Application of Support Vector Machine Method in Tobacco Leaf Usability Forecast

In order to evaluate the classification of tobacco leaf usability, the prediction model of the classification in tobacco leaf usability was established by using support vector machine (SVM) method with different kernel functions, and compare accuracy of the predicted result with Fisher methods. The results indicated the prediction accuracy with SVM method was higher than that of Fisher method. Among other kernel functions based on RBF, The SVM model was the best, and the prediction accuracy reached 90%. Therefore, the SVM method was an effective tool for classifying and predicting tobacco leaf usability.