AI-Driven Wildlife Detection and Management System for Agricultural Protection

Environmental harm, safety hazards, and large financial losses are all consequences of human-wildlife conflict, which continues to be a major obstacle to agricultural sustainability. Conventional deterrence techniques, like sound deterrents, personal intervention, and electric fence, have been shown to be ineffective, expensive, and frequently detrimental to wildlife. The AI-driven wildlife identification and deterrence system presented in this study addresses these problems by utilising computer vision, deep learning, IoT-based monitoring, and ultrasound deterrence techniques. Real-time animal detection from live video feeds is achieved by the suggested method using YOLO v11 and OpenCV, precisely identifying species that are a hazard to farmlands. When an animal is detected, an automated ultrasonic deterrence system is triggered, which releases high-frequency sound waves that successfully and safely ward off the animal .In order to help build long-term conservation and conflict mitigation methods, movement monitoring is also integrated to analyse animal migration patterns from reserve forests to places where humans live .Additionally, an Internet of Things-based real-time alert mechanism is included by the system, providing farmers and authorities with immediate notifications by SMS, email, and mobile applications so they may take prompt action. The complete system is made to be economical, environmentally friendly, and scalable, guaranteeing its widespread use in agricultural areas where wildlife intrusions are a problem.