Improving detection of dairy cow estrus using fuzzy logic

Brunassi, Leandro dos Anjos; Moura, Daniella Jorge de; Nääs, Irenilza de Alencar; Vale, Marcos Martinez do; Souza, Silvia Regina Lucas de; Lima, Karla Andréa Oliveira de; Carvalho, Thayla Morandi Ridolfi de; Bueno, Leda Gobbo de Freitas

doi:10.1590/s0103-90162010000500002

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…In studies carried out using fuzzy logic, aiming to estimate estrus in dairy cows, NÄÄS et al (2008) obtained a mean hit percentage of 62%, while BRUNASSI et al (2010) observed hit rate of 84.2% in the detection of estrus. According to the authors, these results indicate that the system can efficiently detect estrus improving automatic detection in dairy cows.…”

Section: Twb Tdb Twb Tbgmentioning

confidence: 99%

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

Campos¹,

Castro²,

Schiassi

et al. 2013

Eng. Agríc.

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ABSTRACT:The goal of this study was to develop a fuzzy model to predict the occupancy rate of free-stalls facilities of dairy cattle, aiding to optimize the design of projects. The following input variables were defined for the development of the fuzzy system: dry bulb temperature (T db , °C), wet bulb temperature (T wb , °C) and black globe temperature (T bg , °C). Based on the input variables, the fuzzy system predicts the occupancy rate (OR, %) of dairy cattle in free-stall barns. For the model validation, data collecting were conducted on the facilities of the Intensive System of Milk Production (SIPL), in the Dairy Cattle National Research Center (CNPGL) of Embrapa. The OR values, estimated by the fuzzy system, presented values of average standard deviation of 3.93%, indicating low rate of errors in the simulation. Simulated and measured results were statistically equal (P>0.05, t Test). After validating the proposed model, the average percentage of correct answers for the simulated data was 89.7%. Therefore, the fuzzy system developed for the occupancy rate prediction of free-stalls facilities for dairy cattle allowed a realistic prediction of stalls occupancy rate, allowing the planning and design of free-stall barns. KEYWORDS:Confinement systems, bovine facilities, design, stalls, dairy cattle. PREDIÇÃO DA TAXA DE OCUPAÇÃO DE BAIAS EM INSTALAÇÕES TIPO FREE-STALL PARA BOVINOS DE LEITE POR MEIO DOS CONJUNTOS FUZZY RESUMO:Objetivou-se com o presente trabalho desenvolver um modelo fuzzy para predizer a taxa de ocupação de baias em instalações para gado de leite do tipo free-stall, auxiliando na otimização do dimensionamento de projetos. Para o desenvolvimento do sistema fuzzy, foram definidas como variáveis de entrada: a temperatura de bulbo seco (T bs , °C); temperatura de bulbo úmido (T bu , °C), e temperatura de globo negro (T gn ,ºC). Com base nas variáveis de entrada, o sistema fuzzy prediz a taxa de ocupação (TO,%) de vacas leiteiras em instalações do tipo free-stall. Para a validação do modelo, coletas de dados foram realizadas nas instalações do Sistema Intensivo de Produção de Leite (SIPL), do Centro Nacional de Pesquisa de Gado de Leite (CNPGL) da Embrapa. Os valores de TO estimados pelo sistema fuzzy apresentaram valores de desvio-padrão médio de 3,93%, indicando baixo índice de erros na simulação. Os resultados simulados foram, estatisticamente, iguais aos medidos (P>0,05, Teste t). Após a validação do modelo proposto, a porcentagem de acerto médio para os dados simulados foi de 89,7%. Portanto, o sistema fuzzy, desenvolvido para a predição da taxa de ocupação de baias em instalação free-stall para bovinos leiteiros, possibilitou a predição realística da taxa de ocupação de baias, propiciando o planejamento e o projeto de instalações free-stall. PALAVRAS-CHAVE:Sistema de confinamento, instalações para bovinos, projeto, baias, vacas leiteiras.

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Section: Twb Tdb Twb Tbgmentioning

confidence: 99%

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

Campos¹,

Castro²,

Schiassi

et al. 2013

Eng. Agríc.

