3d acceleration for heat detection in dairy cows
- Autores
- Vanrell, Sebastián R.; Chelotti, José O.; Galli, Julio; Rufiner, Hugo Leonardo; Milone, Diego H.
- Año de publicación
- 2014
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Accurate and reliable detection of heat in dairy cows is essential for a controlled reproduction and therefore, for maintaining milk production. Classical approaches like visual identification are no longer viable on large dairy herds. Several automated techniques of detection have been proposed, but expected results are only achieved by expensive or invasive methods, because practical methods are not reliable. We present a method that aims to be both practical and accurate. It is based on simple attributes extracted from 3D acceleration data and well known classifiers: multilayer perceptrons, support vector machines and decision trees. Results show promising detection ratios, above 90% in several configurations of the detection system. Best results are achieved with multilayer perceptrons. This information could be readily incorporated to the automated system in a dairy farm and help to improve its efficiency.
Sociedad Argentina de Informática e Investigación Operativa (SADIO) - Materia
-
Ciencias Informáticas
Ciencias Agrarias
COMPUTERS IN OTHER SYSTEMS
estrus recognition
dairy cattle
binnary classification - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/3.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/42006
Ver los metadatos del registro completo
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3d acceleration for heat detection in dairy cowsVanrell, Sebastián R.Chelotti, José O.Galli, JulioRufiner, Hugo LeonardoMilone, Diego H.Ciencias InformáticasCiencias AgrariasCOMPUTERS IN OTHER SYSTEMSestrus recognitiondairy cattlebinnary classificationAccurate and reliable detection of heat in dairy cows is essential for a controlled reproduction and therefore, for maintaining milk production. Classical approaches like visual identification are no longer viable on large dairy herds. Several automated techniques of detection have been proposed, but expected results are only achieved by expensive or invasive methods, because practical methods are not reliable. We present a method that aims to be both practical and accurate. It is based on simple attributes extracted from 3D acceleration data and well known classifiers: multilayer perceptrons, support vector machines and decision trees. Results show promising detection ratios, above 90% in several configurations of the detection system. Best results are achieved with multilayer perceptrons. This information could be readily incorporated to the automated system in a dairy farm and help to improve its efficiency.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2014-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf121-134http://sedici.unlp.edu.ar/handle/10915/42006enginfo:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/CAI/12.pdfinfo:eu-repo/semantics/altIdentifier/issn/1851-2526info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:53:46Zoai:sedici.unlp.edu.ar:10915/42006Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:53:47.03SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
3d acceleration for heat detection in dairy cows |
title |
3d acceleration for heat detection in dairy cows |
spellingShingle |
3d acceleration for heat detection in dairy cows Vanrell, Sebastián R. Ciencias Informáticas Ciencias Agrarias COMPUTERS IN OTHER SYSTEMS estrus recognition dairy cattle binnary classification |
title_short |
3d acceleration for heat detection in dairy cows |
title_full |
3d acceleration for heat detection in dairy cows |
title_fullStr |
3d acceleration for heat detection in dairy cows |
title_full_unstemmed |
3d acceleration for heat detection in dairy cows |
title_sort |
3d acceleration for heat detection in dairy cows |
dc.creator.none.fl_str_mv |
Vanrell, Sebastián R. Chelotti, José O. Galli, Julio Rufiner, Hugo Leonardo Milone, Diego H. |
author |
Vanrell, Sebastián R. |
author_facet |
Vanrell, Sebastián R. Chelotti, José O. Galli, Julio Rufiner, Hugo Leonardo Milone, Diego H. |
author_role |
author |
author2 |
Chelotti, José O. Galli, Julio Rufiner, Hugo Leonardo Milone, Diego H. |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Ciencias Agrarias COMPUTERS IN OTHER SYSTEMS estrus recognition dairy cattle binnary classification |
topic |
Ciencias Informáticas Ciencias Agrarias COMPUTERS IN OTHER SYSTEMS estrus recognition dairy cattle binnary classification |
dc.description.none.fl_txt_mv |
Accurate and reliable detection of heat in dairy cows is essential for a controlled reproduction and therefore, for maintaining milk production. Classical approaches like visual identification are no longer viable on large dairy herds. Several automated techniques of detection have been proposed, but expected results are only achieved by expensive or invasive methods, because practical methods are not reliable. We present a method that aims to be both practical and accurate. It is based on simple attributes extracted from 3D acceleration data and well known classifiers: multilayer perceptrons, support vector machines and decision trees. Results show promising detection ratios, above 90% in several configurations of the detection system. Best results are achieved with multilayer perceptrons. This information could be readily incorporated to the automated system in a dairy farm and help to improve its efficiency. Sociedad Argentina de Informática e Investigación Operativa (SADIO) |
description |
Accurate and reliable detection of heat in dairy cows is essential for a controlled reproduction and therefore, for maintaining milk production. Classical approaches like visual identification are no longer viable on large dairy herds. Several automated techniques of detection have been proposed, but expected results are only achieved by expensive or invasive methods, because practical methods are not reliable. We present a method that aims to be both practical and accurate. It is based on simple attributes extracted from 3D acceleration data and well known classifiers: multilayer perceptrons, support vector machines and decision trees. Results show promising detection ratios, above 90% in several configurations of the detection system. Best results are achieved with multilayer perceptrons. This information could be readily incorporated to the automated system in a dairy farm and help to improve its efficiency. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-09 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/42006 |
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http://sedici.unlp.edu.ar/handle/10915/42006 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://43jaiio.sadio.org.ar/proceedings/CAI/12.pdf info:eu-repo/semantics/altIdentifier/issn/1851-2526 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 121-134 |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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