Predictive Modeling of the Annual Insect Population of Chilo suppressali in Major Rice Areas of Sichuan Province by Light and Sex Attraction
[Objective]To construct an optimal prediction model under the light and sex induced condi-tions of the annual population of stem borer(Chilo suppressalis)in major rice areas of Sichuan.[Method]According to the adult moth numbers under the light-and sex-induced conditions from 2018-2023.Firstly,the correlations between the annual population of C.suppressalis and meteorological factors were analyzed using Pearson correlation analysis.Then,the prediction models were further built through the stepwise regression and back propagation(BP)neural network.[Result]Under the light-and sex-induced conditions,the annual population of C.suppressalis in each rice area was closely corre-lated with meteorological factors(i.e.,temperature,humidity,rainfall,and barometric pressure).The average temperature in August in rice area of Chengdu Plain was the most correlated with the annual population of C.suppressalis under the light induced condition(R=0.701).On the contrary,under the sex-induced condition,the barometric pressure in June in the eastern Sichuan rice area showed the larg-est negative correlation with the development dynamics(R=-0.840).After comparing the predicted val-ues,regression fitted values,mean absolute error(MAE)and mean square error(MSE)of the stepwise regression model and the BP neural network model under different induced methods,it was determined that the BP neural network model under the light-induced condition yielded the most accurate predictions in the primary rice-growing areas of Sichuan,and the regression fit values were stable between 78.65%-99.59%,92.38%-99.88%,and 76.97%-99.96%for Chengdu Plain Rice Area,East Sichuan Rice Area,and South Sichuan Rice Area,respectively.The results of the 2023 light induction for independent testing of the BP neural network model showed that the predicted values were basically the same as the ac-tual induction in most of the rice districts.[Conclusion]The BP neural network model under the light in-duction has better prediction and fitting effects than stepwise regression.
Chilo suppressalismeteorological factorslight and sex inductionstepwise regressionBP neural networkprediction model
NETL
NSTL
万方数据
