A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep

Kominakis, A.; Abas, Zafeiris; Maltaris, I.; Rogdakis, E.

doi:10.1016/s0168-1699(02)00051-0

Cited by 40 publications

(31 citation statements)

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“…Such improvement is particularly important early in lactation, when a 305-d milk yield can be difficult to predict, and where such a prediction can have serious implications for the choice of future bull-dams. Early detection of low-producing animals is also important for timely culling decisions, with their associated economic benefits (Kominakis et al, 2002). In dairy production, prediction of milk yield is important, in that much of the selection of genetically superior bulls is based on their ability to produce high-yielding daughters.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Görgülü¹

2012

SA J. An. Sci.

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Artificial neural networks (ANNs) have been shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on the capability of ANNs to predict 305-d milk yield in early lactation of Brown Swiss cattle, based on a few test-day records, and some environmental factors such as age, number of lactation and season of calving. The ANNs that were developed were compared with multiple linear regressions (MLR). The various ANNs were modelled and the best performing number of hidden layers, neurons and training algorithms retained. The best ANN model had input, hidden and output layers of tansig transfer function. The layers had 4, 8, and 1 neurons, respectively. It was determined that the mean predicted values calculated by the ANNs were closer to the real mean values without showing any statistical difference. On the other hand, the predicted mean values calculated by MLR and the real mean values were significantly different from each other. The best prediction in ANN method was seen in 1st, 2nd, 3rd, and 4th test-day records when these were recorded to the system as X 1 -X 8 in the ANN system. In this study, the prediction of 305-d milk yield by ANN gave better results that those of MLR, suggesting that ANN can be used as an alternative prediction tool. ________________________________________________________________________________

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Section: Introductionmentioning

confidence: 99%

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Görgülü¹

2012

SA J. An. Sci.

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“…O sucesso obtido pela rede corrobora estudos realizados por Kominakis et al (2002) para predição da produção diária de leite em ovelhas em lactação com uso de RNA, nos quais foi obtido adequado planejamento do fluxo de produção com base nos parâmetros fisiológicos e ambientais. Xin (1999) desenvolveu um sistema automatizado de análise de imagens, que faz os ajustes térmicos apropriados para melhorar o bem-estar de leitões e a sua eficiência produtiva, usando o comportamento postural como entrada em uma rede neural de retropropagação com três camadas, que foi treinada para classificar o estado de conforto térmico.…”

Section: Resultsunclassified

“…Segundo Kominakis et al (2002), o desempenho da rede poderia ser melhorado com a utilização de um conjunto maior de dados na fase de treinamento, possibilitando direcionar os parâmetros específicos de treinamento e permitindo melhor qualidade da predição. A habilidade de se predizer variadas situações aumenta proporcionalmente ao conjunto de dados (Jaiswal et al, 2005).…”

Section: Resultsunclassified

“…Os autores consideraram ainda que, nesses casos, a adição de mais informações na combinação de fatores pode melhorar a acurácia da rede. Ou seja, o aumento de variáveis de entrada, bem como a qualidade e a importância das mesmas para determinadas respostas a serem preditas, também pode melhorar o desempenho das redes (Lacroix et al, 1997;Park et al, 1998;Kominakis et al, 2002;Vieira et al, 2010). Os pares de entradas e saídas possibilitaram evolução do aprendizado por meio da comparação entre a saída desejada e a real.…”

Section: Resultsunclassified

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Uso de redes neurais artificiais para predição de índices zootécnicos nas fases de gestação e maternidade na suinocultura

et al. 2011

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A utilização desse sistema especialista para a previsão dos índices zootécnicos é viável, pois o sistema tem bom desempenho para esta aplicação.Palavras-chave: sistemas especialistas; suinocultura; zootecnia de precisão Use of artificial neural networks on the prediction of zootechnical indexes on gestation and farrowing stages of swinesABSTRACT -The objective of this work was to evaluate the precision of Artificial Neural Networks (ANNs) to estimate zootechnical indexes, based on thermal and physiological variables of pregnant sows. This study was carried out from January to April 2005, in a swine industrial production farm in the gestation section with 27 primiparous gilts, allocated in individual pens and after on farrowing pens where it was quantified animal production indexes of piglets from the study. Therefore, an ANN backpropagation was implemented, with one input layer, one hidden layer, and one output layer with tangent sigmoidal transference functions. Air temperature and respiratory frequency were considered as input variables and weight of piglet at birth and the number of mummified piglets as output variables. The trained ANN presented a great generalization power, which enabled the prediction of the answer-variables. Characterization of the environment of gestation and maternity was appropriated if compared to the real data, with few under or overestimated tendencies of some values. The use of this specialist system to predict zootechnical indexes is viable because the system shows a good performance for this use.

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“…Some of the ANN applications in recent years have been in livestock-based research: dairy cattle (Grzesiak et al, 2010), sheep (Kominakis et al, 2002;Tahmoorespur and Ahmadi, 2012) and pigs Wongsriworaphon et al, 2015). The performance of classifiers has a significant effect on machine vision outputs (Pourreza et al, 2012), and the feed-forward neural network is one of the most powerful classifiers, which could be fast enough and acceptable for many processes (Khoramshahi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Nasirahmadi

Hensel

Edwards

et al. 2017

Animal

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Machine vision-based monitoring of pig lying behaviour is a fast and non-intrusive approach that could be used to improve animal health and welfare. Four pens with 22 pigs in each were selected at a commercial pig farm and monitored for 15 days using top view cameras. Three thermal categories were selected relative to room setpoint temperature. An image processing technique based on Delaunay triangulation (DT) was utilized. Different lying patterns (close, normal and far) were defined regarding the perimeter of each DT triangle and the percentages of each lying pattern were obtained in each thermal category. A method using a multilayer perceptron (MLP) neural network, to automatically classify group lying behaviour of pigs into three thermal categories, was developed and tested for its feasibility. The DT features (mean value of perimeters, maximum and minimum length of sides of triangles) were calculated as inputs for the MLP classifier. The network was trained, validated and tested and the results revealed that MLP could classify lying features into the three thermal categories with high overall accuracy (95.6%). The technique indicates that a combination of image processing, MLP classification and mathematical modelling can be used as a precise method for quantifying pig lying behaviour in welfare investigations.

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A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep

Cited by 40 publications

References 11 publications

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

Uso de redes neurais artificiais para predição de índices zootécnicos nas fases de gestação e maternidade na suinocultura

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

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