNB) as encoder and a new Transformer Decoder part containing a cascade of Query Refinement Modules (QRM) and Mask Modules (MM), Organ3DNet begins with query points obtained with 3D Edge-preserving Sampling (3DEPS) and gradually refines those queries into masks to effectively represent different organ instances. A high-precision dataset containing 889 samples from five species is also provided in this study. In experiment on this dataset, our Organ3DNet outcompeted four networks including ASIS, JSNet, PlantNet, and PSegNet. On the organ semantic segmentation task, our method surpasses the second best JSNet by 2.10 % on F1 and 3.63 % on IoU; while on the instance segmentation task, Organ3DNet surpasses the second best PSegNet by large margins of 16.46 % on mCov and 13.44 % on mWCov, respectively. Validation tests also show that Organ3DNet performs well on downstream tasks and real agricultural scenarios. The dataset and code associated with Organ3DNet are open to the readers. • An open crop point cloud dataset including 5 different species with organ semantics and instance annotations. • Our Organ3DNet can perform organ-level semantic and instance segmentation tasks directly on dense 3D crops. • Organ3DNet integrates advanced computational modules, and outcompetes several state-of-the-art segmentation networks. • The segmented organs by Organ3DNet can be directly used for precise calculation of organ-level phenotypes.
Why it matches plant phenotyping methods植物3D点群から器官を分割し、器官レベル形質の計算に利用する深層学習手法とデータセットを開発・検証しており、フェノタイピング手法が中心である。
abstractwe propose Organ3DNet—a novel deep-learning-based architecture tailored for organ segmentation on high-precision plant 3D data.
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 32 000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image matching across different land cover types. Mean completeness ranged from 64 % for “glacial and perpetual snow” to 98 % for “sealed surfaces”, with a value of 93 % for the “closed forest” class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.08 to 0.16 m, with a normalized median absolute deviation (NMAD) of around 0.8 m and a maximum root mean square error (RMSE) of 1.20 m. Similar accuracies are obtained when comparing historical DSMs with measured geodetic points. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very-high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史的航空画像から植生高モデルを生成する自動写真測量法を開発・精度評価し、森林の高さ変化という植物キャノピー形質を抽出しているため、測定法が中心的である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's countrywide DSMs, VHMs, and auxiliary rasters (matching mask, vegetation mask, metadata shapefile) for four epochs are deposited publicly on EnviDat with an explicit DOI. These vegetation height models are the paper's plant/canopy phenotyping measurements. No author analysis code or trained models are namedDataset · publicDatasets can be accessed from EnviDat ( https://doi.org/10.16904/envidat.528 , Marty et al., 2024). The following files are available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models (VHMs).Open asset ↗Envidat · 10.16904/envidat.528lines:249-256Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wax gourd ( Benincasa hispida (Thunb.) Cogn.) and Mangalore Cucumber (Cucumis melo L. subsp. agrestis var. conomon) are nutritionally rich, mineral-dense crops with a short growing cycle, making them a preferred choice for cultivation among farmers across the country. The Mangalore cucumber, also known as the culinary cucumber, Indian yellow cucumber, or Japanese pickling melon, is widely used in Asian cuisine for pickling. While proper nutrient management is essential for optimal growth, disease control poses a significant challenge in ensuring healthy yields, as disease can rapidly spread from one leaf to another, affecting larger areas of the field and reducing crop yield. Since cucurbits grow close to the soil, they spread across the ground, exhibit dense canopies, and often overlap with neighboring plants. Early detection is crucial to ensure sustainable cultivation, food security, and increased crop productivity. To address this challenge, we collected a dataset comprising 3200 images that includes image samples of Wax gourd and Mangalore cucumber plants affected by leaf miner, pests and image samples of healthy leaves. The Cucurbitaceae datasets that are available in the public domain lack representation of the Mangalore cucumber and Wax gourd varieties. To the best of our knowledge, no publicly available dataset exists for the Wax gourd. Moreover, existing datasets typically contain images captured under controlled greenhouse conditions with plain backgrounds, featuring a single leaf per image. They exhibit low background complexity and limit the scope to detect diseases at the object level, including multiple diseases present on a single leaf or plant. The uniqueness of the proposed dataset lies in addressing this gap by providing field-level images of cucurbits. These images capture variations in soil, overlapped leaves, complex background, varying angles and distances, weeds, and human interference. This makes the dataset suitable for training object detection models capable of identifying single and multiple disease instances, and it can also be effectively used for classification tasks to distinguish between healthy and diseased leaves. It supports advancement in deep learning, feature extraction, segmentation and pattern recognition tasks. Additionally, the dataset serves as a valuable resource for plant pathologists, agronomists and agricultural experts in disease detection, monitoring and management, thereby promoting sustainable agricultural practices. By offering open access, this dataset promotes collaboration within the scientific community to facilitate the development of robust disease detection, identification, and disease control, thus enhancing farming practices and increasing agricultural yields and advancing food security.
Why it matches plant phenotyping methods植物の病害状態を画像から検出・分類する公開データセットが研究の中心であり、植物病害の画像ベース表現型解析に該当する。
abstractwe collected a dataset comprising 3200 images that includes image samples of Wax gourd and Mangalore cucumber plants affected by leaf miner, pests and image samples of healthy leaves.
Reproduction assets foundThe paper is a Data in Brief article describing the WMC-Leafset dataset of 3200 annotated field images of wax gourd and Mangalore cucumber plants for leaf miner/pest object detection. The dataset is publicly deposited on Mendeley Data with an explicit direct URL and DOI, making it a paper-specific, publicly actionable,Dataset · public6° 44′ 46″ east, latitude of 12.16999° or 12° 10′ 12″ north and Mangalore cucumber images were collected from Hulimahu village in longitude of 76.73329° or 76° 43′ 60″ east, latitude 12.1598° or 12° 9′ 35″ north
Data accessibility
Repository name: WMC-Leafset
Data identification number: 10.17632/8m2ytxd4dg.4
Direct URL to data: https://data.mendeley.com/datasets/8m2ytxd4dg/4
Related research article
[ 11 ] M. A. Keerthi Prasad, N. Shobha Rani, M. A. Sangamesha and K. V. Vinay, ``Identification and Detection of Leaf Miner, Pest Infestation in Cucurbitaceae Family in Real-Time Infield Scenarios using YOLOv5s Object Detection Model,'' 2024 11th International Conference on Computing for SustainaOpen asset ↗10.17632/8m2ytxd4dg.4lines:35-65Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafMorphology / geometry measurementLeaf traits
We present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species representing two distinct leafing phenologies and three growth forms from the central Western Ghats of India. Quantitative assessments were conducted for nine key traits: leaf area (LA), mean thickness (LTH), specific leaf area (SLA), leaf dry matter content (LDMC), leaf tissue density (LTD), leaf nitrogen concentration (Leaf N), carbon-to-nitrogen ratio (C/N), and phytolith yield, following standard protocols. For each species, 30 leaves were sampled from a minimum of five individuals, totalling 2790 leaf samples. Qualitative traits, including leaf shape, margin, surface, texture, apex, base, type, and latex presence, were recorded in the field and validated using field manuals. The majority of species sampled were evergreen (74 %), with deciduous species comprising the remainder. Given the growing importance of plant functional traits in ecological research, this dataset offers valuable species-level leaf trait information at the regional scale. The phytolith yield data, in particular, represent one of the few globally available datasets, providing essential baselines for palaeoecological research and enabling quantitative reconstruction of vegetation composition and environmental change over millennial timescales.
Why it matches plant phenotyping methods植物の葉形質を標準化プロトコルで体系的に収集した再利用可能なデータセット論文であり、データセット自体が中心的な成果である。
abstractWe present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species
Reproduction assets foundThe paper's own leaf functional trait dataset (2790 leaves, 93 woody species, central Western Ghats) is publicly deposited on Zenodo with an explicit DOI/URL given in the article.Dataset · publicduals per species. Quantitative leaf functional traits were analyzed following the standard protocol [ 1 , 2 ].
Data source location
Country: India
Sampling site: Gerusoppa Reserve Forest, Central Western Ghats (14°12′ N to 14°24′ N and 74°36′ E to 74°48′ E)
Data accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.16717435
Direct URL to data: https://doi.org/10.5281/zenodo.16717435
Related research article
None
1
Value of the Data
•
This dataset provides high-resolution leaf-level data ( n = 2790) on 17 functional traits for 93 dominant woody species of the central Western Ghats, supporting trait-based ecological research.
•
It enables assessmentOpen asset ↗Zenodo · 10.5281/zenodo.16717435lines:1-54Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2025Computers and Electronics in Agriculture.
High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection-facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset's utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.
Why it matches plant phenotyping methods植物の3D点群を用いた部位セグメンテーション用データセットの構築・検証が中心で、植物形質の定量化を直接支援するため。
abstractTo address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.)
Field / plotLeafClassificationDisease symptoms / severity
The visual diagnosis based on foliar traits remains a cornerstone technique for the early identification of biotic stress, for instance, disease and pest infestations, in many economically valuable crops, including Aquilaria Malaccensis (agarwood). As a species of immense commercial and ecological significance, Aquilaria Malaccensis is particularly vulnerable to a range of pathogens and insect threats that can severely compromise resin production and tree viability. With the increasing integration of disruptive sustainable agricultural technologies, such as artificial intelligence (AI), especially in plant phenotyping and pathology, the development of robust and generalizable AI models hinges on the availability of large-scale and high-resolution image datasets. However, the current lack of such curated datasets for agarwood poses a substantial bottleneck to progress in automated identification systems. This deficiency limits the ability of scientists, technologists, and plant health experts to leverage machine learning and computer vision techniques for timely, accurate, and scalable solutions to different stresses in agarwood disease and pest management, including nematodes, viroids, viruses, pests, phytoplasmas, bacteria, fungi, and Protozoa. This paper presents a dataset of pests and diseases affecting agarwood trees, which impact farmers. It includes a total of 5472 leaf images classified into 14 categories. These categories consist of 8 types of agarwood diseases, 5 types of pests, and 1 category of healthy leaf images, encompassing both insect-damaged and healthy leaves. The images were captured using a PowerShot G7X Mark III camera. The images were captured from three different agarwood plantation sites of Batong, Benutan, and Bukit Silat in 2024, led by the Institute for Biodiversity and Environmental Research (IBER), Universiti Brunei Darussalam, by Botanical Research Centre (UBD BRC) scientists and biologists. This dataset is particularly valuable for training and validating deep learning (DL), computer vision, and machine learning algorithms aimed at identifying agarwood diseases and pests in agarwood leaves. Offering researchers and learners a robust data resource for analyzing and improving agarwood plant health through the development of advanced computational models. The designed models are vital and hold immense practical value for farmers, equipping them with the tools that timely detect and identify diseases in their agarwood trees, empowering them to make informed decisions and potentially intensify their profits.
Why it matches plant phenotyping methods葉画像から病害・害虫による植物状態を識別するための大規模データセットを構築しており、データ取得と再利用可能な解析基盤が研究の中心であるため。
abstractThis dataset is particularly valuable for training and validating deep learning (DL), computer vision, and machine learning algorithms aimed at identifying agarwood diseases and pests in agarwood leaves.
Reproduction assets foundThe paper is a data descriptor for a public agarwood leaf image dataset (5472 images, 14 classes) deposited on Zenodo and Mendeley, with explicit direct URLs and DOIs provided in the Data Accessibility section. This is the paper's own phenotyping image dataset, publicly available and actionable. No separate author codeDataset · publicc.iber.ubd.edu.bn ), Universiti Brunei Darussalam, Gadong, BE1410, Brunei Darussalam
Data accessibility
Repository name: Zendo and Mendeley
Repository Title: Agarwood Leaf Image Dataset for Pest and Disease Analysis in Real-World Environment
Data identification number: https://doi.org/10.5281/zenodo.14842099
Direct URL to data: https://zenodo.org/records/14842100
Direct URL to data: https://data.mendeley.com/datasets/8f8wtr9zwn/2
Related research article
Shafik, W., Tufail, A., De Silva, L.C. et al. A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture. Sci Rep 15, 12,195 (2025). https://doi.org/10.1038/s41598-025-90487-Open asset ↗Zenodo · 10.5281/zenodo.14842099lines:36-67Dataset · publicg, BE1410, Brunei Darussalam
Data accessibility
Repository name: Zendo and Mendeley
Repository Title: Agarwood Leaf Image Dataset for Pest and Disease Analysis in Real-World Environment
Data identification number: https://doi.org/10.5281/zenodo.14842099
Direct URL to data: https://zenodo.org/records/14842100
Direct URL to data: https://data.mendeley.com/datasets/8f8wtr9zwn/2
Related research article
Shafik, W., Tufail, A., De Silva, L.C. et al. A lightweight deep learning model for multi-plant biotic stress classification and detection for sustainable agriculture. Sci Rep 15, 12,195 (2025). https://doi.org/10.1038/s41598-025-90487-1 .
1.
Value of the Data
•
The dataset comprises 5472 high-quOpen asset ↗Mendeley · 8f8wtr9zwnlines:36-67Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Plant disease analysis is crucial for the better yield of the crops, and correct detection of particular infection on the plant parts provide the basis of better control of the plant disease. The prediction and control of disease in crop plants is essential for the food security. The technological advancements, particularly, in the field of the artificial intelligence and machine learning have provided impetus for newer dimensions of application of technology in different fields including the plant crop disease. The fundamental database of different infections in different crop plants forms the basis of the standard training of the machine learning algorithms which further predicts the disease on the test samples. The more detailed dataset of plant diseases with corresponding large number of sample examples helps in better training of the machine learning (ML) modules. The collections of disease dataset by the PlantVillage and evaluated PlantDoc dataset are being extensively used for the ML training and prediction of disease. This perspective discussion delves in the fundamental different types of plant diseases of the different parts of plant (leaf, fruits, flowers, stem), particularly of the crop plants, with emphasis on PlantVillage and PlantDoc datasets. The evaluation of ML techniques for conclusive detection of the disease possibilities has further been included in the discussion.
Why it matches plant phenotyping methods植物病害の画像データセットと機械学習による植物病害検出を中心に扱うレビューであり、植物の病害状態を観測・推定するフェノタイピング手法に該当する。
abstractThe collections of disease dataset by the PlantVillage and evaluated PlantDoc dataset are being extensively used for the ML training and prediction of disease.
ABSTRACT The development of remote sensing methods to estimate plant functional diversity is limited by mismatches between ecology and remote sensing sampling schemes, and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” encompassing a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions: Q1. How should remote sensing characterize functional diversity in large surfaces (sites)? Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples should be used instead. Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale? Accounting for background effects is the key. Optical traits (remote sensing estimates of plant traits) are the best estimators for plant functional diversity. Other variables succeed when filtered out of the soil pixels; their combination did not yield additional advantages. Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements? Leaf area index (LAI) is a better proxy of abundance than the pixel for Q Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution. Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity? The estimation error decreased with LAI and stabilized at values above 1 m²/m². Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution? Optical traits, fluorescence, and reflectance factors were the most robust variables. Still, field data resolution needs to be degraded to match the sensor’s resolution. We found a relative spatial resolution threshold of ∼30 % (where the pixel is around three times larger than the plants). Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.
