The automation and standardization of hyperspectral imaging, particularly at high spatial resolution, are essential for advancing plant phenomics and supporting diverse plant science research. We developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments. System calibration established a spatial resolution of 24.3 μ m (full width at half maximum; FWHM), a spectral resolution of 1.78 nm (FWHM), and a depth of field of 4.53 mm, producing hyperspectral data cubes of 2195 × 2000 × 950 pixels across the 400–1000 nm spectral range. System-level and biological-sample consistency assessments confirmed stable spectral measurements during extended scanning sessions and across independently prepared trays. Thermal evaluation confirmed that the 150 W illumination design introduced minimal sample heating during scanning. HyperBird was first evaluated using grape downy and powdery mildews, where no consistent measurable effect of repeated imaging on pathosystem development was detected under the tested experimental conditions. Subsequently, HyperBird was used to characterize spatiotemporal spectral progression in grapevine leaves inoculated with Plasmopara viticola . An automated regional spectral tracing pipeline was developed to isolate spectra from retrospectively traced regions and compare them with whole-leaf averaged spectra across 0–9 days post-inoculation (DPI). Categorical mixed-effects modeling showed that these spatially resolved regional spectra exhibited significant disease-associated spectral change by 5 DPI, with a sharp transition at 6 DPI, whereas whole-leaf spectra showed delayed and weaker disease-aligned changes beginning at 7 DPI. These results demonstrate that HyperBird enables high-throughput, spatially resolved hyperspectral imaging for quantifying localized plant disease progression and provides a scalable platform for studying spectral biology across plant phenotyping applications.
Why it matches plant phenotyping methodsHyperBirdの高スループット・ハイパースペクトル画像取得ロボットを開発し、校正、再現性、加熱影響、病害進展の定量性能を検証しており、植物フェノタイピング手法が中心である。
abstractWe developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments.
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the authors' code and processed data supporting the phenotyping analyses; raw hyperspectral image data are request-only.Code · publicData Availability
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The code and processed data supporting the findings of this study are available in the GitHub
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repository at https://github.com/jy773Cornell/HyperBird-Robot. Raw hyperspectral image
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data are available from the corresponding author upon reasonable request due to file size and storage
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constraints.
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Supplementary Materials
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Supplementary materials accompany this article as a separate document (supplementary.pdf).
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Supplementary Figure S1. Representative GSAM-based segOpen asset ↗HyperBird-Robotpdf-raw-page:39 lines:1-75Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Accurate crop disease detection supports precision agriculture, but field-deployable identification remains hindered by complex backgrounds, varying illumination, and heavy deep learning models. This work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions. Built on the YOLO11n backbone, the method integrates three customized modules: C3k2-UltraLightBlock for efficient feature representation, LeafRepFusionStem for low-level feature enhancement, and RCSA-HSFPN for refined multi-scale fusion with residual channel-spatial attention. A dedicated dataset with complex backgrounds is constructed via augmentation and background replacement. Experiments show the model achieves 92.0% precision, 92.9% recall, and 93.0% mAP@0.5, with only 2.9 GFLOPs and 1.73 M parameters, representing 54.7% and 33.2% reductions over the baseline. Heatmap visualization confirms improved lesion focusing and background suppression, while cross-crop tests validate strong generalization. This framework provides an efficient solution for real-time, edge-deployable plant disease monitoring, balancing accuracy and computational efficiency for practical agricultural visual computing applications.The implementation code for this study is available at:https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-Complex-Background-Grape-Leaf-Disease-Identification.git
Why it matches plant phenotyping methodsブドウ葉の病斑・病害状態を画像から推定する軽量な視覚検出手法を開発・評価しており、植物病害表現型の取得が中心である。
abstractThis work presents a lightweight visual detection approach for grape leaf diseases under unconstrained field conditions.
Reproduction assets foundThe authors explicitly state that the implementation code for this study is publicly available on their GitHub repository. The paper's grape leaf disease dataset itself is not stated as deposited (only the public PlantVillage source is cited), so only the authors' code qualifies as a paper-specific public asset.Code · publicThe implementation code for this study is avail-
able at:https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-
Complex-Background-Grape-Leaf-Disease-Identification.gitOpen asset ↗https://github.com/aitizc/Lightweight-Visual-Detection-Framework-for-pdf-page:2 lines:1-43Code / 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:255-349Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Early disease diagnosis plays a key role in grape production for minimizing crop risk and maximizing yield. Downy Mildew, Powdery Mildew, and Bacterial Leaf Spot are some of the major diseases that threaten productivity and require timely and accurate diagnosis. This research introduces a new multi-model framework that integrates AI-based image segmentation triggered by Environmental Susceptibility Conditions to inform precision grape farming. The proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy, by understanding environment data to evaluate the risk of disease propagation. Major contributions of the study are the understanding of environmental conditions for context-aware disease propagation, an efficient ensemble segmentation method for accurate leaf disease segmentation and severity analysis, performed on a self-collected dataset from a grape farm in Nashik, Maharashtra, India. The system enables early warning and decision support mechanisms to promote sustainable disease management in grape cultivation, with potential implications for reducing unnecessary pesticide usage. Experimental results show the efficacy of the proposed method, with segmentation accuracy of 96.81% and precision of 99.09%, with a Dice score of 0.95 and a mean Intersection over Union (mIoU) of 0.91, demonstrating excellent robustness under noise conditions. Unlike existing studies either image or sensor-approaches, this work introduces the integration of image data and knowledge of environmental insights offers a scalable, reliable, and real-time disease monitoring solution aligned with the goals of smart and sustainable farming.
Why it matches plant phenotyping methodsブドウ葉の病斑領域を画像分割し、病害の重症度を推定する手法を開発・評価しており、植物の病害状態の取得が研究の中心です。
abstractThe proposed method combines a soft-voting ensemble of the DeepLabV3+, U-Net, and FCN-8’s models for segmentation of diseased and healthy leaf areas with high accuracy
Reproduction assets foundThe paper's grape leaf disease image dataset (NGLDD/NGLD) used for segmentation phenotyping is publicly deposited on Mendeley Data by the authors. No code or model checkpoints are reported as publicly available.Dataset · publicThe dataset used in this study is publicly available in the Mendeley Data repository as the Niphad Grape Leaf Disease
Dataset (NGLD) (DOI: https://doi.org/10.17632/8nnd2ypcv3.5).Open asset ↗Mendeley Data · 10.17632/8nnd2ypcv3.5pdf-page:25 lines:1-65Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Climate change, particularly increasing frequency and intensity of spring frost events, poses a serious threat to viticulture by reducing yield and product quality. This study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP). A unique dataset called FGVL dataset from Sultana seedless grape vineyards in Manisa, Türkiye, following a severe frost event in April 2025. FGVL includes 418 frost-damaged grapes, 510 frost-damaged leaves, 395 healthy grapes, and 698 healthy leaves, all manually annotated by experts under natural field conditions. By integrating ASPP into YOLOv11s, proposed model improved multi-scale contextual feature extraction and achieved mAP@50 of 0.7686, demonstrating stronger performance in instance segmentation of small, overlapping, and visually similar grapevine organs. In addition, Dynamic Confidence Thresholding (DCT) strategy was introduced to improve prediction reliability in dense and visually complex vineyard scenes. Despite challenges such as background clutter, object overlap, and small target structures, model maintained stable performance with low computational demand, requiring only 6.45 GB of GPU memory. Proposed framework offers an accurate, efficient, and practically deployable early recognition system for frost damage assessment in viticulture.
Why it matches plant phenotyping methodsブドウの器官における霜害状態を画像からセグメンテーションする手法を開発・評価しており、植物の病害・障害状態の取得が研究の中心である。
abstractThis study proposes an image processing and machine learning-based framework for early, rapid, and accurate segmentation of frost damage in vineyards using YOLOv11s enhanced with Atrous Spatial Pyramid Pooling (ASPP).
Reproduction assets foundThe paper's Data availability statement explicitly shares the FGVL frost-damage dataset and source code in the corresponding author's public GitHub repository, matching an allowed URL.Code · publicSource code and dataset are publicly shared in GitHub repository of corresponding author. GitHub repo: https://github.com/kaanarikk/Grape-Instance-Segmentation-For-ViticultureOpen asset ↗https://github.com/kaanarikk/Grape-Instance-Segmentation-For-Viticulturelines:230-236Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:93-144Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.
Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.
Reproduction assets foundThe paper's MRI phenotyping data (~160 GB raw, 1.4 TB processed) is only available upon request, but the authors' processing pipeline (scripts and parameters) is publicly deposited on Zenodo with an explicit URL.Code · publicThe processing pipeline (including scripts and parameters required to reproduce the processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369 .Open asset ↗Zenodo · 10.5281/zenodo.17944369lines:370-484Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This study addresses the challenge of detecting white grape clusters (Vitis vinifera L) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled with occlusion issues. In this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility. Convolutional Neural Network (CNN) architectures were compared, highlighting YOLOv8 as superior to Mask R-CNN in both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance: 84.9% precision, 72.6% recall, and mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision, 26% recall). The model successfully detected partially hidden clusters, including those invisible to human experts, better than previous studies that required controlled backgrounds or artificial lighting. Results confirm that combining RGB equalization with data augmentation optimizes detection. These findings underscore the potential of deep learning and low-cost RGB imaging systems to enable automated, scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under field conditions, overcoming previous limitations.
Why it matches plant phenotyping methodsブドウ房を対象としたRGB画像とCNNによる検出手法を開発・比較し、精度を定量評価しているため、植物器官の表現型取得が中心である。
abstractIn this work, over 100 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation, with various preprocessing strategies tested to enhance cluster visibility.
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public GitHub repository containing the original grape-cluster image dataset and annotations used in this study. The ultralytics repository is a generic third-party library, not a paper-specific asset.Dataset · publicData Availability Statement: The original data presented in the study are openly available at
[https://github.com/upmValeriano/racimosUva.git.]Open asset ↗upmValeriano/racimosUvapdf-page:13 lines:1-66Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
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:1-56Code / 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
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Value of the Data
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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 15 Sept 2026
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
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Value of the Data
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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.
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It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published14 Feb 2026International Journal of Engineering Trends and TechnologyCited by 0 · OpenAlex ↗
The study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data, focusing on the ability to withstand salt-and-pepper and Gaussian noises. The image datasets used in agriculture are usually affected by noise because of changes in light, sensor defects, and environmental conditions, which may lead to lower diagnostic accuracy. In order to address this, the proposed system incorporates high noise reduction methods whereby a median filter and a Gaussian filter are used to restore the image quality without compromising on the important leaf texture information. After processing, colour, texture, and shape are used to extract features, which are effective in extracting disease-specific visual representations. These fine features are then trained on various optimized machine learning models, such as Light Gradient Boosted Machine (LGBM), Quantum Support Vector Machine (QSVM), a Modified Random Forest (MRF) with adaptive weighted features, and a Multi-SVM classifier with a custom kernel to map nonlinear features. Through experimental analyses, the proposed ensemble framework is shown to be highly accurate, robust, and noise-tolerant as opposed to the traditional frameworks. The hybrid method is effective in recognizing the significant cucumber diseases and grapes, including powdery mildew, downy mildew, and anthracnose, which will be utilized in the noisy agricultural conditions in the real world. In general, this system offers a noise-resistant, reliable, and computationally efficient system to detect early signs of plant diseases, which can be used in sustainable crop monitoring and precision farming.
Why it matches plant phenotyping methods植物葉の画像から病徴を推定する画像処理・特徴抽出・機械学習システムの設計開発が中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThe study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (cucumber and grape) as its phenotyping inputs; both are publicly accessible with URLs given in the references. No author code or model checkpoints are reported.Dataset · publicGrape Disease Dataset, which was collected on Kaggle
[17], is an extensive collection of images created for the
classification and analysis of different diseases in grape
leaves.Open asset ↗Kagglepdf-raw-page:7 lines:1-64Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summary Leaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.
Why it matches plant phenotyping methods葉身と葉脈の形状を深層学習による相互セグメンテーションと形状解析で抽出・予測する方法が研究の中心であり、植物形態表現型の方法開発に該当する。
abstractUsing a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature.
Reproduction assets foundThe Data Availability Statement explicitly lists three public Zenodo deposits containing data and code to reproduce the paper's analyses: reciprocal U-Net blade/vein prediction, the two-tower CNN 1:1 vein:blade relationship, and interspecies/intraspecies developmental series swaps. All URLs are in allowed_urls and the Code · public393
which the relationship between blade and vasculature is conserved or diversified across the
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spectacular variety of leaf shapes remains to be seen.
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Data Availability Statement
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Data and code to reproduce this work can be found for the following analyses: Reciprocal
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prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
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1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
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developmental series swaps, https://zenodo.org/records/17013783
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Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the aOpen asset ↗zenodo · 16920155pdf-raw-page:22 lines:1-53Code · publicd across the
394
spectacular variety of leaf shapes remains to be seen.
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Data Availability Statement
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuitOpen asset ↗zenodo · 17014105pdf-raw-page:22 lines:1-53Code · publicment
396
Data and code to reproduce this work can be found for the following analyses: Reciprocal
397
prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
398
1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
399
developmental series swaps, https://zenodo.org/records/17013783
400
Conflict of Interest Statement
401
.
CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 29, 2026.
;
https:Open asset ↗zenodo · 17013783pdf-raw-page:22 lines:1-53Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
3
Plant Phenomics Page 26 of 29Open asset ↗zenodo · 10.5281/zenodo.17944369pdf-layout-page:26 lines:1-14Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
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。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Owing to changing climatic and environmental conditions, plant diseases are becoming increasingly prevalent, posing a serious threat to global agriculture. Timely and accurate diagnosis remains challenging, especially where scouting still relies on manual inspection. We propose B2-GraftingNet, a deep learning framework for automated detection of grape leaf diseases. B2-GraftingNet is a streamlined variant of our earlier B4-GraftingNet, retaining its strengths while simplifying blocks for faster inference and deployment. The architecture combines a VGG16 backbone with Inception-style blocks inside a custom CNN to extract robust, multi-scale features based on color, size, and shape. To reduce redundancy and improve generalization, Binary Particle Swarm Optimization (BPSO) selects informative features prior to classification. We evaluate Support Vector Machines (SVM) and k-Nearest Neighbors (KNN); a cubic SVM attains 99.56% peak accuracy on the public Kaggle grape-leaf dataset. For context, we also benchmarked standard pretrained CNNs on the same data, observing validation accuracies of 34.04% (VGG16), 34.04% (VGG19), 97.95% (Xception), 94.91% (Darknet), and 98.44% (ResNet-50); B2-GraftingNet matches or exceeds these while remaining lighter and faster to train and deploy. To enhance transparency and actionability, we pair Grad-CAM, LIME, and occlusion-sensitivity visualizations with a local gpt-oss:20b assistant (served via Ollama) that converts evidence into plain, grower-focused guidance and supports interactive chat validated by horticulturists. Results are further checked against expert-annotated ground-truth labels, confirming high accuracy and computational efficiency. Overall, B2-GraftingNet offers a reliable, interpretable, and scalable solution for early grape-leaf disease detection. The complete setup (code, model, web platform, configuration, and assets) is available on Zenodo: https://doi.org/10.5281/zenodo.17353656.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像から検出する深層学習手法を開発し、複数モデル・専門家アノテーションと比較検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose B2-GraftingNet, a deep learning framework for automated detection of grape leaf diseases.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the grape leaf images used for disease classification, which is a paper-specific, publicly actionable asset. The underlying Kaggle source dataset is also cited, but the Zenodo record is the authors' own public deposit matching an exactDataset · publicICCK Journal of Image Analysis and Processing
reproducible runs, API examples for mobile image https://zenodo.org/records/18401218.
uploads and programmatic retrieval of classifications
and explainability overlays, as well as additional Funding
figures and code listings that mirror the production
This work was supported without any funding.
repository.
Conflicts of Interest
4 Conclusion
Syed Adil Hussain Shah is affiliated with the
In this study, we introdOpen asset ↗Zenodo · 18401218pdf-layout-page:16 lines:1-68Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Early identification of grapevine diseases is critical for reducing yield losses and ensuring sustainable viticulture. CNNs trained on benchmark datasets such as PlantVillage often achieve near-perfect accuracy, yet this performance fails to translate to real-world field conditions where lighting, backgrounds, and lesion appearance vary widely. To address challenges of data scarcity and imbalance, this study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions. VitiForge is systematically evaluated against GAN-based augmentation through a data ablation study on PlantVillage and FieldVitis, a curated field dataset, using MobileNetV2, InceptionV3, and ResNet50V2 classifiers. Results show that VitiForge significantly improves performance in low-data regimes, enabling model training even without real samples, whereas GAN augmentation proves more effective once sufficient real data is available. On field imagery, VitiForge often matched or surpassed GAN-based methods, particularly when paired with MobileNetV2. These findings highlight the complementary roles of procedural and GAN-based synthetic data: VitiForge offers flexibility and scalability under cross-domain and data-scarce conditions, while GANs enhance realism and variability when ample data exists. Together, they support the development of robust and generalizable models for automated grape disease detection in precision agriculture.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像から識別するための合成画像生成パイプラインを開発し、GAN augmentationと比較評価しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions.
Reproduction assets foundThe paper introduces FieldVitis, a curated field grapevine leaf image dataset assembled from public sources, explicitly deposited on Zenodo with a DOI matching an allowed URL. No explicit public availability of the VitiForge pipeline code or trained models is stated in the supplied blocks.Dataset · publicThe introduction of FieldVitis, a curated dataset of grapevine leaves collected from multiple public sources to reflect the real-world variability of vineyard imagery, providing a valuable benchmark for evaluating model generalization under realistic field conditions. It is available in Zenodo at https://doi.org/10.5281/zenodo.17307846 .Open asset ↗Zenodo · 10.5281/zenodo.17307846lines:310-320Code / 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
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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:1-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
GrapevineField / plotRGB / grayscaleObject detectionGrowth / development / phenology
Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge-computing camera system based on Raspberry Pi 5 and integrated with LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Samll Fruit Research from mid February to mid March, 2024, monitoring three wine cultivars (A-27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 minutes between 7:00 to 19:00, generating 2656 high-resolution (4656×3456 pixels) bud break images as database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified top performers for both the single-stage (mAP@0.5=86.0%) and two-stage (mAP@0.5=85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations during the 2025 growing season confirmed the system’s effectiveness, with the two-stage model showing greater robustness to environmental noise like lens fog. A time-series filter smooths the raw daily counts to reveal a clear phenological trend for visualization. In its final deployment, the autonomous BudCAM system captures an image, runs inference on-device, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management.
Why it matches plant phenotyping methodsブドウの芽数・芽吹きという植物の表現型を、エッジカメラ、画像データセット、検出アルゴリズム、時系列処理で取得・推定するシステムを開発・評価しており、方法が研究の中心である。
abstractThis study presents BudCAM, a low-cost, solar-powered, edge-computing camera system based on Raspberry Pi 5 and integrated with LoRa radio board, developed for real-time bud detection.
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Humans need food to sustain their lives. Therefore, agriculture is one of the most important issues in nations. Agriculture also plays a major role in the economic development of countries by increasing economic income. Early diagnosis of plant diseases is crucial for agricultural productivity and continuity. Early disease detection directly impacts the quality and quantity of crops. For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. Disease classification was performed using seven state-of-the-art pretrained convolutional neural network architectures: VGG16, ResNet50, SqueezeNet, Xception, ShuffleNet, DenseNet121 and MobileNetV2. A simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification. GAPNet was designed to be a lightweight and fast model with 337.872 parameters. To address the data imbalance between classes, oversampling was carried out using the synthetic minority oversampling technique. The proposed model achieves accuracy rates of 99.72%, 99.53%, and 99.83% for grape, apple and potato leaf disease classification, respectively. A success rate of 99.64% was achieved in multiplant leaf disease classification when the grape, apple and potato datasets were combined. Compared with the state-of-the-art methods, the lightweight GAPNet model produces promising results for various plant species.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する軽量CNN手法を開発・評価しており、病害表現型の抽出が研究の中心です。
abstractIn this study, a simple and effective leaf disease classification method was developed.
Reproduction assets foundAuthors publicly release GAPNet implementation code via GitHub and Zenodo, and the paper's leaf image datasets (PlantVillage, New Plant Disease, Plant Pathology 2020) are publicly available at listed URLs.Code · publiccle, and approved the final draft.
Asuman Günay Yılmaz conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft.
Data Availability
The following information was supplied regarding data availability:
The data and code are available at GitHub and Zenodo:
- https://github.com/ozgenurr/GAPNet.git .Open asset ↗ozgenurr/GAPNetlines:727-762Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases uOpen asset ↗PlantVillage-Datasetlines:763-789Dataset · public- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing SympOpen asset ↗lines:763-789Dataset · publicGAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 .
The datasets are publicly available at:
- Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color .
- New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data .
- Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .
References
Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing Symposium (IDAP-2023) 2023:67–74. doi: 10.53070/bbd.1349566.
BanjaOpen asset ↗lines:763-789Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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ブドウ葉の病徴・健全状態を画像で取得した注釈付きデータセットを提供し、分類モデルで検証しているため、植物病害表現型のデータセット開発・検証が中心です。
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.
Reproduction assets foundThe paper is a data descriptor for the Niphad Grape Leaf Disease Dataset (NGLD), 2,726 annotated grape leaf images, publicly deposited on Mendeley Data with direct URL and DOI. No author analysis code is shared.Dataset · publicges were labelled sequentially for clear association within the dataset.
Data source location
Niphad Grapes farms, located at District Nashik 422209, MH-India
Longitude and Latitude: 20.0771° N, 74.1094° E
Data accessibility
Repository Name: Niphad Grape Leaf Disease Dataset (NGLD)
DOI: 10.17632/8nnd2ypcv3.5
Direct URL to Data: https://data.mendeley.com/datasets/8nnd2ypcv3/5
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Value of the Data
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Comprehensive Dataset : The Dataset is comprehensive and consists of 2726 high-quality images, in four subfolder such as Downy Mildew, Powdery Mildew, Bacterial Leaf Spot and Healthy Grapes Leaf. Unlike existing public datasets that primarily focus on diseases such as Esca, Black Rot, and Leaf BligOpen asset ↗10.17632/8nnd2ypcv3.5lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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ブドウ樹の病害状態を対象とするマルチスペクトル画像データセットで、画像・校正・アノテーション・利用例を含む再利用可能な資源として構築されており、植物フェノタイピング手法の基盤が中心です。
titleA dataset for vineyard disease detection via multispectral imaging.
Reproduction assets foundThe paper is a data descriptor for a multispectral vineyard disease detection dataset deposited by the authors on Zenodo, including raw/processed images, annotations, and Python usage examples. The two Micasense GitHub repositories are generic vendor libraries, not paper-specific assets.Dataset · publicgio Emilia, Emilia-Romagna, Italy). It is managed by the RIMLab laboratory at the University of Parma, Parco Area delle Scienze 181/A, 43100 Parma, Italy.
Data accessibility
Repository name: A Dataset for Vineyard Disease Detection via Multispectral Imaging
Data identification number: 10.5281/zenodo.14936376
Direct URL to data: https://zenodo.org/records/14936376
Related research article
1
Value of the Data
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The dataset features grapevine images which is a high-value plant used for wine production. Italy and other European nations are among the world's largest wine exporters. For this reason, diseases such as Flavescence Dorée (FD) and Esca, that cause severe damage to both the plant aOpen asset ↗Zenodo · 10.5281/zenodo.14936376lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Crop pests significantly reduce crop yield and threaten global food security. Conventional pest control relies heavily on insecticides, leading to pesticide resistance and ecological concerns. However, crops and their wild relatives exhibit varied levels of pest resistance, suggesting the potential for breeding pest-resistant varieties. This study integrates deep learning (DL)/machine learning (ML) algorithms, plant phenomics, quantitative genetics, and transcriptomics to conduct genomic selection (GS) of pest resistance in grapevine. Building deep convolutional neural networks (DCNNs), we accurately assess pest damage on grape leaves, achieving 95.3% classification accuracy (VGG16) and a 0.94 correlation in regression analysis (DCNN-PDS). The pest damage was phenotyped as binary and continuous traits, and genome resequencing data from 231 grapevine accessions were combined in a Genome-Wide Association Studies, which maps 69 quantitative trait locus (QTLs) and 139 candidate genes involved in pest resistance pathways, including jasmonic acid, salicylic acid, and ethylene. Combining this with transcriptome data, we pinpoint specific pest-resistant genes such as ACA12 and CRK3, which are crucial in herbivore responses. ML-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting pest resistance as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facilitating genomic breeding of pest-resistant grapevine.
Why it matches plant phenotyping methodsブドウ葉の害虫被害を深層学習で画像評価し、分類・回帰性能を検証した植物フェノタイピング手法が中心である。
abstractBuilding deep convolutional neural networks (DCNNs), we accurately assess pest damage on grape leaves, achieving 95.3% classification accuracy (VGG16) and a 0.94 correlation in regression analysis (DCNN-PDS).
