Analysis of Multi-Environment-Driven Variations in Net Photosynthetic Rate and Predictive Model Development for Tomatoes During Early Flowering and Fruit Development Stages in Winter Solar Greenhouses

In protected horticulture, precise regulation of light intensity [i.e., photosynthetic photon flux density (PPFD)], ambient temperature, and ambient CO2 concentration is crucial for optimizing crop photosynthesis. Tomatoes, a key greenhouse crop, exhibit temporal variations in photosynthetic efficiency across their growth cycle. However, the differences in the dynamic responses of net photosynthetic rate (Pn) of tomatoes to environmental factors during flowering and fruit development stages in winter solar greenhouses, as well as how to utilize these differences respectively to achieve more precise on-demand environmental regulation, still require in-depth exploration. Based on measured data, this study employed decision tree (DT), random forest (RF), and XGBoost (XGB) models to predict net photosynthetic rate (Pn) across two growth periods. The results demonstrated that, in comparison with the early flowering stage, the photosynthetic potential of tomato leaves increased during the fruit development stage, with the Pn peak increasing by 11.5%. The proportion of observed data points in the high Pn range (25–35 μmol m−2 s−1) at the fruit development stage was 14.2%, which was significantly higher than the 6.7% observed at the early flowering stage. Meanwhile, the sensitivity of tomato leaves to changes in environmental factors also increased during the fruit development stage. On the independent test set, the XGB model exhibited the best predictive performance: the root mean square error (RMSE) for the early flowering stage model was 0.47 μmol m−2 s−1, with a mean absolute error (MAE) of 0.36 μmol m−2 s−1; for the fruit development stage, the RMSE was 0.60 μmol m−2 s−1, and the MAE was 0.41 μmol m−2 s−1. This study demonstrated the variation patterns of photosynthetic characteristics of tomatoes at different growth stages in response to environment factors. The established XGB model and the generated three-dimensional visualized Pn prediction surfaces provide a quantitative basis and decision-support tools to facilitate precise environmental management strategies for the coordinated dynamic regulation of light, temperature, and CO2 in solar greenhouses.