A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis

Light intensity, CO2 concentration, and temperature are three primary environmental factors with high temporal variability, nonlinearity, and strong coupling, which directly influence the photosynthetic rate of plants. To investigate the combined influence of these factors on the photosynthetic rate of chili pepper plants, a predictive model was developed for their net photosynthetic rate (Pn) during the peak fruiting period. A multifactorial nested experimental design with irregular sampling intervals was used to systematically examine the interacting effects of light intensity, CO2 concentration, and temperature on photosynthesis. Based on the collected data, a support vector regression (SVR) algorithm was trained and its performance was compared with that of a backpropagation (BP) neural network, a radial basis function (RBF) neural network, and a random forest (RF) algorithm. To optimize performance, a grid search with five-fold cross-validation was conducted to identify optimal hyperparameters; this process yielded a cost parameter (C) of 38 and a gamma parameter (γ) of 8, which minimized the root mean square error (RMSE) on the training set. On the test set, the SVR model achieved a coefficient of determination (R2) of 0.9941 and an RMSE of 0.6988 μmol m−2 s−1 (relative to the full Pn range of −4.19 to 39.2 μmol m−2 s−1). A linear fit between measured and predicted Pn values yielded a slope of 0.992 and an intercept of 0.07, indicating near-perfect agreement and surpassing the performance of the BP, RBF, and RF models. These results demonstrate that the SVR-based model outperformed the other approaches and exhibited superior predictive ability, establishing it as a robust theoretical foundation and a practical tool for dynamic environmental optimization in controlled-environment agriculture.