Automated, vision-based early warning systems have been developed to detect behavioural changes in groups of pigs to monitor their health and welfare status. In commercial settings, automatic recording of feeding behaviour remains a challenge due to problems of variation in illumination, occlusions and similar appearance of different pigs. Additionally, such systems, which rely on pig tracking, often overestimate the actual time spent feeding, due to the inability to identify and/or exclude non-nutritive visits (NNV) to the feeding area. To tackle these problems, we have developed a robust, deep learning-based feeding detection method that (a) does not rely on pig tracking and (b) is capable of distinguishing between feeding and NNV for a group of pigs. We first validated our method using video footage from a commercial pig farm, under a variety of settings. We demonstrate the ability of this automated method to identify feeding and NNV behaviour with high accuracy (99.4% ± 0.6%). We then tested the method's ability to detect changes in feeding and NNV behaviours during a planned period of food restriction. We found that the method was able to automatically quantify the expected changes in both feeding and NNV behaviours. Our method is capable of monitoring robustly and accurately the feeding behaviour of groups of commercially housed pigs, without the need for additional sensors or individual marking. This has great potential for application in the early detection of health and welfare challenges of commercial pigs.
|Early online date||12 Jul 2020|
|Publication status||Published - Sep 2020|