Automatic recognition of feeding and foraging behaviour in pigs using deep learning

Alameer, A ORCID: https://orcid.org/0000-0002-7969-3609, Kyriazakis, I, Dalton, HA, Miller, AL and Bacardit, J 2020, 'Automatic recognition of feeding and foraging behaviour in pigs using deep learning' , Biosystems Engineering, 197 , pp. 91-104.

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Abstract

Highlights • An automated detection method of pig feeding and foraging behaviour was developed. • The automated method is based on convolutional deep neural networks. • The automated method does not rely on pig tracking to estimate behaviours. • Detection of feeding behaviour is highly accurate (99.4%) and fast (0.02 sec/image). • The robust method can be applied under different husbandry/ management conditions. 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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Biosystems Engineering
Publisher: Elsevier
ISSN: 1537-5110
Related URLs:
Depositing User: A Alameer
Date Deposited: 26 May 2022 12:50
Last Modified: 13 Jun 2022 09:36
URI: http://usir.salford.ac.uk/id/eprint/63696

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