Abstract:

The study carries out temperature forecast of solar greenhouse, aiming to provide reference for agricultural production, guide greenhouse temperature control to ensure a suitable condition for crop growth, and promote agriculture products’ quality and yield. Meteorological factors such as temperature and sunshine outside the greenhouse were selected to build a prediction model based on random forest algorithm, and then the indoor minimum and maximum temperatures were fitted for prediction analysis and the importance of the prediction factors were evaluated. Results showed that the fitting degree of the fitting value and the observed value of the lowest and the highest air temperature in greenhouse was 99.69% and 99.85%, respectively. The lowest air temperature outside the greenhouse was an important predictor of the indoor minimum air temperature, and the outdoor sunshine was an important predictor of the indoor maximum air temperature. At the same time, the support vector machine, neural network, multiple regression and stepwise regression models were established. By comparing the mean absolute error and root-mean-square error in each model, the prediction accuracy of the random forest model was better than that of other models. The air temperature prediction model based on random forest algorithm is more accurate, which can be popularized in the air temperature prediction of solar greenhouse.

Key words: solar greenhouse, maximum and minimum air temperature, prediction, stochastic forest algorithm, model study