上海农业学报2016，Vol.32Issue(6)：26-32,7.DOI:10.15955/j.issn1000-3924.2016.06.05
基于遗传粒子群算法的番茄幼苗光合优化调控模型
A regulatory model of tomato seedlings’photosynthesis based on genetic algorithm-particle swarm optimization
摘要
Abstract
Tomato plants’photosynthetic rate is mainly influenced by temperature and photon flux density, and acquisition of dynamic information of light saturation points at different temperatures is the crux of improving the regulating efficiency of light environment.According to genetic algorithm-particle swarm optimization (GA-PSO)the paper proposes a regulatory model of photosynthetic optimization:multidimensional data are acquired by means of the two-factor nested tests of photosynthetic rate,a multivariate nonlinear regression model of photosynthetic rate coupling temperature and photon flux density is built and then optimized by using GA-PSO, thus acquiring the light saturation point at any discrete temperature,and lastly with the saturated light intensity as a desired value,a regulatory model of photosynthetic optimization is established.The model is verified by taking tomato seedlings,and the results show that the light saturation points at different temperatures can dynamically be acquired by the proposed method,the determination coefficient between the light saturation points’measured and calculated values is 0.9873,and the maximum relative error is less than 4.6%,indicating that the proposed method has a high precision and an important significance of improving the regulating efficiency of light environment.关键词
番茄/幼苗/光合速率/光饱和点/遗传算法/粒子群算法/调控模型Key words
Tomato/Seedling/Photosynthetic rate/Light saturation point/Genetic algorithm/Particle swarm optimization/Regulatory model分类
农业科技引用本文复制引用
王东,王智永,裴雪,辛萍萍,张佐经..基于遗传粒子群算法的番茄幼苗光合优化调控模型[J].上海农业学报,2016,32(6):26-32,7.基金项目
国家自然科学基金资助项目（31501224）；陕西省农业科技创新与攻关项目 （）
