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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/42006

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network_name_str SEDICI (UNLP)
spelling 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
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http://purl.org/coar/resource_type/c_5794
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format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/42006
url http://sedici.unlp.edu.ar/handle/10915/42006
dc.language.none.fl_str_mv eng
language eng
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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)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
dc.format.none.fl_str_mv application/pdf
121-134
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