Automated detection and quantification of contact behaviour in pigs using deep learning

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 infrastructure. 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 behaviour 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 applications to the field of precision livestock farming of pigs that may transform the industry.