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.INDEX TERMS long short-term memory (LSTM), random forests (RF), egg laying rate, feature importance selection

Hen Egg Production Forecasting: Capabilities of Machine Learning Models in Scenarios with Limited Data Sets

“…This could be important for smaller farms that might not have multi-sensor equipment to measure various parameters that affect hen egg production (such as CO 2 , NH 3 ). A study carried out by [42] was aimed to determine the environmental factors that influence the egg production of laying waterfowls' (lion-head goose). The authors found that the optimal number of parameters was equal to four (i.e., laying rate, carbon dioxide, temperature, and dust) when forecasting, using a combination of LSTM and RF.…”

“…Long Short-Term Memory (LSTM)-is a model based on recurrent neural network that has been used in precision agriculture [41] and, regarding poultry industry, for laying rate prediction [42] as well.…”

“…XGBoost-a "tree boosting system" [45] based on decision trees and the gradient boosting approach. This model was used for feature selection in order to predict the laying rate of waterfowl [42].…”