Prediction of Egg Production Using Artificial Neural Network

Iranian Journal of Applied Animal Science

مقاله 2، دوره 1، شماره 1، خرداد 2011، صفحه 11-16   

نوع مقاله: Research Articles

نویسندگان

S. Ghazanfari¹؛ K. Nobari^{* 2}؛ M. Tahmoorespur²

¹Department of Animal and Poultry Sciences, College of Aboureihan, University of Tehran, Tehran, Iran

²Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

چکیده

Artificial neural networks (ANN) have shown to be a powerful tool for system modeling in a wide range of applications. The focus of this study is on neural network applications to data analysis in egg production. An ANN model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variables. High R² and T for ANN model revealed that ANN is an efficient method of predicting egg production for pullet and hen flocks. We also estimated ANN parameters of a number of eggs on four data sets of individual hens. By increasing the summary intervals to 2 wk, 4 wk and then to 6 wk, ANN power was increased for prediction of egg production. The results suggested that the ANN model could provide an effective means of recognizing the patterns in data and accurately predicting the egg production of laying hens based on investigating their age.

کلیدواژه‌ها

Artificial Neural Networks؛ back propagation algorithm؛ egg production

مراجع

Bohren B.B., Kinney T.B. and Wilson S.P. (1970). Genetic gains in annual egg production from selection on part-record percent production in the fowl. Genetics 65, 655-667.
Cason J.A. (1991). Egg production models for molted flocks. Poult. Sci. 70, 2232-2236.
Cravener T. and Roush W. (1999). Improving Neural Network Prediction of Amino Acid Levels in Feed Ingredients. Poult. Sci. 78, 983-991.
Edriss M.A., Hosseinnia P., Edriss M., Rahmani H.R. and Nilforooshan A. (2008). Prediction of second parity milk performance of dairy cows from first parity information using artificial neural network and multiple linear regression methods. Asian j. Anim. Vet. adv. 3, 222-229.
Fairfull R.W. and Gowe R.S. (1990). Genetics of egg production in chickens. Poult. Breed. Genet. 705-759. R. D. Crawford, ed. Elsevier Science Publishers B.V., The Netherlands.
Fernandez C., Soria E., Martin J.D. and Serrano A.J. (2006). Neural networks for animal science applications: two case studies. Exp. Sys. Applic. 31, 444-450.
Grossman M., Grossman T.N. and Koops W.J. (2000). A model for persistency of egg production. Poult. Sci. 79, 1715-1724.
Khazaei J., Chegini G.R. and Kianmehr M.H. (2005). Modeling physical damage and percentage of threshed pods of chickpea in a finger type thresher using artificial neural networks. J. Lucrari¸ Stiin¸ sifice Seria Agronomie. 48, 594-607.
Khazaei J., Shahbazi F., Massah J., Nikravesh M. and Kianmehr M.H. (2008). Evaluation and Modeling of Physical and Physiological Damage to Wheat Seeds under Successive Impact Loadings: Mathematical and Neural Networks Modeling. Crop Sci. 48, 1532-1544.
Koops W.J. and Grossman M. (1992). Characterization of poultry egg production using a multiphasic approach. Poult. Sci. 71, 399-405.
Lek S., Delacoste M., Baran P., DimopoulosI., Lauga J. and Aulagnier S. (1996). Application of neural networks to modeling nonlinear relationships in ecology. Ecol. Modell. 90, 39-52.
Park S.J., Hwang C.S. and Vlek P.L.G. (2005). Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric. Syst. 85, 59-81.
Mittal G.S. and Zhang J. (2000). Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Meat Sci. 55, 13-24.
Muir W.M. (1990). Association between persistency of lay and partial record egg production in white leghorn hens and implications to selection programs for annual egg production. Poult. Sci. 69, 1447-1454.
North M.O. and BellD.D. (1990). Commercial Chicken Production Manual. 4^th Ed. Chapman & Hall, New York, NY.
Roush W.B., Cravener T.L., Kochera Kirby Y. and Wideman R.F. (1997). Probabilistic Neural Network Prediction of Ascites in Broilers Based on Minimally Invasive Physiological Factors. Poult. Sci. 76, 1513-1516.
Salle C.T.P., Guahyba A.S., Wald V.B., Silva A.B., Salle F.O. and Nascimento V.P. (2003). Use of artificial neural networks to estimate production variables of broilers breeders in the production phase. Br. Poult. Sci. 44, 211-217.
Zhang Q., Yang S.X., Mittal G.S. and Yi S. (2002). Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosystems Eng. 83, 281-290