Reproduction assets foundThe authors publicly host the grape leaf image dataset, YOLO model weights, and all analysis scripts in a GitHub repository explicitly cited for data and code availability.Code · publicCode availability
All scripts performed in this study are available on Github: https://github.com/zhouyflab/Pest-Resistance .Open asset ↗zhouyflab/Pest-Resistancelines:183-224Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published27 Feb 2025Journal of Information Systems Engineering and ManagementCited by 0 · OpenAlex ↗
The rapid advancement of artificial intelligence has paved the way for innovative solutions in agriculture, particularly in crop disease detection. Diagnosing plant diseases often rely on manual inspection and expert knowledge that time-consuming and prone to errors. As agriculture faces increasing challenges from pests, diseases and climate change, there is a pressing require for efficient, automated systems to monitor crop health. In this manuscript, Development of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images is (PCNN-ResNet-COA) proposed. Initially the data is collected from Plant Disease Classification Merged Dataset. This dataset includes both healthy and diseased leaves for various crop types. The collected images are organized into categories based on the crop type such as crop, grape and soybean and disease condition healthy or various types of diseases. Then the categorized images are fed to Pyramid Convolutional Neural Network with Residual Network (PCNN-ResNet), for identifying and classifying the Leaf Images as Corn_healthy, Corn_northern_leaf_blight, Corn_gray_leaf_spot, Grape_healthy, Grape_leaf_blight, Grape_black_rot, Grape_black_measles, corn_common_rust, soybean_bacterial_blight, Soybean_downy_mildew, Soybean_mosaic_virus, Soybean_powdery_mildew, Soybean_healty, Soybean_rust, and Soybean_southern_blight. In general, PCNN-ResNet does not express any adaption of optimization methods for determining optimal parameters to assure precise detection and classification of Leaf Images. Coati Optimization Algorithm (COA) is proposed for improving the weight parameter of PCNN-ResNet classifier that accurately predicts crop yield. The proposed PCNN-ResNet-COA method is implemented and analyzed with help of performance metrics like accuracy, precision, F1-score, computational time is evaluated. The proposed ResNet-COA approach attains 18.97%, 24.57% and 32.68% higher accuracy and 19.84%, 24.93% and 31.62% lower computational time with existing method respectively.
Why it matches plant phenotyping methods葉画像から植物病害状態を自動分類する新規CNN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す。
titleDevelopment of an AI-Based Pyramid Convolutional Neural Network Model with ResNet and Coati Optimization for Multi-Crop, Multi-Disease Identification in Leaf Images
Reproduction assets foundThe paper's leaf-image disease classification experiments are built entirely on the public Kaggle 'Plant Disease Classification Merged Dataset' (88 classes, >76,000 images), which is explicitly cited with its Kaggle URL in the references. No author code, models, or checkpoints are reported as available.Dataset · publicThe input data are obtained from Plant Disease Classification Merged Dataset [17]. A huge number of images, at
least one healthy plant and one disease per plant, the most prevalent diseases, annotated images, laboratory and
field photographs, significant staple foods and the plant species with the highest worldwide production were the
self-imposed conditions for the dataset.Open asset ↗Plant Disease Classification Merged Datasetpdf-layout-page:4 lines:1-64Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Viticulture benefits significantly from rapid grape bunch identification and counting, enhancing yield and quality. Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. On one hand, drone, or Unmanned Aerial Vehicles (UAV) imagery combined with deep learning algorithms has revolutionised agriculture by automating plant health classification, disease identification, and fruit detection. However, these advancements often remain inaccessible to farmers due to their reliance on specialized hardware like ground robots or UAVs. On the other hand, most farmers have access to smartphones. This article proposes a novel approach combining UAVs and smartphone technologies. An AI-based framework is introduced, integrating a 5-stage AI pipeline combining object detection and pixel-level segmentation algorithms to automatically detect grape bunches in smartphone images of a commercial vineyard with vertical trellis training. By leveraging UAV-captured data for training, the proposed model not only accelerates the detection process but also enhances the accuracy and adaptability of grape bunch detection across different devices, surpassing the efficiency of traditional and purely UAV-based methods. To this end, using a dataset of UAV videos recorded during early growth stages in July (BBCH77-BBCH79), the X-Decoder segments vegetation in the front of the frames from their background and surroundings. X-Decoder is particularly advantageous because it can be seamlessly integrated into the AI pipeline without requiring changes to how data is captured, making it more versatile than other methods. Then, YOLO is trained using the videos and further applied to images taken by farmers with common smartphones (Xiaomi Poco X3 Pro and iPhone X). In addition, a web app was developed to connect the system with mobile technology easily. The proposed approach achieved a precision of 0.92 and recall of 0.735, with an F1 score of 0.82 and an Average Precision (AP) of 0.802 under different operation conditions, indicating high accuracy and reliability in detecting grape bunches. In addition, the AI-detected grape bunches were compared with the actual ground truth, achieving an R 2 value as high as 0.84, showing the robustness of the system. This study highlights the potential of using smartphone imaging and web applications together, making an effort to integrate these models into a real platform for farmers, offering a practical, affordable, accessible, and scalable solution. While smartphone-based image collection for model training is labour-intensive and costly, incorporating UAV data accelerates the process, facilitating the creation of models that generalise across diverse data sources and platforms. This blend of UAV efficiency and smartphone precision significantly cuts vineyard monitoring time and effort.
Why it matches plant phenotyping methodsスマートフォン画像とUAVデータを用いてブドウ房を検出・計数するAIパイプラインを開発・評価し、実測値との比較も行っているため、植物器官形質の取得手法が中心である。
abstractAn AI-based framework is introduced, integrating a 5-stage AI pipeline combining object detection and pixel-level segmentation algorithms to automatically detect grape bunches in smartphone images of a commercial vineyard with vertical trellis training.
Reproduction assets foundThe paper's Data Availability Statement points to a public, paper-specific dataset (EscaYard: geotagged smartphone vineyard images, phytosanitary status, UAV 3D point clouds and orthomosaics) published as a Data Brief with a DOI, which directly underpins the smartphone/UAV grape detection phenotyping analysis. No code,Dataset · publicData is available at https://doi.org/10.1016/j.dib.2024.110497 [ 55 ].Open asset ↗10.1016/j.dib.2024.110497lines:202-204Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物のマルチスペクトル・ハイパースペクトル画像と葉の水分状態・化学形質を含む評価用データセットを構築しており、フェノタイピング手法開発のためのベンチマークが中心である。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper's multispectral images, hyperspectral reflectance, and trait measurements (weight, chlorophyll, nitrogen, fuel moisture) are publicly deposited on Figshare with an explicit DOI. The authors' sample Matlab code is included within that dataset. The MicaSense imageprocessing repository is a generic third-party工具Dataset · publicAll the data is available at this repository DOI: https://doi.org/10.6084/m9.figshare.26950660.v2.Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2pdf-page:14 lines:1-35Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness.
Why it matches plant phenotyping methodsブドウ葉の毛密度という形態形質を画像とCNNで自動定量する高スループット手法を開発し、専門家評価および大規模集団で検証しており、表現型取得・抽出法が研究の中心である。
abstractTherefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN).
Reproduction assets foundThe authors publicly released the ResNet CNN training code, the leaf disc image datasets, and the full leaf hair quantification pipeline in a GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicAll datasets and the code to train the CNNs are available in the GitHub repository.Open asset ↗lines:91-99Dataset · publicThe script of the ResNet CNN along with the images are available in the GitHub repository: https://github.com/1708nagarjun/ResNet-CNN-Leaf-hair.Open asset ↗1708nagarjun/ResNet-CNN-Leaf-hairlines:143-183Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed us to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection in images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70% and 86.5% with inference times of around 89 ms for an input size of 1280 × 1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.
Why it matches plant phenotyping methodsブドウの節という明示的な植物器官形質をYOLO画像解析で検出する手法を開発・比較検証し、異なる品種・環境で評価しているため、フェノタイピング手法が中心である。
abstractIn this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10.
Reproduction assets foundThe paper's authors created and openly released a paper-specific grapevine node-detection image dataset (Dão and Douro vineyard images) on Zenodo, cited in the Data Availability Statement.Dataset · publicThe data presented in this study are openly available in the digital repository Zenodo: Douro & Dão Grapevines Dataset for Node Detection— https://doi.org/10.5281/zenodo.10991688 .Open asset ↗Zenodo · 10.5281/zenodo.10991688lines:552-565Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Abstract Grape disease image recognition is a crucial part of agricultural disease detection, and accurate identification of grape leaves plays a vital role in agricultural production. This study proposes a deep learning-based method for grape disease classification and recognition to address issues such as the complexity of grape disease features, uneven distribution of disease features, and data imbalance. First, the adversarial generative network FastGAN is used to generate grape disease images to enrich the sample information for different categories in the dataset and address the data imbalance problem. Then, a novel Transformer structure called LVT Block and a CNN structure called MARI Block are proposed to process the global and local information of images, respectively. Dense connections between these structures result in the DLVT Block, which leads to the lightweight neural network model DLVTNet. Additionally, a lightweight self-attention mechanism combined with CNN called CLSHSA is introduced , which maintains high recognition performance while reducing the model size. Moreover, a multi-scale attention mechanism (MELA) is proposed, which combines positional and multi-scale information to obtain attention weights. Experimental results show that this method achieves an average recognition accuracy of 98.48% in grape leaf disease detection, outperforming mainstream CNN and Transformer models, and effectively focuses on disease areas in leaf images. The method also demonstrates high recognition accuracy in tomato disease detection, indicating good generalization ability and suitability for detecting and recognizing various leaf diseases. The proposed method provides an effective solution for detecting and recognizing grape and other plant leaf diseases, offering a new research approach that combines CNN and Transformer structures.
Why it matches plant phenotyping methods植物葉の病害領域・病害状態を画像から認識する深層学習法の開発が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning-based method for grape disease classification and recognition
Reproduction assets foundThe paper's grape (and tomato) leaf disease image inputs come directly from the public New Plant Diseases Dataset on Kaggle, which is the image dataset used for the paper's phenotyping measurements. No author code, trained models, or generated FastGAN dataset is publicly deposited; the data availability statement only'Dataset · publicformation to obtain attention weights in images, aiding
the model in effectively extracting diseased areas.
2. Materials and Methods
2.1. Image datasets and preprocessing
The grape leaf disease dataset used in this study comes from the publicly available plant disease classification dataset New
Plant Diseases Dataset on Kaggle (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset). Grape leaf images
from this dataset were selected as the image dataset for this study. The dataset includes images of three types of grape leaf diseases
as well as healthy leaves, totaling 7,222 images, divided into four categories. The images have been resized to 256×256 pixels and
processed usingOpen asset ↗Kaggle · new-plant-diseases-datasetpdf-raw-page:4 lines:1-36Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Vine disease detection is considered one of the most crucial components in precision viticulture. It serves as an input for several further modules, including mapping, automatic treatment, and spraying devices. In the last few years, several approaches have been proposed for detecting vine disease based on indoor laboratory conditions or large-scale satellite images integrated with machine learning tools. However, these methods have several limitations, including laboratory-specific conditions or limited visibility into plant-related diseases. To overcome these limitations, this work proposes a low-altitude drone flight approach through which a comprehensive dataset about various vine diseases from a large-scale European dataset is generated. The dataset contains typical diseases such as downy mildew or black rot affecting the large variety of grapes including Muscat of Hamburg, Alphonse Lavallée, Grasă de Cotnari, Rkatsiteli, Napoca, Pinot blanc, Pinot gris, Chambourcin, Fetească regală, Sauvignon blanc, Muscat Ottonel, Merlot, and Seyve-Villard 18402. The dataset contains 10,000 images and more than 100,000 annotated leaves, verified by viticulture specialists. Grape bunches are also annotated for yield estimation. Further, tests were made against state-of-the-art detection methods on this dataset, focusing also on viable solutions on embedded devices, including Android-based phones or Nvidia Jetson boards with GPU. The datasets, as well as the customized embedded models, are available on the project webpage.
Why it matches plant phenotyping methodsブドウ葉の病徴を低高度ドローン画像で検出する大規模データセットを構築し、葉アノテーションと手法比較・組込み機器での評価を行っており、植物状態の画像ベース計測が中心です。
abstractthis work proposes a low-altitude drone flight approach through which a comprehensive dataset about various vine diseases from a large-scale European dataset is generated.
Reproduction assets foundThe authors state their UAV vine-disease dataset (10,000 images, 100,000+ annotated leaves), preprocessing scripts, and pre-trained embedded models are publicly available on the project website, whose URL (github.com/tamaslevente/vineye) appears in the supplied blocks. Other URLs (ultralytics, CVAT, labelImg, Zenodo, kDataset · publicThe dataset and useful preprocessing scripts and pre-trained models are available on the project website.Open asset ↗lines:43-81Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson's r 2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.
Why it matches plant phenotyping methodsSAMを用いたブドウ房画像からの個別果粒セグメンテーション、検証、補正、および房構造・コンパクトネス形質の算出が研究の中心であるため。
abstractThis study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images.
Reproduction assets foundThe authors state that all data and code to reproduce the study's grapevine cluster segmentation and architecture analysis are publicly available in their GitHub repository. Other URLs (SAM checkpoint, pycocotools, RMBG, arXiv refs) are generic third-party resources, not paper-specific assets.Code · publicAll the data and code to reproduce the results of this study are available at https://github.com/diazgarcialab/SAM-cluster-segmentation .Open asset ↗diazgarcialab/SAM-cluster-segmentationlines:105-230Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Detecting crop diseases before they spread poses a significant challenge for farmers. While both deep learning (DL) and computer vision are valuable for image classification, DL necessitates larger datasets and more extensive training periods. To overcome the limitations of working with constrained datasets, this paper proposes an ensemble model to enhance overall performance. The proposed ensemble model combines the convolution neural network (CNN)-based models as feature extractors with random forest (RF) as the output classifier. Our method is built on popular CNN-based models such as VGG16, InceptionV3, Xception, and ResNet50. Traditionally, these CNN-based architectures are referred to as one-way models, but in our approach, they are connected in parallel to form a two-way configuration, enabling the extraction of more diverse features and reducing the risk of underfitting, particularly with limited datasets. To demonstrate the effectiveness of our ensemble approach, we train models using the grape leaf dataset, which is divided into two subsets: original and modified. In the original set, background removal is applied to the images, while the modified set includes preprocessing techniques such as intensity averaging and bilateral filtering for noise reduction and image smoothing. Our findings reveal that ensemble models trained on modified images outperform those trained on the original dataset. We observe improvements of up to 5.6 % in accuracy, precision, and sensitivity, thus validating the effectiveness of our approach in enhancing disease pattern recognition within limited datasets.
