Analysis of Calving Cow Posture Recognition, Behavioral Changes, and Influencing Factors Based on Machine Vision

This study introduces a non-contact, single-target method for real-time monitoring of dairy cow calving posture and behavior using the YOLOv8 model. In total, 600 videos were collected, from which 10,544 image samples were extracted through frame-by-frame processing. Complete video recordings of 86 cows (30 primiparous and 56 multiparous) were utilized to investigate changes in calving behavior. The YOLOv8 model achieved excellent performance with precision (<i>P</i>), recall (<i>R</i>), and mean average precision (m<i>AP</i>) of 96.72%, 96.53%, and 97.41%, respectively, and recognition <i>P</i> of 89.19% for lying postures and 82.61% for standing postures. Behavioral analysis revealed that lying postures were more frequent than standing, and primiparous cows had more frequent posture transitions (9.07 changes) than multiparous cows (5.29 changes), particularly during early parturition. Primiparous cows also showed significantly higher average times for parturition and lying as well ashigher frequency of behavioral changes compared to multiparous cows. Additionally, calf birth weight was positively correlated with maternal behaviors, especially in primiparous cows. Our proposed model effectively and accurately recognizes calving postures in dairy cows, enabling the early detection of abnormal calving events. This provides a scientific basis and technical support for intelligent farm management.