Automating Poultry Farm Management with Artificial Intelligence: Real-time Detection and Tracking of Broiler Chickens for Enhanced and Efficient Health Monitoring

Abstract The broiler industry plays a vital role in meeting the growing global demand for poultry meat. However, maintaining the health and well-being of broiler birds is crucial to ensure both optimal productivity and animal welfare. The increasing mortality rate of broiler chickens developed into an unavoidable issue that required attention. The major goal of this research is to monitor individual chickens for early disease identification, which will then allow for prompt isolation and treatment of sick birds, stopping the spread of pathogens and preserving the health of the flock as a whole. With an accuracy of 96%, the chosen model, YOLOv5s (You Only Look Once), performed the best. Based on their age, the algorithm was able to categorize broiler chickens. The model is converted to ONNX (Open Neural Network Exchange) format after custom training, and the centroid tracker is used for real-time tracking. After that, the output data is kept in a MySQL (My Structured Query Language) database for later use. The OpenCV (Open Source Computer Vision Library) library is used to deploy this model on a local machine. This model seeks to identify the broiler chicken in the video frame, classify them, and maintain track of them using the tracker. Based on their age, the birds are divided into categories. Since most monitoring is required between one and four weeks of age, they are divided into four age groups. The potential application of this model is in the detection of temperature, weight, flock behavior, etc.