AI-Integrated Video Analytics for Real-Time Animal Grazing Detection and Buzzer Alert Activation

Agriculture is one of the most essential sectors contributing to the economic growth of a country, and protecting crops from external threats is crucial for ensuring productivity and sustainability. One of the major challenges faced by farmers is the intrusion of animals into agricultural land, which leads to significant crop damage and financial loss. Traditional methods such as fencing, manual monitoring, and basic intrusion detection systems are not efficient and require continuous human effort. This paper proposes an AI-integrated video analytics system for real-time animal grazing detection and alert activation. The system utilizes CCTV cameras to continuously monitor agricultural fields and capture live video streams. The captured video is processed using image preprocessing techniques, including noise removal, resizing, and enhancement, to improve the quality of frames. A Convolutional Neural Network (CNN) model is used to analyze the processed frames and accurately classify objects as animals or non-animals. Once an animal is detected, the system activates an automated alert mechanism consisting of a buzzer and cracker-based deterrent to scare animals away safely. Additionally, notifications can be sent to farmers for immediate awareness. The proposed system provides real-time monitoring, reduces manual effort, improves detection accuracy, and ensures efficient crop protection. This solution is cost-effective, scalable, and suitable for smart agriculture applications.