A decision-making model for light environment control of tomato seedlings aiming at the knee point of light-response curves

Abstract

Light, the energy source for crop photosynthesis, is a key factor for plant growth. The present study proposes a decision-making model of light environment control. The photosynthesis rate of tomato seedlings under different light intensities, temperatures, and CO2 concentrations was determined in a nested experiment. These data were used to construct a predictive model of the photosynthesis rate using the support vector regression method, with an R2 of 0.9862, a root mean square error of 1.39 μmol·m−2·s−1, and a mean absolute error of 1.18 μmol·m−2·s−1. In total, 861 discrete light-response curves were obtained based on the predictive model, and their knee points were computed using the U-chord curvature method. These knee points were used to form a dataset for constructing a decision-making model for light environment control, with an R2 of 0.984 and a root mean square error of 9.55 μmol·m−2·s−1. The results of the validation experiment suggested that the average relative error of the model was 1.92%, indicating the robustness of the model. Compared with those of the light saturation control method, the average light demand for the decision-making model decreased by 60.49%, whereas the average photosynthesis rate reduced by 24.40%. Although the photosynthesis rate lost a bit, the rate of light saving is almost three times more than the rate of photosynthesis rate decreased slightly, which improved the production efficiency of tomato.