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

番茄光合速率主要受温度和光子通量密度影响,动态获取不同温度条件下的光饱和点信息是提高光环境调控效率的关键。该文结合遗传粒子算法提出了一种光合优化调控模型:利用光合速率双因素嵌套试验获取多维数据,构建温度、光子通量密度耦合的光合速率多元非线性回归模型,采用遗传算法对光合速率模型进行优化,获取任意离散温度值下的光饱和点,以饱和光照强度作为目标值建立光合优化调控模型。以番茄幼苗为例进行了验证,试验结果表明:提出的方法可动态获取不同温度条件下光饱和点,光饱和点实测值与计算值决定系数为0.9873,最大相对误差小于4.6%,具有较高精度,对提高设施光环境调控效率具有重要的意义。

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.