Development of an early warning system for a broiler house using computer vision

Abstract

Anomalous animal behaviour and reduced growth rate are just a few signs that can indicate an undesired situation in a broiler house. It is important that problems such as malfunctioning feeding and drinking lines are detected at an early stage to avoid a negative impact on the welfare or the production of broilers. This paper introduces an automated method to detect problems in a broiler house using cameras and image analysis software. In an experiment with Ross 308 broilers conducted in a commercial broiler house with 28,000 animals, three top-view cameras were mounted at a 5 m height and continuously monitored a floor space of 19.8 by 63.5 m. Analysis software then translated these images into an animal distribution index. Animal distribution index is known to be related to welfare and it can be affected by equipment malfunctioning in a broiler house. Thus, the final objective was to develop a system that could report malfunctioning in a broiler house to the farmer in real-time. Based on these data, a linear real-time model was developed and tested to model the animal distribution index in response to light input. Using this model, the animal distribution index could be predicted online. Comparing these predicted values with the real-time measurements makes it possible to detect any malfunctioning. Results showed that this method could report 95.24% of events (20 out of 21) in real-time, demonstrating a high potential of using automatic monitor tools for broiler production over a complete growing period.