Why it matches plant phenotyping methodsブドウ葉の画像から病徴・病害状態を推定する画像解析モデルを提案し、前処理条件とアンサンブル構成を比較検証しているため、植物フェノタイピング手法が中心である。
abstractOur findings reveal that ensemble models trained on modified images outperform those trained on the original dataset.
Reproduction assets foundThe paper's grape leaf disease image dataset is publicly available on Kaggle; no author analysis code is released.Dataset · publicarious alterations affect these models' functionality in future study. Moreover, exploring how these results translate to different datasets and domains can provide new perspectives on how well enhanced images improve model performance.
Data availability
The datasets analyzed in the paper are available in the Kaggle repository, https://www.kaggle.com/datasets/piyushmishra1999/plantvillage-grape .
CRediT authorship contribution statement
Farian S. Ishengoma: Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Neema N. Lyimo: Writing – review & editing,Open asset ↗Kaggle · plantvillage-grapelines:710-828Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The "EscaYard" dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.
Why it matches plant phenotyping methodsブドウの病徴・生産性・房形質を対象とするマルチモーダル画像／UAVデータセットであり、植物フェノタイピングや病害・収量推定アルゴリズムの開発と評価を主目的としているため。
abstractThe "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.
Reproduction assets foundThe paper is a Data in Brief article describing the EscaYard dataset, publicly deposited on Zenodo with explicit DOI and direct URL. The dataset contains the paper's own phenotyping measurements (geotagged smartphone images, phytosanitary status, grape cluster counts, UAV orthomosaics, 3D point clouds, RTK GNSS trunk-Dataset · publics
City/Town/Region: Tomiño, Pontevedra, Galicia
Country: Spain
Coordinates: Vineyard B7, X: 517183.8, Y: 4645072.8; Vineyard B9, X: 516987.8, Y: 4644823.7 (ETRS89 / UTM zone 29N, EPSG:25829).
Data accessibility
Repository name: Zenodo
Data identification number: https://zenodo.org/doi/10.5281/zenodo.10362567
Direct URL to data: https://zenodo.org/records/10362567
1.
Value of the Data
•
The dataset offers a unique combination of multimodal data, including geotagged smartphone images, UAV orthomosaics, 3D point clouds, and precise geolocation data, enabling a multifaceted analysis of vineyard health and productivity.
•Open asset ↗Zenodo · 10.5281/zenodo.10362567lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNN RGB -LSTM GLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.
Why it matches plant phenotyping methodsブドウ葉のRGB画像から病害状態を推定する深層学習手法とデータセット、診断ソフトウェアを開発・評価しており、植物表現型取得が中心である。
abstractThe objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare
Reproduction assets foundThe authors publicly deposited the Python script, the trained hybrid deep network model, real-world grape disease sample images, and the standalone AI GrapeCare software on Google Drive. The PDD grape leaf image dataset (295 images) is also public at pdd.jinr.ru, but that URL is not in the allowed list, so only the verCode · publicface [ 32 ]. To ensure cross-platform compatibility, including Windows, Linux, and Mac OS, PyInstaller [ 33 ] was applied. The Python script, the hybrid deep network that was generated, grape disease samples from real-world conditions, and the stand-alone version of this software are all available for download on Google Drive ( https://drive.google.com/file/d/1uOVAMiFDWBZsm8U9alzSdSk2c-A8zWBN , accessed on 10 December 2023), packaged in a RAR file with a size of 1.03 GB. As depicted in the overarching flowchart ( Figure 7 ), the pseudo-code explains the establishment of the AI GrapeCare software and its associated functions. The software workflow is organized into five primary stages: (1) loOpen asset ↗Google Drivelines:148-246Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
Why it matches plant phenotyping methodsLiDARによるブドウ樹冠・剪定木体積の取得を、従来法との相関、遺伝率、QTL解析で評価しており、植物形質の高スループット計測法が研究の中心です。
abstractThe detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine.
Reproduction assets foundThe paper's Data availability statement provides a public repository deposit (DOI 10.57745/PETTGY) for the study data and an authors' public ImageJ script for estimating foliage coverage used in the RGB-image phenotyping analysis.Dataset · publictyping but also his expertise and helped with the manuscript review. D.M. supervised the program and helped with manuscript writing. É.D. supervised the whole study and wrote the first draft of the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data availability
Data are available at https://doi.org/10.57745/PETTGY . ImageJ script for estimating foliage coverage: https://forgemia.inra.fr/eric.duchene/image-analysis-scripts/-/blob/main/FoliageCoverage_PC_EN.txt
Supplementary Materials
Supplementary 1
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Tables S1 to S5
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References
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Potential phOpen asset ↗10.57745/PETTGY · 10.57745/PETTGYlines:825-1015Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Introduction The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. Methods In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Results Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). Discussion This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.
Why it matches plant phenotyping methods植物病害画像を対象に拡散モデルとGANを比較し、病害画像データ拡張の性能をFID・KIDで検証する研究であり、植物病害状態の画像ベース評価を支える方法が中心である。
titleHarnessing the power of diffusion models for plant disease image augmentation
Reproduction assets foundThe paper's experiments use a subset of the public PlantVillage image dataset, which the authors explicitly link in the data availability statement. No author analysis code, trained models, or generated-image deposits are mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/mohitsingh1804/plantvillage .Open asset ↗Kaggle · mohitsingh1804/plantvillagelines:841-873Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Detailed and precise knowledge of production parameters (yield, quality, health status, etc.) in agriculture is the basis for analyzing the effect of any agricultural practice. Fine mapping of production parameters makes it possible to identify the origin of observed variability, whether associated with environmental factors or with agricultural practices. In viticulture, in real commercial context, these data are rare because monitoring systems embedded on harvesting machines for grape yield and quality are not yet available. As a result, they are costly and/or cumbersome to acquire manually. As an alternative, a research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture. The data set was acquired as part of this research. The methodology was applied on a commercial vineyard of 30 ha during the whole 2022 harvest season. The method has identified harvest sectors (HS) associated to measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant, percentage of unproductive plants) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured from samples on grape must at a commercial laboratory specialized in oenological analysis (Institut Coopératif du Vin, Montpellier, France) with standardized protocols. The percentage of unproductive plants of a harvest sector was calculated from the manually geolocation of each unproductive plants (dead plants + missing plants) over the entire vineyard, the plantation density of blocks, and the geolocalization of the harvest sector. The mean area of these harvest sectors is 0.3 ha. The data set is supplemented by climatic data from a weather station deployed in the center of the vineyard. It provided three climatic parameters (relative humidity, rainfall, air temperature) every 15 min, for the 2020, 2021 and 2022 years. It was also supplemented by a complete description of the vineyard blocks (grape variety, plantation year, area, inter-row distance and vine distance). The proposed data set constitutes a unique and interesting resource for research in agronomy, vine ecophysiology and remote sensing. It can be used for any research in vine ecophysiology aimed at identifying potential relationships between yield and harvest quality parameters for different grape varieties. The data set only covers one year, which is a limitation for studying inter-annual variability of the parameters measured. Another limitation of the method concerns the footprint (0.3 ha on average) of the parameters measured.
Why it matches plant phenotyping methodsGNSSを用いた低コストのブドウ収量・収穫品質マッピング手法と、その大規模データセットが研究の中心であり、収量や不生産株割合などの植物・圃場形質を抽出している。
abstracta research project has been proposed to test low-cost methods using GNSS tracking devices for yield and harvest quality mapping in viticulture.
Reproduction assets foundThe article is a Data in Brief describing the authors' own public Zenodo deposit containing the vineyard phenotyping measurements (block, agronomic/harvest-sector, and weather data as .shp and .csv files), plus an example analysis script (Yield_vs_variety.py) and yield map, all hosted at the stated Zenodo DOI.Dataset · publicce), as well as the geolocation of unproductive wines were obtained from the vineyard Farm Management Information System.
Data source location
Institution: Institut Agro Montpellier
City: Montpellier
Country: France
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.8328384
Direct URL to data: https://doi.org/10.5281/zenodo.8328384
Related research article
J-P. Gras, S. Moinard, T. Crestey and B. Tisseyre. Mapping grape yield with low-cost vehicle tracking devices, In Precision agriculture’23 , Wageningen Academic Publishers. (2023) 555-561. https://doi.org/10.3920/978-90-8686-947-3_70
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Value of the Data
The dataset presented in this paper is a particulOpen asset ↗Zenodo · 10.5281/zenodo.8328384lines:32-61Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
O_LIAccurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data is limited. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. C_LIO_LIWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine. Feature importance was quantified by AutoGluon and SHAP value. The final model was evaluated and compared with previous models for its performance under different climate conditions. C_LIO_LIThe final model achieved an overall 1.36 {degrees}C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model might have adequately extracted some biological mechanisms during training. C_LIO_LIThe final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-2023 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time. C_LI
Why it matches plant phenotyping methodsブドウの凍結耐性という植物状態を大規模・リアルタイムに推定する自動機械学習モデルを開発し、既存モデルとの性能比較と実運用展開まで行っており、表現型推定手法が中心です。
abstractWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine.
Reproduction assets foundThe authors publicly released the original LT50 training data and the source code for feature extraction, model training, and deployment in a GitHub repository explicitly stated in the Data availability section. The ACIS URL is a generic external climate data service, not a paper-specific asset.Code · publiclly yielding with a more generalizable model to help understand the biology of grapevine
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freezing tolerance and quantify the threat of freezing under climate change.
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5. Data availability
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All the original training data and source code for feature extraction, modeling training and model
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deployment are available at https://github.com/imbaterry11/NYUS.2
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6. Acknowledgements
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The authors would like to thank Lynn Mills (WA), Beth Ann Workmaster (WI), Katherine
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Benedict (NS), Alexander Campbell and Jessee Tinslay (QC), Don Smith and Meredith Persico
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(PA) and Hanna Martins, Felex Pike, and Bill Wilsey (NY) for their help in LT50 data collection.
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This work was parOpen asset ↗https://github.com/imbaterry11/NYUS.2 · NYUS.2pdf-raw-page:25 lines:1-64Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Feature selection, reducing number of input variables to develop classification model, is an important process to reduce computational and modelling complexity and affects the performance of image process. In this paper, we have proposed new statistical approaches for feature selection based on sample selection. We have applied our new approaches to grapevine leaves data that possesses properties of shape, thickness, featheriness, and slickness are investigated in images. To analyze such kind of data by using image process, thousands of features are created and selection of features plays important role to predict the outcome properly. In our numerical study, Convolutional Neural Networks (CNNs) have been used as feature extractors and then obtained features from the last average pooling layer to detect the type of grapevine leaves from images. These features have been reduced by using our suggested four statistical methods: Simple random sampling (SRS), ranked set sampling (RSS), extreme ranked set sampling (ERSS), Moving extreme ranked set sampling (MERSS). Then selected features have been classified with Artificial Neural Network (ANN) and we have obtained the best accuracy of 97.33% with our proposed approaches. Based on our empirical analysis, it has been determined that the proposed approach exhibits efficacy in the classification of grapevine leaf types. Furthermore, it possesses the potential for integration into various computational devices.
Why it matches plant phenotyping methodsCNN特徴抽出と新規特徴選択法を用いてブドウ葉画像の形態的な葉タイプ分類を行う手法開発が中心であり、画像から植物器官の表現型を推定する研究と判断します。
abstractwe have proposed new statistical approaches for feature selection based on sample selection
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 · publicThis dataset was created by Koklu et al. ( 2022 ) and obtained from the website http://www.muratkoklu.com/datasets/Grapevine_Leaves_Image_Dataset.rar .Open asset ↗http://www.muratkoklu.com/datasets/Grapevine_Leaves_Image_Dataset.rar · Grapevine_Leaves_Image_Dataset.rarlines:151-284Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
SegmentatFinding plant diseases early on is essential for reducing damage while improving the quality of the yield. This paper describes an intelligent cloud-edge-based system for identifying and categorizing diseases in grape plants. The system is based on the support vector machine (SVM), a supervised machine learning technique to classify data. The traits of healthy and diseased plants are identified using digital photographs of grape plants. To determine color and texture information, we retrieved global features from the grape images. Patterns or structures (such as corners or edges) are discovered using the speeded-up robust features (SURF) method. The K-means clustering approach is used to quantify feature space, which lowers the number of feature descriptors. The training set for the SVM classifier is made up of feature descriptors. The system classified the unlabeled grape images using the trained SVM classifier during testing. 1600 RGB pictures from four classes: Black-rot, Black-measles, Leaf-blight, and Healthy-leaf make up the original data set. To evaluate the system and provide accuracy and confusion matrices, simulations are run in four different color spaces (grayscale, RGB, YCbCr, and L*a*b*). In the L*a*b* color space, the system attained a maximum average accuracy of up to 90.63% at a ratio of 70:30 training to testing data.
