Leopard Detection and Alert System using YOLOv11 and IoT

The increasing frequency of leopard encounters near human settlements poses critical challenges to public safety and wildlife conservation. This research presents an AI-powered Leopard Detection and Alert System leveraging the YOLOv11 deep learning model and Internet of Things (IoT) technologies. The system performs real-time detection of leopards from surveillance footage and generates instant multi-channel alerts via the Telegram Bot API and an Arduino Uno R3-based piezoelectric buzzer. Experimental results demonstrate detection accuracy with confidence scores of 77–81%, real-time processing at 15–20 FPS, and overall system reliability, making the solution suitable for deployment in forest-border and rural regions. The modular architecture supports future extension to multi-species detection and edge device deployment.