Atesoglu, FatihBingol, Harun2026-06-192026-06-1920250765-00191958-5608https://doi.org/10.18280/ts.420220https://hdl.handle.net/20.500.12899/5383The problem of determining the type of grapevine leave (GL) has an important place in the agricultural field and especially in the field of viticulture. It is a foodstuff that is consumed as a table especially in every Middle Eastern country and its export to Europe has been increasing in recent years. GL are usually consumed as wraps. Considering the economic situation of such a widely used plant, the determination of the plant type is very important. Because early intervention is required for diseases that will occur in the plant. As it is known, early diagnosis will facilitate treatment. In this study, leaf types were classified using artificial intelligence techniques in order to help experts diagnose the type of vine leaf. In addition, a hybrid model was proposed for classification processes. In this proposed hybrid model, feature maps were first extracted from the Resnet50 and Inceptionv3 deep models and these features were combined. AThen, the most valuable features in the feature map were selected with the Neighborhood Component Analysis (NCA) method. Then, the feature map containing the most valuable features was classified in the best-known supervised classification methods. The proposed hybrid model reached an accuracy value of 89.2%. Thus, it has been determined that the proposed hybrid model achieves a highly competitive accuracy value in the classification of vine leaf images and can be used for this purpose.eninfo:eu-repo/semantics/openAccessCnnGrapevine LeavesNcaMachineLearningArticle10.18280/ts.420220422835841WOS:001484318400020Q4