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

Publication history: Accepted - 5 October 2022; Published online - 22 October 2022Change 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.We are grateful to Dr Katarina Buckova, Melanie McAuley, Zoe Tey and Joy McMillen for help with the collection and annotation of the datasets. This research was part of the EU-China HealthyLivestock project https://healthylivestock.net/. The authors wish to acknowledge that HealthyLivestock is funded by the European Union H2020 research and innovation program under grant agreement number 773436. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The automated detection work was supported in part by the UK Centre for Innovation Excellence in Livestock (CIEL) and Zoetis Inc. Zoetis did not influence the data selection, interpretation, conclusions drawn or the decision on how or what to publish. The study was made possible by a grant from the Green Development and Demonstration Programme under the Ministry of Food, Agriculture and Fisheries, Denmark (project IntactTails j. nr. 34,009-13-0743)