Document Type : Research Paper

Authors

1 Department of Animal Science, Golestan Agricultural and Natural Resources Research and Education, AREEO, Gorgan, Iran

2 1Animal Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran

3 Livestock affairs Department, Agricultural Jihad Organization of Golestan, Ministry of Agriculture-jahad, Gorgan, Iran

4 Animal Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran

Abstract

This study was conducted for predicting performance traits of the whole production period of Arian broiler breeder via its initial performances using artificial neural network (ANN). The input variables of modeling were house, number of hens in the house, weeks of production, body weight at 20-24 weeks of age and outputs of the model were body weight, egg number, egg mass, egg weight and feed intake at the 25 to 47 weeks of age. The used ANN model for prediction of body weight had 5 inputs, 5 neurons at 1st hidden layer, 2 neurons at 2nd hidden layer and 1 output, thus we write it as 5-5-3-1. Similarly, the optimized ANN model structure for feed intake, egg number, egg weight and egg mass were 7-7-4-1, 8-8-4-1, 7-7-3-1 and 7-7-3-1, respectively. R2 of adequate models for BW, FI, EN, EW and EM were 0.991, 0.998, 0.989, 0.993 and 0.996, and Root Mean Square Error were 1.55, 0.992, 0.266, 3.838 and 0.506, respectively. The results of the study shown that architecture and the specification of the neural networks such as inputs, outputs, number of neurons and number of hidden layers can affect the performance of the ANN model. The results indicated the possibility of predicting whole production period of Arian broiler breeder using early stage production records.

Keywords

  1. راهنمای پرورش مرغ مادر گوشتی آرین (1399). کمیته ملی احیای مرغ لاین آرین. معاونت علمی و فناوری ریاست جمهوری.
  2. Faridi, A., France, J. and Golian, A. (2013). Neural network models for predicting early egg weight in broiler breeder hens. Journal of Applied Poultry Research. 22:1–8.
  3. Ghazanfari, S. and Nobari, K. (2011). Prediction of egg production using artificial neural network. Iranian Journal of Animal Science. 1(1): 11-16.
  4. Ghazanfari, S. (2014). Application of Linear Regression and Artificial NeuralNetwork for Broiler Chicken Growth Performance Prediction. Iranian Journal of Animal Science. 4(2): 411-416.
  5. Ghiassi, M. and Saidane, H. (2005). A dynamic architecture for artificial neural networks. Neurocomputing. 63: 397:413.
  6. Jahan, M., Maghsoudi, A., Rokouei, M. and Faraji-Arough H. (2020). Prediction and optimization of slaughter weight in meat-type quails using artificial neural network modeling. Poultry Science, 99(3): 1363-1368.
  7. Mohammadrezaei, M., Gheisari, A., Toghyani, M. and Toghyani, M. (2011). Modeling daily feed intake of broiler chicks. 4th International Conference In Aminal Nutrition, Malaysia.
  8. Narinc, D., Uckardes, F. and Aslan, E. (2014). Egg production curve analysis in poultry science. World’s Poultry Science Journal. 70: 817–828.
  9. Otwinowska-Mindur, A., Gumułka, M. and Kania-Gierdziewicz, J. (2016). MatheMatical Models for egg production in broiler breeder hens. Annals of Animal Science. 16(4):1185–1198.
  10. Sairanya, N.J., Susmitha, L., Thomas George, S. and Subathra, M.S.P. (2019). Hybrid Approach for Classification of Electroencephalographic Signals Using Time–Frequency Images With Wavelets and Texture Features. P: 253-273. In: Hemanth, D.J., D. Gupta and V.E. Balas (eds.) Intelligent Data Analysis for Biomedical Applications. Academic Press.
  11. Safari-Aliqiarloo, A., Faghih-Mohammadi, , Zare, M., Seidavi, A., Laudadio, V., Selvaggi, M. and Tufarelli, V. (2017). Artificial neural network and non-linear logistic regression models to fit the egg production curve in commercial-type broiler breeders. European Poultry Science. 81. DOI: 10.1399/eps.2017.212.
  12. Takma, Ç. and Gevrekci, Y. (2018). Use of Neural Network Model to Predict of Egg Yield. Journal of Agricultural Faculty of Gaziosmanpasa University. 35 (2):147-151.
  13. Van der Klein, S.A.S., Bedecarrats, G.Y. and Zuidhof, M.J. (2020). Modeling life-time energy partitioning in broiler breeders with differing body weight and rearing photoperiods. Poultry Science. 99:4421–4435.
  14. Wolc, A., Graczyk, M., Settar, P., Arango, J.,. O’Sullivan, N.P., Szwaczkowski, T. and Dekkers, J.C.M. (2015). Modifed Wilmink curve for egg production analysis in layers. XXVII International Poultry Science Symposium PB WPSA “Science to practice – practice to science”, Bydgoszcz, Poland, p. 56.
  15. Yakubu, A. and Madaki, J. (2017). Modelling growth of dual-purpose Sasso hens in the tropics using different algorithms. Journal of Genetics and Molecular Biology. 1(1):1-9.
  16. You, J., Lou, E., Afrouziyeh, M., Zukiwsky, NM. and Zuidhof 2021. Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens. Poultry Science, 100(8).
  17. 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 Engineering. 83: 281-290.