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

Abstract

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

Keywords

• Long short-term memory (LSTM)
• random forests (RF)
• egg laying rate
• feature importance selection