Abstract
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.
Highlights
The accurate quantification of feeding and associated behaviours is an important challenge for the early detection of health and welfare challenges in livestock
Results show that our method provides sustainable and long-term segments of behaviour in “noisy” environments where pigs are more likely to be touching and frequently occluded by each other, overcoming problems associated with systems that rely on pig tracking to identify behaviours, e.g (Mittek et al, 2017)
Automation in animal husbandry is a tool that has the capability for capturing early changes in key behaviours that occur due to welfare and health compromises. Such changes are impractical to quantify manually and early detection, through automation, allows for timely intervention to prevent a further reduction in animal welfare and associated economic losses
Summary
The accurate quantification of feeding and associated behaviours is an important challenge for the early detection of health and welfare challenges in livestock. Feeding is a fundamental behaviour which can be quantified in a number of different ways when considering a group of pigs. These include recording the amount of food consumed, recording the duration of time spent chewing/ biting food, or recording the amount of time and/or frequency that the head of the animal is in the food trough. Animals will visit the feeding area without consuming any feed, to sample or explore the area where food is, or should be, distributed This is classified as a non-nutritive visit (NNV) (Miller et al, 2019; Weary et al, 2009). To date, quantifying NNV behaviour in group housed animals has only been possible retrospectively via highly time-consuming manual analysis and has limited use in a real world scenario
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