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 environmental factors were considered to improve the accuracy of egg production rate prediction. A method was proposed by combining the Random For...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.115488-115496
Main Authors: Yin, Hang, Liu, Chuanyun, Gao, Yacui, Fan, Wenting, Xiao, Bin, Cao, Liang, Hassan, Shahbaz Gul, Liu, Shuangyin
Format: Article
Language:English
Subjects:
Accuracy
Computer architecture
egg laying rate
feature importance selection
Impact analysis
Indexes
Livestock
Logic gates
Long short-term memory (LSTM)
Predictive models
Production
Radio frequency
Random forests
random forests (RF)
Reliability analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3105189