Identification of butterfly species with a single neural network system

Growing interest in conservation and biodiversity increased the demand for accurate and consistent identification of biological objects, such as insects, at the level of individual or species. Among the identification issues, butterfly identification at the species level has been strongly addressed because it is directly connected to the crop plants for human food and animal feed products. However, so far, the widely-used reliable methods were not suggested due to the complicated butterfly shape. In the present study, we propose a novel approach based on a back-propagation neural network to identify butterfly species. The neural network system was designed as a multi-class pattern classifier to identify seven different species. We used branch length similarity (BLS) entropies calculated from the boundary pixels of a butterfly shape as the input feature to the neural network. We verified the accuracy and efficiency of our method by comparing its performance to that of another single neural network system in which the binary values (0 or 1) of all pixels on an image shape are used as a feature vector. Experimental results showed that our method outperforms the binary image network in both accuracy and efficiency.