Studies On The Forecasting Of The Second-Generation Corn Borer By The Meteorological Factors

The corn borer, known as a famous pest, belongs to Lepidoptera Pyralidae. In our country the corn borer is comprised of the Asian corn borer and the Europe corn borer. The Asian corn borer is the preponderant species in our country and distributes all over our country with serious occurrence. The corn borer is the focus of the pest forecasting and the pest manage. The corn is the primary grain crop and fodder, so the corn borer brings high loss to the corn planting and the livestock breeding.This paper studies the effect of the meteorological factors to the second-generation corn borer, and then the feasibly meteorological factors for forecasting are filtrated, and the usage of the statistical forecasting methods in order to increase the precision of forecasting, and the usage of the Fuzzy math method to forecasting the second corn bore.1. Using the occurred data of the corn borer and the synchronous meteorological data of NingYang, ShanDong province in 1989-2003 we analyze the effect of main meteorological factors to the population dynamics of the second corn borer with the correlation analysis; the multinomial step-wise regression analysis; path analysis and the grey correlation analysis. The result shows that the effect of the meteorological factors accord with the biology characteristic of the second-generation corn borer.Integrated with the above ways the conclusion is gained that the main effect factor to the amount of the second-generation corn borer is the precipitation of 5-8 month.2. Filtrating the appropriate forecasting factors is the bottle-neck of forecasting. In this paper the meteorological factors for forecastinging the amount of the second-generation corn borer are gained by the multinomial step-wise regression analysis. These are the average daily temperature in June ( x₁ ); the average precipitation in June ( x₂); the average precipitation in July ( x₄); the average precipitation in May ( x₆); the average daily temperature in August ( x₇); the average precipitation in August ( x₈).3. After using the poly-linear regression model and the multinomial step-wise regression analysis, the BP (Back Propagation) artificial neural network (ANN) model is studied in forecastinging the population dynamics of the second-generation corn borer with the meteorological factor filtered by using poly-linear step-wise regression. It has very high forecasting precision by applied. Using the BP ANN the average forecasting precision reach 95.53567%, and have 93.33% samples with the 90% veracity at least. All samples have 85% veracity at least.In order to elevate the forecasting precision more, the combination forecasting model based on using entropy to ascertain the weighting coefficients is used to forecasting the corn borer and a satisfactory effect is acquired. Using this method the average forecasting precision reach 95.63094%, and have 93.33% sample with the 92% veracity at least. All samples have 86.9% veracity at least.4. The influence on the occurrence and the development of the pest is uncertain and fuzzy, so the Fuzzy theory is tried to forecasting the corn borer. The Fuzzy pattern recognition and the Fuzzy matter element decision model are used to forecasting the corn borer and gain satisfied results. After put into test the two methods have 100% veracity. Fuzzy theory sufficiently takes the fuzzy and the complexity of the pest developmental system into account, so it has feasible usage in the pest forecasting.