Kayci, LokmanKaya, Yilmaz2024-12-242024-12-2420140939-71402326-2680https://doi.org/10.1080/09397140.2014.892340https://hdl.handle.net/20.500.12604/6947We 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.eninfo:eu-repo/semantics/closedAccessButterfly identificationexpert systemgrey-level co-occurrence matrixmultinomial logistic regressiontexture analysisArticle6015764Q4WOS:000331654700010Q32-s2.0-8489759643810.1080/09397140.2014.892340