Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs

Detalhes bibliográficos
Autor(a) principal: Lagua, Eddiemar
Data de Publicação: 2024
Outros Autores: Mun, Hong-Seok, Ampode, Keiven Mark B., Chem, Veasna, Park, Hae-Rang, Kim, Young-Hwa, Yang, Chul-Ju
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of Animal Behaviour and Biometeorology
Texto Completo: https://malque.pub/ojs/index.php/jabb/article/view/1805
Resumo: Respiratory disease is the most significant issue in the pig industry and leads to high mortality and reduced growth rates. Housing conditions are crucial for maintaining the health and welfare of animals to optimize growth rates. This study aimed to evaluate the respiratory health status of pigs raised in different housing conditions using artificial intelligence (AI) technology and to determine the relationships between respiratory health and environmental factors. Eighty growing pigs (Largewhite × Landrace) × Duroc) were randomly distributed into two housing conditions: the control group, which had normal housing conditions, and the treatment group, which had poor housing conditions. A two-sample t test was performed to compare the groups, and a cross-correlation analysis was conducted to determine associations between respiratory health and days of exposure to environmental factors. The results show significant (p < 0.001) lower incidence of respiratory health status along with a significant (p < 0.001) greater frequency of coughing in pigs raised under poor housing conditions. Respiratory health is negatively associated with high exposure to temperature, relative humidity, and CO2 at lags 0 (R = -0.873, -0.887, and -0.869, respectively), -1 (R = -0.708, -0.742, and -0.705, respectively), and -2 (R = -0.545, -0.590, and -0.599, respectively). Furthermore, respiratory health was negatively associated with high exposure to NH3 (R = -0.420) at lag 0. In conclusion, AI technology is an effective tool for monitoring respiratory health. However, further evaluation and calibration of the alarm system threshold are recommended. These findings suggest that maintaining proper control of environmental factors can help prevent respiratory diseases in pigs.
id UFERSA-2_5940296aee451a9d8455209ab2abcfb5
oai_identifier_str oai:ojs2.malque.pub:article/1805
network_acronym_str UFERSA-2
network_name_str Journal of Animal Behaviour and Biometeorology
repository_id_str
spelling Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigssmart farmingartificial intelligenceanimal welfarecoughingRespiratory disease is the most significant issue in the pig industry and leads to high mortality and reduced growth rates. Housing conditions are crucial for maintaining the health and welfare of animals to optimize growth rates. This study aimed to evaluate the respiratory health status of pigs raised in different housing conditions using artificial intelligence (AI) technology and to determine the relationships between respiratory health and environmental factors. Eighty growing pigs (Largewhite × Landrace) × Duroc) were randomly distributed into two housing conditions: the control group, which had normal housing conditions, and the treatment group, which had poor housing conditions. A two-sample t test was performed to compare the groups, and a cross-correlation analysis was conducted to determine associations between respiratory health and days of exposure to environmental factors. The results show significant (p < 0.001) lower incidence of respiratory health status along with a significant (p < 0.001) greater frequency of coughing in pigs raised under poor housing conditions. Respiratory health is negatively associated with high exposure to temperature, relative humidity, and CO2 at lags 0 (R = -0.873, -0.887, and -0.869, respectively), -1 (R = -0.708, -0.742, and -0.705, respectively), and -2 (R = -0.545, -0.590, and -0.599, respectively). Furthermore, respiratory health was negatively associated with high exposure to NH3 (R = -0.420) at lag 0. In conclusion, AI technology is an effective tool for monitoring respiratory health. However, further evaluation and calibration of the alarm system threshold are recommended. These findings suggest that maintaining proper control of environmental factors can help prevent respiratory diseases in pigs.Malque Publishing2024-02-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch Articlesapplication/pdfhttps://malque.pub/ojs/index.php/jabb/article/view/180510.31893/jabb.2024008Journal of Animal Behaviour and Biometeorology; Vol. 12 No. 1 (2024): January; 20240082318-12652318-1265reponame:Journal of Animal Behaviour and Biometeorologyinstname:Universidade Federal Rural do Semi-Árido (UFERSA)instacron:UFERSAenghttps://malque.pub/ojs/index.php/jabb/article/view/1805/1139Copyright (c) 2024 Malque Publishinghttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessLagua, EddiemarMun, Hong-SeokAmpode, Keiven Mark B.Chem, VeasnaPark, Hae-RangKim, Young-HwaYang, Chul-Ju2024-03-03T18:07:16Zoai:ojs2.malque.pub:article/1805Revistahttps://periodicos.ufersa.edu.br/index.php/jabbPUBhttp://periodicos.ufersa.edu.br/revistas/index.php/jabb/oai||souza.jr@ufersa.edu.br2318-12652318-1265opendoar:2024-03-03T18:07:16Journal of Animal Behaviour and Biometeorology - Universidade Federal Rural do Semi-Árido (UFERSA)false
dc.title.none.fl_str_mv Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
title Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
spellingShingle Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
Lagua, Eddiemar
smart farming
artificial intelligence
animal welfare
coughing
title_short Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
title_full Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
title_fullStr Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
title_full_unstemmed Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
title_sort Monitoring using artificial intelligence reveals critical links between housing conditions and respiratory health in pigs
author Lagua, Eddiemar
author_facet Lagua, Eddiemar
Mun, Hong-Seok
Ampode, Keiven Mark B.
