Estimating broiler heat stress using computer vision and machine learning

Abstract

To optimize and enhance the efficiency of broiler chicken farming, it is essential to maintain the chicken’s welfare, as heat stress can decrease growth efficiency. The temperature-humidity index (THI) is a key indicator used to determine if chickens are experiencing heat stress. Precision livestock farming (PLF) based on computer vision is one method that can assist farmers in continuously and automatically monitoring the condition of their chickens. This research developed a computer vision-based PLF system to observe chickens with CP 707 strain in a commercial farm using the Mask region-based convolutional neural network (Mask R-CNN) method and object tracking algorithms to analyze features such as the cluster index, unrest index, and the distance traveled by broilers. The results indicated that all features tend to inversely correlate with the THI value, with the cluster index showing the most noticeable tendency. Additionally, it was found that external factors, such as the presence of farmers around the observation area, can affect the chickens' behavior, although the cluster index feature is relatively resilient to disturbances if the operator is not captured by the camera. It was concluded that there is a relationship between the features and the THI value; however, these features are not yet sufficient to distinguish the condition of chickens under high and low THI conditions in real-time.