Abstract

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments.

Highlights

  • For instance, large-scale genetic selection programmes are based on the measurements of several hundreds of farm animals [5]

  • To test the efficiency of the radar tracking system, we compared the data obtained from the infrared cells, the video and the radar

  • Proximity scores and crossing rates obtained from infrared cells were positively correlated with data obtained from the radar (Pearson correlation; proximity: r = 0.77, p < 0.001; crossing rate: r = 0.87, p < 0.001) and the video (Pearson correlation test; proximity: r = 0.91, p < 0.001; crossing rate: r = 0.34, p < 0.001)

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Summary

Introduction

Behavioural research increasingly requires automated recording and analyses of animal movements [1] This is exemplified by emerging methods for high-throughput monitoring and statistical analyses of movements that enable the quantitative characterisation of behaviour on large numbers of individuals, the discovery of new behaviours, and the objective comparison of behavioural data across studies and species [2,3]. These quantitative approaches are powerful to study inter-individual behavioural variability or personalities in animal populations [4]. In these studies behavioural measures are frequently obtained from direct observations by the experimenters or farmers [8], which considerably limits the possibility to quantify behavioural traits at the experimental or commercial farm level

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