View full text Add to dashboard Cite

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“…The result accuracy of a classifier system is the ratio of number of system success and number of evaluated cases (BRUNASSI, 2010;eq.[3]). The sensitivity (Sens) and specificity (Spec) analysis was performed as suggested by FIRK et al (2002), shown in eq.…”

Section: Methodsmentioning

confidence: 99%

“…The Receiver Operating Characteristic (ROC) method was used to determine sensitivity and specificity values (BRUNASSI et al, 2010). It refers to a graphical representation in Cartesian plane of true positive selection (sensitivity) and false positive fraction (1-specificity), providing a cut-off point that allows precise separation of "Lame" and "Possible Lame" or "Healthy".…”

Section: Methodsmentioning

confidence: 99%

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Preventive diagnosis of dairy cow lameness

Neto¹,

Nääs²,

Carvalho³

et al. 2014

Eng. Agríc.

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This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H 1 and H 2 ), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H 1 and H 2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H 1 and H 2 , respectively. The system simulation presented 3.40% difference from real cattle lameness data for H 1 , while for H 2 , it was 0.23%; indicating the system efficiency in decision-making.

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“…A metodologia fuzzy tem sido utilizada em várias áreas, como bem-estar animal (PANDORFI et al, 2007), análises de custo de produção , sistemas de monitoração para redes de transmissão de energia elétrica (ALMEIDA & KAGAN, 2010), detecção de câncer (SILVEIRA et al, 2008) e detecção de cio em vacas leiteiras (FERREIRA et al, 2007;BRUNASSI et al, 2010).…”

Section: Introductionunclassified

Procedimento fuzzy aplicado à avaliação da insalubridade em atividades agrícolas

et al. 2012

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RESUMO:Diante do alto grau de mecanização a que as atividades agrícolas estão sendo submetidas, objetivou-se, com esta pesquisa, desenvolver um modelo fuzzy capaz de avaliar e classificar o nível de insalubridade em diversos ambientes de trabalho. O modelo desenvolvido tem como variáveis de entrada: o índice de bulbo úmido e temperatura de globo (IBUTG, ˚C), o nível de ruído (dBA), a taxa de metabolismo (W m -2 ) e o tempo de descanso (%) e, como variável de saída, o índice de bem-estar humano (IBEH). O método de inferência utilizado foi o de Mandani e, na defuzificacão, utilizou-se o método do centro de gravidade. O sistema de regras foi desenvolvido com base nas combinações das variáveis de entrada. Foram definidas 400 regras com pesos iguais a 1, sendo que, na elaboração das regras, um especialista da área foi consultado. Foram utilizados dados de campo visando a testar o sistema desenvolvido, e os resultados mostraram que a modelagem proposta é uma ferramenta promissora na determinação do IBEH, apresentando tempo de descanso ideal variando de 64,2% (motosserra, próximo ao ouvido do operador) até 25% (derriçadora, 20 m de distância do operador), sendo que, diante de um cenário predefinido do ambiente térmico e acústico, foi possível determinar o grau de bem-estar humano e o tempo de descanso ideal para cada equipamento avaliado. PALAVRAS-CHAVE:bem-estar humano, modelagem matemática, sistema especialista. FUZZY PROCEDURE APPLIED TO EVALUATE THE INSALUBRITY LEVEL IN AGRICULTURE ACTIVITIESABSTRACT: Given the high degree of mechanization to which agricultural activities are being submitted, the objective of this research was to develop a fuzzy model able to evaluate and classify the insalubrity level in different work environments. The model developed has as input variables: wet bulb globe temperature index (WBGTI, ˚C), noise level (dBA), metabolic rate (W m -2 ) and rest time (%); and has as output variable the human well-being index (HWBI). The Mamdani inference method was used, and for the defuzzification, the Center of Gravity method was used. The system of rules was developed based on combinations of input variables. Four hundred rules were defined, with weights equal to one, considering that in the development of rules, a specialist was consulted. Field data were used in order to test the developed system. The results showed that the proposed model is a promising tool in determining the HWBI, presenting the ideal rest time ranging from 64.2% (chainsaw, near the operator's ear) to 25% (harvester, 20 meters away from the operator), and, given a pre-defined scenario of thermal and acoustic environment, it was possible to determine the degree of human well-being and the ideal rest time for each equipment evaluated.

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Improving detection of dairy cow estrus using fuzzy logic

Cited by 15 publications

References 12 publications

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

Prediction of free-stall occupancy rate in dairycattle barns through fuzzy sets

Preventive diagnosis of dairy cow lameness

Procedimento fuzzy aplicado à avaliação da insalubridade em atividades agrícolas

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