Integrating YOLOv8-pose for localization and behavior tracking of calves in precision livestock farming

Good health and welfare are of great importance when rearing calves. Stress can negatively impact health. Changes in a calf’s behavior may indicate stress. To recognize these changes at early stages, continuous monitoring is required. The aim of this study was to evaluate a model of rapid video analysis of calf behavior. The focus was on the ‘standing’ and ‘lying’ behavior, time spent at the feeder or drinker, and the localization of the area in the pen where the animals stayed, including body orientation. The one-stage detection model YOLOv8-Pose was utilized for this objective. It combines object detection and keypoint estimation, and it was adapted to train a new model. Additionally, further programming work was done to determine the duration of each individual event of the various behaviors in the video recordings. The start and end times of the duration were compared with the manually coded observations of the same videos. The trained YOLOv8-pose model showed a high accuracy. It detected and differentiated the postures ‘standing’, ‘lying_prone’ and ‘lying_lateral’ with a mean average precision (mAP) of 96.78%. The mAP was 97.16% in the case of consumption. The mAP for detecting the calves’ location and orientation was 92.38% and 92.66%, respectively. With a recognition time of 5.6 ms per frame, it can be used for real-time monitoring. This model enables simultaneous monitoring of multiple observations and provides comprehensive behavior monitoring. Consequently, it offers a valuable basis for early warning systems and makes a decisive contribution to forward-looking health management in calf rearing.