Why it matches plant phenotyping methodsブドウ葉の画像から健全・病害状態を抽出し、画像特徴量、セグメンテーション／分類、複数色空間で性能評価するシステム開発が中心であるため。
abstractThis paper describes an intelligent cloud-edge-based system for identifying and categorizing diseases in grape plants.
Reproduction assets foundThe paper's grape disease classification uses the public PlantVillage grape leaf image dataset from Kaggle, and the authors also deposited their selected 1600-image dataset on Figshare with an explicit availability statement. No author analysis code or trained model is shared.Dataset · publicAvailability of data and materials
The data set is available at the following share repository:
https://figshare.com/ndownloader/files/37001836Open asset ↗figsharepdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The grapevine is vulnerable to diseases, deficiencies, and pests, leading to significant yield losses. Current disease controls involve monitoring and spraying phytosanitary products at the vineyard block scale. However, automatic detection of disease symptoms could reduce the use of these products and treat diseases before they spread. Flavescence dorée (FD), a highly infectious disease that causes significant yield losses, is only diagnosed by identifying symptoms on three grapevine organs: leaf, shoot, and bunch. Its diagnosis is carried out by scouting experts, as many other diseases and stresses, either biotic or abiotic, imply similar symptoms (but not all at the same time). These experts need a decision support tool to improve their scouting efficiency. To address this, a dataset of 1483 RGB images of grapevines affected by various diseases and stresses, including FD, was acquired by proximal sensing. The images were taken in the field at a distance of 1-2 meters to capture entire grapevines and an industrial flash was ensuring a constant luminance on the images regardless of the environmental circumstances. Images of 5 grape varieties (Cabernet sauvignon, Cabernet franc, Merlot, Ugni blanc and Sauvignon blanc) were acquired during 2 years (2020 and 2021). Two types of annotations were made: expert diagnosis at the grapevine scale in the field and symptom annotations at the leaf, shoot, and bunch levels on computer. On 744 images, the leaves were annotated and divided into three classes: 'FD symptomatic leaves', 'Esca symptomatic leaves', and 'Confounding leaves'. Symptomatic bunches and shoots were, in addition of leaves, annotated on 110 images using bounding boxes and broken lines, respectively. Additionally, 128 segmentation masks were created to allow the detection of the symptomatic shoots and bunches by segmentation algorithms and compare the results to those of the detection algorithms.
Why it matches plant phenotyping methodsブドウ病害の症状を画像から抽出するための専門家アノテーション付きデータセットであり、植物体・葉・枝・果房の病徴状態を対象とするフェノタイピング手法・ベンチマークとして中心的です。
abstractTo address this, a dataset of 1483 RGB images of grapevines affected by various diseases and stresses, including FD, was acquired by proximal sensing.
Reproduction assets foundThis Data Brief describes a paper-specific grapevine disease image dataset (1483 RGB images with expert annotations) publicly deposited on Mendeley Data, with a direct URL provided in the article.Dataset · publice:
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City/Town/Region: Rions, Gironde
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City/Town/Region: Saint-Martin, Gironde
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Plot 1: 44.5712274, -0.1697558
Data accessibility
Repository name: Mendeley Data
Direct URL to data: https://data.mendeley.com/datasets/3dr9r3w3jn/2
Related research article
Tardif, M., Amri, A., Keresztes, B., Deshayes, A., Martin, D., Greven, M., & Da Costa, J.-P. (2022). Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study. OENO One, 56(3), 371–384. https://doi.oOpen asset ↗Mendeley Data · 3dr9r3w3jn/2lines:46-137Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
In most of the countries, grapes are considered as a cash crop. Currently huge research is going on in development of automated grape harvesting systems. Speedy and reliable grape bunch detection is prime need for various deep learning based automated systems which deals with object detection and object segmentation tasks. But currently very few datasets are available on grape bunches in vineyard, because of which there is restriction to the research in this area. In comparison to the vineyard in outside countries, Indian vineyard structure is more complex, so it becomes hard to work in real-time. To overcome these problems and to make vineyard dataset for suitable for Indian vineyard scenarios, this paper proposed four different datasets on grape bunches in vineyard. For creating all datasets in GrapesNet, natural environmental conditions have been considered. GrapesNet includes total 11000+ images of grape bunches. Necessary data for weight prediction of grape cluster is also provided with dataset like height, width and real weight of cluster present in image. Proposed datasets can be used for prime tasks like grape bunch detection, grape bunch segmentation, and grape bunch weight estimation etc. of future generation automated vineyard harvesting technologies.
Why it matches plant phenotyping methodsブドウ果房画像データセットを構築し、果房の検出・セグメンテーションに加えて重量推定用の寸法と実重量を提供することが中心で、再利用可能な植物表現型データセットに該当する。
abstractthis paper proposed four different datasets on grape bunches in vineyard.
Reproduction assets foundThe paper is a data descriptor for GrapesNet, a public Mendeley Data repository of Indian vineyard RGB/RGB-D grape bunch image datasets with ground-truth cluster height, width, and weight measurements used for phenotyping tasks (detection, segmentation, weight estimation). The dataset is the paper's core asset and is aDataset · publicRepository name: GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets
Data identification number (DOI): 10.17632/mhzmzd5cwx.1
Direct URL to data: https://data.mendeley.com/datasets/mhzmzd5cwx/1Open asset ↗10.17632/mhzmzd5cwx.1lines:1-95Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Even though mechanization has dramatically decreased labor requirements, vineyard management costs are still affected by selective operations such as winter pruning. Robotic solutions are becoming more common in agriculture, however, few studies have focused on grapevines. This work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines. The Faster R-CNN network was fine-tuned using 1215 RGB images collected in different vineyards and annotated through bounding boxes. The network was tested on 232 RGB images, PRs were categorized by wood type (W), orientation (Or) and visibility (V), and performance metrics were calculated. PR detection was dramatically affected by visibility. Highest detection was associated with visible intermediate complex spurs in Merlot (0.97), while most represented coplanar simple spurs allowed a 74% detection rate. The Mask R-CNN network was trained for grapevine organs (GOs) segmentation by using 119 RGB images annotated by distinguishing 5 classes (cordon, arm, spur, cane and node). The network was tested on 60 RGB images of light pruned (LP), shoot-thinned (ST) and unthinned control (C) grapevines. Nodes were the best segmented GOs (0.88) and general recall was higher for ST (0.85) compared to C (0.80) confirming the role of canopy management in improving performances of hi-tech solutions based on artificial intelligence. The two fine-tuned and tested networks are part of a larger control framework that is under development for autonomous winter pruning of grapevines. Supplementary information The online version contains supplementary material available at 10.1007/s11119-023-10006-y.
Why it matches plant phenotyping methods深層学習によるブドウ樹の剪定領域検出と器官セグメンテーションを開発・評価しており、植物器官状態の画像ベース取得が中心である。
abstractThis work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines.
Reproduction assets foundThe paper's annotated grapevine organ segmentation dataset (images with polygon/bounding-box annotations for cordon, arm, spur, cane, node) is publicly deposited on Zenodo. The pruning region detection dataset is not public and must be requested from the corresponding author. No author analysis code is available (code:Dataset · publicd Research, PRIN 20172HHNK5 Project.
Data availability
The pruning region detection dataset generated and/or analyzed during the presented study is currently not publicly available, but can be requested from the corresponding author on reasonable request. The annotated segmentation dataset is published on the zenodo platform at https://zenodo.org/record/5501784 .
Code availability
Not applicable.
Declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
The authors comply with the Journal’s Ethics guidelines confirming to respect third parties rights such as copyright and/or moral rights.
Consent to participate
NoOpen asset ↗zenodo · 5501784lines:583-615Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
In this paper, we present a direct drive brush-shaped tool developed for the use of robotic vine suckering. Direct drive design philosophy allows for precise and high bandwidth control of the torque exerted by the brush. Besides limiting the torque exerted onto the plant, this kind of design philosophy allows the brush to be used as a torque sensor. High bandwidth torque feedback from the tool is used to enable a position controlled robot arm to perform the suckering task without knowing the exact position and shape of the trunk of the vine. An experiment was conducted to investigate the dependency of the applied torque on the overlap between the brush and the obstacle. The results of the experiment indicate a quadratic relationship between torque and overlap. This quadratic function is estimated and used for compliant trunk shape following. A trunk shape following experiment demonstrates the utility of the presented tool to be used as a sensor for compliant robot arm control. The shape of the trunk is estimated by tracking the motion of the robot arm during the experiment.
Why it matches plant phenotyping methodsブドウ樹の幹形状をトルクセンサ付きロボット工具で推定する手法を開発・実験検証しており、植物形状の取得が中心的である。
abstractDirect drive design philosophy allows for precise and high bandwidth control of the torque exerted by the brush. Besides limiting the torque exerted onto the plant, this kind of design philosophy allows the brush to be used as a torque sensor.
Reproduction assets foundThe paper's authors explicitly state that their implementation of the prioritized task-space control algorithm (used for the compliant trunk shape following experiments) is publicly available on GitHub. No phenotype/trait datasets or image/sensor data deposits are mentioned; the video link is supplementary footage of aCode · publicThis implementation of the prioritized task-space control
algorithm is available on GitHub (https://github.com/ivatavuk/ptsc_eigen, accessed on
29 November 2022).Open asset ↗github.com/ivatavuk/ptsc_eigen · ivatavuk/ptsc_eigenpdf-page:10 lines:1-29Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Counting the number of grape bunches at an early stage of development offers relevant information to the winegrower about the potential yield to be harvested. However, manual counting on the fields is laborious and time-consuming. Remote sensing, and more precisely unmanned aerial vehicles mounted with RGB or multispectral cameras, facilitate this task rapidly and accurately. This dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches. The videos were acquired throughout four UAV flights with an RGB camera tilted at 60 degrees. Each flight recorded one side of a row of the vineyard. The grape berries were between pea-size (BBCH75) and bunch closure (BBCH79) stage, which is two months before harvesting. No operations other than those usual in a commercial vineyard, such as pruning, cane tying, fertilization, and pest treatment, have been carried out, hence, the dataset presents leaf occlusion. The dataset was gathered and labelled to train object detection and tracking algorithms for grape bunch counting. Furthermore, it eases the work of winegrowers to check the sanitary status of the vineyard.
Why it matches plant phenotyping methodsブドウ房数という植物の収量関連形質をUAV画像から推定するための、ラベル付き動画データセットであり、物体検出・追跡手法の開発を支援する中心的な成果である。
abstractThis dataset contains 40 RGB videos from a 1.06-ha vineyard located in northern Spain. Moreover, the dataset includes mask labels of visible grape bunches.
Reproduction assets foundThis Data in Brief article describes its own public dataset: 40 UAV RGB videos over a vineyard with grape bunch mask annotations (MOTS-style PNG labels) for object detection/tracking and phenotyping, deposited on Zenodo with an explicit direct URL and DOI. This is a paper-specific, publicly available, directly reproducDataset · publice location
Institution: Wageningen University & Research
City/Town/Region: Tomiño, Pontevedra, Galicia
Country: Spain
Latitude and longitude (and GPS coordinates) for collected samples/data:
41°57′18.3″N 8°47′41.9″W
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.7330951
Direct URL to data: https://zenodo.org/record/7330951#.Y3tU3nbMKUk
Related research article
Ariza-Sentís, M., Vélez, S., Baja, H., & Valente, J. (2022). IPPS 2022 Conference Book . 231.
Value of the Data
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Dataset is useful for researchers interested in instance segmentation, as it allows the detection and tracking of the clusters [2] .
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Dataset can be employed to count the number of viOpen asset ↗Zenodo · 10.5281/zenodo.7330951lines:1-68Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. Results For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. Conclusion NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
Why it matches plant phenotyping methodsブドウのスペクトルを用いたフェノミック予測法を開発・評価し、ゲノム予測との比較や予測能力の検証を行っており、植物形質推定手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour.
Reproduction assets foundThe paper explicitly deposits its grapevine phenotypic/genotypic data and its NIRS spectra, R analysis scripts, and result tables in the INRAE data portal under two DOIs, both listed in allowed_urls. These are paper-specific, publicly actionable assets directly reproducing the phenotyping measurements and computationalDataset · publicGenotypic values and genotypic data for half-diallel and diversity panel populations are available at https://doi.org/10.15454/PNQQUQOpen asset ↗10.15454/PNQQUQlines:204-268Dataset · publicSpectra, R scripts and result tables have been deposited in the INRAE data portal: https://doi.org/10.15454/BICRFXOpen asset ↗INRAE data portal · 10.15454/BICRFXlines:204-268Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.
Why it matches plant phenotyping methodsブドウ葉の病徴・病害面積をハイパースペクトル画像から自動推定し、SVMの検証と病害重症度評価を行う方法中心の研究である。
abstractdeveloped an automatic detection tool based on a Support Vector Machine (SVM) classifier
Reproduction assets foundThe paper's hyperspectral image dataset of downy mildew on grapevine leaves is explicitly stated as publicly available on Recherche Data Gouv with a DOI (10.57745/AV1ETI), matching an allowed URL. No author analysis code repository is deposited (only generic library citations), so only the dataset qualifies.Dataset · publicThe hyperspectral images used in this work came from a database publicly available [ 40 ] at https://doi.org/10.57745/AV1ETI , accessed on 19 July 2022.Open asset ↗10.57745/AV1ETIlines:135-137Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.) in vineyards where sustainability was threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of wood quality and plant health across plant species.
