Improving detection of dairy cow estrus using fuzzy logic
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002 |
Resumo: | Production losses due to lack of precision in detecting estrus in dairy cows are well known and reported in milk production countries. Nowadays automatic estrus detection has become possible as a result of technical progress in continuously monitoring dairy cows using fuzzy pertinence functions. Dairy cow estrus is usually visually detected; however, solely use of visual detection is considered inefficient. Many studies have been carried out to develop an effective model to interpret the occurrence of estrus and detect estrus; however, most models present too many false-positive alerts and because of this they are sometimes considered unreliable. The objective of this research was to construct a system based on fuzzy inference functions evaluated with a receiver-operating characteristic curve, capable of efficiently detect estrus in dairy cows. For the input data the system combined previous estrus cases information and prostaglandin application with the data of cow activities. The system outputs were organized in three categories: 'in estrus', 'maybe in estrus" and 'not in estrus'. The system validation was carried out in a commercial dairy farm using a herd of 350 lactating cows. The performance of the test was measured by calculating its sensitivity towards the right estrus detection; and its specificity towards the precision of the detection. Within a six months period of tests, over 25 thousands cases of estrus were analyzed from a database of the commercial farm. The sensitivity found was 84.2%, indicating that the system can detect estrus efficiently and it may improve automatic estrus detection. |
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Improving detection of dairy cow estrus using fuzzy logicestrus cycleartificial intelligenceexpert systemProduction losses due to lack of precision in detecting estrus in dairy cows are well known and reported in milk production countries. Nowadays automatic estrus detection has become possible as a result of technical progress in continuously monitoring dairy cows using fuzzy pertinence functions. Dairy cow estrus is usually visually detected; however, solely use of visual detection is considered inefficient. Many studies have been carried out to develop an effective model to interpret the occurrence of estrus and detect estrus; however, most models present too many false-positive alerts and because of this they are sometimes considered unreliable. The objective of this research was to construct a system based on fuzzy inference functions evaluated with a receiver-operating characteristic curve, capable of efficiently detect estrus in dairy cows. For the input data the system combined previous estrus cases information and prostaglandin application with the data of cow activities. The system outputs were organized in three categories: 'in estrus', 'maybe in estrus" and 'not in estrus'. The system validation was carried out in a commercial dairy farm using a herd of 350 lactating cows. The performance of the test was measured by calculating its sensitivity towards the right estrus detection; and its specificity towards the precision of the detection. Within a six months period of tests, over 25 thousands cases of estrus were analyzed from a database of the commercial farm. The sensitivity found was 84.2%, indicating that the system can detect estrus efficiently and it may improve automatic estrus detection.Escola Superior de Agricultura "Luiz de Queiroz"2010-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002Scientia Agricola v.67 n.5 2010reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/S0103-90162010000500002info:eu-repo/semantics/openAccessBrunassi,Leandro dos AnjosMoura,Daniella Jorge deNääs,Irenilza de AlencarVale,Marcos Martinez doSouza,Silvia Regina Lucas deLima,Karla Andrea Oliveira deCarvalho,Thayla Morandi Ridolfi deBueno,Leda Gobbo de Freitaseng2010-09-20T00:00:00Zoai:scielo:S0103-90162010000500002Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2010-09-20T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Improving detection of dairy cow estrus using fuzzy logic |
title |
Improving detection of dairy cow estrus using fuzzy logic |
spellingShingle |
Improving detection of dairy cow estrus using fuzzy logic Brunassi,Leandro dos Anjos estrus cycle artificial intelligence expert system |
title_short |
Improving detection of dairy cow estrus using fuzzy logic |
title_full |
Improving detection of dairy cow estrus using fuzzy logic |
title_fullStr |
Improving detection of dairy cow estrus using fuzzy logic |
title_full_unstemmed |
Improving detection of dairy cow estrus using fuzzy logic |
title_sort |
Improving detection of dairy cow estrus using fuzzy logic |
author |
Brunassi,Leandro dos Anjos |
author_facet |
Brunassi,Leandro dos Anjos Moura,Daniella Jorge de Nääs,Irenilza de Alencar Vale,Marcos Martinez do Souza,Silvia Regina Lucas de Lima,Karla Andrea Oliveira de Carvalho,Thayla Morandi Ridolfi de Bueno,Leda Gobbo de Freitas |
author_role |
author |
author2 |
Moura,Daniella Jorge de Nääs,Irenilza de Alencar Vale,Marcos Martinez do Souza,Silvia Regina Lucas de Lima,Karla Andrea Oliveira de Carvalho,Thayla Morandi Ridolfi de Bueno,Leda Gobbo de Freitas |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Brunassi,Leandro dos Anjos Moura,Daniella Jorge de Nääs,Irenilza de Alencar Vale,Marcos Martinez do Souza,Silvia Regina Lucas de Lima,Karla Andrea Oliveira de Carvalho,Thayla Morandi Ridolfi de Bueno,Leda Gobbo de Freitas |
dc.subject.por.fl_str_mv |
estrus cycle artificial intelligence expert system |
topic |
estrus cycle artificial intelligence expert system |
description |
Production losses due to lack of precision in detecting estrus in dairy cows are well known and reported in milk production countries. Nowadays automatic estrus detection has become possible as a result of technical progress in continuously monitoring dairy cows using fuzzy pertinence functions. Dairy cow estrus is usually visually detected; however, solely use of visual detection is considered inefficient. Many studies have been carried out to develop an effective model to interpret the occurrence of estrus and detect estrus; however, most models present too many false-positive alerts and because of this they are sometimes considered unreliable. The objective of this research was to construct a system based on fuzzy inference functions evaluated with a receiver-operating characteristic curve, capable of efficiently detect estrus in dairy cows. For the input data the system combined previous estrus cases information and prostaglandin application with the data of cow activities. The system outputs were organized in three categories: 'in estrus', 'maybe in estrus" and 'not in estrus'. The system validation was carried out in a commercial dairy farm using a herd of 350 lactating cows. The performance of the test was measured by calculating its sensitivity towards the right estrus detection; and its specificity towards the precision of the detection. Within a six months period of tests, over 25 thousands cases of estrus were analyzed from a database of the commercial farm. The sensitivity found was 84.2%, indicating that the system can detect estrus efficiently and it may improve automatic estrus detection. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-90162010000500002 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.67 n.5 2010 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936462001242112 |