基于SVM-BSA的光环境调控模型的构建

植物的生长环境对提高植物经济效益具有重大意义。近年来的研究表明,光合速率是验证植物有效光合作用的重要参数,因此将环境因子作为参量构建光合速率模型成为现阶段的研究重点。文章通过支持向量机找寻出温度、CO2浓度、光子通量密度与光和呼吸速率之间的对应关系,在这个基础上,使用鸟群算法完成对光环境最优目标值进行寻优,并完成光环境最优模型的构建。模型预测数据与实际量测数据的拟合结果的决定系数为0.989,均方误差为14.58,表明本文构建的光合速率模型可以有效根据环境参数计算出植物的最优光饱和点,为农业大棚环境的精准调控提供依据。

The environment for plant to grow is of great significance to improve plant economic benefits. In recent years, studies have shown that photosynthetic rate is an important parameter to verify plant effective photosynthesis, so the construction of photosynthetic rate model based on environmental factors has become the focus of research at the present stage. In this paper, the corresponding relations among temperature, CO2 concentration, photon flux density and light and respiration rate are found by support vector machine. On this basis, the bird swarm algorithm is used to optimize the optimal target value of light environment and complete the construction of the optimal model of light environment. The determination coefficient of the fitting result between the predicted data and the actual measured data is 0.989, and the mean square error is 14.58, indicating that the photosynthetic rate model constructed in this paper can effectively calculate the optimal light saturation point of plants according to environmental parameters. The purpose of this paper is to provide a basis for the precise regulation and control of agricultural greenhouse environment.