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 does not rely on pig tracking and is capable of distinguishing between feeding and NNV for a group of pigs.
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