A New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50

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dc.contributor.authorBingöl, Harun
dc.contributor.authorAslan, Serpil
dc.date.accessioned2025-10-24T17:59:13Z
dc.date.available2025-10-24T17:59:13Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractRice is extremely important for individuals and countries, both in terms of nutritional value and financial value. It is necessary to protect such an important plant from diseases and increase the yield. However, early detection of diseases on plant leaves can prevent the spread of this disease and is also very important in terms of treating the plant. Artificial intelligence has become very popular in recent years thanks to its success in terms of disease classification. CNN architectures used in image classification perform very successful work. Within the scope of this study, it is recommended that the diseases on rice leaves be classified using artificial intelligence techniques, without mixing them with each other, with very high accuracy values, and without any problems caused by humans. With this proposed model, a support vector machine-based model is proposed that classifies five (5) of the most common rice diseases with a very high accuracy of %98.
dc.identifier.doi10.54565/jphcfum.1499620
dc.identifier.endpage26
dc.identifier.issn2651-3080
dc.identifier.issn2651-3080
dc.identifier.issue2
dc.identifier.startpage22
dc.identifier.urihttps://doi.org/10.54565/jphcfum.1499620
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1993
dc.identifier.volume7
dc.language.isoen
dc.publisherNiyazi BULUT
dc.relation.ispartofJournal of Physical Chemistry and Functional Materials
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzDergiPark_20251023
dc.subjectBioinformatics and Computational Biology (Other)
dc.subjectBiyoinformatik ve Hesaplamalı Biyoloji (Diğer)
dc.titleA New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50
dc.typeArticle

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