Prediction of Wheat Stripe Rust using Neural Network
By comprehensive analysis on wheat stripe rust disease and meteorological data from 1984 to 2008 in Pingliang, the main factors were selected, and based on it three modified BP prediction models were built to realize the prediction of wheat stripe rust bacteria amount during summer, the occurrence degree in autumn and in the next spring. The node numbers of three networks are 6-12-1, 3-10-1 and 610-1, and the training functions of hidden and output layers are both traingdx. The simulation results show that for training samples of three BP networks the prediction results are completely matching with real levels. The mean square deviations between the prediction results and real values of test samples are all within the range of 0.1~0.4 level. It proves that modified BP prediction model has better accuracy than stepwise regression method and can meet production needs.
BP neural network wheat stripe rust disease prediction simulation meteorological factor
Lihong Mo
Faculty of Electronic and Electrical Engineering Huaiyin Institute of Technology Huaian, China
国际会议
厦门
英文
475-479
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）