Abstract: 【Aim】 This study aims to build up a pest and disease forecast model based on wavelet neural network (WNN), so as to provide a basis for taking measures to prevent pests and diseases, reducing crop damage by pests and diseases and improving quantity and quality of crop yields. 【Methods】 Based on the occurrence degree of wheat aphids from 1980 to 2014 and the meteorological factors in Ruicheng County, Yuncheng City, Shanxi Province, we integrated and created 9 new independent variable input models from 40 fundamental meteorological factors through Principal Component Analysis (PCA) and screened hidden layer nodes by trial and error method, conducted training with data from 1980 to 2009 and retested the occurrence degree of wheat aphids from 2010 to 2014. Finally, the study built up a WNN model by taking wavelet function as transfer function and contrasted itself with BP neural network (BPNN) model which takes Sigmoid function as transfer function. 【Results】 The average fitting accuracy of both models, namely, WNN and BPNN models, were above 80% in at least 10 years. Their MAPE values were 89.83% and 83.07%, and their MSE values were 0.0578 and 0.6192, respectively. 【Conclusion】 Both models can well illustrate the occurrence degree of wheat aphids. As for the forecast accuracy and model stability, however, WNN is better than BPNN.

Key words: Wheat aphid, wavelet neural network (WNN), back propagation neural network, occurrence degree, forecast