A Hybrid RF–XGBoost Model for Soil Temperature Prediction in Solar Greenhouses

Solar greenhouses are indispensable infrastructure for winter vegetable production in high-latitude regions, maintaining favorable growing conditions without auxiliary heating through superior thermal insulation and solar radiation capture. However, extreme meteorological events disrupt this equilibrium, and soil temperature prediction remains challenging due to thermal hysteresis—time-lagged responses to environmental drivers that threaten crop viability during pre-dawn periods. Existing studies focus on air temperature or short-term horizons (3–6 h), leaving the critical 12 h prediction window inadequately addressed. This study develops a hybrid machine learning framework for 12 h soil temperature prediction in a solar greenhouse. High-resolution data were collected using soil temperature sensors at 200 mm depth (tomato root zone) during the winter growing season. Five algorithms were evaluated: RF and XGBoost (Bagging/Boosting for trend/residual capture), LSTM and GRU (temporal memory), and a novel RF-XGBoost hybrid with two-stage residual correction. The hybrid model achieved R2 = 0.9927, improving standalone RF (R2 = 0.9883) by 0.45% with MSE, RMSE, MAE reductions of 18.1%, 9.6%, 13.9%. Maximum 12 h error was 2.52 °C versus 3.12–4.60 °C for standalone models. The 12 h horizon enables preemptive heating activation, mitigating frost risk while avoiding unnecessary energy expenditure.