Prediction of air temperature and humidity in greenhouses via artificial neural network

Abstract

Accurate prediction of greenhouse temperature and relative humidity is critical for developing environmental control systems. Effective regulation strategies can help improve crop yields while reducing energy consumption. In this study, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks were used for short-term prediction of temperature and relative humidity in a double-film greenhouse. The prediction models used indoor soil temperature, light intensity, and historical measurements of temperature and humidity from the previous 10 minutes as inputs. Results show that the MLP model with Levenberg-Marquardt optimization performs best in predicting the current temperature and humidity, with an RMSE of 0.439°C and R2 of 0.997 for temperature prediction and an RMSE of 1.141% and R2 of 0.996 for relative humidity prediction. For 30-minute short-term prediction, the Bayesian optimized RBF model showed better temperature prediction with an RMSE of 1.579°C and an R2 of 0.958, while the MLP model performed better in relative humidity prediction with an RMSE of 4.299% and an R2 of 0.948. This study provides theoretical support for advancing the intelligent regulation of greenhouse environmental factors in cold and arid regions, and the application of predictive models to intelligent environmental management systems could help optimize cultivation practices and energy efficiency.