Why it matches plant phenotyping methodsBOSSEを用いてリモートセンシングによる植物形質・機能多様性推定手法をシミュレーションベンチマークし、検証・比較する研究であり、植物フェノタイピング手法が中心である。
abstractThe Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies.
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 · publicvariables, we used the “pyGNDiv” package (https://github.com/JavierPachecoLabrador/pyGNDiv-Open asset ↗JavierPachecoLabrador/pyGNDiv- · pyGNDivpdf-page:11 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Wheat is an important food crop, wheat seedling count is very important to estimate the emergence rate and yield prediction. Timely and accurate detection of wheat seedling count is of great significance for field management and variety breeding. In actual production, the method of artificial field investigation and statistics of wheat seedlings is time-consuming and laborious. Aiming at the problems of small targets, dense distribution and easy occlusion of wheat seedling in the field, a wheat seedling number detection model (DM_IOC_fpn) combining local and global features was proposed in this study. Firstly, the wheat seedling image is preprocessed, and the wheat seedling dataset is built by using the point annotation method. Secondly, the density enhanced encoder module is introduced to improve the network structure and extract local and global contextual feature information of wheat seedling. Finally, the total loss function is constructed by introducing counting loss, classification loss, and regression loss to optimize the model, so as to enable accurate judgment of wheat seedling position and category information. Experiment on self-built dataset have shown that the root mean square error (RMSE) and mean absolute error (MAE) of DM_IOC_fpn were 2.91 and 2.23, respectively, which were 1.78 and 1.04 lower than the original IOCFormer. Compared with the current mainstream object detection models, DM_IOC_fpn has better counting performance. DM_IOC_fpn can accurately detect the number of small target wheat seedling, and better solve the problem of occlusion and overlapping of wheat seedling, so as to achieve the accurate detection of wheat seedling, which provides important theoretical and technical support for automatic counting of wheat seedlings and yield prediction in complex field environment.
Why it matches plant phenotyping methods小麦幼苗数という植物形質をUAV画像から自動抽出・計数するモデルを開発し、自作データセット上で性能評価しており、表現型取得手法が中心である。
abstractExperiment on self-built dataset have shown that the root mean square error (RMSE) and mean absolute error (MAE) of DM_IOC_fpn were 2.91 and 2.23, respectively
With the rapid advancement of artificial intelligence (AI) and computer vision, intelligent agricultural systems have become a crucial component of smart farming. Among them, automatic crop disease recognition plays a vital role in ensuring agricultural productivity and food security. This study proposes an AI‑based crop disease recognition system that integrates deep learning, image processing, and edge computing. A large‑scale dataset of crop disease images was constructed, and transfer learning was employed to enhance model generalization. A convolutional neural network (CNN) was optimized by incorporating attention mechanisms and multi‑scale feature fusion to improve accuracy. Experiments show an average accuracy of 97.8% on the PlantVillage dataset [9] and stable performance under real‑field lighting variations. A lightweight deployment framework based on TensorFlow Lite enables real‑time disease detection on mobile and embedded platforms. The system provides a feasible, efficient AI‑driven solution for precision agriculture and contributes to the digital transformation of modern farming.
Why it matches plant phenotyping methods植物病害画像から病害状態を推定する深層学習システムの開発・検証が中心であり、植物フェノタイプ測定に該当する。
abstractThis study proposes an AI‑based crop disease recognition system that integrates deep learning, image processing, and edge computing.
Lychee is a high-value subtropical fruit. The adoption of vision-based harvesting robots can significantly improve productivity while reduce reliance on labor. High-quality data are essential for developing such harvesting robots. However, there are currently no consistently and comprehensively annotated open-source lychee datasets featuring fruits in natural growing environments. To address this, we constructed a dataset to facilitate lychee detection and maturity classification. Color (RGB) images were acquired under diverse weather conditions, and at different times of the day, across multiple lychee varieties, such as Nuomici, Feizixiao, Heiye, and Huaizhi. The dataset encompasses three different ripeness stages and contains 11,414 images, consisting of 878 raw RGB images, 8,780 augmented RGB images, and 1,756 depth images. The images are annotated with 9,658 pairs of lables for lychee detection and maturity classification. To improve annotation consistency, three individuals independently labeled the data, and their results were then aggregated and verified by a fourth reviewer. Detailed statistical analyses were done to examine the dataset. Finally, we performed experiments using three representative deep learning models to evaluate the dataset. It is publicly available for academic
Why it matches plant phenotyping methodsライチ果実の成熟段階という植物器官の状態をRGB-D画像から分類するデータセットを構築し、アノテーション検証と深層学習モデル評価を行っており、表現型取得・評価手法が中心である。
abstractwe constructed a dataset to facilitate lychee detection and maturity classification.
Reproduction assets foundThe authors publicly release the paper's lychee RGB-D image dataset (raw/augmented RGB images, depth maps, detection and maturity annotations) and the Python scripts for data augmentation, image similarity comparison, and annotation in the same GitHub repository.Dataset · publicchees, the non-augmented models
produced misclassifications with lower recognition and accuracy, whereas the augmented models
avoided these issues. Overall, the results demonstrate that the data augmentation method effectively
improves the comprehensive performance of the models.
5. Data Availability
The dataset is available at：https://github.com/SeiriosLab/Lychee. The Python scripts for data
augmentation, image similarity comparison, and annotation are available within the same
repository under the tree/main/script directory.Open asset ↗SeiriosLab/Lycheepdf-raw-page:13 lines:1-55Plant phenotyping relevance match · UnverifiedarXiv · checked 13 Sept 2026
Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation. Unlike previous parametric models, Demeter handles varying shape topology across various species and models three sources of shape variation: articulation, subcomponent shape variation, and non-rigid deformation. To advance crop plant modeling, we collected a large-scale, ground-truthed dataset from a soybean farm as a testbed. Experiments show that Demeter effectively synthesizes shapes, reconstructs structures, and simulates biophysical processes. Code and data is available at https://tianhang-cheng.github.io/Demeter/.
Why it matches plant phenotyping methods植物形態のトポロジー・形状・関節・変形を表現する3Dパラメトリックモデルを開発し、実世界の作物データセットで再構成・シミュレーションを評価しているため、形態フェノタイピング手法が中心である。
abstractwe present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation.
3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.
Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法と、風による葉の動きを補正する技術を開発しており、植物表現型取得が中心です。データセット提供も含みます。
abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.
Field / plotRGB / grayscaleLeafClassificationDisease symptoms / severity
Agriculture is key to sustaining life and economic development, and crops like turmeric are essential for everyday application and economic viability. Turmeric crops are very prone to foliar disease, which has a great impact on yield and quality. Early detection of the diseases is of great significance to farming practitioners since manual observation is generally time-consuming and unreliable. To surpass this challenge, a comprehensive dataset has been developed to facilitate the generation of an automatic disease recognition system. The dataset comprises 865 images of original turmeric leaves and 3496 images of augmented turmeric leaves, both infected and healthy, with four classes of diseases: aphid attack, blotch, leaf spot, and healthy leaves. All the leaves were captured from different angles to offer variability and clarity, with particular emphasis on high-quality and diversified data. Through this dataset, a precise and efficient identification process can be realized, which will aid agriculture practitioners in recognizing diseases at an early stage and reducing crop losses. This paper seeks to improve agricultural productivity, crop quality, and the overall growth and sustainability of the agricultural economy using state-of-the-art deep learning models, such as EfficientNetB7 and ResNet152, for precise and interpretable disease classification. The proposed approach achieves high accuracy, with EfficientNetB7 attaining 98.67 % and ResNet152 reaching 97.87 %. Additionally, this research lays the groundwork for scalable and affordable disease detection technology, allowing agricultural practitioners to maximize crop yield and achieve long-term food security using smart tools.
Why it matches plant phenotyping methodsターメリック葉の病害状態を画像から分類するデータセットと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。
abstracta comprehensive dataset has been developed to facilitate the generation of an automatic disease recognition system.
Reproduction assets foundThe paper is a Data in Brief article describing a turmeric leaf disease image dataset (865 original and 3496 augmented smartphone images) collected by the authors, with the dataset publicly deposited on Mendeley Data. This is a paper-specific, publicly available plant image/phenotyping asset with a direct URL matching,Dataset · publicEkdonto village turmeric field in Pabna (latitude: 24.071123635779465, longitude: 89.34558471048882)
4. Tebunia village turmeric field in Pabna (latitude: 24.070634252228754, longitude: 89.20332505175169)
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/jtttfbx342.1
Direct URL to data: https://data.mendeley.com/datasets/jtttfbx342/1
1.
Value of the Data
•
This dataset generates a wealth of visual information on leaf diseases of turmeric, which is a good resource to train machine learning models. The models can be constructed to differentiate well between healthy and diseased leaves so that the diseases can be diagnosed early and accurately in agricuOpen asset ↗Mendeley Data · 10.17632/jtttfbx342.1lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
The tea industry plays a vital role in China's green economy. Tea trees (Melaleuca alternifolia) are susceptible to numerous diseases and pest threats, making timely pathogen detection and precise pest identification critical requirements for agricultural productivity. Current diagnostic limitations primarily arise from data scarcity and insufficient discriminative feature representation in existing datasets. This study presents a new tea disease and pest dataset (TDPD, 23-class taxonomy). Five lightweight convolutional neural networks (LCNNs) were systematically evaluated through two optimizers, three learning rate configurations and six distinct scheduling strategies. Additionally, an enhanced MnasNet variant was developed through the integration of SimAM attention mechanisms, which improved feature discriminability and increased the accuracy of tea leaf disease and pest classification. Model validation employs both our proprietary TDPD dataset and an open-access dataset, with performance evaluation metrics including average accuracy, F1 score, recall, and parameter size. The experimental results demonstrated the superior classification performance of the model, which achieved accuracies of 98.03% based on TDPD and 84.58% based on the public dataset. This research outlines an effective paradigm for automated tea disease and pest detection, with direct applications in precision agriculture through integration with UAV-mounted imaging systems and mobile diagnostic platforms. This study provides practical implementation pathways for intelligent tea plantation management.
Why it matches plant phenotyping methods茶葉画像から病害・害虫状態を推定するCNN、データセット構築、モデル比較・検証が研究の中心であり、植物の病害状態を対象とする実質的なフェノタイピング手法研究である。
titleA lightweight convolutional neural network for tea leaf disease and pest recognition
In the present scenario, medicinal plants play a crucial role in promoting a healthy lifestyle by protecting against numerous diseases. They also hold significant potential as a source of income, particularly for rural populations across the globe. Plants used for herbal medicine are known as medicinal plants, and each part of these plants may be utilized for medicinal purposes. Further, medicinal plants are beneficial in enhancing the human immune system. In this research, a new medicinal plant named as Central India Medicinal Plant Dataset (CIMPD) has been developed to support significant research in human health. The dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species. These images were collected from various locations in central India. The entire work was carried out over a period of five months, which included plant selection, leaf collection, image capturing, and data organization into folders. This dataset provides comprehensive information, including the botanical name, common name, geographical origin, healthy and unhealthy leaf images, and medicinal uses of the plants. It serves as a valuable resource for research in machine learning, computer vision, and related domains. Additionally, it will enable the development and evaluation of methodologies for disease detection, plant identification, and other relevant applications.
Why it matches plant phenotyping methods健康・不健康な葉画像を含む再利用可能なデータセットを構築し、植物の病害状態を画像から判定する研究基盤として提供しているため、画像ベースの植物状態計測に該当する。植物同定も含むが、データセット構築自体が中心である。
abstractThe dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species.
Reproduction assets foundThe paper is a data descriptor for the Central India Medicinal Plant Dataset (CIMPD), a public Kaggle dataset of 9130 healthy/unhealthy medicinal plant leaf images from 23 species, directly reproducing the paper's phenotyping (leaf image) measurements. The ResNet18 feature-visualization analysis code is not explicitly,Dataset · publichas 9130 images from 23 classes. Within the dataset, there’s an unequal distribution of samples among various classes.
Data source location
For this project, a large no of gardens of various places of central India has visited to collect the medicinal plant leaves.
Data accessibility
Repository name: Kaggle
Direct URL to data: https://www.kaggle.com/datasets/satyamtomar08/indian-medicinal-plant-dataset
1.
Value of the Data
•
The development of a medicinal plant dataset plays a crucial role in the exploration of advanced machine learning models for significant investigations such as plant identification, disease detection, crop management, and more [ [1] , [2] , [3] , [4] ].
•
This plant leafOpen asset ↗Kagglelines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The Eggplant Leaf Disease Dataset was meticulously developed to address challenges in accurately identifying diseases that threaten eggplant crops, a vital agricultural resource worldwide. This dataset includes 3116 high-resolution images captured between March and May 2024 from two major agricultural regions in Bangladesh, representing real-world conditions. It comprises 10 distinct disease classes-Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, and Tobacco Mosaic Virus-making it the most comprehensive dataset for eggplant diseases to date. To enhance its utility, rigorous data augmentation techniques, including flipping, rotating, shearing, shifting, noise addition, and brightness adjustment, were applied. This expanded the dataset to 10,000 images, ensuring its robustness for machine learning applications. Expert annotations further enhance its quality, providing critical insights for precise disease classification. Our Proposed CBAM-EfficientNetB0 model had an amazing classification accuracy of 98.70 %, which was much better than the baseline architectures. ResNet50 only got 32.60 %, VGG16 got 73.00 %, and VGG19 got 68.00 %. The proposed model's better performance shows that combining channel and spatial attention through CBAM with EfficientNetB0's feature extraction abilities works well. This architecture does a good job of picking out the distinguishing features in eggplant leaf images, which makes it possible to accurately identify diseases. The dataset and model work together to make AI-powered early disease detection, automated monitoring, and decision support in precision agriculture possible. These tools help farmers use sustainable farming methods by making timely interventions, reducing the need for manual inspection, and increasing crop productivity and food security.
Why it matches plant phenotyping methodsナス葉の病害状態を画像から分類するデータセットと解析モデルを開発・評価しており、植物病害フェノタイピング手法が中心である。
titleA comprehensive annotated image dataset for deep learning analysis of eggplant leaf diseases.
Reproduction assets foundThe paper's eggplant leaf disease image dataset (3116 annotated images, augmented to 10,000) is publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.Dataset · publicSeed Certification Agency, Ministry of Agriculture, Bangladesh, for his invaluable feedback and cooperation .
Data source location
Town/City/Region: Dhaka, Musnshigonj and Jhenaidah Sadar.
