Research on greenhouse environment prediction model based on SO-BP neural network

Predicting change trends in greenhouse environment is an essential part of the construction of an accurate greenhouse control system, due to the hysteretic response characteristic of temperature and humidity regulation process in greenhouse environment. In response to the issue of insufficient accuracy of traditional neural network algorithms in greenhouse prediction, a method for predicting greenhouse environment based on snake optimizer（SO） optimized BP neural network is proposed. Experimental results show that the R2 value of predicting the temperature within 15 minutes by this method is 0. 956 4, which is 14. 87% and 2. 19% higher than that of the BP model and the HHO-BP model, respectively. The prediction indicators of MAE, MAPE, and RMSE values for temperature are 0. 481 3, 2. 237 8, and 0. 672 9, respectively. The R2 value of predicting the humidity within 15 minutes is 0. 982 1, which is 13. 12% and 2. 37% higher than that of the BP model and the HHO-BP model, respectively.The prediction indicators of MAE, MAPE, and RMSE values for humidity are 1. 709 0, 2. 584 2, and 2. 283 8, respectively. The prediction results of this model are quite ideal and can be used for greenhouse temperature and humidity prediction, providing technical support for the establishment of an accurate greenhouse control system.