Why it matches plant phenotyping methodsブドウ樹内部組織と病害状態を、MRI・X線CT・自動ボクセル分類によって非破壊的に定量する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractwe developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants
Reproduction assets foundThe paper's imaging datasets (MRI, X-ray CT, photographic volumes, annotations) are only available 'upon reasonable request', but the authors' extended Trainable Segmentation plugin used for the machine-learning voxel classification is explicitly open-source on GitHub.Code · publicFernandez et al. 24
DATA AND CODE AVAILABILITY
The datasets generated and analyzed during the current study are available from the corresponding
author upon reasonable request.
The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable
Segmentation on GitHub: https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.
.
CC-BY-NC-ND 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 this
this version posted February 3, 2023.
;
https://doi.org/10.1101/2022.06.09.495457
doOpen asset ↗Rocsg/Trainable_Segmentation · Hyperwekapdf-raw-page:24 lines:1-16Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model.
Why it matches plant phenotyping methodsブドウ樹の枝を画像から分割する公開データセットを提案し、セグメンテーション性能も評価しており、植物器官の画像計測手法・ベンチマークが中心です。
abstractan image dataset is proposed for grapevine canes’ segmentation for a pruning task.
Reproduction assets foundThe paper's own grapevine pruning image dataset (100 RGB images with hand-annotated segmentation masks) is publicly available on GitHub under CC BY 4.0, as stated in the abstract and Data Availability Statement. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicData Availability Statement: Data are available at https://github.com/humain-lab/Buds-Dataset
under Creative Commons Attribution 4.0 International license.Open asset ↗humain-lab/Buds-Datasetpdf-page:9 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.
Why it matches plant phenotyping methodsブドウ房を対象とする画像・ラベル dataset の構築と公開が中心で、房の検出・可視数の記録という植物器官の表現型取得に直接関係するため。
abstractThe present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties.
Reproduction assets foundThe paper is a data descriptor for wGrapeUNIPD-DL, an open dataset of 373 vineyard images with YOLO-format bunch bounding-box labels, publicly deposited on Zenodo (10.5281/zenodo.4066730). The Yolo_Label GitHub link is a generic third-party annotation tool, not a paper-specific asset.Dataset · publicuired with a distance from the side canopy from 1.5 up to 3 meters.
Data source location
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Institution: Department of Land Environment Agriculture and Forestry, University of Padova;
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City: Legnaro;
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Country: Italy;
Data accessibility
Repository name: ZenodoData identification number: 10.5281/zenodo.4066730Direct URL to data: https://zenodo.org/record/4066730#.YofMr9hBxPY Instructions for accessing these data: data are Open Access in Creative Commons Attribution 4.0 International
Related research article
Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., & Marinello, F. (2022). Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms. AgOpen asset ↗Zenodo · 10.5281/zenodo.4066730lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
GrapevineField / plotDisease symptoms / severityGrowth / development / phenology
Phytosanitary bulletins released at weekly interval by eight Italian regional plant protection services in the growing seasons 2012-2017 were used to derive an harmonized dataset of grapevine downy mildew infection risk and phenological observations. The downy mildew infection risk ( n = 8816) was classified using a 5-point Likert response item ranging from 'very low' (1) to 'very high' (5) by six independent evaluators with domain expertise in agronomy, phytopathology and agrometeorology. Common criteria have been used in the risk assessment, considering (i) the presence of disease symptoms in field surveys, (ii) the host phenological susceptibility, (iii) the weather forecasts in the next week from the bulletin release date, (iv) the advice to apply a fungicide treatment and (v) the outputs of epidemiological models. The phenological observations are provided as BBCH codes ( n = 1689), which have been either transcribed from the phytosanitary bulletins or derived from the narrative description of the visual observation. Phenological data refer to the main early and late grapevine varieties in the eight regions (NUTS-2 administrative unit). Each record is associated with the NUTS-2 and NUTS-3 (31 provinces) administrative unit of reference, to the growing season (2012-2017), and refers to the individual risk assessment by the six evaluators. The dataset is hosted by the Centre for Agriculture and Environment of the Italian Council for Agricultural Research and Economics. These data could be helpful to researchers who develop either grapevine phenological models or process-based epidemiological predictive algorithms in order to refine their calibration and evaluation, as well as being a valuable resource for stakeholders in charge of evaluating the effective implementation of Integrated Pest Management in the decision-making process of public plant protection services in Italy. The dataset is freely available here.
Why it matches plant phenotyping methodsブドウのフェノロジー（BBCHコード）と病害リスクを体系化した再利用可能な調和データセットであり、植物状態の取得・整理自体が中心的な貢献である。
abstractused to derive an harmonized dataset of grapevine downy mildew infection risk and phenological observations
Reproduction assets foundThe paper is a Data in Brief describing a public Mendeley Data repository containing the paper's own grapevine downy mildew risk assessments and BBCH phenological observations, directly deposited by the authors.Dataset · publicCity: Bologna
Country: Italy
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/3jsh4y2bw4.1
Direct URL to data: https://data.mendeley.com/datasets/3jsh4y2bw4/1
Related research article
S. Bregaglio, F. Savian, E., Raparelli, D., Morelli, R., Epifani, F., Pietrangeli, C., Nigro, R., Bugiani, S., Pini, P., Culatti, D., Tognetti, F., Spanna, M., Gerardi, I., Delillo, S., Bajocco, D., Fanchini, G., Fila, F., Ginaldi, L. M., Manici, A public decision support system for the assessment of plOpen asset ↗Mendeley Data · 10.17632/3jsh4y2bw4.1lines:46-101Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew ( Peronospora ) on grapevine ( Vitis vinifera ) leaves at early stages of development using thermal imaging technology and to determine the best time during the day for image acquisition. In controlled experiments, 1587 thermal images of grapevines grown in a greenhouse were acquired around midday, before inoculation, 1, 2, 4, 5, 6, and 7 days after an inoculation. In addition, images of healthy and infected leaves were acquired at seven different times during the day between 7:00 a.m. and 4:30 p.m. Leaves were segmented using the active contour algorithm. Twelve features were derived from the leaf mask and from meteorological measurements. Stepwise logistic regression revealed five significant features used in five classification models. Performance was evaluated using K-folds cross-validation. The support vector machine model produced the best classification accuracy of 81.6%, F1 score of 77.5% and area under the curve (AUC) of 0.874. Acquiring images in the morning between 10:40 a.m. and 11:30 a.m. resulted in 80.7% accuracy, 80.5% F1 score, and 0.895 AUC.
Why it matches plant phenotyping methods熱画像と画像解析・分類モデルを用いてブドウ葉の病害状態を早期推定する手法を開発・評価しており、植物病害表現型の取得が中心です。
abstractThis study sought to detect downy mildew ( Peronospora ) on grapevine ( Vitis vinifera ) leaves at early stages of development using thermal imaging technology
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' phenotyping datasets (thermal-image-derived leaf temperature features, meteorological measurements, and disease severity labels) as Excel files in two public GitHub repositories, both listed in allowed_urls. These directly reproduce the paper's 1,Dataset · publicThe datasets generated and analyzed during the current study are available in GitHub: Data sets (Excel): https://github.com/BarCohenBGU/database.git (29 September 2021). ‘All data new’—the classification dataset included 1403 records.Open asset ↗BarCohenBGU/databaselines:635-637Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB's latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m 2 compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB's modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management. Supplementary information The online version contains supplementary material available at 10.1007/s00271-022-00787-x.
Why it matches plant phenotyping methodsリモートセンシングによる3SEBモデルを用いてブドウ樹・被覆作物の蒸発散分離を推定し、渦相関測定および既存モデルと比較検証している。水利用管理への応用を含むが、植物の生理状態推定手法の技術評価が中心である。
abstracta remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system.
Reproduction assets foundThe paper applies the authors' 3SEB model to vineyard ET partitioning and explicitly points to the authors' public GitHub repository as the model source code. No public phenotype/trait datasets or trained models are deposited; the GRAPEX flux/radiometric data are described but no public URL is given.Code · publicRefer to Burchard-Levine et al. ( 2022 ) or the source code ( https://github.com/VicenteBurchard/3SEB ) for model details and specifications.Open asset ↗VicenteBurchard/3SEBlines:112-122Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.
Why it matches plant phenotyping methods植物表現型データのデジタル記録・取得を目的とするオープンソースアプリの開発であり、表現型測定ワークフローが中心的です。
abstractTherefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses.
Reproduction assets foundThe paper describes PhenoApp, an open-source Android phenotyping app. Authors provide the app's source code (Gitea, archived on Zenodo) and underlying example input/output phenotype data files on Zenodo under CC0. The SHAPE II project website is a project page, not a paper-specific data deposit, and is excluded.Code · publice ‘in’ folder of the app main directory and no additional source data is required).
-
Output_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants fOpen asset ↗gitea.julius-kuehn.de · JKI/pheno-applines:333-433Code · publicput_example.xls (sample output file created by PhenoApp).
Data are available under the terms of the
Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).
Software availability
Source code available from:
https://gitea.julius-kuehn.de/JKI/pheno-app
Archived source code at time of publication:
https://doi.org/10.5281/zenodo.5525779
36
License:
Apache-2.0
Acknowledgements
We are grateful to Moritz Cappel, Teresa Claus and Claudia Vogel for ongoing testing, recommendations and bug report of PhenoApp during development.
Funding Statement
This work was supported by grants from the German Federal Ministry of Education and Research to FS (SelWineQ, FKZ 031B0889Open asset ↗Zenodo · 10.5281/zenodo.5525779lines:333-433Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of −0.2, thus suggesting that phenomic prediction could be applied for any trait.
Why it matches plant phenotyping methodsスペクトルに基づくフェノミック予測をブドウで適用し、複数組織・年・集団・形質で遺伝予測との性能比較を行っており、表現型取得・予測手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits.
Reproduction assets foundThe paper's Data availability statement deposits spectra, R scripts, and result tables in the INRAE data portal (DOI 10.15454/BICRFX), and genotypic values/genotypic data at DOI 10.15454/PNQQUQ. Both are paper-specific, public, and actionable.Dataset · publicyear of phenotyping and spectra measurement
are the same. Still, PP has shown its interest for
breeding over a wide range of traits.
Data availability
All analyses were conducted using free and open-
source software, mostly R. Genotypic values and
genotypic data for half-diallel and diversity panel
populations are available at https://doi.org/10.15454/PNQQUQ. Spectra, R scripts and result
tables have been deposited in the INRAE data
15
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this versionOpen asset ↗10.15454/PNQQUQpdf-raw-page:15 lines:1-97Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola ((Berk. and Curt.) Berl. and de Toni) causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to considerably reduce the amount of time during leaf disc phenotyping. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.
Why it matches plant phenotyping methodsブドウ葉ディスクの病害重症度を自動画像評価するSCNNと撮像パイプラインを開発・評価しており、植物表現型取得法が研究の中心である。
abstractHere we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs.
Reproduction assets foundThe authors explicitly state that the complete leaf-disc-scoring pipeline (SCNN training scripts, Jupyter notebook, microscope workflow, and the training/validation image datasets) is publicly available as an open-source GitHub repository. This directly reproduces the paper's phenotyping analysis and includes the plantDataset · publicFinal image datasets for training the two SCNNs are available
for download together with a Jupyter notebook containing detailed instructionsOpen asset ↗pdf-page:5 lines:1-38Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to reduce the amount of time during leaf disc phenotyping considerably. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.
Why it matches plant phenotyping methodsブドウ葉ディスクの病害重症度を自動画像解析・CNNで推定する手法を開発し、手動評価との相関で検証しているため、植物フェノタイピング手法が中心である。
abstractHere we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs.
Reproduction assets foundThe authors publicly released the complete leaf-disc scoring pipeline (SCNN training scripts, classification pipeline, R plotting scripts, ZenBlue imaging workflow) plus the training image datasets in an open-source GitHub repository, enabling reproduction of this paper's phenotyping measurements and analysis.Dataset · publicThe pictures used to train
the two binary SCNNs are included as datasets in the repository.Open asset ↗pdf-page:12 lines:1-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
Why it matches plant phenotyping methods植物器官の可視パーツ数を画像から検出・計数する深層学習手法を開発し、複数作物データセットで評価しているため、表現型取得・推定法が中心です。
abstractWe propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster.
