The automatic identification of butterfly species using deep learning methodologies

Abstract

Automatic identification of butterflies, especially at an expert level, is needed for important topics such as species conservation studies, minimizing the insect damage on plants in agriculture, and biodiversity conservation. An efficient and performing model which can define species even in small datasets may reduce the need for experts on the subject or reduce the time spent for identification. By the model proposed in this study, automatic taxonomic classification of butterflies was studied. Convolutional Neural Network (CNN) applications were applied on 7148 photographs of six butterfly species used in the study. 80 percent of the data set was reserved for training and 20 percent for testing, and the model was run with the relevant parameters. At the end of the study, an accuracy degree of 92.73% was obtained.