Prediction Model Of Photosynthetic Rate Based On Sopso-Lssvm For Regulation Of Greenhouse Light Environment

It is difficult to accurately predict the photosynthetic rate of crops in the greenhouse, which affects the implementation of greenhouse light environment regulation considering photosynthesis demand. Therefore, this paper takes tomato as the test crop, and through the combination experiment on photosynthetic rate with the condition of nested temperature, humidity, photon flux density and CO2 concentration, the sample data is obtained. Then, the training samples are selected from the sample data to train the SOPSO-LSSVM algorithm, and a soft sensing model for predicting the photosynthetic rate is established, which can realize the accurate prediction of tomato photosynthetic rate. Finally, the simulation results indicate when the model is used to predict the photosynthetic rate, it has the smaller maximum relative error and root mean square error, which are 0.0264 and 0.2926 respectively. The average relative prediction error is only 0.0078 and the coefficient of determination is 0.9953. More shows that the established model can provide a reliable objective function for the regulation of greenhouse light environment considering the photosynthesis demand, and then guide the regulation of light environment.