A predictive model of photosynthesis for cucumber

Abstract

Photosynthetic rate (Pn) of plants is determined by environment, such as temperature, carbon dioxide (CO2), and light. Light environment includes light intensity (LI) and light quality (LQ). It is important to build a predictive model of protected crops' Pn where LI, LQ and other environmental factors are comprehensively considered. In this paper, cucumber was taken as experimental material, and a nested experiment was designed to measure the Pn under different temperature, CO2 concentration ([CO2]), LI and LQ. On the bases of these measured data, a predictive model of Pn was built by using support vector regression (SVR) algorithm. The performance of training set with coefficient of determination (DC) of 0.9990, and the root-mean-square error (RMSE) of 0.0478 μmol•m -2 •s -1 demonstrated that the model is highly accurate after training. The validation results of predictive model showed that the fitting slope was 1.0015, and the intercept was 0.0223 between measured and predicted Pn values, which indicated that the model was accurate to calculate the Pn of plants under different environment.