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

Abstract

We present an application of image-processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. Grey-level cooccurrence 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°, 45°, 90° and 135°) 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.