Analysis and Comparison of New-Born Calf Standing and Lying Time Based on Deep Learning

Abstract

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 (p p p = 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.

Keywords

• behaviour monitoring
• animal welfare
• deep learning
• health indicator