Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System

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dc.authorid0000-0002-8611-701Xen_US
dc.contributor.authorÖzyurt, Fatih
dc.contributor.authorSert, Eser
dc.contributor.authorAvcı, Derya
dc.date.accessioned2022-04-27T13:01:53Z
dc.date.available2022-04-27T13:01:53Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe need for automatic disease detection applications that can help farmers in the detection of agricultural product diseases is increasing day by day. Convolutional Neural Network (CNN) is a very popular field in image processing, recognition, and classification. It is seen that CNN architectures are used in the determination of agricultural products. In this study, 3 different ResNet architectures of the features automatically are used in the detection of tomato diseases. The most efficient features obtained from these architectures have been obtained by the NCA algorithm again. The features obtained have been trained with the Cubic SVM machine learning algorithm. Tomato leaves belonging to a total of 10 classes have been trained at 80% and a test performance rate of 98.2% has been achieved.en_US
dc.identifier.citationÖzyurt, F., Sert, E., & Avci, D. (2022). Ensemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification System. Traitement du Signal, 39(1) 71-77.en_US
dc.identifier.doi10.18280/ts.390107
dc.identifier.endpage77en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85128202756en_US
dc.identifier.startpage71en_US
dc.identifier.urihttps://doi.org/10.18280/ts.390107
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1040
dc.identifier.volume39en_US
dc.identifier.wosWOS:000777957800007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorSert, Eser
dc.language.isoenen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectNCAen_US
dc.subjectResidual Networken_US
dc.subjectTomato Leaf Diseaseen_US
dc.titleEnsemble Residual Network Features and Cubic-SVM Based Tomato Leaves Disease Classification Systemen_US
dc.typeArticleen_US

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