重庆大学学报2025，Vol.48Issue(4)：108-114,7.DOI:10.11835/j.issn.1000-582X.2025.04.009
基于XGB-KF模型的农业温室温度预测
Agricultural greenhouse temperature prediction based on the XGB-KF model
摘要
Abstract
To address the challenge of agricultural greenhouse temperature measurement being highly susceptible to noise,which limits direct prediction accuracy,this study proposes an integrated prediction model,XGB-KF,combining XGBoost and the Kalman filter.First,the model estimates the current greenhouse temperature using XGBoost.Then,the Kalman filter dynamically adjusts the estimated result to refine the prediction.Numerical experiments are conducted using sensor data from a greenhouse in Zhuozhou,with root mean square error(RMSE)as the main evaluation metric.Compared with XGBoost,Bi-LSTM,and Bi-LSTM-KF methods,the XGB-KF model reduces RMSE by 5.22%,10.85%and 7.45%respectively.关键词
集成模型/机器学习/时间序列/温室温度Key words
integrated model/machine learning/time series/greenhouse temperature分类
信息技术与安全科学引用本文复制引用
黄威,贾若然,钟坤华,刘曙光..基于XGB-KF模型的农业温室温度预测[J].重庆大学学报,2025,48(4):108-114,7.基金项目
中国科学院重点资助项目(E351600201).Surpported by Key Research Programs of Chinese Academy of Sciences(E351600201). （E351600201）
