农业工程学报Issue(17)：220-227,8.DOI:10.3969/j.issn.1002-6819.2014.17.028
基于遗传算法的番茄幼苗光合作用优化调控模型
Optimal regulation model of tomato seedlings’ photosynthesis based on genetic algorithm
摘要
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, 1500μmol/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.关键词
光合作用/遗传算法/番茄/光合速率/温度/光饱和点/调控模型Key words
photosynthesis/genetic algorithm/tomato/photosynthetic rate/temperature/light saturation point/regulatory model分类
农业科技引用本文复制引用
胡瑾,何东健,任静,刘翔,梁岩,代建国,张海辉..基于遗传算法的番茄幼苗光合作用优化调控模型[J].农业工程学报,2014,(17):220-227,8.基金项目
国家科技支撑计划课题（2012BAD52G01）；陕西省科学技术研究发展计划项目（2013K02-03）；西安市科技计划项目（NC1214（2）） （）
