Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.
