Improving detection of dairy cow estrus using fuzzy logic

Improving detection of dairy cow estrus using fuzzy logic

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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Main Authors: 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
Format: Digital revista
Language:English
Published: Escola Superior de Agricultura "Luiz de Queiroz" 2010
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002
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spelling oai:scielo:S0103-901620100005000022010-09-20Improving detection of dairy cow estrus using fuzzy logicBrunassi,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 Freitas estrus cycle artificial intelligence expert system 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.info:eu-repo/semantics/openAccessEscola Superior de Agricultura "Luiz de Queiroz"Scientia Agricola v.67 n.5 20102010-10-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002en10.1590/S0103-90162010000500002
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libraryname SciELO
language English
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author 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
spellingShingle 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
Improving detection of dairy cow estrus using fuzzy logic
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_sort Brunassi,Leandro dos Anjos
title Improving detection of dairy cow estrus using fuzzy logic
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
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.
publisher Escola Superior de Agricultura "Luiz de Queiroz"
publishDate 2010
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162010000500002
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