Zhang Wenju, Wang Yaowu, Guo Leifeng, Falzon Greg, Kwan Paul, Jin Zhongming, Li Yongfeng, Wang Wensheng
Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China.
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.
Animals (Basel). 2024 Apr 29;14(9):1324. doi: 10.3390/ani14091324.
Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time ( < 0.002), 2.65 more daily lying bouts ( < 0.049), and 4.3 min less daily lying bout duration ( = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.
站立和躺卧是四足动物的基本行为，它们的持续时间比例是犊牛健康的重要指标。在本研究中，我们提出了一种用于非侵入性监测犊牛行为的计算机视觉方法。在四个视角部署摄像头，连续六天监测六头犊牛。训练YOLOv8n以检测站立和躺卧的犊牛。然后基于对未训练数据的自动推理总结并分析每日行为预算。结果显示平均精度为0.995，平均推理速度为每秒333帧。在总共8个犊牛日中，估计每日站立和躺卧时间的最大误差小于14分钟。与健康犊牛相比，腹泻犊牛每日躺卧时间多约2小时（<0.002），每日躺卧次数多2.65次（<0.049），每次躺卧持续时间少4.3分钟（=0.5）。所提出的方法有助于基于自动测量的站立和躺卧时间了解犊牛的健康状况，从而改善农场中犊牛的福利和管理。
