Abstract:

To explore a more accurate and effective method to predict the initial flowering date of Nanguo pear, grey relation analysis was used to determine the winter meteorological factors associated with the initial flowering date, which could be used as BP (Black Propagation) neural network and RBF (Radial Basis Function). The root mean square error (RMSE) and relative error (RE) were used to evaluate the prediction effect of the model. Meanwhile, the stepwise regression equation was established by comparing the winter meteorological factors significantly related to the initial flowering date of Nanguo pear and the prediction results after back substitution. The following are the results. (1) The meteorological factors with greater grey relation with the initial flowering date of Nanguo pear were the daily average temperature, maximum temperature, minimum temperature and relative humidity in winter, and the correlation degree was above 0.6. Therefore, these four factors were used as the input layer of BP and RBF neural network models to predict the initial flowering date of Nanguo pear. (2) The meteorological factors significantly correlated with the onset of flowering were daily mean temperature, daily mean ground temperature of 5 cm, daily minimum temperature and daily maximum temperature, and the correlation coefficients were -0.646, -0.628, -0.638 and -0.663, respectively. All the established regression models passed the significance test and had statistical significance. (3) The accuracy of model fitting established by BP and RBF was generally close. (4) The errors of BP neural network and RBF neural network prediction results based on grey relation were 1 day and 2.25 days respectively, and the flowering date predicted by BP neural network was closer to the actual flowering date. (5) RMSE, RE and R² of BP neural network model based on grey relation were 1, 6.34% and 0.7, respectively, and for RBF neural network model, RMSE was 2.25, RE was 13.13%, and R² was 0.568. In conclusion, BP neural network model established by grey relation analysis method was more accurate than RBF model in predicting the initial flowering date of Nanguo pear.

Key words: initial flowering date, grey relation analysis, BP neural network, RBF neural network, stepwise regression analysis