A Novel Method to Predict Laying Rate Based on Multiple Environment Variables

Realizing an accurate laying rate prediction based on environmental factors plays a vital role in livestock and poultry breeding. In this paper, multiple were considered to improve the accuracy of egg production prediction. A method was proposed by combining Random Forest (RF) Long Short-Term Memory (LSTM) analyze impact external rate. Firstly, using RF, feature importance selection implemented affecting Secondly, extreme Gradient Boosting (XGBoost) introduced as comparison evaluate reliability RF selection. Finally, discarding features with low one one, multi-variable RF-LSTM conducted. Experiment results showed that significantly improved