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Scientific irrigation is very important for saving water in agriculture, increasing output and benefit in our country. In this paper a method of nonlinear character extraction based on kernel canonical correlation analysis(KCCA) is presented in which information of soil and environment are input vectors of model. Nonlinear character are extracted by KCCA, then main character variables are determined which reflects the complex relationship between original input and output data and the array dimension of input data is simplified. At last the model based on least squares support vector machine (SVM) were completed. By comparing simulation results, precision and rapidity of the prediction model based on KCCA-SVM are higher than those of CCA-SVM and LS-SCM model. The experimental results show that the method is very effective.

Research on Model of Predicting Irrigation Water Requirement Based on Kernel Method