A Deep Learning Approach to Animal Footprint Classification

Animal footprint identification is a crucial tool for studying wildlife, protecting species, and monitoring the environment. A deep learning-based approach is presented for automatic footprint identification and classification, focusing on footprints from various animals, including cows, cats, rabbits, squirrels, and elephants. Convolutional Neural Networks (CNNs) are utilized to recognize distinct characteristics of the footprint, ensuring precise classification. A labeled dataset of preprocessed footprint images is used for training, with image augmentation techniques applied to enhance robustness against variations in size, angle, and lighting conditions. For practical application, a user-friendly web interface is developed to enable real-time footprint identification through image uploads. The model demonstrated high accuracy in rapidly identifying animal footprints after testing, with a validation accuracy of 95.65% and a training accuracy of 97.40%. This system offers an effective, non-intrusive approach for monitoring animal populations, examining migration behaviors, and aiding conservation initiatives, rendering it a useful resource for researchers, conservationists, and wildlife enthusiasts.