Application of Linear Regression and Artificial NeuralNetwork for Broiler Chicken Growth Performance Prediction

Document Type : Research/Original Article


Department of Animal Science, College of Abouraihan, University of Tehran, Tehran, Iran


This study was conducted to investigate the prediction of growth performance using linear regression and artificial neural network (ANN) in broiler chicken. Artificial neural networks (ANNs) are powerful tools for modeling systems in a wide range of applications. The ANN model with a back propagation algorithm successfully learned the relationship between the inputs of metabolizable energy (kcal/kg) and crude protein (g/kg) and outputs of feed intake, weight gain and feed conversion ratio variables. High R2 and T values for the ANN model in comparison to linear regression revealed that the artificial neural network (ANN) is an efficient method for growth performance prediction in the starter period for broiler chickens. This study also focused on expanding the experiment with more levels of inputs to predict outputs the using best ANN model.


Ahmadi H. and Golian A. (2010). Growth analysis of chickens fed diets varying in the percentage of metabolizable energy provided by protein, fat and carbohydrate through artificial neural network. Poult. Sci. 89, 173-179.
Ahmadi H., Mottaghitalab M. and Nariman-Zadeh N. (2007). Group method of data handling-type neural network prediction of broiler performance based on dietary metabolizable energy, methionine and lysine. J. Appl. Poult. Res. 16, 494-501.
Ahmadi H., Mottaghitalab M., Nariman-Zadeh N. and Golian A. (2008). Predicting performance of broiler chickens from dietary nutrients using group method of data handling type neural networks. Br. Poult. Sci. 49, 315-320.
Cravener T. and Roush W. (1999). Improving neural network prediction of amino acid levels in feed ingredients. Poult. Sci. 78, 983-991.
Ghazanfari S., Nobari K. and Tahmoorespur M. (2011). Prediction of egg production using artificial neural network. Iranian J. Appl. Anim. Sci. 1, 11-16.
Hruby M., Hamre M.L. and Coon C.N. (1996). Non linear and linear functions in body protein growth. J. Appl. Poult. Res. 5, 109-115.
Huang P., Lin P., Yan S. and Xiao M. (2012). Seasonal broiler growth performance prediction based on observational study. J. Comp. 7, 1895-1902.
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. Agr. 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.
Lacroix R., Wade K.M., Kok R. and Hayes J.F. (1995). Prediction of cow performance with a connectionist model. Trans. Asae. 42, 1573-1579.
Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J. and Aulagnier S. (1996). Application of neural networks to modeling nonlinear relationships in ecology. Ecol. Model. 90, 39-52.
Moharrery M. and Kargar A. (2007). Artificial neural network for prediction of plasma hormones, liver enzymes and performance in broilers. J. Anim. Feed Sci. 16, 293-304.
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.
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.
SAS Institute. (2001). SAS®/STAT Software, Release 8. SAS Institute, Inc., Cary, NC.
Swennen Q., Decuypere E. and Buyse J. (2007). Implications of dietary macronutrients for growth and metabolism in broiler chickens. J. World. Poult. Sci. 63, 541-556.
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.
  • Receive Date: 21 July 2013
  • Revise Date: 01 September 2013
  • Accept Date: 15 September 2013
  • First Publish Date: 01 June 2014