Image-Based Classification of Snake Species Using Convolutional Neural Network

The snake species can be manually identified based on some features such as head shape, body shape, body texture, skin color, and eye shape, which are not common for non-expert people. An automatic classification of a snake species based on its image is already developed using a traditional machine learning technique, but the parameters should be manually tuned. Therefore, in this paper a convolutional neural network (CNN) is used to develop such classification. Three CNN architectures are evaluated using a dataset of 415 snake images from five common hazardous venomous snake species in Indonesia. Five-fold cross-validating shows that CNN is capable of classifying the snake images with a high accuracy of 82%.