Exploration of Egg Production Curve Fitting of Local Chickens Based on Machine Learning
[Objective]In order to improve fitting accuracy on egg production curve in chicken,the machine learning method was explored to model the weekly egg production rate for 2 indigenous breeds,and compared with the nonlinear regression method.[Method]Data were collected from local chickens populations recorded from 22 to 50 weeks.The hens were housed in a single cage in an enclosed house,kept in artificial light for 16 hours during the laying period.The chickens were divided into two groups,each with 150 chickens.Group Ⅰ was a synthetic line of Meat-type Yellow chickens,and group Ⅱ was a local dual-purpose breed.The non-linear regression method in IBM SPSS Statistics 21.0 software was used to fit the egg production curve,including the Logistic,McNally,Yang,and Grossman models.The machine learning model was constructed with MATLAB R2014a,using a multilayer perceptron.The neural network was trained with the quasi-Newton method for 300 iterations.The egg production curve was fitted with least squares support vector machine,and the regularization and kernel function parameters were optimized with Bayesian inference.[Result]According to the evaluation criteria of MSE,R2,and AIC,the Grossman model had the best degree of fitting among the four non-linear regression models,and the McNally model performed the worst.The peak egg production rate predicted by the McNally model deviated from the real value,the peak egg production rates obtained from the Logistic,Yang,and Grossman models were consistent with the observed value.There were differences between the parameter estimates of the curves fitted for the two groups.The persistence of the lay for group Ⅱ was better than that of group Ⅰ.Based on the MSE,R2 and graphical evaluation,the neural network fitted better than the traditional nonlinear regression,and the support vector machine was slightly better than the neural network.The optimized parameters for the neural network were 2 hidden layers,containing 5 neurons in each layer.For support vector machine,the regularization parameter of group Ⅰ was 30.97,and the kernel parameter was 0.0701;The regularization and kernel parameter of group Ⅱ was 566.53 and 0.1754,respectively.[Conclusion]Machine learning method could be used to fit the egg production curve in these populations.Compared to those classic univariate regression,machine learning methods could harbor more variates and provide more accurate predictions.
artificial neural networksupport vector machinenonlinear regressionegg production curvechickens
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