Construction of Yunnan Flue-Cured Tobacco Yield Integrated Learning Prediction Model Driven by Meteorological Data

The timely and accurate prediction of flue-cured tobacco yield is crucial for its stable yield and income growth. Based on yield and meteorological data from 2003 to 2023 (from the NASA POWER database) of Yunnan Province, this study constructed a coupled framework of polynomial regression and a Stacking ensemble model. Four trend yield separation methods were compared, with polynomial regression selected as being optimal for capturing long-term trends. A total of 135 meteorological features were built using flue-cured tobacco’s growth period data, and 17 core features were screened via Pearson’s correlation analysis and Recursive Feature Elimination (RFE). With Random Forest (RF), Multi-Layer Perceptron (MLP), and Support Vector Regression (SVR) as base models, a ridge regression meta-model was developed to predict meteorological yield. The final results were obtained by integrating trend and meteorological yields, and core influencing factors were analyzed via SHapley Additive exPlanations (SHAP). The results showed that the Stacking model had the best predictive performance, significantly outperforming single models; August was the optimal prediction lead time; and the day–night temperature difference in the August maturity stage and the solar radiation in the April transplantation stage were core yield-influencing factors. This framework provides a practical yield prediction tool for Yunnan’s flue-cured tobacco areas and offers important empirical support for exploring meteorology–yield interactions in subtropical plateau crops.