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dc.contributor.advisorTuna Çakaren_US
dc.contributor.authorTek Kara, Seda Emel
dc.date.accessioned2021-12-14T11:21:13Z
dc.date.available2021-12-14T11:21:13Z
dc.date.issued2020en_US
dc.identifier.citationTek Kara, S. E. (2021). The Automatic Identification of Butterfly Species Using Deep Learning Methodologies. MEF Üniversitesi Fen Bilimleri Enstitüsü, Bilişim Teknolojileri Yüksek Lisans Programı. ss. 1-24en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1688
dc.description.abstractAutomatic identification of butterflies, especially at an expert level, is needed for important topics such as species conservation studies, minimizing the insect damage on plants in agriculture, and biodiversity conservation. An efficient and performing model which can define species even in small datasets may reduce the need for experts on the subject or reduce the time spent for identification. By the model proposed in this study, automatic taxonomic classification of butterflies was studied. Convolutional Neural Network (CNN) applications were applied on 7148 photographs of six butterfly species used in the study. 80 percent of the data set was reserved for training and 20 percent for testing, and the model was run with the relevant parameters. At the end of the study, an accuracy degree of 92.73% was obtained.en_US
dc.language.isoengen_US
dc.publisherMEF Üniversitesi Fen Bilimleri Enstitüsüen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectButterfly Classification, Artificial Intelligence, Deep Learning, Species Identification, Convolutional Neural Networken_US
dc.titleThe automatic identification of butterfly species using deep learning methodologiesen_US
dc.title.alternativeDerin öğrenme metodolojilerini kullanarak kelebek türlerinin otomatik olarak tanımlanmasıen_US
dc.typeYL-Bitirme Projesien_US
dc.departmentBilişim Teknolojileri Yüksek Lisans Programıen_US
dc.identifier.startpage1-24en_US
dc.relation.publicationcategoryYL-Bitirme Projesien_US
dc.contributor.institutionauthorTek Kara, Seda Emel


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