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

Footprints are among the oldest forms of evidence in wildlife work. They inform monitoring, species conservation, and environmental research, yet reading them the traditional way demands expert eyes, takes a long time, and invites mistakes. This paper presents Smart Forest Guardian, a deep-learning system that classifies animal footprints and watches for intruders. Using EfficientNet-B0 with transfer learning, it identifies the prints of six species: elephant, Pig, horse,Monkey, lion, and deer. Before classification, every image passes through a preprocessing pipeline of resizing, grayscale conversion, noise reduction, contrast enhancement, and augmentation, which keeps accuracy steady as field conditions change. Preprocessing, feature extraction, classification, real-time prediction through a FastAPI backend, and automated intrusion monitoring with YOLO WORLD all sit inside one application. The model learns each species' signature from shape, texture, and edge characteristics. It reached a test accuracy of 73.68%,and it returns predictions as structured JSON within milliseconds. Evaluation shows the model is most dependable on species with plentiful training data, while rarer species are harder to separate because of class imbalance and visually similar prints. To address this, future work will expand and balance the dataset, strengthen augmentation for underrepresented classes, and port the model to low-power edge devices for deployment in the field. The system also adds a human-intrusion alert that flags suspicious activity for forest officers in real time. Deep learning, computer vision, and web technologies together make a method for wildlife monitoring, animal tracking, and anti-poaching operations that is efficient, accurate, and non-intrusive. Everything is consolidated on a real-time Forest Officer Dashboard.