Classification of walnut dataset by selecting CNN features with whale optimization algorithm

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dc.authoridBASARAN, Erdal/0000-0001-8569-2998
dc.authoridCELIK, YUKSEL/0000-0002-7117-9736
dc.authoridKaradeniz, Alper Talha/0000-0003-4165-3932
dc.contributor.authorKaradeniz, Alper Talha
dc.contributor.authorBasaran, Erdal
dc.contributor.authorCelik, Yueksel
dc.date.accessioned2024-09-29T15:51:24Z
dc.date.available2024-09-29T15:51:24Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSince many years ago, walnuts have been extensively available around the world and come in various quality varieties. The proper variety of walnut can be grown in the right area and is vital to human health. This fruit's production is time-consuming and expensive. However, even specialists find it challenging to differentiate distinct kinds since walnut leaves are so similar in color and feel. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The classification process can now be carried out automatically from leaf photos thanks to technological advancements. The walnut data set was applied to the suggested deep learning model. There aren't many studies on the classification of walnut leaves in the literature, and the most of them were conducted in laboratories. The walnut data set, which consists of 18 different types of 1751 photos, was used to test the suggested deep learning model. The three most successful algorithms among the commonly utilized CNN algorithms in the literature were first selected for the suggested model. From the Vgg16, Vgg19, and AlexNet CNN algorithms, many features were retrieved. Utilizing the Whale Optimization Algorithm (WOA), a new feature set was produced by choosing the top extracted features. KNN is used to categorize this feature set. An accuracy rating of 92.59% was attained as a consequence of the tests.en_US
dc.description.sponsorshipTrabzon Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s11042-024-18586-1
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.scopus2-s2.0-85185508618en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-024-18586-1
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4043
dc.identifier.wosWOS:001167950900012en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofMultimedia Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWalnut dataseten_US
dc.subjectCNNen_US
dc.subjectWOAen_US
dc.subjectFeature selectionen_US
dc.subjectKNNen_US
dc.titleClassification of walnut dataset by selecting CNN features with whale optimization algorithmen_US
dc.typeArticleen_US

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