Wild Guard: Solar Powered Animal Intrusion Detection and Alert System

Abstract—Monitoring animal movement and health is a critical component of veterinary science, livestock management, and wildlife conservation, particularly in regions where human-animal interactions are frequent. This paper presents a solar-powered animal detection and alert system that utilizes real-time image processing and deep learning to enhance animal healthcare monitoring. The system integrates a camera module with the YOLO (You Only Look Once) object detection algorithm to identify animals entering predefined zones such as agricultural fields, roadways, or human habitations. Unlike conventional systems that depend on motion or weight sensors, this solution employs computer vision to deliver accurate and timely detection of animals and their behaviors. Data captured by the camera is processed by an embedded computing device, which generates alerts to notify veterinary authorities or landowners for further action. The entire system is powered by solar energy, ensuring continuous, low-maintenance operation in remote or off-grid areas. By combining artificial intelligence, embedded systems, and renewable energy, this project provides a cost-effective and scalable tool for improving animal healthcare, enabling early detection of threats, and reducing the risk of human-animal conflict. The proposed system demonstrates promising potential for deployment in both rural and conservation settings where real-time animal monitoring is essential. Keywords—Animal healthcare, YOLO, object detection, computer vision, embedded system, solar-powered system, veterinary monitoring, wildlife detection, real-time alert system, AI in agriculture, smart farming, animal surveillance, rural technology, deep learning, animal safety