翻訳待ち：A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 20 Aug 2026] Title:A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection View a PDF of the paper titled A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection, by Petar Canoski and Vlatko Spasev and Ivica Dimitrovski and Ivan Kitanovski and Petre Lameski View PDF HTML (experimental) Abstract:Grape leaf disease recognition is important for precision agriculture, enabling early diagnosis, timely intervention, and improved vineyard management. Although deep learning has achieved strong results, many studies rely on few datasets, often acquired under controlled conditions, and may not reflect real vineyard challenges such as complex backgrounds, variable illumination, occlusion, leaf pose, disease severity, and device differences. This paper presents a dataset-centric benchmark of deep learning methods for grape leaf disease classification and detection. We analyze publicly available datasets in terms of disease categories, annotation types, acquisition conditions, image characteristics, class distributions, provenance, and task suitability. Representative models are evaluated in three settings: image-level classification, region-level classification, and object detection. Classification is assessed using accuracy, while detection is evaluated using mAP@50 and mAP@50:95. Cross-dataset experiments further examine transfer between datasets with compatible disease categories but different visual and annotation characteristics. Results show near-saturated classification performance on several controlled or derivative datasets, greater difficulty on heterogeneous datasets, and substantial variation in detection performance across annotation settings. Cross-dataset performance drops sharply, especially for object detection, indicating that shared disease labels do not necessarily define equivalent recognition tasks. The benchmark emphasizes dataset provenance, realistic field evaluation, annotation compatibility, and external validation for reliable vineyard disease recognition. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.20608 [cs.CV] (or arXiv:2608.20608v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.20608 arXiv-issued DOI via DataCite (pending registration) Submission history From: Ivica Dimitrovski [view email] [v1] Thu, 20 Aug 2026 23:17:09 UTC (11,005 KB) Full-text links: Access Paper: View a PDF of the paper titled A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection, by Petar Canoski and Vlatko Spasev and Ivica Dimitrovski and Ivan Kitanovski and Petre Lameski View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)