Abstract:In order to improve the accuracy of Chinese wolfberry classification, a classification model based on the image of Chinese wolfberry was established. Six kinds of Chinese wolfberry from three different origin areas were studied, Fisher discriminate analysis (FDA) and kernel Fisher discriminate analysis (KFDA) were used to identify the 6 kinds of Chinese wolfberry samples, based on the color and texture feature parameters of Chinese wolfberry image. In the method of KFDA, Radius basis function (RBF) was selected as the kernel function, the measuring method of matrix similarity based on distance discrimination was taken to define the RBF characteristic parameter, and the optimum characteristic parameter was 13.2436. Selects the first 150 principal components, based on the Wilks Λ criterion, the classification of Chinese wolfberry and verify accuracy were 100.00% and 87.80%, respectively, and based on the contribution rate, the classification of Chinese wolfberry and verify accuracy were 100.00% and 81.70%, respectively. The final results showed that 150 most conducive PC were selected by this Wilks Λ criterion screening method, and the identification correct rates were respectively from 91.7% (FDA) up to 100% (KFDA), this method not only improved the correct rate of Chinese wolfberry effectively but also would provide a theoretical guide for the application of the other agriculture products image classification.