Deep Learning Approach to Snake Species Identification

Snake species identification is always challenging but is becoming increasingly intractable by the increasing diversity in snake species. This implies that the traditional methods of identification that heavily rely on the subjective estimates of the appearance can lead to potential inaccuracy in identifying such species. The present study puts forward a deep learning system applied to the identification of snake species, using CNNs and advanced image processing techniques. Our approach involves comprehensive data collection and preprocessing, followed by segmentation using YOLOv8s to accurately isolate snakes in images. Feature extraction is performed using MobileNetV2 and EfficientNetB0, while classification is achieved through various machine learning algorithms. Notably, an ensemble method combining multiple classifiers yields an impressive accuracy of 94%. The results clearly indicate the success of our approach to the automation of classification of snake species, which therefore contributes to better management practices and better public health initiatives on snake-bite prevention.