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/5drkk544k8.1
Direct URL to data: https://data.mendeley.com/datasets/5drkk544k8/1Open asset ↗Mendeley Data · 10.17632/5drkk544k8.1lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cotton, often referred to as "white gold" or the "king of fibers," is one of the most widely used natural fibers in the global textile industry, supporting approximately 250 million people worldwide. However, cotton plants suffer from a variety of diseases, particularly leaf diseases, which can significantly reduce the yield and fiber quality. To overcome this problem, we propose a carefully curated image dataset that enables research toward early and automated disease detection and health monitoring of cotton plants. The dataset comprises 1373 original and 4963 augmented high-resolution images of cotton leaves with healthy, damaged, and infected samples. The images were captured under different environmental conditions from plants grown at the Sher-e-Bangla Agricultural University in Dhaka, Bangladesh to provide natural variability and realism. The dataset considers four common cotton leaf diseases-Fusarium wilt, Alternaria leaf spot, Verticillium wilt, and bacterial blight-each labeled and classified to support machine learning applications. Captured from different angles and devices, the images have rich visual content that enables the development of strong deep learning models for disease classification. The dataset was designed to advance research relevant to precision agriculture by supporting early disease detection studies, crop health monitoring, and sustainable cotton-growing methods.
Why it matches plant phenotyping methods綿花葉の病害・健全状態を画像で分類するためのデータセットであり、植物の病害状態を直接観測する再利用可能なフェノタイピング資源が中心です。
abstractwe propose a carefully curated image dataset that enables research toward early and automated disease detection and health monitoring of cotton plants.
Reproduction assets foundThe paper is a data article describing a cotton leaf image dataset (1373 original + 4963 augmented images) for disease classification, publicly deposited on Mendeley Data with DOI 10.17632/t9hgvk2h9p.1 and a direct URL. This is the paper's own plant-phenotyping (leaf disease image) dataset and is directly actionable.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/t9hgvk2h9p.1
Direct URL to data: https://data.mendeley.com/datasets/t9hgvk2h9p/1Open asset ↗Mendeley Data · 10.17632/t9hgvk2h9p.1lines:1-48Code / 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 · checked 14 Sept 2026
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類するデータセットが研究の中心であり、植物病害フェノタイピング用の画像データセットとして適格です。
abstractThe PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
None
1
Value of the Data
•
The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Fusarium head blight (FHB) is a serious fungal disease that affect small grain cereals, causing significant wheat ( Triticum aestivum L.) yield and quality losses globally. Breeding disease-resistant wheat varieties is key to address FHB-related challenges, but its progress is delayed by traditional methods due to the small-scale, laborious and relatively subjective nature of manual assessment. This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once (YOLO) model to examine FHB in wheat, enabling us to perform large-scale and automated symptomatic analysis of this disease. We first established an Open FHB (OFHB) training dataset, consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression. Then, a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes, followed by an adaptive Excess Green method to identify symptomatic regions and thus FHB-related traits on spikes. To study resistance levels, we employed an unsupervised SHapley Additive exPlanations (SHAP) method to pinpoint key traits between 10 and 20 d after inoculation (DAIs), resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels (i.e., highly and moderately susceptible, and moderately and highly resistant), which were highly correlated with field specialists’ evaluations. Finally, we derived disease developmental curves based on measures of key traits during 10–20 DAI, quantifying varietal disease progression patterns over time. To our knowledge, this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat, providing a valuable toolkit for breeders and plant researchers to assess resistance levels, select disease-resistant varieties, and understand dynamics of the fungal disease.
Why it matches plant phenotyping methodsドローン画像、深層学習、症状領域抽出を組み合わせ、コムギ穂のFHB症状・関連形質と病害進展を自動定量する手法が研究の中心である。
abstractThis study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once (YOLO) model to examine FHB in wheat, enabling us to perform large-scale and automated symptomatic analysis of this disease.
In smart agriculture, the precise acquisition of complete 3D plant phenotypic data is critical for applications such as intelligent breeding and growth monitoring. However, due to equipment constraints, environmental noise, and self-occlusion, the collected 3D point cloud data of plants is often incomplete. This incompleteness significantly hinders key tasks in plant phenotypic analysis, including organ segmentation and surface reconstruction, necessitating effective data completion methods. Supervised point cloud completion methods face challenges due to the inherent incompleteness of collected data and the need for extensive labeled datasets. To address these issues, we propose UnPlantPC, an unsupervised plant point cloud completion model built on a self-supervised encoder–decoder paradigm. To effectively capture regions with complex geometric structures in plant point clouds, the model employs a keypoint down-sampling strategy that integrates Euclidean and cosine distances, ensuring the extracted key points are both representative and directionally informative. Additionally, a geometric-aware attention module enhances feature extraction in these regions, further improving the model’s ability to capture intricate geometric details. To align plant point cloud distributions under self-supervised learning, we introduce a novel Region-Aware Contrastive Distance, which provides accurate supervisory information. This innovation enables the model to deliver more precise completion results. UnPlantPC demonstrates state-of-the-art performance across several metrics on the PlantPCom dataset, achieving a notable 15.73% improvement in CDL2 compared to existing models.
Why it matches plant phenotyping methods植物3D点群の欠損補完という表現型データ取得・復元手法を開発し、データセット上で既存手法と比較評価しているため、植物フェノタイピング手法が中心である。
abstractthe precise acquisition of complete 3D plant phenotypic data is critical
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The Transition to a Data-Driven Agriculture (TTADDA) project focuses on advancing the shift toward high-tech, circular agriculture. By developing cutting-edge sensor technologies and AI-driven tools, the project aims to boost productivity through a data-centric potato production system that supports circular agricultural practices. Potato phenotyping is crucial for creating high-yielding, resilient, and sustainable potato crops, which are essential in global food systems. Specifically in the Netherlands, the global leader in seed potato production and Japan that produces certified seed potatoes under strict quality controls and phytosanitary regulations. A multi-season drone dataset from five potato trials—three in Japan and two in the Netherlands was collected. Each trial field was divided into small plots, each planted with a specific cultivar to assess varietal performance. Data included drone imagery (RGB and multispectral), manual yield and ground coverage measurements, and weather data. The combination of sensor versatility, diverse potato varieties, and varying climate and soil conditions between Japan and the Netherlands makes this dataset highly valuable and potentially reusable for a wide range of applications. Using MIAPPE for this dataset ensures consistent, clear documentation of sensors, varieties, and conditions, making the data findable, reusable, and easy to integrate with other studies. It also supports reproducibility and automated analysis across the multi-location trials.
Why it matches plant phenotyping methodsジャガイモの表現型解析を目的とした、RGB・マルチスペクトルUAV画像と地上測定を含む多季節・多地点データセットであり、再利用性と再現性を意識したフェノタイピング基盤として中心的です。
abstractA multi-season drone dataset from five potato trials—three in Japan and two in the Netherlands was collected.
Field / plotLeafClassificationDisease symptoms / severity
The Ash Gourd dataset is valuable since it was collected from the diverse regions within the district of Dhaka in Bangladesh. This dataset represents one of the first attempts to document, elicit, and categorize the health conditions of Ash Gourd (Benincasa hispida) plants in Bangladesh based on healthy samples, aphid plurality, downy mildew, leaf curl, and leaf miner-infested categories. Ash Gourd is one of the region's most important vegetables because of its nutritional and economic value; thus, it is essential to know diseases' manifestation in the improvement of agricultural productivity. The Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner. All images in all categories are raw which can be used flexibly according to the needs of analysis and model training. Concretely, the Healthy class consists of 803 images, while the four other classes contain 1,873 images. This structured way of collecting data will, in turn, enable deeper analysis and help construct machine learning models for disease classification, hence providing worthy insights into Ash Gourd plant health.
Why it matches plant phenotyping methodsアッシュゴード葉の画像データセットを構築し、健全状態と4種類の病害・害虫状態を分類可能にした研究であり、植物の病害状態を観測する再利用可能な表現型データセットが中心です。
abstractThe Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner.
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods高スループット画像表現型データセットが中心で、画像から農業形質を抽出したデータ、処理済み画像、解析スクリプトを提供しているため。
titleA high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress
This study presents a large, meticulously curated and manually validated dataset aimed at classifying leaf quality into five critical categories: Healthy, Bacterial Spot, Shot Hole, Yellow, and Powdery Mildew. The dataset encompasses four distinct plant species—Cinnamomum Camphora (Camphor), Terminalia Chebula (Haritaki), Moringa Oleifera (Sojina), and Azadirachta Indica (Neem)—each represented across three or four disease categories, depending on observed symptoms and final number of classes is thirteen (13 classes). Data collection was conducted between November 1, 2024, and January 5, 2025, utilizing four different mobile cameras to ensure diversity in image resolution, lighting, and environmental conditions. The original dataset comprised 10,858 high-resolution images, which were subsequently expanded to 65,148 through the application of six comprehensive data augmentation techniques, including rotations (45°, 60°, and 90°), horizontal flipping, zooming and brightness adjustment. All images were standardized to 512×512 pixels to ensure uniformity and seamless compatibility with machine learning and computer vision models. This enriched dataset serves as a crucial resource for the development of automated plant disease detection systems and supports advancements in precision agriculture. It not only addresses the pressing need for scalable, high-quality data in agricultural research but also establishes a solid foundation for benchmarking novel deep learning architectures. By enabling more accurate and efficient leaf disease classification, the dataset contributes significantly to enhancing tree health monitoring, improving crop yield, and promoting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉の病徴を画像で分類する大規模データセットの構築・検証が中心であり、植物病害状態の表現型計測に該当する。
abstractThis study presents a large, meticulously curated and manually validated dataset aimed at classifying leaf quality into five critical categories: Healthy, Bacterial Spot, Shot Hole, Yellow, and Powdery Mildew.
The Ash Gourd dataset is valuable since it was collected from the diverse regions within the district of Dhaka in Bangladesh. This dataset represents one of the first attempts to document, elicit, and categorize the health conditions of Ash Gourd (Benincasa hispida) plants in Bangladesh based on healthy samples, aphid plurality, downy mildew, leaf curl, and leaf miner-infested categories. Ash Gourd is one of the region's most important vegetables because of its nutritional and economic value; thus, it is essential to know diseases' manifestation in the improvement of agricultural productivity. The Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner. All images in all categories are raw which can be used flexibly according to the needs of analysis and model training. Concretely, the Healthy class consists of 803 images, while the four other classes contain 1,873 images. This structured way of collecting data will, in turn, enable deeper analysis and help construct machine learning models for disease classification, hence providing worthy insights into Ash Gourd plant health.
Why it matches plant phenotyping methodsアッシュゴード葉の画像データセットを構築し、植物の健康状態・病徴カテゴリを分類するための再利用可能なデータ資源を提供しており、植物病害状態の画像ベース表現型解析が中心です。
abstractThe Ash Gourd dataset contains 2676 images, structured into the five categories of Healthy, Aphid, Downy Mildew, Leaf Curl, and Leaf Miner.
Reproduction assets foundThe paper's own ash gourd leaf image dataset (2676 images, five classes) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching the paper's phenotyping measurements.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/zj4th6xvdp.2
Direct URL to data:https://data.mendeley.com/datasets/zj4th6xvdp/2Open asset ↗Mendeley Data · 10.17632/zj4th6xvdp.2html-lines:1-98Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
RGB / grayscaleClassificationGrowth / development / phenology
Okra is a highly nutritious farming product that combats malnutrition issues while supporting sustainable agricultural methods. In order to keep its quality and versatility in preparation applications, it is important to classify its maturity stages into under-mature, mature, and over-mature categories. Classification is vital to identify the best time to harvest, satisfy market demands, minimize post-harvest losses, and optimize cooking uses. This data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera. The dataset contains okra samples that were sourced from various farms and vegetable markets, ensuring that it encapsulates the natural variability found in real-world farm and market environments. The availability of such a large dataset enables the creation of precise classification models that can assist farmers in optimizing the time of harvest, fulfilling consumers' requirements, and improving market results. The research has great relevance to promoting agricultural quality evaluation and boosting market readiness using non-invasive techniques.
Why it matches plant phenotyping methodsRGB画像によるオクラ果実の成熟段階分類データセットを提供しており、非破壊的な植物器官形質の取得・分類が中心である。
abstractThis data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera.
Coffee is one of the most popular beverages consumed worldwide and is also an important economic driver in agricultural economies. However, nutritional deficiencies in coffee plants have a major effect on the quality and yield of the crop. Detection of these deficiencies early and accurately is critical for effective intervention and management. In this work, we introduce a novel deep-learning framework for detection and classification of nutritional deficiencies in coffee plants. DenseNet-201, AlexNet, and MobileNet-V2 are integrated to extract discriminative features from coffee leaf images, and an attention-based feature fusion mechanism is proposed using squeeze-and-excitation blocks to improve feature representation. A differential evolution algorithm is used to optimize a Kernel extreme learning machine for learning efficiency and generalization to classify the extracted features. A benchmark dataset is used for the evaluation of the proposed model and its performance is assessed against multiple performance metrics such as accuracy, precision, recall, specificity, F1-score, and the Matthews correlation coefficient. The proposed method is compared with existing deep learning models, and it is found that the proposed method outperforms the other models with a classification accuracy of 99.50%, precision of 99.22%, recall of 99.24%, specificity of 0.9947, F1-score of 0.9957, and M correlation coefficient of 0.9908. The model is able to identify nutritional deficiencies accurately, and these results confirm the model’s effectiveness as a practical and scalable solution for precision agriculture and sustainable coffee cultivation.
Why it matches plant phenotyping methodsコーヒー葉画像から栄養欠乏という植物状態を検出・分類する深層学習手法を開発し、ベンチマークデータセットと比較評価しており、植物フェノタイピング手法が中心です。
abstractwe introduce a novel deep-learning framework for detection and classification of nutritional deficiencies in coffee plants.
Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R 2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.
Why it matches plant phenotyping methodsマルチビュー画像によるイネ幼苗の3D再構成プラットフォームとデータセットを開発し、再構成精度および抽出形質を実測値と検証しており、表現型取得手法が研究の中心である。
abstractdeep learning-based feature extraction and matching methods are employed in this study
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub; the phenotype/image dataset is only available upon request, so it does not qualify as a public asset.Code · publicThe code used in this study is available at https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.git .Open asset ↗https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.gitlines:433-485Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Field / plotSegmentationStress / disease detectionDisease symptoms / severity
Accurate segmentation of areas affected by pests and diseases is essential for precisely assessing the severity and spread of infections, thereby facilitating the development of effective management and intervention strategies. Obtaining high-quality pixel-level annotations for training deep learning models in agricultural environments poses considerable challenges. To overcome this limitation, the present work introduces a novel semantic segmentation approach (SegPPD-FS) that employs few-shot learning techniques to reduce annotation demands while effectively segmenting plant pests and diseases. The proposed SegPPD-FS comprises two key components: the similarity feature enhancement module (SFEM) and the hierarchical prior knowledge injection module (HPKIM). The SFEM refines foreground targets by employing a lightweight attention mechanism to mitigate irrelevant background interference in natural images and further enhances the discriminative capability of query features. The HPKIM is designed to address the difficulties associated with identifying pests and diseases that vary widely in terms of shape and size within field images, which is achieved through a hierarchical integration of multiscale contextual data into the query feature representations. In addition, this study constructed and publicly released a high-quality few-shot semantic segmentation (FSS) dataset that included 101 distinct categories of plant pests and diseases, which supports further research on the precise monitoring of plant health issues. The experimental results demonstrate that the proposed method achieves mIoU values of 71.19 % and 71.58 % with the 1-shot and 2-shot settings, respectively, on the released dataset. This performance surpasses that of other FSS techniques, such as SegGPT and PerSAM, providing a promising and label-efficient solution for pest and disease monitoring. The collected dataset, which focuses on plant pests and diseases, has been publicly released at https://doi.org/10.5281/zenodo.15114159, providing a valuable resource for evaluating various FSS techniques.
