针对农业温室大棚温度测量受噪声影响大而不宜直接预测的问题，提出一种将XGBoost和Kalman Filter相结合的集成预测模型XGB-KF。该模型首先基于XGBoost对温室内部当前时刻的温度值进行初步估计，然后使用Kalman Filter对得到的估计结果进行动态修正，得到最终的预测结果。基于涿州地区农业温室大棚的传感器数据进行了数值实验，并以均方根误差（RMSE）作为主要指标对模型进行性能评估。与XGBoost、Bi-LSTM和Bi-LSTM-KF方法相比较，XGB-KF的RMSE分别降低5.22%、10.85%、7.45%。

Aiming at the problem that agricultural greenhouse temperature measurement is greatly affected by noise and is not suitable for direct prediction, an integrated prediction model XGB-KF, which combines XGBoost and Kalman Filter, was proposed. The model firstly estimated the temperature of the greenhouse at the current moment based on XGBoost, and then Kalman Filter was used to dynamically modify the estimated result. Finally, the prediction result is obtained. Based on the sensor data of agricultural greenhouse in Zhuozhou, the numerical experiment was carried out. At the same time, root mean square error (RMSE) was used as the main indicator to evaluate the model. Compared with XGBoost, Bi-LSTM and Bi-LSTM-KF methods, the root mean square error (RMSE) of XGB-KF model was reduced by 5.22%, 10.85% and 7.45% respectively.