Change in the frequency of contact between pigs within a group may be indicative of a
change in the physiological or health status of one or more pigs within a group, or indicative of the occurrence of abnormal behaviour, e.g. tail-biting. Here, we developed a novel framework that detects and quantifies the frequency of interaction, i.e., a pig head to another pig rear, between pigs in groups. The method does not require individual pig tracking/identification and uses only inexpensive camera-based data capturing infra- structure. We modified the architecture of well-established deep learning models and further developed a lightweight processing stage that scans over pigs to score said interactions. This included the addition of a detection subnetwork to a selected layer of the base residual network. We first validated the automated system to score the interactions between individual pigs within a group, and determined an average accuracy of 92.65% ± 3.74%, under a variety of settings, e.g., management set-ups and data capturing.
We then applied the method to a significant welfare challenge in pigs, that of the detection
of tail-biting outbreaks in pigs and quantified the changes that happen in contact behav-
iour during such an outbreak. Our study shows that the system is able to accurately
monitor pig interactions under challenging farming conditions, without the need for
additional sensors or a pig tracking stage. The method has a number of potential appli-
cations to the field of precision livestock farming of pigs that may transform the industry.
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