A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression

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dc.authoridkayci, lokman/0000-0003-4372-5717
dc.contributor.authorKayci, Lokman
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-12-24T19:28:11Z
dc.date.available2024-12-24T19:28:11Z
dc.date.issued2014
dc.departmentSiirt Üniversitesi
dc.description.abstractWe present an application of image-processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. Grey-level co-occurrence matrix (GLCM) matrices are utilised to evaluate the surface texture features of butterflies' wings, which is an important character for identification. Eleven textural features were extracted from butterfly images and characterised by the texture average in four directions (0 degrees, 45 degrees, 90 degrees and 135 degrees) and distances (d = 1, 2, 3 and 4 pixels). We used 190 butterfly images belonging to 19 different species of the family Pieridae. The identification accuracy of the GLCM+MLR was 96.3% with tenfold cross validation. The methodology presented here classified the butterflies effectively. These findings suggest that the proposed MLR algorithm and GLCM texture features technique are feasible for the identification and classification of butterfly species.
dc.description.sponsorshipScientific Research Projects Unit of Siirt University, Turkey [2013-SIUMUH-M1]
dc.description.sponsorshipThis study is supported by the Scientific Research Projects Unit of Siirt University, Turkey, with 2013-SIUMUH-M1 project number.
dc.identifier.doi10.1080/09397140.2014.892340
dc.identifier.endpage64
dc.identifier.issn0939-7140
dc.identifier.issn2326-2680
dc.identifier.issue1
dc.identifier.scopus2-s2.0-84897596438
dc.identifier.scopusqualityQ3
dc.identifier.startpage57
dc.identifier.urihttps://doi.org/10.1080/09397140.2014.892340
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6947
dc.identifier.volume60
dc.identifier.wosWOS:000331654700010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofZoology in The Middle East
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectButterfly identification
dc.subjectexpert system
dc.subjectgrey-level co-occurrence matrix
dc.subjectmultinomial logistic regression
dc.subjecttexture analysis
dc.titleA vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression
dc.typeArticle

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