Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Abstract

The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.