Cow Health Monitoring System using Deep Learning

Continuous monitoring of cow health is required in the dairy sector in order to meet the goals of increasing output and preventing diseases at an earlier stage. The conventional approaches to health monitoring are laborious, time-consuming, and primarily reliant on the use of manual observation. The purpose of this research is to offer an automated cow health monitoring system that makes use of a YOLO (You Only Look Once) object detection model that is based on Deep Learning. The system performs an analysis of video or picture data in real time in order to identify important health markers such as abnormal posture, lameness, restricted movement, and feeding behaviour. Because YOLO provides detection that is both quick and accurate, the technology is appropriate for agricultural settings that occur in real time. Following the processing of the detected data, the conditions of cows are classified as either healthy or unhealthy. Through the use of this approach, human interaction is minimized, and prompt health assessment is ensured. The approach that is being proposed improves the welfare of animals, maximizes the efficiency of dairy farms, and provides assistance for precision livestock production. The results of the experiments show that the detection accuracy and real-time performance have been significantly enhanced.