Why it matches plant phenotyping methods植物の病害・害虫による影響領域を画像からセグメンテーションし、被害の重症度・拡大を評価する手法を開発しており、植物状態の取得が中心です。公開データセットの構築・ベンチマークも含みます。
abstractthe present work introduces a novel semantic segmentation approach (SegPPD-FS) that employs few-shot learning techniques to reduce annotation demands while effectively segmenting plant pests and diseases
Reproduction assets foundThe paper publicly releases its SegPPD-101 pest/disease segmentation dataset (2263 pixel-annotated images, 101 categories) on Zenodo and its model weights via the authors' GitHub repository, both explicitly stated in the data availability statement.Dataset · publicThe dataset used in this study is available at https://doi.org/10.5281/zenodo.15114159, and the model weights can be accessed at https://github.com/zihan303/SegPPD-FS.Open asset ↗Zenodo · 10.5281/zenodo.15114159html-lines:393-417Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Accurate monitoring of crop phenotypic traits is essential for efficient farm management and automation in agriculture. Multi-object tracking (MOT) and video instance segmentation (VIS) offer promising approaches to enhance agricultural robotic-vision systems, yet a major limitation is the scarcity of high-quality spatial-temporal datasets. In this paper, we introduce BUP-ST20, a novel weakly labelled spatial-temporal dataset for sweet pepper tracking and segmentation captured on a robotic platform. Our dataset is generated by leveraging still image annotations and utilizing a neural radiance field approach (PAg-NeRF) to automatically obtain consistent object semantics and identities across video sequences. BUP-ST20 contains 16,240 images from 275 sequences, with weak labels for training and validation, and human-annotated ground truth for evaluation. We describe how this pseudo-labelling approach can be adapted to any robotic platform with the required inputs, greatly reducing the annotation requirements for dataset creation, with a focus on agriculture and horticulture. Utilizing BUP-ST20, we evaluate state-of-the-art MOT approaches and propose two novel tracklet matching criteria, enhancing robustness in frame-skipped scenarios and low frame rate cameras. When we decrease the frame rate to approximately 1 frame per second our offline MOT based matching criteria is able to improve performance by an absolute value of 19.63, outlining its validity as a tracklet aggregation technique in this scenario. Our experiments demonstrate the effectiveness of the dataset in benchmarking MOT and VIS techniques within the agricultural domain. This also allows us to highlight challenges such as occlusion, shape variations, and weak-labelling limitations. BUP-ST20 serves as a valuable resource for further advancements in robotic crop monitoring and agricultural automation, while demonstrating the ability to create future weakly labelled datasets using robotic platforms.
Why it matches plant phenotyping methods植物の追跡・セグメンテーションを対象とする時空間データセットを開発し、ロボット撮像、弱ラベル生成、ベンチマークまで扱っており、植物フェノタイピング手法が中心である。
abstractOur experiments demonstrate the effectiveness of the dataset in benchmarking MOT and VIS techniques within the agricultural domain.
Abstract Accurate yield prediction before maize harvest is crucial for advancing agricultural management and ensuring food security. Unlike conventional approaches that rely on phenotypes from a single growth stage, this study models multiple traits across different developmental stages, all targeting final yield, thereby uncovering their dynamic and cumulative contributions. We introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction. The method employs a bipartite graph structure to impute missing trait values at each stage and leverages a hypergraph attention mechanism to capture high-order sample relationships. Comprehensive benchmark experiments demonstrate that Yield-Graph consistently outperforms traditional machine learning and graph-based models in both trait completion and yield prediction. Moreover, the framework exhibits strong robustness across growth stages, high adaptability to regional variations, and effective generalization across datasets. These findings highlight the potential of graph-enhanced multi-stage modeling for early-stage yield prediction, offering a scalable solution for precision agriculture and intelligent crop management.
Why it matches plant phenotyping methods多段階の植物形質を補完・統合し、収量という植物形質を予測するグラフ手法を開発・ベンチマークしており、形質取得・推定ワークフローが中心です。
abstractWe introduce Yield-Graph, an innovative framework that integrates multi-stage phenotypic data for yield prediction.
Reproduction assets foundThe paper's authors publicly release their Yield-Graph analysis code on GitHub; the phenotype datasets themselves are only available on request.Code · publicthe
manuscript. All authors read and approved the final manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable
request.
Code availability
The code developed to generate the results and analysis in this article is available at https://github.com/wjhhh2928/Yield-GraphOpen asset ↗https://github.com/wjhhh2928/Yield-Graphpdf-raw-page:14 lines:1-38Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
This study highlights the growing significance of flowers, especially roses, in the global agricultural market, where they are cultivated for both personal enjoyment and commercial purposes. Among these, roses are considered one of the most popular and widely cultivated flowers. However, rose cultivators often encounter substantial challenges due to diseases that affect the plants, which can lead to significant economic losses in the agricultural sector. Timely and accurate detection of these diseases is crucial to mitigating their impact, potentially saving millions of dollars in crop losses. The dataset utilized in this research consists of 10,000 high-quality images collected from an initial set of 3113 images taken from several rose gardens located in Amin Model Town, Khagan, Ashulia, and Savar, Bangladesh. The data collection process spanned from October 30 to November 6, 2024. These images are categorized into four distinct classes: Healthy Leaf, Black Spot, Leaf Hole, and Dry Leaf, representing various stages of disease development in rose plants. The images were captured using a Vivo IQOO Z9x phone, ensuring high resolution and detailed imagery necessary for research analysis. This dataset serves as a valuable resource for researchers and developers working on creating efficient algorithms for the early and accurate identification of rose leaf diseases. By leveraging machine learning and image processing techniques, these algorithms could significantly enhance disease detection and prevention, helping to safeguard crops and reduce economic losses in the agricultural sector.
Why it matches plant phenotyping methodsバラ葉の病徴を画像データセットとして体系的に収集・分類し、植物病害状態の画像ベース推定を支援する研究であり、データセット構築が中心です。
titleRoseLeafSet: Real-world leaf image dataset for AI-based agricultural solutions.
Reproduction assets foundThe paper is a data descriptor for RoseLeafSet, a public rose leaf image dataset (3113 original images, augmented to 10,000) deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/9g668bfhy5.3). This is a paper-specific, publicly available plant image dataset directly reproducing the paper's phenotypyDataset · publiclocation
City: Amin Model Town, Khagan, Ashulia, Savar, Dhaka Country: Bangladesh. Local location: Shumi Nursery, Shetu Nursery, Bismillah Nursery etc. Geographical Location: 23 ° 53′ 2″ N and 90 ° 19′ 28″ E.
Data accessibility
Repository name: Mendeley Data Data identification number: 10.17632/9g668bfhy5.3 Direct URL to data: https://data.mendeley.com/datasets/9g668bfhy5/3
Related research article
None
1.
Value of the Data
•
The dataset presented here, a collaborative effort of researchers and industry professionals, is suitable for training machine learning models for rose leaf disease classification and detection. This makes it a valuable resource for all of us, as we work together to devOpen asset ↗Mendeley Data · 10.17632/9g668bfhy5.3lines:1-48Plant 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
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R 2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R 2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
Why it matches plant phenotyping methods葉の傾斜角分布という植物形質を画像から推定する深層学習手法を開発し、大規模データセット、既存モデル比較、レーザースキャンによる検証、水ストレス下での追跡評価まで実施しており、フェノタイピング手法が研究の中心です。
abstractWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public access to the authors' analysis code (Anonymous GitHub), the phenotyping image/trait dataset (Zenodo), and the pretrained AngleCam V2 model weights (Zenodo). All three are paper-specific, public, and actionable.Code · publicLK and TK conceived the ideas, designed the methodology, and led the analysis. TK, JP, RR, JF, LK,
26
and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available underOpen asset ↗anonymous.4open.science/r/AngleCamV2-2B38pdf-raw-page:2 lines:1-30Dataset · publicK, JP, RR, JF, LK,
26
and DL collected the data. LK and TK led the writing of the manuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, whoOpen asset ↗zenodo · 10.5281/zenodo.17086253pdf-raw-page:2 lines:1-30Model / weights · publicanuscript. All authors contributed
27
critically to the drafts and gave final approval for publication.
28
Data Availability Statement
29
The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
30
is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
31
at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
33
All authors declare that they have no conflicts of interest.
34
2
.
CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for tOpen asset ↗zenodo · 10.5281/zenodo.17101166pdf-raw-page:2 lines:1-30Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Street trees are vital to urban livability, providing ecological and social benefits. Establishing a detailed, accurate, and dynamically updated street tree inventory has become essential for optimizing these multifunctional assets within space-constrained urban environments. Given that traditional field surveys are time-consuming and labor-intensive, automated surveys utilizing Mobile Mapping Systems (MMS) offer a more efficient solution. However, existing MMS-acquired tree datasets are limited by small-scale scene, limited annotation, or single modality, restricting their utility for comprehensive analysis. To address these limitations, we introduce WHU-STree, a cross-city, richly annotated, and multi-modal urban street tree dataset. Collected across two distinct cities, WHU-STree integrates synchronized point clouds and high-resolution images, encompassing 21,007 annotated tree instances across 50 species and 2 morphological parameters. Leveraging the unique characteristics, WHU-STree concurrently supports over 10 tasks related to street tree inventory. We benchmark representative baselines for two key tasks--tree species classification and individual tree segmentation. Extensive experiments and in-depth analysis demonstrate the significant potential of multi-modal data fusion and underscore cross-domain applicability as a critical prerequisite for practical algorithm deployment. In particular, we identify key challenges and outline potential future works for fully exploiting WHU-STree, encompassing multi-modal fusion, multi-task collaboration, cross-domain generalization, spatial pattern learning, and Multi-modal Large Language Model for street tree asset management. The WHU-STree dataset is accessible at: https://github.com/WHU-USI3DV/WHU-STree.
Why it matches plant phenotyping methods樹木の個体セグメンテーションと形態パラメータを含むマルチモーダルデータセットを構築し、ベンチマークする研究であり、植物個体の状態・形態抽出手法が中心である。
abstractWHU-STree, a cross-city, richly annotated, and multi-modal urban street tree dataset.
Reproduction assets foundThe paper's core asset is the WHU-STree multi-modal street tree dataset (point clouds, panoramic images, 21,007 annotated tree instances, 50 species, height/DBH), which the authors state is publicly accessible via their GitHub organization WHU-USI3DV. The Zenodo DOIs in the reference list belong to cited prior datasetsDataset · publicticular, we
identify key challenges and outline potential future works for fully exploit-
ing WHU-STree, encompassing multi-modal fusion, multi-task collaboration,
cross-domain generalization, spatial pattern learning, and Multi-modal Large
Language Model for street tree asset management. The WHU-STree dataset
is accessible at: https://github.com/WHU-USI3DV /WHU-STree.
Keywords: Deep learning, Tree inventory, Individual tree segmentation,
Tree species classification, Multi-modal, Mobile mapping system
1. Introduction
Street trees, vital to urban ecosystems, provide ecological benefits (e.g.,
shade (Kumar et al., 2024), air purification (Grundstrém and Pleijel, 2014),
noise reductiOpen asset ↗WHU-STreepdf-raw-page:2 lines:1-35Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Motivation Plant heat tolerance data are increasingly valued for their potential to help increase our understanding of species’ responses to extreme temperatures, but these efforts are hindered by methodological inconsistencies and missing contextual information. To address this issue, we collated data that compiles heat tolerance estimates and documents key sources of variation attributable to taxonomy, methodology, geography, and cultivation to improve data clarity and usability. This resource is designed to catalyze more rigorous and ecologically meaningful syntheses by enabling researchers to identify, account for, and test the drivers of variation in plant heat tolerances and their consequences. Main types of variable contained Heat tolerance estimated in degrees Celsius from photosynthetic tissue Spatial location and grain Global in scope with undersaturated taxonomic sampling and underrepresented geographic regions. Time period and grain 1935-2024 Major taxa and level of measurement Primarily vascular plants encompassing >1700 taxa, >1000 genera and >200 families. Software format Comma-separated values
Why it matches plant phenotyping methods植物の熱耐性という生理形質を体系的に収集・整理したデータベースであり、方法論の不一致や変動要因も記録する再利用可能なリソースであるため、フェノタイピングデータセットとして対象に含める。
abstractwe collated data that compiles heat tolerance estimates and documents key sources of variation attributable to taxonomy, methodology, geography, and cultivation
Cotton is one of the most important natural fiber crops worldwide, yet harvesting remains limited by labor-intensive manual picking, low efficiency, and yield losses from missing the optimal harvest window. Accurate recognition of cotton bolls and their maturity is therefore essential for automation, yield estimation, and breeding research. We propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions. Building on YOLOv11n, Cott-ADNet enhances spatial representation and robustness through improved convolutional designs, while introducing two new modules: a NeLU-enhanced Global Attention Mechanism to better capture weak and low-contrast features, and a Dilated Receptive Field SPPF to expand receptive fields for more effective multi-scale context modeling at low computational cost. We curate a labeled dataset of 4,966 images, and release an external validation set of 1,216 field images to support future research. Experiments show that Cott-ADNet achieves 91.5% Precision, 89.8% Recall, 93.3% mAP50, 71.3% mAP, and 90.6% F1-Score with only 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet.
Why it matches plant phenotyping methods綿花の花・ボール認識を対象とする画像解析手法を開発し、データセット作成、外部検証、性能評価まで行っており、植物器官の表現型取得が中心である。
abstractWe propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions.
Reproduction assets foundThe paper explicitly states that its code and curated cotton boll/flower detection dataset (4,966 labeled images plus a 1,216-image external validation set) are publicly released at the authors' GitHub repository. The ultralytics repository is a generic third-party library, not a paper-specific asset.Code · publicy 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet .
