Design And Development Of A Low-Power Embedded Edge-Computing Framework For Real-Time Wildlife Monitoring and Deterrence

In modern agriculture, protecting crops from animal intrusions is a major challenge. This project presents a real-time wildlife monitoring and deterrence system using the YOLO V8 object detection algorithm. The system employs AI-based image processing with OpenCV for pre processing and integrates automatic notification and control mechanisms for enhanced farm security. A camera continuously captures images, and YOLO V8 detects and classifies animals in real time. Detected images are uploaded to a remote server for analysis and then deleted to save storage. Pre-processing steps like noise reduction, resizing, and normalization improve detection accuracy, while compression and feature extraction ensure real-time performance. When an animal is detected, the system sends an email alert with the timestamp and type of animal, activates a buzzer, and displays details on an LCD screen. LED floodlights turn on in low light to increase visibility and deter nocturnal animals. The YOLO V8 model is continuously refined for accuracy and adaptability, offering a practical, efficient solution for smart farm wildlife monitoring and deterrence.