Reproduction assets foundThe paper's grape experiments use the public Embrapa WGISD dataset, extended by the authors with berry dot annotations that they state were made publicly available as part of that dataset extension. The banana and wheat datasets (Israel Phenomics Consortium) and the authors' code/models have no stated public release,; Dataset · publicThe dot annotations were made
publicly available as part of Embrapa WGISD dataset extension.Open asset ↗Embrapa WGISDpdf-page:5 lines:1-59Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
GrapevineField / plotWhole plant / canopy / plot / field
Very often, the root of problems found to produce food sustainably, as well as the origin of many environmental issues, derive from making decisions with unreliable or inexistent data. Data-driven agriculture has emerged as a way to palliate the lack of meaningful information when taking critical steps in the field. However, many decisive parameters still require manual measurements and proximity to the target, which results in the typical undersampling that impedes statistical significance and the application of AI techniques that rely on massive data. To invert this trend, and simultaneously combine crop proximity with massive sampling, a sensing architecture for automating crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how monitoring vehicles must be configured for optimally tracking crop parameters at high resolution. This paper structures the architecture for such vehicles in four subsystems, examines the most common components for each subsystem, and delves into their interactions for an efficient delivery of high-density field data from initial acquisition to final recommendation. Its main advantages rest on the real time generation of crop maps that blend the global positioning of canopy location, some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar characteristics. The information contained in the maps delivered by the robot may help growers systematically apply differential harvesting, evidencing the suitability of the proposed architecture for massive monitoring and subsequent data-driven actuation. While many crop parameters still cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit from them, an efficient and trustable sensing architecture becomes indispensable.
Why it matches plant phenotyping methods作物の高密度な形質・キャノピー情報を取得する地上センシング車両のアーキテクチャを主題とし、取得からマップ生成までの方法論を構築しているため、単なる農業実験ではない。
abstracta sensing architecture for automating crop scouting from ground vehicles is proposed.
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 · publicData Availability Statement
Original data derived from the VineScout research project, from which this paper extracts relevant information and experience, can be accessed in the following address: https://zenodo.org/record/4432057#.X_w94BZ7mXJ .Open asset ↗Zenodo · 4432057lines:78-93Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Ampelography, the botanical discipline dedicated to the identification and classification of grapevine cultivars, was grounded on the description of morphological characters and more recently is based on the application of DNA polymorphisms. New methods of image analysis may help to optimize morphological approaches in ampelography. The objective of this study was the classification of representative cultivars of Vitis vinifera conserved in the Spanish collection of IMIDRA according to seed shape. Thirty eight cultivars representing the diversity of this collection were analyzed. A consensus seed silhouette was defined for each cultivar representing the geometric figure that better adjusted to their seed shape. All the cultivars tested were classified in ten morphological groups, each corresponding to a new model. The models are geometric figures defined by equations and similarity to each model is evaluated by quantification of percent of the area shared by the two figures, the seed and the model (J index). The comparison of seed images with geometric models is a rapid and convenient method to classify cultivars. A large proportion of the collection may be classified according to the new models described and the method permits to find new models according to seed shape in other cultivars.
Why it matches plant phenotyping methods種子画像から形状を抽出し、幾何モデルと面積共有率でブドウ品種を分類する画像解析手法が研究の中心であり、再利用可能な植物形態フェノタイピング法に該当する。
abstractNew methods of image analysis may help to optimize morphological approaches in ampelography.
Reproduction assets foundThe paper deposits its seed-image datasets and analysis materials in public Zenodo records: composed images of 30 seeds per accession (record 4433813), a video protocol for obtaining average silhouettes (record 4478344), a video of the J index calculation process (record 4478315), and the Mathematica code for the ten新的Dataset · publicified in ten groups defined by their
similarity to each of the respective models.
2.4.1. Obtention of an average silhouette for each cultivar
The average silhouette is a representative image of seed shape for each cultivar. It
was obtained in Corel Photo Paint, by the following protocol (a detailed video is available
at Zenodo: https://zenodo.org/record/4478344#.YBPOguhKiM8):
The layers containing the seeds are superimposed and the opacity is given a value of
3 in all layers. All the layers are combined, and the brightness is adjusted to a minimum
value. From this image we are interested in the inner region representing the area where
most of the seeds coincide, which is the darkest area. ToOpen asset ↗Zenodo · 4478344pdf-raw-page:3 lines:1-37Code · publicbelow, the model in white.
Right: Reed zones show the areas quantified in each of the figures. ImageJ gives the total area for
the seed with the model in black, while shared area is obtained with the white model.
3. Results
3.1. New models
The Mathematica code for the ten new models described in this work is stored in
Zenodo: (https://zenodo.org/record/4478500#.YBPetOhKiM8). The following nine models
were obtained from modifications in Model 7 [24] (between parenthesis the cultivars to
which the model applies):
Model Listán Prieto (Listán Prieto and Tortozona Tinta):
(
2
17
(√3300 − 90𝑥2 −
400
24 + 5𝑥2
) + 𝑦) (
25
187
(−√3300 − 90𝑥2 −
1200
60 + 𝑥6
) + 𝑦) = 0;
Model Sylvestris (wild varietiesOpen asset ↗Zenodo · 4478500pdf-raw-page:4 lines:1-48Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Esca is carried out manually through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants that helps in preventing it to spread in the vineyards and in minimizing the financial loss to the wine producers. In this article, an image dataset of grapevine leaves is presented. The dataset holds grapevine leaves images belonging to two classes: unhealthy leaves acquired from plants affected by Esca disease and healthy leaves. The data presented has been collected to be used in a research project jointly developed by the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy and the STMicroelectronics, Italy, under the cooperation of the Umani Ronchi SPA winery, Osimo, Ancona, Marche, Italy. The dataset could be helpful to researchers who use machine learning and computer vision algorithms to develop applications that help agronomists in early detection of grapevine plant diseases. The dataset is freely available at http://dx.doi.org/10.17632/89cnxc58kj.1.
Why it matches plant phenotyping methodsブドウ葉画像によるエスカ病の症状分類データセットを提供しており、植物病害状態の画像ベース表現型取得・解析を中心とする研究である。
abstractIn this article, an image dataset of grapevine leaves is presented.
Abstract We propose a novel, canopy density estimation solution using a three‐dimensional (3D) ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous‐time 3D SLAM (simultaneous localization and mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data are processed through a Continuous‐Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analyzed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 km, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (relative root mean square error) per vineyard panel, for acquisition speeds of 5–6 km/h, and under half the standard deviation in estimated densities when compared with an industry standard gap‐fraction based solution. The code and field data sets are available at https://github.com/csiro-robotics/agscan3d .
Why it matches plant phenotyping methods3D LiDARとSLAMを用いてブドウ樹冠密度を推定する取得・解析手法を開発し、シミュレーションおよび大規模圃場実験で反復性と既存法を検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel, canopy density estimation solution using a three‐dimensional (3D) ray cloud representation for perennial horticultural crops at the field scale.
Reproduction assets foundThe paper's abstract explicitly states that the authors' code and field datasets (AgScan3D lidar data used for canopy density estimation) are publicly available at the CSIRO Robotics GitHub repository, which matches the allowed URL.Code · publicThe code and field datasets are available at
https://github.com/csiro-robotics/agscan3d .Open asset ↗csiro-robotics/agscan3dlines:1-63Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Published1 Nov 2020Journal of the American Society for Horticultural ScienceCited by 29 · OpenAlex ↗
Grape ( Vitis vinifera ) cluster compactness is an important trait due to its effect on disease susceptibility, but visual evaluation of compactness relies on human judgement and an ordinal scale that is not appropriate for all populations. We developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape ( Vitis sp.) population for 2 years. Images were collected from grape clusters immediately after harvest, segmented by color, and analyzed using a custom script. Both automated and conventional phenotyping methods were used, and comparisons were made between each method. A partial least squares (PLS) model was constructed to evaluate the prediction of physical cluster compactness using image-derived measurements. Quantitative trait loci (QTL) on chromosomes 4, 9, 12, 16, and 17 were associated with both image-derived and conventionally phenotyped traits within years, which demonstrated the ability of image-derived traits to identify loci related to cluster morphology and cluster compactness. QTL for 20-berry weight were observed between years on chromosomes 11 and 17. Additionally, the automated method of cluster length measurement was highly accurate, with a deviation of less than 10 mm ( r = 0.95) compared with measurements obtained with a hand caliper. A remaining challenge is the utilization of color-based image segmentation in a population that segregates for fruit color, which leads to difficulty in differentiating the stem from the fruit when the two are similarly colored in non-noir fruit. Overall, this research demonstrates the validity of image-based phenotyping for quantifying cluster compactness and for identifying QTL for the advancement of grape breeding efforts.
Why it matches plant phenotyping methodsブドウ房の画像解析パイプラインを開発し、従来法との比較、PLSによる予測評価、測定精度検証を行っており、画像ベース表現型計測が中心である。
abstractWe developed an image analysis pipeline and used it to quantify cluster compactness traits in a segregating hybrid wine grape ( Vitis sp.) population for 2 years.
Reproduction assets foundThe paper explicitly states public availability of both the grape cluster images (University of Minnesota Conservancy) and the custom MATLAB image analysis script (GitHub), both directly supporting this paper's phenotyping measurements and analysis.Dataset · publicStien Iverson and David Tork, who helped with data collection. Soon Li Teh and
James Luby built the GE1025 linkage map.
Cluster images are available at https://conservancy.umn.edu/handle/11299/202560. Image analysis script is available at https://github.com/underhil-lanna/GrapeImageAnalysis.Current address for A.U.: Grape Genetics Research Unit, U.S. Department of
Agriculture, Agricultural Research Service, 630 West North Street, Geneva, NY
14456
M.C. is the corresponding author. Email: clark776@umn.edu.
This is an open accOpen asset ↗conservancy.umn.edu · 11299/202560pdf-raw-page:1 lines:74-81Code · publicStien Iverson and David Tork, who helped with data collection. Soon Li Teh and
James Luby built the GE1025 linkage map.
Cluster images are available at https://conservancy.umn.edu/handle/11299/202560. Image analysis script is available at https://github.com/underhil-lanna/GrapeImageAnalysis.Current address for A.U.: Grape Genetics Research Unit, U.S. Department of
Agriculture, Agricultural Research Service, 630 West North Street, Geneva, NY
14456
M.C. is the corresponding author. Email: clark776@umn.edu.
This is an open access article distributed under the CC BY-NC-ND license
(https://creativecommons.org/licenses/by-nc-ndOpen asset ↗github.com/underhil-lanna/GrapeImageAnalysispdf-raw-page:1 lines:74-81Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.
Why it matches plant phenotyping methodsブドウ葉・果実のNIRスペクトルと機械学習により煙汚染状態を非破壊推定する検出システムを開発しており、植物状態の取得・推定手法が研究の中心である。
abstractThis study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination.
Reproduction assets foundThe article's supplementary materials link (MDPI) hosts Table S1 with the paper's own smoke-taint chemical measurements (volatile phenol concentrations in grape juice and glycoconjugates in grape homogenate). No public code, model checkpoints, or raw spectral/image datasets are described; the ANN code is only describedSupplement · publicw.arcwinecentre.org.au ), which is funded as part of the ARC’s Industrial Transformation Research Program (Project No. ICI70100008), with support from Wine Australia and industry partners. The authors greatly acknowledge the Digital Agriculture, Food, and Wine Group.
Supplementary Materials
The following are available online at https://www.mdpi.com/1424-8220/20/18/5099/s1 , Table S1: Concentrations of volatile phenols in grape juice (µg/L) and their glycoconjugates in grape homogenate (µg/kg) one hour after smoke treatments.
Click here for additional data file.
Author Contributions
Conceptualization, V.S., and S.F.; data curation, V.S., C.G.V., and S.F.; formal analysis, V.S.; funding acquisOpen asset ↗lines:87-170Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The shapes of grapevine leaves have been critical to correctly identify economically important varieties throughout history. The correspondence of homologous features in nearly all grapevine species and varieties has enabled advanced morphometric approaches to mathematically classify leaf shape. These approaches either model leaves through the measurement of numerous vein lengths and angles or measure a finite number of corresponding landmarks and use Procrustean approaches to superimpose points and perform statistical analyses. Hand illustrations, too, play an important role in grapevine identification, as details omitted using the above methods can be visualized. Here, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations. Using these points, averaged leaf shapes for 60 varieties of wine and table grapes are calculated that preserve features. A pairwise Procrustes distance matrix of the overall morphological similarity of each variety to the other classifies leaves into two main groups--deeply lobed and more entire--that correspond to the measurements of sinus depth by Pierre Galet. Using the system of Galet, pseudo-landmarks are converted into relative distance and angle measurements. Both Galet-inspired and Procrustean methods allow increased accuracy in predicting variety compared to a finite number of landmarks. Using Procrustean pseudo-landmarks captures grapevine leaf shape at the same level of detail as drawings and provides a quantitative method to arrive at mean leaf shapes representing varieties that can be used within a predictive statistical framework.
Why it matches plant phenotyping methodsブドウ葉の形状を擬似ランドマークとプロクルステス解析で定量化し、品種識別・平均葉形状推定に用いる手法の開発が中心である。
abstractHere, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations.
Reproduction assets foundThe paper explicitly links public GitHub repositories and a Dryad DOI containing its leaf photographs, hand-traced landmark/pseudo-landmark raw data, visual-check outputs, interpolation code and outputs, and Procrustes analysis code and outputs — all paper-specific, public, and actionable.Dataset · public) the photo ID of the leaf indicating the vineyard position of
206 the vine it was collected from, 2) an enumerating value 1 through 4 specifying which of four
207 leaves for the variety the data corresponds to, and 3) which vector the data file represents.
