Automatic identification of butterfly species based on local binary patterns and artificial neural network

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dc.authoridUYAR, Murat/0000-0001-7243-7939
dc.authoridkayci, lokman/0000-0003-4372-5717
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKayci, Lokman
dc.contributor.authorUyar, Murat
dc.date.accessioned2024-12-24T19:25:20Z
dc.date.available2024-12-24T19:25:20Z
dc.date.issued2015
dc.departmentSiirt Üniversitesi
dc.description.abstractButterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. (C) 2014 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2014.11.046
dc.identifier.endpage137
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-84919665856
dc.identifier.scopusqualityQ1
dc.identifier.startpage132
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2014.11.046
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6365
dc.identifier.volume28
dc.identifier.wosWOS:000348452500014
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectButterfly identification
dc.subjectLocal binary patterns
dc.subjectTexture features
dc.subjectArtificial neural network
dc.titleAutomatic identification of butterfly species based on local binary patterns and artificial neural network
dc.typeArticle

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