Application of artificial neural network for automatic detection of butterfly species using color and texture features
Butterflies can be classified by their outer morphological qualities, genital characteristics that can be obtained using various chemical substances and methods which are carried out manually by preparing genital slides through some certain processes or molecular techniques which is a very expensive method. In this study, a new method which is based on artificial neural networks (ANN) and an image processing technique was used for identification of butterfly species as an alternative to conventional diagnostic methods. Five texture and three color features obtained from 140 butterfly images were used for identification of species. Texture features were obtained by using the average of gray level co-occurrence matrix (GLCM) with different angles and distances. The accuracy of the purposed butterfly classification method has reached 92.85%. These findings suggested that the texture and color features can be useful for identification of butterfly species.
artificial neural network glcm butterfly identification expert system lbp texture analysis