Detecting Dairy Cow Behavior Using Vision Technology

McDonagh, John; Tzimiropoulos, Georgios; Slinger, Kimberley R.; Huggett, Zoë J.; Down, Peter M.; Bell, Matt J.

Abstract

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management.

Citation

McDonagh, J., Tzimiropoulos, G., Slinger, K. R., Huggett, Z. J., Down, P. M., & Bell, M. J. (2021). Detecting Dairy Cow Behavior Using Vision Technology. Agriculture, 11(7), Article 675. https://doi.org/10.3390/agriculture11070675

• Journal Article Type: Article
• Acceptance Date: Jul 15, 2021
• Online Publication Date: Jul 17, 2021
• Publication Date: 2021-07
• Deposit Date: Jul 22, 2021
• Publicly Available Date: Jul 22, 2021
• Journal: Agriculture
• Electronic ISSN: 2077-0472
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 7
• Article Number: 675
• DOI: https://doi.org/10.3390/agriculture11070675
• Keywords: Plant Science; Agronomy and Crop Science; Food Science
• Public URL: https://nottingham-repository.worktribe.com/output/5813119
• Publisher URL: https://www.mdpi.com/2077-0472/11/7/675