The Application Of Artificial Neural Network In The Forecasting Of Wheat Midge

Wheat midge is the important pest on wheat in Northern Hemisphere, and it is also one of important pests on wheat in our country. It belongs to the Diptera Cecidomyiidae. There are two kinds midges on wheat, one is Contarinia tritici (Kirby) and another is Sitodiplosis mosellana (Gehin). Wheat midge can result in the destructive disaster to wheat. It had broken out many times on our country history, and has great impact on wheat yield. Therefore, the forecasting of wheat midge is very important to the control of wheat midge.Data of 14 weather factors during 1933~2000 are studied by using stepwise regression analysis. Key factors affected wheat midge occurrence of Guanzhong district are the average daily temperature in January (Xi), the average daily temperature in March (X3), the average daily temperature in August of last year (X5) and precipitation in February (X8).Six prediction methods are selected to predict occurrence level of wheat midge. To make occurrence degree prediction, single factor regression algorithms, stepwise regression algorithms, discriminatory analysis, Markovian model, fuzzy mathematics theory and Back propagation neural network algorithms are studied and models are builded.Back prediction accurate ratio of six methods are 71. 4%, 82.5%, 74.6%, 77.8%, 69.8%, 100% respectively. Prediction accurate ratio are 60%, 60%, 40%, 40%, 60%, 100% respectively. The accurate rate of return of the previous five methods are higher than their prediction, but their accurate rates of return are also not good, all these show that these predicting methods are not good in the forecasting of wheat midge than neural network. And particularly is the discriminatory analysis and Markovian model, their accurate rate of return are more higher than their prediction, this show that their prediction resolutions are unsteady and they have good application value in the prediction of wheat midge. Only can the result of back prediction and prediction under back propagation neural network reach to 100%. This method has good prediction result, and its prediction is stable. So it has the good applied prospect.Artificial neural network (ANN) is one of the quick developing processing techniques in artificial intelligence. ANN has convergent speed, perfect trace performance, and high tolerant errors as well. Specially, it is suitable for complex non-linear prediction. The neural network is primarily using on patternrecognition, photo treated, economic prediction and management. In recent year, it alse is applied in the prediction of plant disease and pest, but there are still no reports on wheat midge prediction. In this paper, ANN was applied in the prediction of wheat midge.The algorithm is used to imitate the brain' s ability to make decisions and draw conclusions when presented with complex, noisy and/or partial information. It has three layers: input layer, hidden layer and output layer. This prediction model has been passed in Visual Basic 6. 0. This model has in common use, and can be used in the prediction of other diseases and pest insect.