Abstract:

Laying rate is one of the important indexes to evaluate laying performance of hens, it has the characteristics of time-varying, nonlinearity, and complex coupling. So it is difficult to predict the laying rate accurately. The traditional neural network prediction model does not have memory function and cannot be used on the time sequence prediction. So the LSTM-Kalman prediction model is proposed. Firstly, principal component analysis was used to extract the key influencing variables of laying rate of hens. Then LSTM neural network was used as a static prediction model to predict the laying rate of hens. Kalman filter was used to dynamically adjust the result of LSTM prediction to obtain the final prediction results. The data analysis showed that the model’s average absolute error, mean square error and Pearson correlation coefficient were 0.312 8, 0.435 3 and 0.975 2, respectively, which was significantly better than traditional prediction methods, including BP neural network and extreme learning machine. The mutual testing and verification, based on the production data of two hen coops, showed the prediction accuracy of the model were 97.14% and 98.71%, respectively. The model had strong generalization ability and could meet the actual needs of layer production rate prediction. This paper provided a reference for precise control of layer breeding environment data.

Key words: laying rate of hens, Kalman filter, LSTM neural network, prediction accuracy