208 These files, the raw data, are available at the following link:
209 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/ampel
210 ometry_data. Tracing all data for a single leaf took approximately 15 minutes. Because the data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicthe data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Hunter,
213 2007) to plot the data on the actual photo. The code for plotting vectors onto the original photo
214 can be found here:
215 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/0_visual_check/ampel
216 ometry_visual_check.ipynb. The visual checks for each of the 240 leaves analyzed in this study
217 can be found here:
218 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/outpu
219 t_visual_check
220
5Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicith assigned numbers of points to every vector, interpolation was
240 used to calculate equidistant pseudo-landmarks. A function was created using the scipy
241 (Virtanen et al., 2020) interp1d function to interpolate the correct number of equidistance
242 points for each vector. The code used to interpolate points is here:
243 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/ampe
244 lometry_interpolation.ipynb. The interpolated points can be found here:
245 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/outp
246 ut_interpolated_points.txt
247
248 With corresponding points between all leaves, a Procrustes analysis could be peOpen asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicesults saved as a pairwise distance matrix. The hclust() function in R using the
259 “mcquitty” method was used to hierarchically cluster varieties based on the pairwise distance
260 matrix and overall morphological similarity. The code for performing a Procrustes analysis for
261 each variety and outputs can be found here:
262 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/2_procrustes_by_vari
263 ety
264
6Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicll Procrustes mean
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shape, super-imposed Procrustes coordinates for all leaves, and eigenvalues and eigenleaves
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from a PCA. The superimposed Procrustes coordinates of all leaves and the mean shape were
267
plotted together. The code for the Procrustes analysis for all 240 leaves and the outputs can be
268
found here:
269
https://github.com/DanChitwood/grapevine_ampelometry/tree/master/3_overall_procrustes
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Data analysis
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To calculate allometry for each line segment, distances between all points were converted to
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cm using the pixel to cm scale measured for each leaf. The lm() function in R was used to model
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the natural log of the distance from each point to the neOpen asset ↗DanChitwood/grapevine_ampelometrypdf-raw-page:7 lines:1-92Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Grape berry color is an economically important trait that is controlled by two major genes influencing anthocyanin synthesis in the skin. Color is often described qualitatively using six major categories; however, this is a subjective rating that often fails to describe variation within these six classes. To investigate minor genes influencing berry color, image analysis was used to quantify berry color using different color spaces. An image analysis pipeline was developed and utilized to quantify color in a segregating hybrid wine grape population across two years. Images were collected from grape clusters immediately after harvest and segmented by color to determine the red, green, and blue (RGB); hue, saturation, and intensity (HSI); and lightness, red-green, and blue-yellow values (L∗a∗b∗) of berries. QTL analysis identified known major QTL for color on chromosome 2 along with several previously unreported smaller-effect QTL on chromosomes 1, 5, 6, 7, 10, 15, 18, and 19. This study demonstrated the ability of an image analysis phenotyping system to characterize berry color and to more effectively capture variability within a population and identify genetic regions of interest.
Why it matches plant phenotyping methodsブドウ果実色を画像解析で定量するパイプラインを開発・適用し、従来の主観評価より集団内変異を捉える手法が中心である。
abstractAn image analysis pipeline was developed and utilized to quantify color in a segregating hybrid wine grape population across two years.
Reproduction assets foundThe paper's image-processing MATLAB script is publicly available on the authors' GitHub repository. The grape cluster images are deposited in DRUM but only available upon request, so they are not a public asset.Code · publicThe image processing MATLAB script is publicly available at https://www.github.com/underhillanna/GrapeImageAnalysis .Open asset ↗underhillanna/GrapeImageAnalysislines:63-170Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Inflorescence architecture provides the scaffold on which flowers and fruits develop, and consequently is a primary trait under investigation in many crop systems. Yet the challenge remains to analyse these complex 3D branching structures with appropriate tools. High information content datasets are required to represent the actual structure and facilitate full analysis of both the geometric and the topological features relevant to phenotypic variation in order to clarify evolutionary and developmental inflorescence patterns. We combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture (the rachis and all branches without berries) among 10 wild Vitis species. Clustering and correlation analyses revealed unexpected relationships, for example pedicel branch angles were largely independent of other traits. We identified multivariate traits that typified species, which allowed us to classify species with 78.3% accuracy, versus 10% by chance. Twelve traits had strong signals across phylogenetic clades, providing insight into the evolution of inflorescence architecture. We provide an advanced framework to quantify 3D inflorescence and other branched plant structures that can be used to tease apart subtle, heritable features for a better understanding of genetic and environmental effects on plant phenotypes.
Why it matches plant phenotyping methodsX線CT画像と計算解析を組み合わせ、ブドウの3D花序構造を定量化する再利用可能な表現型解析フレームワークを開発・適用しており、手法が研究の中心である。
abstractWe combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: the full X-ray tomography PLY dataset (7.85 GB) of 392 scanned grapevine inflorescences hosted on the Danforth Center Topp lab resources page, and the authors' Matlab analysis code (persistence barcodes, bottleneck distances, berry potential simulation,几何/Dataset · publicThe full PLY dataset for this work is 7.85 GB, and can be downloaded from: https://www.danforthcenter.org/scientists-research/principal-investigators/chris-topp/resources .Open asset ↗lines:39-133Code · publicAll Matlab functions used to calculate persistence barcodes, bottleneck distances, simulation for berry potential, other geometric features used in this study, and the script for extracting phylogenetic information can be found at the following GitHub repository: https://github.com/Topp-Roots-Lab/Grapevine-inflorescence-architecture .Open asset ↗Topp-Roots-Lab/Grapevine-inflorescence-architecturelines:174-184Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Powdery mildews present specific challenges to phenotyping systems that are based on imaging. Having previously developed low-throughput, quantitative microscopy approaches for phenotyping resistance to Erysiphe necator on thousands of grape leaf disk samples for genetic analysis, here we developed automated imaging and analysis methods for E. necator severity on leaf disks. By pairing a 46-megapixel CMOS sensor camera, a long-working distance lens providing 3.5× magnification, X-Y sample positioning, and Z-axis focusing movement, the system captured 78% of the area of a 1-cm diameter leaf disk in 3 to 10 focus-stacked images within 13.5 to 26 seconds. Each image pixel represented 1.44 μ m 2 of the leaf disk. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of E. necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity, with a training validation accuracy of 94.3%. For an independent image set the CNN was in agreement with human experts for 89.3% to 91.7% of subimages. This live-imaging approach was nondestructive, and a repeated measures time course of infection showed differentiation among susceptible, moderate, and resistant samples. Processing over one thousand samples per day with good accuracy, the system can assess host resistance, chemical or biological efficacy, or other phenotypic responses of grapevine to E. necator . In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays.
Why it matches plant phenotyping methodsブドウ葉ディスク上のうどんこ病重症度を画像とCNNで定量する高スループット表現型解析システムを開発・検証しており、植物表現型取得法が研究の中心である。
abstracthere we developed automated imaging and analysis methods for E. necator severity on leaf disks
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 imaging control software is available at https://github.com/LightingResearchCenter/Plant-Imaging- Platform .Open asset ↗LightingResearchCenter/Plant-Imaging- · Plant-Imaging-lines:359-372Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY’s primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease.
Why it matches plant phenotyping methodsブドウ葉の画像から植物体の病徴（黄化病）をCNNで検出する手法を開発・評価しており、病害状態の取得が研究の中心であるため。
abstractwe present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings.
Reproduction assets foundThe authors explicitly state that the trained neural networks, code to reproduce the experiments, and the leaf clipping image dataset are publicly available on GitHub (Salento-Grapevine-Yellows-Dataset repository).Dataset · publicThe
trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the
internet.Open asset ↗pdf-raw-page:1 lines:1-82Code · publicCode is publicly available on GitHub.Open asset ↗pdf-raw-page:7 lines:1-75Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.
Why it matches plant phenotyping methods植物根系の三次元画像化と活動根密度の推定を目的とした非侵襲的センシング手法を提案し、合成データおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractIn this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM).
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 · publicMeasured and simulated raw data, electrical imaging, and MALM data used to generate the
figures can be accessed at https://doi.org/10.5281/zenodo.1464825 (Mary et al., 2018).Open asset ↗Zenodo · 10.5281/zenodo.1464825lines:872-946Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Quantitative phenotyping of downy mildew sporulation is frequently used in plant breeding and genetic studies, as well as in studies focused on pathogen biology such as chemical efficacy trials. In these scenarios, phenotyping a large number of genotypes or treatments can be advantageous but is often limited by time and cost. We present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient. The pipeline was tested on images from leaf disc assay experiments involving two F 1 grapevine families, one that had glabrous leaves (Vitis rupestris B38 × ‘Horizon’ [RH]) and another that had leaf trichomes (Horizon × V. cinerea B9 [HC]). Correlations between computer vision and manual visual ratings reached 0.89 in the RH family and 0.43 in the HC family. Additionally, we were able to use the computer vision system prior to sporulation to measure the percent leaf trichome area. We estimate that an experienced rater scoring sporulation would spend at least 90% less time using the computer vision system compared with the manual visual method. This will allow more treatments to be phenotyped in order to better understand the genetic architecture of downy mildew resistance and of leaf trichome density. We anticipate that this computer vision system will find applications in other pathosystems or traits where responses can be imaged with sufficient contrast from the background.
Why it matches plant phenotyping methods画像からブドウ葉のべと病胞子形成面積と毛状突起面積を定量推定するコンピュータビジョン手法の開発・検証が中心であり、植物表現型手法に該当する。
abstractWe present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient.
Reproduction assets foundThe paper's authors publicly deposited the four Python/OpenCV scripts (crop.py, values.py, circles.py, lines.py) used to quantify downy mildew sporulation and leaf trichome area from smartphone leaf-disc images, with an explicit availability statement and URL. No phenotype dataset or image deposit is stated in the textCode · publicd the threshold and Hough
circle transform algorithm parameters and lines.py is used to find the
Hough line transform algorithm parameters. All of the scripts are
parallelized, such that, when run, they automatically use all available
CPU cores for faster image processing. The scripts and a guide for the
scripts can be found at https://github.com/kdivilov/downymildew-CV.For the computer vision system, the images were initially cropped
so that only leaf discs that were fully contained in an image were kept
(Fig. 1). The cropped images were then converted to Lab color space,
which, unlike RGB color space, includes all colors visible to the
human eye, with all of the layers thresholded using usOpen asset ↗kdivilov/downymildew-CVpdf-raw-page:2 lines:79-134Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100–1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60% of it, and scaled up to window patches containing a proportion of 20–80% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.
Why it matches plant phenotyping methodsブドウ芽の画像検出を中心に、SIFT・Bag of Features・SVMによる植物器官の表現型取得手法を開発・評価しており、単なる生物学的実験での測定ではない。
abstractthis paper presents a classification method for images of grapevine buds
Reproduction assets foundThe paper's grapevine bud image datasets (labeled bud/non-bud patch corpus) and the authors' .Net image-manipulation/annotation software and code are publicly available at the authors' dharma.frm.utn.edu.ar URLs, as stated in footnotes and the discussion.Dataset · publicy the
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region, with a pre-selected patch step size and dimensions. This method
269
works similarly to a scanning-window algorithm, but we limit it to scan in
270
a restricted region. With this procedure we could obtain a lot of examples,
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orders of magnitude more than the bud patches.
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3All images datasets available in http://dharma.frm.utn.edu.ar/papers/vise/bc/4.Net software and code available in http://dharma.frm.utn.edu.ar/papers/vise/bc/11Open asset ↗dharma.frm.utn.edu.arpdf-raw-page:11 lines:1-55Code · public269
works similarly to a scanning-window algorithm, but we limit it to scan in
270
a restricted region. With this procedure we could obtain a lot of examples,
271
orders of magnitude more than the bud patches.
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3All images datasets available in http://dharma.frm.utn.edu.ar/papers/vise/bc/4.Net software and code available in http://dharma.frm.utn.edu.ar/papers/vise/bc/11Open asset ↗dharma.frm.utn.edu.arpdf-raw-page:11 lines:1-55Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
CoffeeGrapevineLeafMorphology / geometry measurementPhysiological trait estimationLeaf traitsWater status / transpiration
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1-1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400-900 kHz and 200-400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.
Why it matches plant phenotyping methods植物葉の水分量・膨圧を非接触超音波で測定するセンサー方式の要件分析、試作、応用を中心に扱っており、植物表現型取得法が明確に中心的である。
abstractThis work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure.
Reproduction assets foundThe paper's inverse-problem analysis code for extracting leaf parameters (thickness, density, ultrasound velocity, attenuation) from measured resonance spectra is explicitly stated to be publicly available via the authors' GitHub repository and the US-BIOMAT resource page. No phenotype dataset deposit is mentioned.Code · publicUS-BIOMAT
Available online: https://us-biomat.com/resources/code-2/ or https://github.com/usbiomat/ultrasonic-thickness-resonance (accessed on 12 July 2016)Open asset ↗usbiomat/ultrasonic-thickness-resonancelines:327-413