Feeding frequency and thermal environment are key factors influencing the behavior and welfare of lactating sows, as well as piglet access to milk. This study employed a deep learning-based computer vision approach using the YOLOv8l (You Only Look Once version 8 – large) model to automate multi-class behavior detection in lactating sows. Eight distinct behaviors were classified: feeding, not feeding, suckling, not suckling, lateral lying, sternal lying, standing, and sitting. The model achieved high performance (precision = 0.943, recall = 0.950, F1-score = 0.946, mAP@50 = 0.965). Three treatment groups were evaluated: Group 1 (feeding five times/day, air-conditioned), Group 2 (hourly feeding, air-conditioned), and Group 3 (hourly feeding, non-air-conditioned). Group 2 exhibited the highest suckling frequency (38.75; p = 0.035), feeding frequency (32.97; p p p p p < 0.001) but of shorter duration. These findings demonstrate that hourly feeding under air-conditioned conditions promotes more frequent feeding and suckling behaviors, while heat stress influences lying patterns. The YOLOv8l model proved to be a robust tool for automated behavior detection, supporting its applicability in precision livestock monitoring systems.
Mehtab et al. (Mon,) studied this question.