Smart Animal Monitoring System for Farms using YOLO and OpenCV

Animal detection in agricultural environments is a crucial component of modern farming practices, enabling farmers to monitor livestock, enhance security, and optimize resource management. This paper proposes a solution for animal detection in farm settings, leveraging the speed and accuracy of the You Only Look Once (YOLO) object detection algorithm and the versatile computer vision capabilities of the Open-Source Computer Vision Library (OpenCV). Utilizing the YOLO algorithm, recognized for its real-time object detection capabilities, enables the identification and localization of animals in farm images or video frames. OpenCV complements this process by providing a robust framework for image processing, enabling tasks such as image preprocessing, feature extraction, and camera interfacing. Integrating YOLO and OpenCV aims to create an efficient and reliable system for realtime animal detection, offering farmers valuable insights into livestock activities and facilitating prompt decision-making. The proposed approach helps to enhance farm management applications, contributing to increased productivity and addressing challenges related to animal monitoring in agricultural areas. The proposed work has been implemented using a Python tool, and the results gained 98% accuracy.