† † footnotetext: ∗ * Corresponding author: cuij@wfu.edu
Index Terms :
cotton, cotton boll detection, lightweight object detection, rotational convolution
1 Introduction
Cotton is one of the most critical economic crops worldwide, accounting for nearly 35% of global natural fiber production. It underpins industries such as Open asset ↗SweefongWong/Cott-ADNetlines:1-57Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
A leading concern for global wheat production, Fusarium head blight (FHB) can cause yield losses of up to 50% during severe epidemics. The cultivation of FHB-resistant wheat varieties is widely acknowledged as a highly effective and economical approach to disease management. The disease resistance breeding task depends on accurately evaluating the severity level of FHB. However, existing approaches may fail to distinguish among healthy and slightly infected wheats due to insufficient fine-grained feature learning, resulting in unreliable predictions. To tackle these challenges, this paper proposed the FHBNet model for evaluating the severity level of FHB under an end-to-end manner by simply using image-level annotated RGB images. In total, 6035 RGB aerial images taken from the wheat field were used to construct the dataset and each image was labelled by the light, moderate, or severe category. In FHBNet, we first utilized the multi-scale criss-cross attention (MSCCA) block to capture the global contextual relationships from each pixel, thereby modelling the spatial context of wheat ears. Furthermore, in order to accurately locate small lesions in wheat ears, we applied the bi-level routing attention (BRA) module, which suppressed the most irrelevant key-value pairs and only retained a small portion of interested regions. The experimental results demonstrated that FHBNet achieved an accuracy of 79.49% on the test se5t, surpassing the mainstream neural networks like MobileViT, MobileNet, EfficientNet, RepLkNet, ViT, and ConvNext. Moreover, visualization heatmaps revealed that FHBNet can accurately locate the FHB lesions under complex conditions, e.g., varying severity levels and illuminations. This study validated the feasibility of rapid and nondestructive FHB severity level evaluation with only image-level annotated aerial RGB images as an input, and the research result of this study can potentially accelerate the disease resistance breeding task by providing high-throughput and accurate phenotype analysis.
Why it matches plant phenotyping methods小麦個体のFHB病斑・重症度を航空RGB画像から推定するモデルとデータセットを開発・評価しており、植物病害状態の表現型取得が研究の中心です。
abstractthis paper proposed the FHBNet model for evaluating the severity level of FHB under an end-to-end manner by simply using image-level annotated RGB images.
Money plants are widely recognized for their significant spiritual and air-purifying benefits. Research has proven that daily interaction with these vibrant indoor plants effectively reduces anxiety and stress. This paper introduces a robust dataset of 4302 healthy, unhealthy, combined, real and college premises images of money plants captured using smartphones. The dataset was collected from the educational hub, Dr. D. Y. Patil Institute of Technology, Pune campus, Maharashtra, India. Under controlled conditions, images were taken from a mobile device to ensure consistency and quality. From different angles and different backgrounds, images are captured. The aim of creating the dataset was to support researchers in achieving their objectives in the agricultural field and to explore our dataset so that it may be used for further research, investigation, and training of artificial intelligence models using our dataset.
Why it matches plant phenotyping methods植物の健康・不健康状態を画像で収集・分類したデータセットが論文の中心であり、病害・状態フェノタイピング用データセットとして適格です。
abstractThis paper introduces a robust dataset of 4302 healthy, unhealthy, combined, real and college premises images of money plants captured using smartphones.
Reproduction assets foundThis Data in Brief article describes its own public plant-phenotyping asset: a 4302-image money plant (Epipremnum aureum) leaf dataset with manual healthy/unhealthy classification, deposited on Mendeley Data (V4, DOI 10.17632/kd8hs7ch6t.4) with a Zenodo mirror and an authors' GitHub repository. The dataset is the paperDataset · publicRepository name : Epipremnum aureum (Money Plant Leaf) datasets
Data identification number : Version: V4, 10.17632/kd8hs7ch6t.4
Direct URL to data: https://data.mendeley.com/datasets/kd8hs7ch6t/4Open asset ↗10.17632/kd8hs7ch6t.4lines:1-73Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published10 Sept 2025International Journal of Basic and Applied SciencesCited by 0 · OpenAlex ↗
This article is coined for investigating the Village Plant dataset. Many researchers worldwide, carrying out their research in the domain of agriculture, are dependent on this open source dataset. A plant is vulnerable to several infirmities during its period of growth. Detection of the plant’s ill health and monitoring the environmental parameters is the most challenging task in agriculture. Plant disease epidemic may have a significant effect on crop production, reducing the country’s wealth. Early diagnosis of the occurrence of ill health in plants and the remedies are feasible using Artificial Intelligence (AI). Currently, methods like Deep Learning (DL) algorithms, machine vision techniques, and robotics play an important role in monitoring plant diseases and the growth status. This dataset contains multi-fold in-information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato. An Internet of Things (IoT) based plant data collection and integration system will provide data for this research, which optimizes the feature set through Ant Colony Optimization (ACO) for improving prediction in feature selection using deep learning models like DenseNet, ResNet 50, VGG 19, and Long Short-Term Memory (LSTM) networks, which in turn enhances plant productivity with advances in AI-driven agricultural diagnostics for plant stress prediction.
Why it matches plant phenotyping methods植物の正常・罹病画像から植物の病気・ストレス状態を推定する画像解析ワークフローとデータセット利用が研究の中心であり、植物状態のフェノタイピング手法に該当する。
abstractThis dataset contains multi-fold in-information about the plants. They include the normal and diseased images of plants like Bell Pepper, Tomato, Cucumber, and Potato.
The development of computer vision-based rice phenotyping techniques is crucial for precision field management and accelerated breeding, which facilitate continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into ecophysiological processes. However, owing to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both because of a lack of large, representative collections of rice field images and because of the time-intensive nature of the annotation. To address this gap, we created the first comprehensive multiclass rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing more than 6000 genotypes across all growth stages. From these original images, 3078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the subdataset from China spans all major genotypes and rice-growing environments from northeastern to southern regions. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and when multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at www.global-rice.com.
Why it matches plant phenotyping methodsイネの器官レベル表現型抽出を目的とした大規模画像セグメンテーションデータセットを作成し、複数モデルでベンチマークしているため、方法論が中心である。
abstractThe development of computer vision-based rice phenotyping techniques is crucial for precision field management and accelerated breeding
Woody canopies regulate exchanges of energy, water and carbon, and their three-dimensional (3D) structure supports much of terrestrial biodiversity. Remote sensing technologies such as airborne laser scanning (ALS) now enable the 3D mapping of entire landscapes. However, we lack the large, harmonized and geographically representative ALS collections needed to build a global picture of woody ecosystem structure. To address this challenge, we developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m2 resolution. The GCA covers 56,554 km2 across all major biomes. 19% of this area has been scanned multiple times, and 87% of all GCA products are openly available, covering 95% of the total area. To showcase its wide range of applications, we applied the GCA in three case studies. First, we validated three global satellite-derived canopy height maps, finding poor performance at native resolution (1-30 m, R2 < 0.38) and moderate performance at 250 m resolution (R2 < 0.65). Second, analyzing global patterns in canopy gap size frequency we discovered an unexpectedly large variation of power law exponents from branch to stand level ( = 1.52 to 2.38), pointing to a fundamental scale-dependence of forest structure. Third, we developed a framework to standardize forest turnover quantification from multi-source, multi-temporal ALS. In a temperate forest in North America it revealed that 21% of canopy gaps closed within 12 years of opening and would thus be missed by infrequent monitoring. As demonstrated by these case studies, the GCA provides a novel data source for ecologists, foresters, remote sensing scientists and the ecosystem modelling community that substantially advances our ability to understand the structure and dynamics of woody ecosystems at global scales.
Why it matches plant phenotyping methodsALSから樹冠高・標高などの植物群落構造形質を標準化して提供する大規模データ基盤を開発し、既存マップの検証や森林構造解析にも用いており、フェノタイピング手法・データ基盤が中心である。
abstractwe developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m2 resolution.
Computer vision has emerged as a critical enabler of sustainable production in protected agriculture by offering efficient and non-invasive crop disease diagnosis. The development of accurate disease recognition models relies heavily on the availability of high-quality image datasets. This study introduces a tomato disease image dataset collected in 2024 from greenhouse facilities within a modern agricultural park in Sichuan Province, China. The dataset comprises 1026 high-resolution images, including 417 images of viral disease, 82 images of gray mold, and 527 images of bacterial wilt, totaling approximately 2.78 GB. Captured under real-world greenhouse conditions and from multiple angles and distances, the images effectively capture multi-scale phenotypic disease features. Manual annotation was conducted using the LabelImg tool under the guidance of plant pathology experts, with labeled regions covering leaves, fruits, and stems. Annotation files are stored in XML format, each corresponding to a specific image. This dataset is well-suited for research in disease classification, object detection, and phenotyping, and supports deep learning model training and cross-crop transfer learning applications.
Why it matches plant phenotyping methodsトマト病害の症状を画像で捉え、分類・検出モデル用に専門家アノテーションした再利用可能なデータセットであり、植物病害状態の表現型取得が中心である。
abstractThe development of accurate disease recognition models relies heavily on the availability of high-quality image datasets.
Reproduction assets foundThe paper is a Data in Brief article describing a public tomato disease image dataset (1026 annotated images) deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL exactly.Dataset · publicwas conducted at the Modern Agricultural Science and Technology Innovation Demonstration Park of the Sichuan Academy of Agricultural Sciences (30.7797° N, 104.2082° E), located in Sichuan Province, China.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c2×8rynybg.1
Direct URL to data: https://data.mendeley.com/datasets/c2×8rynybg/1
Related research article
None.
1
Value of the Data
The dataset contains 1026 annotated images of tomato plants exhibiting three major disease types, collected in 2024 from greenhouse environments in Sichuan’s Modern Agricultural Demonstration Park. Plant pathology specialists manually labeled all samples. Its technical sOpen asset ↗Mendeley Data · 10.17632/c2×8rynybg.1lines:1-52Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.
Why it matches plant phenotyping methodsコムギ穂の罹病小穂率という植物病害形質を画像から自動推定する手法を開発し、データセット構築と専門家評価による検証を行っており、フェノタイピング手法が中心である。
abstractrequires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding.
Reproduction assets foundThe paper's Data availability statement explicitly deposits both the spikelet-level annotated wheat spike image dataset (620 RGB images, 5,222 annotations) and the FHBDSR-Net analysis code in a public GitHub repository under the authors' account, matching an allowed URL.Dataset · publicThe dataset and code generated in this study are available at https://github.com/WeizhenLiuBioinform/Wheat-FHB-DSR-Measurement .Open asset ↗WeizhenLiuBioinform/Wheat-FHB-DSR-Measurementlines:901-961Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2025Computers and Electronics in Agriculture.
Drones have enabled large-scale breeding and cultivation experiments. However, extracting individual breeding plots from aerial images is a key prerequisite and urgent demand for extracting variety-level traits. The main difficulties in plot extraction include irregular rotation angles of the plots, ambiguous gaps both within and between plots, and variable color contrasts between the vegetation and the background. To solve these challenges, a novel oriented instance segmentation network (OSNet) is proposed by leveraging a global context transformer (GCT) and an oriented region proposal network (RPN). The performance was assessed using a well-labeled dataset with 960 plots of 160 wheat varieties across two years. Results show that OSNet achieved the AP@0.5 of 0.917, F1-score of 0.959, Accuracy of 0.966, IoU of 0.912, Recall of 0.934, and Plot-a of 0.999. OSNet outperformed five state-of-the-art (SOTA) networks with an average improvement of 3.08 %, 1.42 %, 1.19 %, 1.70 %, 1.79 %, and 0.04 % in AP@0.5, F1-score, Accuracy, IoU, Recall, and Plot-a, respectively. The sensitivity analysis proved that OSNet consistently achieved stable segmentation accuracy across different rotation angles and growth stages. The interpretability through ablation analysis showed that OSNet benefits from the oriented proposal and global information. Furthermore, OSNet can be transferred to new datasets with various years, crops, and data dimensions, supporting typical phenotyping tasks such as 2D wheat spike detection (r = 0.91) and 3D canopy height measurement (r = 0.89). The innovative methodology will be a fundamental tool for processing drone imagery, accelerating phenotypic trait extraction across various varieties and thereby expediting the breeding process.
Why it matches plant phenotyping methodsUAV画像から育種区画を抽出する新規インスタンスセグメンテーション手法を開発・検証しており、植物形質抽出への適用性能も評価しているため、方法が研究の中心である。
abstractTo solve these challenges, a novel oriented instance segmentation network (OSNet) is proposed by leveraging a global context transformer (GCT) and an oriented region proposal network (RPN).
Wheat diseases have severely threatened global food security, making prompt and accurate detection methods crucial for disease control. However, large-scale detection methods face challenges such as low accuracy, labor-intensive labeling processes, and limited applicability across different wheat diseases. This study proposes a novel method using an Unsupervised Domain Adaptation (UDA) for semantic segmentation, termed DATS-ASSFormer, to detect wheat rust, wheat scab, and wheat yellow dwarf from UAV-based multispectral images. The proposed method employs Domain Adaptation via Teacher-Student networks (DATS) to generate pseudo-labels for unlabeled data in the target domain, and Adaptive Semantic Segmentation Former (ASSFormer) to precisely segment diseased areas, thus minimizing the dependency on laborious manual labeling. To support the development and evaluation of the model, the Northwest A&F University-Wheat Disease Remote Sensing Dataset (NWAFU-WDRSD) was developed, encompassing 8,628 images of the three predominant wheat diseases. Extensive testing confirmed that the DATS-ASSFormer model outperformed existing models in six domain adaptation tasks, achieving average Oracle (supervised training within the target domain) and UDA mIoU scores of 85.80 % and 65.96 %, respectively. These results significantly enhance detection accuracy and robustness across various diseases in real-world agricultural settings. The efficacy of DATS-ASSFormer highlights its potential for practical applications in precision agriculture, offering a scalable and efficient solution for large-scale wheat disease detection and management. The project is accessible at https://github.com/YcZhangSing/DATS-ASSFormer.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から小麦病害領域を抽出するセマンティックセグメンテーション手法を開発・評価し、専用データセットも構築しているため、植物病害表現型の取得が中心である。
abstractThis study proposes a novel method using an Unsupervised Domain Adaptation (UDA) for semantic segmentation, termed DATS-ASSFormer, to detect wheat rust, wheat scab, and wheat yellow dwarf from UAV-based multispectral images.
