Animal Detection In Farmlands Using Artificial Intelligence And IoT: A Case Study Of Thalavady Region, Erode District

Agriculture remains the backbone of the Indian economy, providing livelihood for a large portion of the population. However, farmers living near forest boundaries frequently experience severe crop losses due to wildlife intrusion. In regions such as Thalavady in the Erode district of Tamil Nadu, animals including elephants, wild boars, deer, monkeys, and cattle often enter agricultural lands and destroy crops. Traditional crop protection methods such as manual monitoring, fencing, and scare devices are inefficient and require continuous human effort. This research proposes an intelligent animal detection system based on Artificial Intelligence (AI), computer vision, and Internet of Things (IoT) technologies to monitor farmland and detect wildlife intrusion in real time. The system utilizes camera modules and edge computing devices to process images using deep learning algorithms such as Convolutional Neural Networks (CNN) and YOLO object detection models. When animals are detected, alerts are sent to farmers through mobile notifications, and deterrent mechanisms such as sound alarms and lights are activated. The proposed system aims to reduce crop damage, enhance farmland security, and support coexistence between agriculture and wildlife. Experimental results suggest that AI-based detection systems can achieve high accuracy and significantly reduce farmer workload.