Prediction of Air Temperature and Relative Humidity in Greenhouse based on Neural Network Model

Abstract Greenhouse environmental factors such as temperature and humidity are critical environmental factors influencing plant development, quality, and production quantity, therefore, it is crucial to predict the temperature and humidity. In this study, a back propagation(BP)neural network was proposed to model the temperature and relative humidity of a greenhouse and have obtained a high accuracy pridiction modole. The model considered both internal and external environmental factors influencing crop growth, the best pridiction result accurred when input featurears are outdoor temperature, relative humidity, irradiation intensity and previors indoor temperature and relative humidity,with 9d of training data set, in which the R² of temperature is 0.999, the RMSE is 0.299℃,and error range is[ -0.240℃, 0.781℃], and the R² of relative humidity is 0.999, the RMSE is 0.657%, and the error range is [-0.766%, 1.995%]. This model can be used to forecast the temperature and humidity of a greenhouse and guide the control of the temperature and humidity of a greenhouse.