Harvesting is a critical task in the tree fruit industry, demanding extensive manual labor and substantial costs, and exposing workers to potential hazards. Recent advances in automated harvesting offer a promising solution by enabling efficient, cost-effective, and ergonomic fruit picking within tight harvesting windows. However, existing harvesting technologies often indiscriminately harvest all visible and accessible fruits, including those that are unripe or undersized. This study introduces a novel foundation-model-based framework for efficient apple ripeness and size estimation. Specifically, we curated two public RGBD-based Fuji apple image datasets, integrating expanded annotations for ripeness (“Ripe” vs. “Unripe”) based on fruit color and image capture dates. The resulting comprehensive dataset, Fuji-Ripeness-Size Dataset, includes 4,027 images and 16,257 annotated apples with ripeness and size labels. To the best of our knowledge, this is the first published dataset on apples with ripeness and size annotations. Leveraging Grounding-DINO, a foundation-model-based object detector, we achieved robust apple detection and ripeness estimation, with mean Average Precision being 72.8, outperforming other state-of-the-art models in the evaluation on our dataset. Additionally, we developed six size estimation algorithms, made a comprehensive comparison using box-plots, and identified the best algorithm with lowest error and variation. The Fuji-Ripeness-Size Dataset and the apple detection and size estimation algorithms are made publicly available¹1The code and dataset is available at https://github.com/zhukeyi-stan/Fuji_Ripeness_And_Size_Estimation., which provides valuable benchmarks for future studies in automated and selective harvesting.
Why it matches plant phenotyping methodsリンゴの熟度・サイズという植物器官の形質を画像から推定する手法を開発・比較し、データセットとベンチマークも提供しているため、フェノタイピング手法が中心である。
abstractThis study introduces a novel foundation-model-based framework for efficient apple ripeness and size estimation.
The identification of germplasm resources and analysis of phenotypic traits in lettuce hold significant importance for screening superior varieties and advancing genetic research. To uncover phenotypic differences among lettuce types and their dynamic changes during growth, this study utilized high-throughput phenotyping platform (HTPP) to collect and analyze time-series image data of eight lettuce types. Lettuce phenotypic traits were classified into five major categories: morphology, structure, color, texture, and size, which were further subdivided into 24 subcategories. Using multivariate statistical analysis, the study explored the relationships between phenotypic traits and lettuce types. Combining time-series analysis, the study examined the associations among phenotypic traits, lettuce types, and temporal sequences, revealing the dynamic changes of different lettuce types during their growth processes. The results showed that there were significant differences in phenotypic traits among different lettuce types throughout the growth cycle. These differences reflect the genetic characteristics and phenotypic variation patterns among lettuce types, revealing that genotype guides phenotype formation, while different phenotypes directly influence lettuce growth dynamics. Furthermore, by integrating multidimensional phenotypic traits, we constructed a phenotypic fingerprint for lettuce, providing each lettuce plant with a unique identifier that enables rapid detection of phenotypic differences among lettuce individuals and assists in the selection of elite cultivars. This study provides technical support for precise and rapid identification of lettuce germplasm resources, and can be used as the data basis for genetic research.
Why it matches plant phenotyping methodsレタスの高スループット表現型解析プラットフォームを用いた時系列画像の取得・解析が研究の中心で、多次元形質の抽出とフェノタイプ・フィンガープリント構築を扱っているため。
abstractthis study utilized high-throughput phenotyping platform (HTPP) to collect and analyze time-series image data of eight lettuce types.
The Transition to a Data-Driven Agriculture (TTADDA) project focuses on advancing the shift toward high-tech, circular agriculture. By developing cutting-edge sensor technologies and AI-driven tools, the project aims to boost productivity through a data-centric potato production system that supports circular agricultural practices. Potato phenotyping is crucial for creating high-yielding, resilient, and sustainable potato crops, which are essential in global food systems. Specifically in the Netherlands, the global leader in seed potato production and Japan that produces certified seed potatoes under strict quality controls and phytosanitary regulations. A multi-season drone dataset from five potato trials-three in Japan and two in the Netherlands was collected. Each trial field was divided into small plots, each planted with a specific cultivar to assess varietal performance. Data included drone imagery (RGB and multispectral), manual yield and ground coverage measurements, and weather data. The combination of sensor versatility, diverse potato varieties, and varying climate and soil conditions between Japan and the Netherlands makes this dataset highly valuable and potentially reusable for a wide range of applications. Using MIAPPE for this dataset ensures consistent, clear documentation of sensors, varieties, and conditions, making the data findable, reusable, and easy to integrate with other studies. It also supports reproducibility and automated analysis across the multi-location trials.
Why it matches plant phenotyping methodsジャガイモの表現型解析を目的としたUAV RGB・マルチスペクトル画像と圃場測定を含む、多季節・多地点の再利用可能なデータセットの構築・標準化が中心である。
abstractA multi-season drone dataset from five potato trials-three in Japan and two in the Netherlands was collected.
Reproduction assets foundThe paper is a data descriptor for the TTADDA-UAV potato phenotyping dataset (RGB/multispectral orthomosaics, DSMs, yield, ground coverage, weather) publicly deposited on 4TU.ResearchData with a DOI, plus an authors' GitHub repository for loading the MIAPPE-formatted data.Dataset · publicThe dataset is part of the following collection:
Data identification number: doi.org.10.4121/936b5772–09fc–4856–983d-1f9cc2f38d15
Direct URL to data: ( https://doi.org/10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15 )
The collection consist of metadata, and five related studies: TTADDA_NARO_2021, TTADDA_NARO_2022, TTADDA_NARO_2023, TTADDA_WUR_2022, TTADDA_WUR_2023
To visualise the metadata and download the dataset we recommend the following GIT:
https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset
Related research article
None
1
Value of the DOpen asset ↗10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15lines:57-83Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
RootSeed / grainMorphology / geometry measurementObject detectionSegmentationRoot system architecture
spp.), ecologically and economically significant, pose unique challenges due to their curved seedling morphology. Traditional manual measurement methods are time-consuming, prone to human error, and often lack consistency. Moreover, automated approaches remain limited and often fail to accurately process seedlings with nonlinear or curved morphologies. In this study, we introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures. It leverages a dual-path architecture that combines pixel-level spatial features with instance-level semantic information, enabling robust measurement of curved radicles. To support training, we construct GermElmData, a curated dataset of annotated elm seedling images, and introduce a novel synthetic data generation pipeline that produces high-fidelity, morphologically diverse germination images. This reduces the dependence on extensive manual annotations and improves model generalization. Experimental results demonstrate that GLEN achieves an estimation error on the order of millimeters, outperforming existing models. Beyond quantifying germinating elm seeds, the architectural design and data augmentation strategies in GLEN offer a scalable framework for morphological quantification in both plant phenotyping and broader biomedical imaging domains.
Why it matches plant phenotyping methods発芽エルム種子の曲がった幼根長を深層学習で推定する手法を開発し、注釈付き画像データセットと合成データ生成パイプラインも構築しているため、植物表現型取得が中心である。
abstractwe introduce GLEN, a deep learning-based model for detecting germinating elm seeds and accurately estimating their lengths of germinating structures.
This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions. The images have been meticulously annotated by agronomy experts for reliable disease labeling. To enhance dataset diversity, data augmentation methods such as rotation, scaling, brightness adjustment, and horizontal flipping were systematically applied, expanding the dataset by creating additional variants from the original images. The dataset serves as a rich resource for developing machine learning models for the automatic detection of rice diseases. This initiative aims to enable early disease detection, promote sustainable farming practices, and improve food security, particularly in rice-dependent developing countries.
Why it matches plant phenotyping methodsイネ葉画像を用いて病害状態を表現型として扱う、専門家注釈付きデータセットの構築・提供が中心であり、植物病害フェノタイピング手法の基盤となる。
Reproduction assets foundThe paper is a Data in Brief article describing a rice leaf disease image dataset (19,000 images, 7 classes) publicly deposited on Mendeley Data with DOI 10.17632/vwv3nry3wr.1. This is the paper's own phenotyping image dataset and is directly actionable. No separate analysis code or trained model checkpoint is reportedDataset · publicData accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/vwv3nry3wr.1
Direct URL to data: https://data.mendeley.com/datasets/vwv3nry3wr/1Open asset ↗Mendeley Data · 10.17632/vwv3nry3wr.1lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
The Rose (genus Rosa) has become a significant factor in the Bangladeshi flower industry, both in terms of exports and local consumption. However, rose farming in this country faces serious challenges due to diseases affecting its leaves, which weaken the plants and result in lower flower yields and financial losses for farmers. Rosa (genus Rosa) is one of the most attractive and commercially valuable flower genera. However, agricultural rose production faces several challenges, such as pesticide resistance, which affects plant growth and results in a reduced quantity and quality of healthy flowers. Several natural factors also cause interference with rose production. Most farmers involved in this industry have limited education, which hinders their ability to identify early-stage rose-leaf disease solely through visual inspection. Furthermore, limited communication with agricultural experts exacerbates the situation, leading to delayed interventions and economic losses. This study presents the rose leaf disease dataset, which would help enhance disease tracking, diagnosis, and research in roses. From October 2024 to January 2025, large-scale field surveys were conducted to capture quality images for each condition class in rose leaves. In this paper, four classes comprise 'Black Spot,' 'Insect Hole,' 'Yellow Mosaic Virus,' and 'Healthy,' representing different stages in disease progression. There are 3,228 original images, categorized as follows: Black Spot (409), Insect Hole (453), Yellow Mosaic Virus (680), and Healthy (1,686). During the pre-processing stage, the images are resized to 3000×3000 pixels, and low-quality, duplicate, or irrelevant images are removed to ensure high quality. We have employed various augmentation techniques, including rotation, flipping, contrast adjustment, blurring, shearing, zooming, and noise addition, to increase the dataset size and enhance model generalization. Datasets like this one are in high demand for agricultural research, leading to improved disease management and increased yields. These goals can be achieved through high-accuracy machine-learning models for early disease detection and cause identification. This gives the farmers more time to take necessary actions for disease prevention and pest control. This tech-based system combines the field of agriculture with the cutting edge of computer science and AI, making precision agriculture even more effective and efficient. Our dataset is designed to meet the need for data to train these models and provide a baseline benchmark for disease detection in our specific crop, the Rose. Improvements in different generations of models, as well as numerous other forms of scientific advancements, can lead to further increases in efficiency and ultimately result in better, smarter farms. In our initial testing for categorizing rose leaves, we employed two well-known transfer learning models. Among them, MobileNetV2 performed exceptionally well, achieving an accuracy of 96.79% in image classification. This dataset can be integrated with innovative farming equipment, such as drones and sensors, to monitor large fields in real-time. This dataset serves as a benchmark for training deep learning models, enabling enhanced automated monitoring and decision-making in precision agriculture.
Why it matches plant phenotyping methodsバラ葉の病徴を画像で分類する大規模データセットとベンチマークを構築しており、植物の病害状態を直接推定する画像ベース手法が中心である。
titleRoseLeafInsight: A high-resolution image dataset for rose leaf disease recognition.
Reproduction assets foundThe paper's own rose leaf disease image dataset (3,228 original images plus processed/augmented versions) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/8chrjdxn79.1
Direct URL to data: https://data.mendeley.com/datasets/8chrjdxn79/2
The dataset is publicly available and can be accessed via the provided Mendeley Data repository link.Open asset ↗Mendeley Data · 10.17632/8chrjdxn79.1lines:31-66Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Apple leaf diseases severely affect the quality and yield of apples, and accurate classification is crucial for reducing losses. However, in natural environments, the similarity between backgrounds and lesion areas makes it difficult for existing models to balance lightweight design and high accuracy, limiting their practical applications. In order to resolve the aforementioned problem, this paper introduces a lightweight converged attention multi-branch network named LCAMNet. The network integrates depthwise separable convolutions and structural re-parameterization techniques to achieve efficient modeling. To avoid feature loss caused by single downsampling operations, a dual-branch downsampling module is designed. A multi-scale structure is introduced to enhance lesion feature diversity representation. An improved triplet attention mechanism is utilized to better capture deep lesion features. Furthermore, a dataset named SCEBD is constructed, containing multiple common disease types and interference factors under natural environments, realistically reflecting orchard conditions. Experimental results show that LCAMNet achieves 92.60% accuracy on the SCEBD and 95.31% on a public dataset, with only 0.03 GFLOPs and 1.30M parameters. The model maintains high accuracy while remaining lightweight, enabling effective apple leaf disease classification in natural environments on devices with limited resources.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から分類する軽量モデルを開発し、自然環境データセットを構築・評価しており、植物病害状態の画像ベース表現型推定が中心である。
abstractthis paper introduces a lightweight converged attention multi-branch network named LCAMNet.
Reproduction assets foundThe paper's data availability statement links three public image datasets directly used in its experiments: the FGVC8 Plant Pathology 2021 Kaggle dataset, the AppleLeaf9 GitHub dataset, and the ATLDSD dataset on ScienceDB. No author analysis code or trained model is released, and the self-constructed SCEBD has no own公开Dataset · publicce Foundation Project (No. 2024MS06002), the Inner Mongolia Autonomous Region universities innovative research team project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources,Open asset ↗plant-pathology-2021-fgvc8 · plant-pathology-2021-fgvc8lines:727-753Dataset · publicomous Region universities innovative research team project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources, Writing – review & editing. XF: Project administration, SupervisOpen asset ↗JasonYangCode/AppleLeaf9 · JasonYangCode/AppleLeaf9lines:727-753Dataset · publicteam project (No. NMGIRT2313) and the Inner Mongolia Natural Science Foundation Project (No. 2025ZD012).
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/competitions/plant-pathology-2021-fgvc8 ; https://github.com/JasonYangCode/AppleLeaf9 ; https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
YJ: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HL: Funding acquisition, Resources, Writing – review & editing. XF: Project administration, Supervision, Writing – review & editing. BW: Project aOpen asset ↗0e1f57004db842f99668d82183afd578lines:727-753Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Laboratory / benchtopRGB / grayscaleFruitClassificationGrowth / development / phenology
Okra is a highly nutritious farming product that combats malnutrition issues while supporting sustainable agricultural methods. In order to keep its quality and versatility in preparation applications, it is important to classify its maturity stages into under-mature, mature, and over-mature categories. Classification is vital to identify the best time to harvest, satisfy market demands, minimize post-harvest losses, and optimize cooking uses. This data in brief uses a non-destructive approach to okra maturity classification based on a dataset of images taken under controlled illumination using a standard RGB camera. The dataset contains okra samples that were sourced from various farms and vegetable markets, ensuring that it encapsulates the natural variability found in real-world farm and market environments. The availability of such a large dataset enables the creation of precise classification models that can assist farmers in optimizing the time of harvest, fulfilling consumers' requirements, and improving market results. The research has great relevance to promoting agricultural quality evaluation and boosting market readiness using non-invasive techniques.
Why it matches plant phenotyping methodsRGB画像データセットによるオクラ果実の成熟段階分類が研究の中心であり、植物器官の状態を画像から推定する再利用可能なフェノタイピング資源に該当する。
titleRGB image dataset for okra maturity classification to enhance agricultural quality and market readiness.
Reproduction assets foundThe paper is a Data in Brief article whose core contribution is a public RGB okra image dataset (364 images across three maturity classes) deposited on Mendeley Data, directly serving as the paper's phenotyping image asset. No separate analysis code repository is described.Dataset · publicVellore Institute of Technology - Chennai Campus.
