Study of Lori growth traits using nonlinear models and artificial neural network optimized by genetic algorithm

Machine learning methods such as artificial neural network (ANN) are already widely used in agriculture because these methods are fast, powerful and flexible tools for classification and forecasting requirements. In the field of animal science, these methods are used for the detection of mastitis, estrous and removal reasons of animals (Shahinfar et al. 2012). ANN is a machine learning method that simulates brain function. The most important advantage of ANN is related to its ability to accept large volumes of data and find interesting and complex relationships between these data. Feed-forward neural network is a type of neural network training methods that is a training monitored. The network contains neurons that are composed of several layers. The first layer of input data, the last layer of the data is output, and between these two layers are hidden layers. In this way the genetic algorithm is programming technique that uses a process of genetic evolution as a problem solving model (Ahmed and Simonovic 2005). Non-linear regression models are developed form of classical models. This models are includes fixed and random effects that used to describe the growth of their data (Bahreini Behzadi et al. 2014). Often growth traits of livestock described by non-linear growth models such as Gompertz, Logistic, Richards, Weibull, Brody and von Bertalanffy (Aman Ullah et al. 2013).
Material and The data for this research was related to number of 7054 Lori sheep and including birth weight, weaning weight, weight six and nine months of age that were collected by the Agricultural Organization of the Lorestan province between the years 2001 to 2010 years. This data was related to nomadic herds in the Khorramabad city. At first, this data was edited using Excel 2010 and Fox Pro 3 (Hentzen 1995) software. To check the normality of the data, the software SAS (Institute SAS 2004) univariate procedures was used. Also for evaluation of growth traits, Gompertz, Brody and Logistics models were used. These models were performed by non-linear procedure (PROC NLIN) and the Gauss - Newton Iterative methods using SAS 9.2 (2003) software and then the growth parameters were calculated. Different models were validation and compared with each other based on the coefficient of determination (R2), mean squared error (MSE), the number of iteration and Akaike information criterion (AIC). In the ANN environmental effects such as sex of lamb, type of birth, birth season, birth year, mother's age and birth weight, weaning weight and weight at six months of age were introduced as input to the neural network and ultimately weight at nine months of age was predicted. When neural network structure was formed, Mean Square Error (MSE) was used to evaluate and determine the optimal number of neurons in the middle layer.
Compare models based on the coefficient of determination shows that the models are not much different from each other and coefficient of determination range was varied between 96.79 to 98.84 percent. The highest R2 for male and female was related to the Brody model. High R2 and low iteration for all 3 models show that these models are suitable to describe the growth curve of Lori sheep. Year of birth, birth type, lamb sex, mother age and birth season had significant effect on the A, B and K parameters (P Based on R2 in this study suggest that the Brody model is the best model for fit the growth traits and also three models of Brody, Logistic and Gompertz have the higher performance for forecasting and analysis of growth traits in Lori sheep than ANN. The birth and weaning weights and the other growth traits in Lori lambs is impressed by the change in weather conditions, followed by changes in natural conditions.