Li Xia, Song Yifeng, An Xiaoping, An Yuning, Wang Yuan, Liu Na, Gu Jiaxu, Qi Jingwei
College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China.
National Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot 010018, China.
Animals (Basel). 2025 Aug 22;15(17):2470. doi: 10.3390/ani15172470.
The 'head back' posture is a pronounced and significant behavioral trait during bovine parturition, commonly interpreted as a natural response to the pain associated with parturition. Leveraging computer vision technology for real-time monitoring of parturition behaviors can provide timely assistance during calving and enhance animal welfare. This study initially evaluated the head back posture in cows of different types, finding that primiparous cows and those delivering calves weighing over 43 kg exhibited prolonged durations of both labor and head back posture. A model was developed using the YOLOv8 algorithm with 25,617 images to recognize and classify changes in head posture during parturition, including positions like lying with or without head back. The model demonstrated robust predictive performance with a precision () of 69.76%, recall () of 75.35%, average precision () of 70.12%, and score of 0.71. Furthermore, the model's capability to recognize postures from different camera angles and under varying environmental conditions was assessed. Notably, images captured from an abdominal angle achieved exceeding 90%, with consistent stability under varying lighting conditions, including sunny and overcast weather, during both daytime and nighttime. Behavioral analysis showed that the parturition duration and total duration of head back posture in primiparous cows were significantly higher than those in multiparous cows ( < 0.05), and the changing trends of motor performance between primiparous and multiparous cows were consistent across different parturition stages. Additionally, the correlation between calf birth weight and maternal behavior was stronger in primiparous cows than in multiparous cows, further indicating obvious differences in physiological and behavioral responses of cows during primiparous and multiparous parturition. This study underscores the potential of computer vision applications in enhancing real-time intervention and promoting welfare during bovine parturition.
“头部后仰”姿势是奶牛分娩过程中一种明显且重要的行为特征,通常被解释为对分娩相关疼痛的自然反应。利用计算机视觉技术实时监测分娩行为可为产犊过程提供及时协助并提高动物福利。本研究最初评估了不同类型奶牛的头部后仰姿势,发现初产奶牛和分娩体重超过43千克犊牛的奶牛,其产程和头部后仰姿势的持续时间都较长。使用YOLOv8算法和25617张图像开发了一个模型,以识别和分类分娩过程中头部姿势的变化,包括有无头部后仰的躺卧姿势等。该模型表现出强大的预测性能,精确率()为69.76%,召回率()为75.35%,平均精确率()为70.12%,F1分数为0.71。此外,还评估了该模型从不同摄像头角度和不同环境条件下识别姿势的能力。值得注意的是,从腹部角度拍摄的图像F1分数超过90%,在包括晴天和阴天的不同光照条件下,白天和夜间都具有一致的稳定性。行为分析表明,初产奶牛的产程和头部后仰姿势的总持续时间显著高于经产奶牛(<0.05),并且初产奶牛和经产奶牛在不同分娩阶段的运动表现变化趋势一致。此外,初产奶牛犊牛出生体重与母体行为之间的相关性比经产奶牛更强,进一步表明初产和经产奶牛在生理和行为反应上存在明显差异。本研究强调了计算机视觉应用在加强奶牛分娩实时干预和促进福利方面的潜力。