A CNN-SVM study based on selected deep features for grapevine leaves classification

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dc.contributor.authorKoklu, Murat
dc.contributor.authorUnlersen, M. Fahri
dc.contributor.authorOzkan, Ilker Ali
dc.contributor.authorAslan, M. Fatih
dc.contributor.authorSabanci, Kadir
dc.date.accessioned2024-02-23T14:13:10Z
dc.date.available2024-02-23T14:13:10Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractThe main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this study, deep learning-based classification is conducted by using images of grapevine leaves. For this purpose, images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods. The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2. As the second approach, features were extracted from pre-trained MobileNetv2 ' s Logits layer and classification was made using various SVM kernels. As the third approach, 1000 features extracted from MobileNetv2 ' s Logits layer were selected by the Chi-Squares method and reduced to 250. Then, classification was made with various SVM kernels using the selected features. The most successful method was obtained by extracting features from the Logits layer and reducing the feature with the Chi-Squares method. The most successful SVM kernel was Cubic. The classification success of the system has been determined as 97.60%. It was observed that feature selection increased the classification success although the number of features used in classification decreased.en_US
dc.identifier.doi10.1016/j.measurement.2021.110425
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85119057932en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2021.110425
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12339
dc.identifier.volume188en_US
dc.identifier.wosWOS:000742844600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectTransfer Learningen_US
dc.subjectSvmen_US
dc.subjectGrapevine Leavesen_US
dc.subjectLeaf Identificationen_US
dc.titleA CNN-SVM study based on selected deep features for grapevine leaves classificationen_US
dc.typeArticleen_US

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