Abstract:

In order to explore a more accurate and effective method for predicting the initial stage of full flowering of rose, based on the observation data and meteorological data in Pingyin County, Shandong Province from 1994 to 2024, the time changing trend of the initial stage of full flowering of rose was analyzed. The key meteorological factors were selected through correlation analysis, which was used to establish the prediction model by BP neural network, and was compared with stepwise multiple linear regression method. Root mean square error (RMSE), relative error (RE) and coefficient of determination (R²) were used to evaluate the prediction accuracy of the model. The results showed that the initial stage of full flowering of rose was advanced in 1994-2024, with an average advance of 0.4 days per 10 years. From 1994 to 2020, 16 meteorological factors were significantly correlated with the ordinal number of the initial stage of full flowering (P<0.01), among which the heat condition in mid-early April was the main meteorological factor affecting the flowering period. The RMSE, RE and R² of the training set of BP neural network model were 0.75 d, 0.62% and 0.92, and the mean absolute error was 0.44 d. The RMSE, RE and R² of the stepwise multiple linear regression model were 1.31 d, 1.08%, 0.77, and the mean absolute error was 1.04 d. Both models can forecast the initial stage of full flowering of roses in late April. Data from 2021-2024 were used to verify the prediction effect of the model. The years in which the forecast values of the BP neural network model were consistent with the actual values accounted for 75.0%; the years in which the forecast values of the stepwise multivariate linear regression model were consistent with the actual values accounted for 25.0%. In summary, BP neural network model has better prediction effect than stepwise multiple linear regression model, and has higher reliability and application potential in the initial stage of full flowering of rose forecast.

Key words: rose, the initial stage of full flowering, meteorological prediction, BP neural network, stepwise multiple linear regression