In order to recognize citrus leaf canker disease accurately and quickly, a diagnosis method of citrus leaf canker disease based on naive Bayesian classification was proposed. The digital images of leaves with different severities of citrus leaf canker disease were used as the data source. According to the characteristics of color space, a disease spot recognition model based on naive Bayesian classification was established for rapid diagnosis of citrus leaf canker disease, and the diagnostic abilities of naive Bayesian classification, fixed threshold, adaptive threshold and support vector machine for citrus leaf canker disease were compared. The results showed that the method based on naive Bayesian classification was effective in the segmentation of citrus leaf canker disease, and the incorrect segmentation rate was only 3.58%, which was far better than the threshold methods and support vector machine. In terms of performance efficiency, the time order of the four algorithms was fixed threshold method＜adaptive threshold＜naive Bayesian＜support vector machine, all of which were within a reasonable range. Combined with the preparation time, naive Bayesian method had the best performance efficiency. Therefore, the naive Bayesian classification algorithm has a rapid and accurate application ability in the diagnosis of citrus leaf canker disease, and can provide a new way for the accurate diagnosis of fruit tree disease severities.

Keywords： citrus ; canker disease ; naive Bayesian classification ; threshold segmentation