City/Country: Chennai, India.
Latitude and longitude for collected samples/data: (12.8406° N, 80.1534° E), Vellore Institute of Technology - Chennai.
Data accessibility
Repository name: Okra Image Dataset
Data identification number: DOI: 10.17632/jmhz4826f2.1
Direct URL to data: https://data.mendeley.com/datasets/jmhz4826f2/1
Related research article
[ 1 ]
1
Value of the Data
•
Agricultural quality assessment is advancing through non-invasive methods that include the use of RGB image analysis for effective determination of okra maturity stages.
•
Utilization of ML and DL methods in recognizing visual characteristics, i.e., color, texture, andOpen asset ↗10.17632/jmhz4826f2.1lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Rice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. To address the challenges of insufficient feature extraction and poor multi-scale disease adaptability in existing deep learning approaches under complex field environments, this study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR. We constructed the RiDDET-5 dataset containing 9,303 images covering five major disease categories. The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出する深層学習手法を開発し、データセット上で性能評価しており、植物病害状態のフェノタイピング手法が中心である。
abstractthis study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR.
Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 %. However, the precision for stems with 54 % was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.
Why it matches plant phenotyping methods小麦器官の画素レベルセグメンテーション用データセットを構築し、モデル性能を検証する研究であり、植物形質抽出のための画像解析手法が中心です。
abstractThe labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer.
Reproduction assets foundThe paper's GWFSS wheat organ segmentation dataset (1096 pixel-labelled images plus 52,078 unlabelled images, subset/imaging-setup metadata) and the benchmark segmentation model are publicly deposited in the ETH Research Collection and mirrored on Hugging Face, with links also listed on the Global Wheat site.Dataset · publicThe full dataset (GWFSS_v1.0_full) including the 1096 ground-truth labelled images (GWFSS_v1.0_labelled), the descriptions of the datasets (GWFSS_v1.0_subsets.csv) and imaging setups (GWFSS_v1.0_imaging_setups.csv) is available in the ETH research collection (https://doi.org/10.3929/ethz-b-000734546)Open asset ↗ETH research collection · 10.3929/ethz-b-000734546html-lines:1006-1041Dataset · publicTo facilitate access, the labelled data and the benchmark model will also be available at (https://huggingface.co/datasets/GlobalWheat/GWFSS_v1.0).Open asset ↗huggingface · GlobalWheat/GWFSS_v1.0html-lines:1129-1192Dataset · publicLinks to these datasets can be found at: https://www.global-wheat.com/gwfss.html.Open asset ↗html-lines:1006-1041Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction Yam is an important medicinal and edible crop, but its quality and yield are greatly affected by leaf diseases. Currently, research on yam leaf disease segmentation remains unexplored. Challenges like leaf overlapping, uneven lighting and irregular disease spots in complex environments limit segmentation accuracy. Methods To address these challenges, this paper introduces the first yam leaf disease segmentation dataset and proposes BiSeNeXt, an enhanced method based on BiSeNetV2. Firstly, dynamic feature extraction block (DFEB) enhances the precision of leaf and disease edge pixels and reduces lesion omission through dynamic receptive-field convolution (DRFConv) and pixel shuffle (PixelShuffle) downsampling. Secondly, efficient asymmetric multi-scale attention (EAMA) effectively alleviates the problem of lesion adhesion by combining asymmetric convolution with a multi-scale parallel structure. Finally, PointRefine decoder adaptively selects uncertain points in the image predictions and refines them point-by-point, producing accurate segmentation of leaves and spots. Results Experimental results indicated that the approach achieved a 97.04% intersection over union (IoU) for leaf segmentation and an 84.75% IoU for disease segmentation. Compared to DeepLabV3+, the proposed method improves the IoU of leaf and disease segmentation by 2.22% and 5.58%, respectively. Additionally, the FLOPs and total number of parameters of the proposed method require only 11.81% and 7.81% of DeepLabV3+, respectively. Discussion Therefore, the proposed method can efficiently and accurately extract yam leaf spots in complex scenes, providing a solid foundation for analyzing yam leaves and diseases.
Why it matches plant phenotyping methodsヤム葉と病斑を画像から分割・抽出する手法とデータセットを開発し、性能比較まで行っており、植物の病害状態を取得する方法が中心である。
abstractthis paper introduces the first yam leaf disease segmentation dataset and proposes BiSeNeXt, an enhanced method based on BiSeNetV2.
Reproduction assets foundThe paper's authors publicly released their self-constructed yam leaf disease segmentation dataset (1,097 annotated images of anthracnose, brown spot, and gray spot) via a Google Drive link in the Data availability statement. No code or trained model deposit is explicitly stated.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://drive.google.com/drive/folders/1_ojcb_84TMbkZwYfm0dgsL1NjiGw7GRF?usp=sharing .Open asset ↗lines:1046-1093Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This study presents a large, meticulously curated and manually validated dataset aimed at classifying leaf quality into five critical categories: Healthy, Bacterial Spot, Shot Hole, Yellow, and Powdery Mildew. The dataset encompasses four distinct plant species-Cinnamomum Camphora (Camphor), Terminalia Chebula (Haritaki), Moringa Oleifera (Sojina), and Azadirachta Indica (Neem)-each represented across three or four disease categories, depending on observed symptoms and final number of classes is thirteen (13 classes). Data collection was conducted between November 1, 2024, and January 5, 2025, utilizing four different mobile cameras to ensure diversity in image resolution, lighting, and environmental conditions. The original dataset comprised 10,858 high-resolution images, which were subsequently expanded to 65,148 through the application of six comprehensive data augmentation techniques, including rotations (45°, 60°, and 90°), horizontal flipping, zooming and brightness adjustment. All images were standardized to 512×512 pixels to ensure uniformity and seamless compatibility with machine learning and computer vision models. This enriched dataset serves as a crucial resource for the development of automated plant disease detection systems and supports advancements in precision agriculture. It not only addresses the pressing need for scalable, high-quality data in agricultural research but also establishes a solid foundation for benchmarking novel deep learning architectures. By enabling more accurate and efficient leaf disease classification, the dataset contributes significantly to enhancing tree health monitoring, improving crop yield, and promoting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉の病害症状を画像で分類する大規模データセットであり、植物の病害状態を直接評価する再利用可能なベンチマーク資源が中心です。
abstractThis study presents a large, meticulously curated and manually validated dataset aimed at classifying leaf quality into five critical categories: Healthy, Bacterial Spot, Shot Hole, Yellow, and Powdery Mildew.
Reproduction assets foundThe paper is a Data in Brief article describing AI-MedLeafX, a public leaf-image dataset for medicinal plant disease classification, deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/zz7r5y4dc6.1). This is the paper's own phenotyping image dataset (10,858 original images, 65,148 augmented, 13疾病/类Dataset · publicification tasks and future agricultural research applications .
Data source location
National Botanical Garden, Mirpur-2, Dhaka – 1216
Latitude: 23.8121° N
Longitude: 90.3531° E
Zone: Dhaka
Country: Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/zz7r5y4dc6.1
Direct URL to data: https://data.mendeley.com/datasets/zz7r5y4dc6/1
The dataset is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Related research article
None
1.
Value of the Data
•
This is a unique and complete dataset of images from different categories, including healthy, bacterial spot, shot hole, powdery mildew, and yellow leaf. This datasetOpen asset ↗Mendeley Data · 10.17632/zz7r5y4dc6.1lines:1-49Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
ArabidopsisTomatoLiDAR / point cloudLeafStem / branchSegmentation
The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science.
Why it matches plant phenotyping methods3D点群による植物の表現型データセットを構築し、茎・葉の意味分割と葉インスタンス分割の手法を開発・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 introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds
Plant phenotyping has emerged as a cornerstone for deciphering the complex interplay between plant genetics and environmental factors. To acquire reliable plant phenotypes, it is important to have an accurate, robust, and high-throughput plant phenotyping system platform. During the last decade, the community seems to have a consensus that such systems should be deployed and work in daytime. Many phenotyping results, however, can be inaccurate and unstable in daytime due to rapid changes in lighting and shadows, particularly for vision-based systems. In this work, we build upon a commercial vision-based high-throughput plant phenotyping (HTPP) platform TraitDiscover and customize a nighttime working mode for the platform. In particular, we incorporate several hardware designs tailored to the nighttime environment such as the array-style lighting equipment and the three-axis high-precision automated control system. On the software side, we also integrate state-of-the-art YOLOv8 object detection and K-Net semantic segmentation frameworks to enable high-performance nighttime image analysis. The feasibility and robustness of the system are demonstrated with a case study on rice. To quantify the effectiveness of phenotyping, a high-quality nighttime rice image segmentation dataset is collected, with 360 finely annotated masks of rice plants. Experimental results show that our customized system is able to achieve surprisingly high segmentation performance up to 93.52% mask IoU, which is significantly higher than the metrics reported from daytime phenotyping. From the mage analysis results, we further extract and validate 28 phenotyping parameters related to color, morphology, and texture status. The average R2 between the inferred phenotype parameters and the actual values reached 0.95, demonstrating the reliability and robustness of the system in nighttime phenotyping. Our results and findings may encourage phenotyping practitioners to rethink the current de facto choice of deploying ‘daytime plant phenotyping platforms’.
Why it matches plant phenotyping methods夜間の植物表現型取得プラットフォームをハードウェア・画像解析・データセットとして開発し、イネ形質の抽出精度と頑健性を検証しており、方法が研究の中心である。
abstractwe build upon a commercial vision-based high-throughput plant phenotyping (HTPP) platform TraitDiscover and customize a nighttime working mode for the platform.
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提示し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を直接支援するため、方法論が中心である。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicIn addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUROpen asset ↗WUR-ABE/TomatoWURlines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提供し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を中心に扱っている。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
Field / plotLeafClassificationSegmentationStress / disease detectionDisease symptoms / severity
The early diagnosis of okra leaf diseases is crucial for maintaining crop health and ensuring high agricultural productivity. To facilitate the development of robust deep learning models for automated disease detection, we present a comprehensive dataset of 2500 okra leaf images collected from real-time agricultural fields in India. The dataset consists of six classes, including healthy leaves (Class 0) and five diseased categories: Leaf Curly Virus (Class 1), Alternaria Leaf Spot (Class 2), Cercospora Leaf Spot (Class 3), Phyllosticta Leaf Spot (Class 4), and Downy Mildew (Class 5). Each image is resized to 224 × 224 pixels to ensure compatibility with standard deep learning models. The primary objective of this dataset collection is to provide a benchmark resource for researchers working on early-stage plant disease classification, detection and segmentation. This dataset is unique as it is one of the first publicly available Indian okra leaf disease datasets captured in real-world conditions, incorporating natural variations in lighting, leaf positioning, and environmental factors. It serves as a valuable resource for future young researchers in the field of smart agriculture, enabling advancements in machine learning-based disease diagnosis, smart farming applications, and precision agriculture. Future enhancements will focus on expanding the dataset with more images, including different growth stages and environmental conditions, to improve model generalization and real-world applicability.
Why it matches plant phenotyping methodsオクラ葉の病害状態を画像で分類・セグメンテーションするための公開データセットおよびベンチマーク資源が研究の中心であり、植物表現型（病害状態）の取得・解析手法に該当する。
abstractTo facilitate the development of robust deep learning models for automated disease detection, we present a comprehensive dataset of 2500 okra leaf images collected from real-time agricultural fields in India.
The present dataset is a collection of multispectral images designed for development of detection algorithms for grapevine diseases like Flavescence dorée (FD) and Esca (ED). Although FD severely threatens viticulture, there are few public datasets and none with multispectral data collected in the field. The collected images have been taken from a frontal perspective of vineyard plants that highlights details of leaves and trunks facilitating detailed disease analysis. The data were collected using a Micasense RedEdge-P multispectral camera, capturing six spectral bands across 172 image captures of three different grapevine varieties used in Lambrusco wines: Ancellotta, Marani, and Salamino. The dataset includes raw and processed images, calibration images for the multispectral camera, annotations detailing plant health conditions, and Python-based usage examples for researchers. Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. The dataset is a valuable resource for advancing remote and proximal sensing in precision agriculture.
Why it matches plant phenotyping methodsブドウ病害という植物状態を対象に、マルチスペクトル画像・注釈・校正データを含む再利用可能なデータセットを構築しており、表現型取得基盤が研究の中心です。
titleA dataset for vineyard disease detection via multispectral imaging
Tea is an extremely popular beverage around the world due to its exquisite taste and flavor. Unfortunately, it is prone to different types of illness, which can reduce the amount of harvest along with its standard. Among these, leaf infections are a serious concern since they negatively affect the quality of tea leaves. As a consequence, tea producers often encounter a great deal of obstacles and financial losses. Keeping this in mind, a thorough dataset has been compiled, which contains 5278 images of diseased and healthy leaves. The purpose of this dataset is to improve our knowledge of how these conditions impact cultivating tea plants and tea production. These images are collected from a variety of locations and meteorological circumstances, which provide an extensive knowledge of the disease patterns unique to tea leaves. The pictures have been captured with the help of some high-quality devices from different angles and in high resolution to ensure the standard and increase the usability of the dataset. Rigorous steps were followed when preparing the dataset that would be of great help in building a precise artificial intelligence model. The dataset carefully determined and classified six tea leaf diseases: Tea algal leaf spot, Brown Blight, Gray Blight, Helopeltis, Red spider, and Green mirid bug. There is one more class in the dataset containing images of healthy leaves. These illnesses are known for their devastating impact on tea leaves. An automated disease classification system can be made utilizing deep learning techniques that will enable estate managers to take timely action to stop the spread of the disease, and this meticulously collected dataset will immensely help to train that model.
Why it matches plant phenotyping methods茶葉の健全・病害状態を画像で取得し、疾患分類用データセットとして構築した研究であり、植物の病害表現型データの整備が中心である。
abstracta thorough dataset has been compiled, which contains 5278 images of diseased and healthy leaves
AppleMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration (% Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.
Why it matches plant phenotyping methodsリンゴ果実の糖度・成熟度・品種という植物器官の形質を対象に、マルチスペクトル撮像システムを構築・最適化し、注釈付き大規模データセットを作成しているため、フェノタイピング手法とデータセットが中心です。
abstractThe study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system.
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, andDataset · publicason, therefore,
from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the
field. The factor to convert radiation in MJ m−
2 to PPFR was 2.04 according to [26].
4.1 Data Files and Structure
The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing
pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable
and widely-used ‘.csv‘ and ‘.png‘ format.
• data/Design 2015 2022 Eschikon.csv: Experimental design file, including pOpen asset ↗ETH Research Collection · 10.3929/ethz-b-000742401pdf-raw-page:6 lines:1-47Code · publicCollection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing
interoperability and standardization with other datasets.
