Sheep health behavior analysis in machine learning: A short comprehensive survey
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/23952 |
Resumo: | Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production. |
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Sheep health behavior analysis in machine learning: A short comprehensive survey231102Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production.Repositório Científico do Instituto Politécnico do PortoNoor, AlamCorke, Murray J.Tovar, Eduardo2023-11-23T11:22:49Z2023-11-212023-11-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/23952enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T01:47:51Zoai:recipp.ipp.pt:10400.22/23952Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:19:59.036736Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Sheep health behavior analysis in machine learning: A short comprehensive survey 231102 |
title |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
spellingShingle |
Sheep health behavior analysis in machine learning: A short comprehensive survey Noor, Alam |
title_short |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
title_full |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
title_fullStr |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
title_full_unstemmed |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
title_sort |
Sheep health behavior analysis in machine learning: A short comprehensive survey |
author |
Noor, Alam |
author_facet |
Noor, Alam Corke, Murray J. Tovar, Eduardo |
author_role |
author |
author2 |
Corke, Murray J. Tovar, Eduardo |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Noor, Alam Corke, Murray J. Tovar, Eduardo |
description |
Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-23T11:22:49Z 2023-11-21 2023-11-21T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/23952 |
url |
http://hdl.handle.net/10400.22/23952 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135499069161472 |