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

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, tw...

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Autores principales:	Ali Alameer, Ilias Kyriazakis, Jaume Bacardit
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70f821c81cfe4b1fb4b119bc609c4819
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spelling	oai:doaj.org-article:70f821c81cfe4b1fb4b119bc609c48192021-12-02T19:06:33ZAutomated recognition of postures and drinking behaviour for the detection of compromised health in pigs10.1038/s41598-020-70688-62045-2322https://doaj.org/article/70f821c81cfe4b1fb4b119bc609c48192020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70688-6https://doaj.org/toc/2045-2322Abstract 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 $$0.989 \pm 0.009$$ 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.Ali AlameerIlias KyriazakisJaume BacarditNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ali Alameer Ilias Kyriazakis Jaume Bacardit Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
description	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 $$0.989 \pm 0.009$$ 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.
format	article
author	Ali Alameer Ilias Kyriazakis Jaume Bacardit
author_facet	Ali Alameer Ilias Kyriazakis Jaume Bacardit
author_sort	Ali Alameer
title	Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
title_short	Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
title_full	Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
title_fullStr	Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
title_full_unstemmed	Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
title_sort	automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/70f821c81cfe4b1fb4b119bc609c4819
work_keys_str_mv	AT alialameer automatedrecognitionofposturesanddrinkingbehaviourforthedetectionofcompromisedhealthinpigs AT iliaskyriazakis automatedrecognitionofposturesanddrinkingbehaviourforthedetectionofcompromisedhealthinpigs AT jaumebacardit automatedrecognitionofposturesanddrinkingbehaviourforthedetectionofcompromisedhealthinpigs
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Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

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