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Research article - Peer-reviewed, 2021

Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning

Ren, Keni; Bernes, Gun; Hetta, Mårten; Karlsson, Johannes

Abstract

There is a need for reliable and efficient methods for monitoring the activity and social behaviour in cows, in order to optimise management in modern dairy farms. This research presents an embedded system that could track individual cows using Ultra-wideband technology. At the same time, social interactions between individuals around the feeding area were analysed with a computer vision module. Detections of the dairy cows’ negative and positive interactions were performed on foreground video stream using a Long-term Recurrent Convolution Networks model. The sensor fusion system was implemented and tested on seven dairy cows during 45 days in an experimental dairy farm. The system performance was evaluated at the feeding area. The real-time locating system based on Ultra-wideband technology reached an accuracy with mean error 0.39 m and standard deviation 0.62 m. The accuracy of detecting the affiliative and agonistic social interactions reached 93.2%. This study demonstrates a potential system for monitoring social interactions between dairy cows.

Keywords

ultra-wideband; computer vision; dairy cows; social interactions; machine learning

Published in

Journal of Systems Architecture
2021, volume: 116, article number: 102139

Authors' information

Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics
Umeå University
Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden
Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden
Karlsson, Johannes
Umeå University

UKÄ Subject classification

Agricultural Science
Other Engineering and Technologies not elsewhere specified

Publication Identifiers

DOI: https://doi.org/10.1016/j.sysarc.2021.102139

URI (permanent link to this page)

https://res.slu.se/id/publ/111542