Automated Flock Density and Activity Recognition for Welfare Monitoring on Commercial Egg Farms

Monitoring poultry behaviour provides the opportunity to aid egg production and animal welfare. With the current development in machine learning and computer vision, automated content analysis has become a practical way for low-cost and continuous monitoring of animal behaviours. In this demo, we will show a simple yet effective flock monitoring system based on computer vision and machine learning techniques for egg farmers that allows them to reduce labour yet improve performance. This demo shows that it is possible to auto-analyse flock activities thereby providing early warning of welfare issues, by applying object detection, tracking and crowd-counting techniques. Summaries of individual bird activity and their distribution are closely related to the flock behaviour, which in turn reflects the welfare status. Specifically, the density and movement patterns of birds provide reliable information on the welfare status of the flock. For example, the real-time monitoring of density and movement can give early warnings of pile-ups. To observe these and other important flock activities, we developed a low-cost and easy-use system based on recent computer vision techniques to auto-estimate the density and movement of birds on commercial egg farms.