Image Searching Method of Tobacco Disease Based on Disease Spot Feature Fusion

PU Yongxian;Computer Science Department,Dehong Teachers College;

content-based disease image searching;;image segmentation;;disease image feature extraction;;feature fusion;;image similarity;;tobacco disease

Recently,tobacco disease diagnosis expert system relies on the naked eye to diagnose disease characteristics,which causes disease diagnostic uncertainty and misjudgment. This study proposes a disease spot image searching method to diagnose tobacco diseases based on disease spot feature fusion.The color,texture and shape features of a disease are extracted after disease spot image segmentation,and then disease spot feature dimension reduction is conducted based on double coding genetic algorithm and support vector machine( SVM) model to obtain effective features for characterization of the disease characteristics and respective weights. The image similarity distance calculation is processed between the normalization of effective characteristics and the disease spot features of the image database system. According to the distance a number of similar images are returned,by which the descriptions of diseases and preventing measures are presented. Experiments with seven common diseases of tobacco showed that the precision and comprehensiveness of disease image retrieval based on fusion of color,texture and shape features were significantly higher than those using a single feature retrieval. Diagnosis of tobacco disease in this way,not only has higher recognition rate of diseases,but also realizes visualization of diagnosis results. Application of this method in tobacco disease diagnosis expert system will improve the robustness of the system,providing conditions for realizing remote online diagnosis of diseases.