Integrating CBAM and Squeeze-and-Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis

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dc.contributor.authorUnal, Yavuz
dc.date.accessioned2026-04-25T14:19:44Z
dc.date.available2026-04-25T14:19:44Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractThe vine plant holds significant importance beyond grape farming due to its diverse products. Various grape-derived products, such as wine and molasses, highlight the vine plant's role as a valuable agricultural resource. Additionally, traditional cuisines around the world widely utilize grape leaves, contributing to their substantial economic value. However, diseases affecting grape leaves not only harm the plant and its yield but also render the leaves unsuitable for culinary use, leading to considerable economic losses for producers. Detecting diseases on grape leaves is a challenging and time-consuming task when performed manually. Thus, developing a deep learning-based model to automate the classification of grape leaf diseases is of critical importance. This study aims to classify the most common grape leaf diseases grape-scab (grape leaf blister mite) and downy mildew (grapevine downy mildew) alongside healthy leaves using deep learning techniques. Initially, we conducted a basic classification using pre-trained deep learning models. Subsequently, the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation Networks (SE) were integrated into the most successful pre-trained classification model to enhance classification performance. As a result, the classification accuracy improved from 92.73% to 96.36%.
dc.identifier.doi10.1002/fsn3.70377
dc.identifier.issn2048-7177
dc.identifier.issue6
dc.identifier.orcid0000-0002-3007-679X
dc.identifier.pmid40463992
dc.identifier.scopus2-s2.0-105007535813
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1002/fsn3.70377
dc.identifier.urihttps://hdl.handle.net/11486/8133
dc.identifier.volume13
dc.identifier.wosWOS:001517193700007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorUnal, Yavuz
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofFood Science & Nutrition
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260420
dc.subjectCBAM
dc.subjectdeep learning
dc.subjectgrapevine leaf disease
dc.subjectSENet
dc.titleIntegrating CBAM and Squeeze-and-Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis
dc.typeArticle

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