Optimal regulation model of tomato seedlings' photosynthesis based on genetic algorithm

Abstract: As one of the world's major greenhouse crops, the yield and quality of tomato is significantly affected by photosynthesis. Temperature and photon flux density are important factors affecting photosynthesis; how to effectively evaluate their effects on tomato's photosynthesis, establish optimal model of photosynthetic rate and improve the rate of photosynthesis have become urgent problems in the field of crop cultivation. In view of these requirements, an optimal photosynthesis regulation model of tomato seedlings based on genetic algorithm was proposed in the paper. Firstly, the two-factor nested test of photosynthetic rate was conducted with Li-6400XT under the conditions that the tomato seedling " Wool powder 802" was used as the test sample, the temperature gradients were set at 16℃, 21℃, 25℃, 29℃, 33℃, 37℃, respectively, and the photon flux density gradients were set at 0, 50, 100, 200, 400, 600, 800, 1000, 1200, 1500 μmol/m2·s, respectively. Secondly, a photosynthetic rate model coupling temperature and photon flux density was built by processing multivariate nonlinear regression of the experimental data obtained. Then, the optimization algorithm of photosynthetic rate based on genetic algorithm was designed under different temperature gradients, by which the light saturation points under different temperature conditions were obtained. Furthermore, the optimal regulatory model of tomato seedlings' photosynthesis was established aiming at light saturation points. Finally, the model verification test was conducted by comparing and analyzing the measured data and calculated data by the model under 17 values of light saturation points at different temperatures. The results showed that the correlation coefficient of the values between the measured and the calculated was 0.920, the slope of the fitted line was 1.011, and the ordinate intercept was 0.236, which indicated that these two values had good correlation and similarity. Besides, the maximum of relative error was less than 6%, which proved that the proposed model had a high accuracy and had access to the light saturation points at different temperatures dynamically. The conclusion provides a theoretical basis for the optimal regulation of photosynthetic rate and is of great significance for raising the output of tomato in greenhouse and improving the economic benefits.