Chem, Veasna
Park, Hae-Rang
Kim, Young-Hwa
Yang, Chul-Ju
author_role author
author2 Mun, Hong-Seok
Ampode, Keiven Mark B.
Chem, Veasna
Park, Hae-Rang
Kim, Young-Hwa
Yang, Chul-Ju
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Lagua, Eddiemar
Mun, Hong-Seok
Ampode, Keiven Mark B.
Chem, Veasna
Park, Hae-Rang
Kim, Young-Hwa
Yang, Chul-Ju
dc.subject.por.fl_str_mv smart farming
artificial intelligence
animal welfare
coughing
topic smart farming
artificial intelligence
animal welfare
coughing
description Respiratory disease is the most significant issue in the pig industry and leads to high mortality and reduced growth rates. Housing conditions are crucial for maintaining the health and welfare of animals to optimize growth rates. This study aimed to evaluate the respiratory health status of pigs raised in different housing conditions using artificial intelligence (AI) technology and to determine the relationships between respiratory health and environmental factors. Eighty growing pigs (Largewhite × Landrace) × Duroc) were randomly distributed into two housing conditions: the control group, which had normal housing conditions, and the treatment group, which had poor housing conditions. A two-sample t test was performed to compare the groups, and a cross-correlation analysis was conducted to determine associations between respiratory health and days of exposure to environmental factors. The results show significant (p < 0.001) lower incidence of respiratory health status along with a significant (p < 0.001) greater frequency of coughing in pigs raised under poor housing conditions. Respiratory health is negatively associated with high exposure to temperature, relative humidity, and CO2 at lags 0 (R = -0.873, -0.887, and -0.869, respectively), -1 (R = -0.708, -0.742, and -0.705, respectively), and -2 (R = -0.545, -0.590, and -0.599, respectively). Furthermore, respiratory health was negatively associated with high exposure to NH3 (R = -0.420) at lag 0. In conclusion, AI technology is an effective tool for monitoring respiratory health. However, further evaluation and calibration of the alarm system threshold are recommended. These findings suggest that maintaining proper control of environmental factors can help prevent respiratory diseases in pigs.
publishDate 2024
dc.date.none.fl_str_mv 2024-02-05
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Research Articles
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://malque.pub/ojs/index.php/jabb/article/view/1805
10.31893/jabb.2024008
url https://malque.pub/ojs/index.php/jabb/article/view/1805
identifier_str_mv 10.31893/jabb.2024008
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://malque.pub/ojs/index.php/jabb/article/view/1805/1139
dc.rights.driver.fl_str_mv Copyright (c) 2024 Malque Publishing
https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Malque Publishing
https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Malque Publishing
publisher.none.fl_str_mv Malque Publishing
dc.source.none.fl_str_mv Journal of Animal Behaviour and Biometeorology; Vol. 12 No. 1 (2024): January; 2024008
2318-1265
2318-1265
reponame:Journal of Animal Behaviour and Biometeorology
instname:Universidade Federal Rural do Semi-Árido (UFERSA)
instacron:UFERSA
instname_str Universidade Federal Rural do Semi-Árido (UFERSA)
instacron_str UFERSA
institution UFERSA
reponame_str Journal of Animal Behaviour and Biometeorology
collection Journal of Animal Behaviour and Biometeorology
repository.name.fl_str_mv Journal of Animal Behaviour and Biometeorology - Universidade Federal Rural do Semi-Árido (UFERSA)
repository.mail.fl_str_mv ||souza.jr@ufersa.edu.br
_version_ 1799319802021412864