Smart Forest Guardian: AI-Powered Footprint Recognition and Poaching Detection

Ms.Maddelabhargavi 1, Chetan D2, Darshan G3, Dhanush J4, Ganesh M5 Assistant Professor, Dept of CSE, KSIT, Karnataka, India1

Student, Dept of CSE, KSIT, Karnataka, India2-5

Abstract - Animal footprint identification plays a significant role in wildlife monitoring, species conservation, and environmental research. Traditional methods of footprint analysis require expert observation and are often time- consuming and prone to inaccuracies. This paper proposes a deep learning-based Animal Footprint Classification System that automatically identifies footprints of different animals such as cows, cats, rabbits, squirrels, and elephants using Convolutional Neural Networks (CNNs). The system uses labeled and preprocessed footprint images along with image augmentation techniques to improve classification accuracy under varying environmental conditions.The proposed system integrates preprocessing, feature extraction, classification, and real-time prediction into a user-friendly web application. CNN models are trained to recognize unique footprint patterns based on shape, texture, and edge characteristics. The system achieves high accuracy in identifying animal footprints and provides rapid predictions through image uploads. By combining deep learning techniques with web technologies, the proposed solution offers an efficient and non-intrusive approach for wildlife monitoring, animal tracking, and ecological studies.

Key Words: Animal Footprint Classification, Deep Learning, Convolutional Neural Network, Image Processing, Wildlife Monitoring, Footprint Detection, Web Application, Image Classification