9 Code availability
Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code
is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover
10 Author contributions
BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK,
CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design:
BK, LK, LR, AH; all authors improved and approved the manuscript
5Open asset ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycoverpdf-raw-page:7 lines:1-46Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
This dataset encompasses an extensive collection of 18,248 high-resolution JPEG images, documenting various stages of Taro Leaf Blight (TLB) infection in Taro plants across West Africa. TLB, primarily caused by the pathogen Phytophthora colocasiae, manifests through necrotic leaf spots, white sporangia bands, and orange droplets, severely impacting the agricultural output and economic stability of smallholder farmers in the region. The images represent a range of infection stages—early, mid, late, and healthy conditions—captured during the dry and early rainy seasons in Nigeria and Ghana using smartphones equipped with high-resolution cameras. This dataset was carefully curated to help in the development and training of machine learning models for early and accurate detection of TLB, a crucial step towards effective disease management. By enabling the application of advanced diagnostics through technologies such as smartphone apps and AI-based analysis tools, this dataset not only aims to enhance the technological capabilities within agricultural sectors but also serves as a vital educational resource. Researchers and developers can utilize this dataset to create and refine models that diagnose plant diseases promptly, thereby allowing for timely interventions that can prevent widespread crop damage and subsequent economic losses. Additionally, the dataset supports ongoing efforts to integrate artificial intelligence with traditional farming practices, offering a bridge between advanced technological solutions and accessible applications for resource-limited settings. The potential reuse of this dataset extends beyond disease identification; it encompasses agricultural research, educational purposes, and further development of automated systems for plant health monitoring, making it a cornerstone for future innovations in agricultural technology and management strategies.
Why it matches plant phenotyping methodsタロイモ葉の病斑・感染段階を画像で記録した大規模データセットであり、植物病害状態の画像ベース推定モデル開発を主要目的とするため、植物フェノタイピング手法文献に含める。
abstractThis dataset encompasses an extensive collection of 18,248 high-resolution JPEG images, documenting various stages of Taro Leaf Blight (TLB) infection in Taro plants across West Africa.
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Agriculture plays a vital role in Vietnam’s economy, with durian being a key high-value crop that supports millions of farmers. However, durian leaves are highly susceptible to pests, diseases, and environmental stressors, negatively impacting yield and quality. This study introduces a dataset of 2595 durian leaf images, categorized into six classes: 484 healthy leaves and 2111 diseased leaves spanning Blight (440), Colletotrichum (400), Algal (462), Phomopsis (411), and Rhizoctonia (398). The images were collected from durian orchards across Vietnam under diverse conditions, then background-removed, resized to 400 × 400 pixels, and manually annotated with expert guidance. This dataset provides a valuable resource for advancing research in automated plant disease detection, enabling the development of computer vision models for early diagnosis and precision farming, thereby supporting sustainable durian production and improved crop productivity.
Why it matches plant phenotyping methods植物葉の病徴を画像として収集・注釈したデータセットが研究の中心であり、植物病害状態の画像ベース表現型解析に利用できる。
abstractThis study introduces a dataset of 2595 durian leaf images, categorized into six classes: 484 healthy leaves and 2111 diseased leaves
Field / plotLeafClassificationDisease symptoms / severity
Essentially, visual identification of plant health is vital for research in agriculture and medicinal plants for important crops, both in terms of economics and pharmacology, such as betel leaves. The strong integration of AI-based methods in precision agriculture and herbal medicine quality control makes these systems effective only when trained on well-structured, diversified datasets.The plant betel leaf (Piper betle) is cultivated throughout the world for its medicinal, cultural, and economic importance, but improper classification and quality assessment of this plant occur because of environmental conditions and variations in handling. To solve this problem, we hereby present the Betel Leaf Dataset, which is systematically curated, consisting of 1,800 high-resolution images (1080 × 1080 pixels) exhibiting the three different conditions of betel leaves: Healthy (Fresh), Diseased, and Dried. The dataset was collected from Veer, Taluka-Purandar, Pune, India, under both natural and controlled conditions so that different appearances could be ensured. Categories include images that have been taken under varied light, backgrounds, and orientations, which comprehensively can cover all real variations in betel leaves. Hence, this systematically collected, standardized, and accessible dataset can enhance agricultural research in leaf classification studies and quality assessment techniques to facilitate better documentation and understanding of betel leaf characteristics. This dataset can be utilized in machine learning applications for plant disease detection, precision agriculture, and automated quality control systems.
Why it matches plant phenotyping methodsベテル葉の健全・病変・乾燥状態を対象とする画像データセット自体が研究の中心であり、植物状態の画像ベース評価に再利用可能なデータを提供している。
abstractwe hereby present the Betel Leaf Dataset, which is systematically curated, consisting of 1,800 high-resolution images (1080 × 1080 pixels) exhibiting the three different conditions of betel leaves: Healthy (Fresh), Diseased, and Dried.
Grapes are widely cultivated fruit crops, essential for fresh consumption, winemaking and dried product production. However, their yield and quality are significantly impacted by various fungal diseases. This paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025. The dataset is precisely annotated under the guidance and observation of agriculture domain expert and organized in a well-defined folder structure. The dataset captures the two major categories healthy leaves and unhealthy leaves, during cultivation period. A primary directory containing two main classes Heathy Leaf Images and Unhealthy Leaf images. Further unhealthy class is divided into three subfolders for disease class, namely Downy Mildew, Powdery Mildew and Bacterial Leaf Spot. These are the major fungal disease observed on grape crop causes substantially crop losses and ultimately impact on the yield production. Timely identification of these diseases can significantly reduce the risk of crop loss and help to improve quality of fruit with maximum yield production. This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. These results validate the dataset’s quality and its suitability for deep learning-based grape disease detection. Overall, this open-access resource provides a valuable foundation for computer vision, machine learning, and agricultural technology researchers aims to enhance disease management practices in grape production. thus, this is an effective source of data for future studies and real-world applications in sustainable grape production.
Why it matches plant phenotyping methodsブドウ葉の健康状態と病害を画像として収集・注釈したデータセットが研究の中心であり、植物病害状態を直接評価する再利用可能な資源として構築・検証されている。
abstractThis paper provides a large dataset of 2,726 high-quality grape leaf disease images collected from grapes farm of Nashik, India in two years of span 2023 to 2025.
Salt stress is a major abiotic stress affecting wheat at various developmental stages and significantly reduces grain yield. Developing salt resilient wheat cultivars alleviate the negative impacts of salt stress and helps in maintaining sustainable grain yield under salt stress. A study was undertaken to assess the response of various seedling traits in a genetically, phenotypically, and geographically diverse panel of 228 hexaploid spring wheat accessions using greenhouse lysimeter system with two irrigation treatments: control (electrical conductivity of irrigation water as deci-Siemens per meter., (ECᵢ𝓌 = 14 dSm⁻¹) and saline (ECᵢ𝓌 = 14 dSm⁻¹). Salt stress was given on 18 days old seedlings and the targeted salinity level (ECᵢ𝓌 = 14 dSm⁻¹) was achieved gradually over two days period, to overcome any osmotic shock. Data on various seedling traits [such as shoot height (SH; inches), root length (RL; inches), tiller number (TN), shoot weight (SW; grams), and root weight (RW; grams)] were collected after three weeks of salt treatment from control and salt stress environment. Shoot and root traits were used to calculate root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Furthermore, the salt tolerance index (STI), was calculated for each trait by dividing trait values of each accession from salt-treated tanks by those from control tanks. Raw data was subjected to mixed linear analysis to derive best linear unbiased prediction (BLUP). BLUP values were also used for Pearson's correlation coefficient analysis and principal component analysis (PCA), which gives intrinsic relationship among various seedling traits. Dataset presented here is a valuable source for identifying tolerant lines for salt stress environment. Moreover, researchers can utilize this information to identify potential genomic regions associated with salt stress tolerance and can be utilized in developing salt resilient wheat cultivars.
Why it matches plant phenotyping methods塩ストレス下のコムギ幼植物について、複数の形態・生体重形質を体系的に収集した再利用可能な表現型データセットであり、植物表現型データの提供が中心です。
titlePhenotypic data related to seedling traits of hexaploid spring wheat panel evaluated under salinity stress
High precision 3D data is becoming crucial for accurate feature extraction. Acquiring 3D data from plants with different growing patterns and thier growth under different environmental conditions is still a challenging task. The utilization of deep learning techniques can overcome some of these challenges, but these techniques often demand good quality training data for 3D point cloud analysis. One of the main challenges in plant phenotyping is the general lack of annotated 3D datasets available to the research community. Constructing such datasets is particularly difficult due to the complexity of capturing high-quality data that accurately represent the intricate structures and diverse morphologies of plants. The development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits, and addressing challenges in modern agriculture. However, the lack of high-quality, annotated datasets for complex plant structures, such as wheat, hinders the development of effective methodologies. To address this, we introduce Wheat3D PartNet, a comprehensive repository of 1303 3D point cloud models of wheat (Triticum L.), comprising three cultivars: Paragon, Gladius, and Apogee. The 3D point clouds are reconstructed from RGB images of real plants that were acquired from multiple viewpoints and represent different plant structures at different growth rates. Wheat3D PartNet samples are manually labeled into two parts i.e., ears (wheat spikes) and non-ears (leaves and stems) and that captured in drought and watered conditions. Wheat3D PartNet is designed to support segmentation-based trait quantification tasks such as spike counting, spike length estimation, and stress detection—facilitating more precise yield prediction and enabling early agronomic intervention. Extensive experiments using several state-of-the-art 3D deep learning models validate the dataset’s utility and challenge level. The methodology behind Wheat3D PartNet is extensible to other crops, including rice and potato, and is expected to significantly boost the research, understanding, and measurements of plants of interest.
Why it matches plant phenotyping methods植物の3D形態解析と形質定量を目的とする注釈付きデータセットを構築し、複数の3Dモデルで有用性を検証しており、フェノタイピング手法・データ資源が中心である。
abstractThe development of robust data sets is critical to advance plant phenotyping, allowing precise quantification of plant traits
Trait-based breeding has been shown to enhance and sustain yield potential of various crops under current and future changing climate. To be successful, trait-based breeding requires extensive phenotyping of plants in large-scale field trials that may include hundreds of genotypes. Computer vision approaches have been used extensively for image-based high-throughput phenotyping of diverse traits. However, studies focused on estimating grain count, a trait that directly influences the overall yield, are limited due to a lack of benchmark grain image datasets. In this work, we focus on grain count estimation in sorghum, a crop that holds immense significance for both food and energy production. We introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes (i.e., 4 panicles per genotype), for a total of approximately 5000 images, as well as approximately 12,500 images containing the corresponding threshed grains, together with machine counts per panicle. To develop baseline models, we manually annotated grains in images from 100 genotypes using bounding boxes. We used the manually annotated images to train baseline models for small object detection and counting. We also trained regression-based models for grain count estimation. The best overall model for count estimation from panicle images was a regression model, which achieved a mean absolute percent error of 29.97 and an R 2 value of 0.75. We make our dataset and baselines publicly available to facilitate further research on grain count estimation in sorghum and other crops. • We curated the Sorghum-Grain-Count dataset, which includes ∼17,500 panicle and threshed grain images covering 316 genotypes. • This is the largest dataset for grain count estimation and can help advance research on small object count estimation. • We trained strong baseline object detection models, as well as regression-based models for grain count estimation. • Our best model for grain count estimation from panicle images was a regression model that achieved an R 2 value of 0.75. • Our models provide a foundation for non-destructive yield estimation tools, which are greatly needed by breeding programs.
Why it matches plant phenotyping methodsソルガムの穂・粒画像から粒数という植物収量関連形質を推定する大規模データセット、ベンチマーク、検出・回帰モデルを開発・公開しており、表現型取得・推定手法が研究の中心です。
abstractWe introduce a large Sorghum-Grain-Count (SGC) dataset consisting of (front and back, with and without flash) images of 1264 panicles from 316 genotypes
The timely detection of viral pathogens in vineyards is a critical aspect of management. Diagnostic methods can be labor-intensive and may require specialized training or facilities. The emergence of artificial intelligence (AI) has the potential to provide innovative solutions for disease detection but requires a significant volume of high-quality data as input. With that purpose, we partnered with wine grape growers to collect a robust dataset of verified images. We used those images to train an AI model and develop a handheld application as a decision support tool for grapevine leafroll and red blotch diseases. The tool allows users to scan a grapevine canopy with a mobile device and view a confidence reading describing the likelihood that the imaged vine has visual symptoms consistent with leafroll, red blotch, or a healthy vine. The 86% accuracy under field conditions and generally positive user experience suggest there is potential for the trained use of AI as an investigative tool to quickly assess visual symptoms associated with these grapevine diseases.
Why it matches plant phenotyping methodsブドウの病徴を画像から推定するAIモデル、データセット、携帯アプリを開発・評価しており、植物病害状態の取得・判定手法が研究の中心である。
abstractWe used those images to train an AI model and develop a handheld application as a decision support tool for grapevine leafroll and red blotch diseases.
Background: Food security being one of the prominent global issues requires strategies to maximize plant productivity with due consideration to sustainability. In this regard, precision agricultural practices are incorporated and are quite promising. The use of technology i.e. integrating AI is rapidly changing the complete agricultural scenario. A quick identification of wilting ensures farmers take early action and remedies. An early detection of plant wilting in a real-time scenario can avoid huge food crop losses but the whole task is humongous. The images collected from open fields over large areas can be analyzed via various image processing techniques. Using the CNN model for identification of early images for quick response to plant care saves the time and effort of the farmers. CNN utilization allows prompt wilting detection and early corrective action to protect the crop. In this paper, CNN trained on image data allows high predictability of wilting detection at early stages in Soybean plants. Methods: A well-defined dataset of 6704 pictures of soybean plants from agricultural fields is considered and allocated appropriately to train, test and validate the CNN model. Using image preprocessing, resizing and rescaling of images is done to ensure consistency of image dimensions. Noise elimination from the images is done via a low-pass filtering method to preserve low-frequency information and the images are converted to grayscale. Using standard Python libraries, data augmentation is ensured and 9158 images across all classes are arranged for the model. The performance evaluation matrix indicating accuracy percentage, precision percentage and recall percentage is estimated. Result: The proposed CNN algorithm is first calibrated using a set of images from the dataset and then tested for an entirely different set of images not used earlier. The overall accuracy is 91%. The model promises unambiguous identification of wilting in soybean leaves by appropriately classifying images set in 5 orders using a substantially ample dataset. Early identification in real time can prove to be of utmost benefit to the agricultural community in terms of eradication of the causes and yield retention of the crop.
Why it matches plant phenotyping methodsCNNによる画像解析でダイズの萎凋状態を段階分類する手法を開発・検証しており、植物の病害・生理状態の取得が研究の中心である。
abstractUsing the CNN model for identification of early images for quick response to plant care saves the time and effort of the farmers.