Use of Neural Network Model to Predict of Egg Yield

Yıl 2018, , 147 - 151, 29.08.2018

Çiğdem Takma Yakut Gavrekçi

https://doi.org/10.13002/jafag4309

Öz

A neural network is a mathematical model of information processing based on the work of the human brain. An artificial neural network (ANN) is composed of a number of simple processing elements connected together in a network. In this study, the egg yield was predicted based on the individually collected hatching period, line, body weight (BW), age at sexual maturity (ASM) and body weight at sexual maturity (BWSM) records of layers using neural network model. A multilayer perceptron (MLP) ANN model trained by back propagation algorithm is developed for feed-forward neural network learning. From the available data set, training and testing sets were extracted. Goodness of fit of the model was determined with the coefficient of determination (R2), root mean square error (RMSE) and Mean Absolute Deviation (MAD) values. The R2 for training and test sets were estimated to be 0.80 and 0.82, respectively. Lower RMSE and MAD values were obtained. The empirical result shows that neural network can be used for the prediction of egg yield.

Anahtar Kelimeler

Neural network, coefficient of determination, layer, egg yield, multilayer perception

Kaynakça

• Ahmadi H, Golian A (2008). Neural network model for egg production curve. Journal of Animal and Veterinary Advances, 7:1168-1170.
• Amraei S, Mehdizadeh S A, Salari S (2017). Broiler weight estimation based on machine vision and artificial neural network. Britsh Poutry Science, 58(2): 200-205.
• Cruz V A R, Savegnago R P, Schmidt G S, Ledur M C, Munari D P (2013). Neural networks on predict breeding values of egg production using phenotypic information. 10th World Congress of Genetics Applied to Livestock Production Proceedings.
• Faridi A, France J, Golian A (2013). Neural network models for predicting early egg weight in broiler breeder hens. Journal of Applied Poultry Research, 22:1-8.
• Fausett L (1994). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, USA, Prentice Hall.
• Felipe V P, Silva M A, Valente B D, Rosa G J (2015). Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes. Poultry Science, 94(4):772-80.
• Ghazanfari S, Nobari K, Tahmoorespur, M (2011). Prediction of egg production using artificial neural network. Iranian Journal of Animal Science, 1(1): 11-16.
• Gianola D, Okut H, Weigel K A, Rosa G J M (2011). Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat. BMC Genetics, 12:87.
• Lin H, Zhao J, Sun L, Chen Q, Zhou F (2011). Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis. Innovative Food Science & Emerging Technologies, 12(2): 182-186.
• Mehdizadeh S A, Minae S, Hancock N H, Torshizi M A K (2014) An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy. Information Processing in Agriculture, 1(2): 105-114.
• Savegnago R P, Nunes B N, Caetano S L, Ferraudo A S, Schimidt G S, Ledur M C, Munari D P (2011). Comparison of logistic and neural network models to fit the egg production curve of White Leghorn hens. Poultry Science, 90:2174-2188.
• Sefat M Y, Borgaee A M, Beheshti B, Bakhoda H (2014). Application of Artificial Neural Network (ANN) for Modelling the Economic Efficiency of Broiler Production Units. Indian Journal of Science & Technology, 7(11):1820-1826.
• Semsarian S, Nasab M P E, Zarehdaran S, Dehghani A A (2013). Prediction of the weight and number of eggs in Mazandaran native fowl using artificial neural network. International Journal of Advanced Biological and Biomedical Research, 5:532-537.
• Tijen W F (1982). Yumurta sektöründe uygulamalı tavuk ıslahı. Uluslar arası Bilimsel Tavukçuluk Kongresi, 53-71, Ankara.
• Ünver Y, Oğuz İ, Akbaş Y (2000). Yumurtacı Damızlıklarda Kısmi Yumurta Verim Kayıtlarına Ait Parametre Tahminleri. Ege Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi.
• Zhang Z, Wang Y, Fan G, Harrington P B (2007). A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples. Phytochem. Anal. 18:109–114.