Improving detection of dairy cow estrus using fuzzy logic

Scientia Agricola

Leandro Dos Anjos Brunassi²  Daniella Jorge De Moura¹  Irenilza De Alencar Nääs¹  Marcos Martinez Do Vale¹  Silvia Regina Lucas De Souza¹  Karla Andrea Oliveira De Lima²  Thayla Morandi Ridolfi De Carvalho²  Leda Gobbo De Freitas Bueno¹ 

[1] ,UNICAMP FEAGRI Programa de Pós-Graduação em Engenharia Agrícola

关键词: estrus cycle;    artificial intelligence;    expert system;    ciclo estral;    inteligência artificial;    sistema especialista fuzzy;   

DOI  :  10.1590/S0103-90162010000500002

来源: SciELO

【 摘 要 】

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.

【 授权许可】

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