Improving detection of dairy cow estrus using fuzzy logic

• Autor(a) principal: Brunassi,Leandro dos Anjos
• Data de Publicação: 2010
• Outros Autores: 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
• 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
• topic: 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.