Accurate prediction of transpiration rate is crucial for precision irrigation and improved water use efficiency in greenhouses. This paper proposes a "Physics + XGBoost + Shapley additive explanation (SHAP)" framework. A physical indicator, E_phys= Cond × VpdL, is constructed using stomatal conductance (Cond) and leaf-air vapor pressure deficit based on leaf temperature (VpdL) to establish a linear physical baseline. Subsequently, an XGBoost residual correction model is built using the residual between observed transpiration rate and the predicted physical value as the learning objective, thus forming the Physics + XGBoost transpiration rate prediction framework. Results show that the coefficient of determination on the test set is approximately 0.9975, and the root mean square error is approximately 0.0583. SHAP analysis indicates that E_phys, Cond, and VpdL are the dominant factors driving residual correction. Furthermore, this paper predicts the canopy daily evapotranspiration at the leaf scale to construct an irrigation scenario, providing a generalizable technical path for water management and physical-data fusion modeling of greenhouse crops.