Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer, A ORCID: https://orcid.org/0000-0002-7969-3609, Kryiazakis, I and Bacardit, J 2020, 'Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs' , Scientific Reports, 10 , p. 13665.

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview

Abstract

Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of 0.989±0.009, under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.

Item Type: Article
Additional Information: Dataset can be found here: https://data.ncl.ac.uk/articles/dataset/Automated_recognition_of_postures_and_drinking_behaviour_for_the_detection_of_compromised_health_in_pigs/13042619
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Scientific Reports
Publisher: Springer Nature
ISSN: 2045-2322
Depositing User: A Alameer
Date Deposited: 26 May 2022 12:55
Last Modified: 17 Aug 2022 08:47
URI: https://usir.salford.ac.uk/id/eprint/63695

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year