Method for recognition of grape disease based on support vector machine

A new method for recognizing grape leaf disease by using computer image processing and Support Vector Machine (SVM) was studied to improve recognition accuracy and efficiency. At first, vector median filter was applied to remove noise of the acquired color images of grape leaf with disease. Then a method of statistic pattern recognition and mathematics morphology was introduced to segment images of grape leaf with disease. At last texture features, shape features and color features of color image of grape leaf with disease were extracted, and classification method of SVM for recognition of grape disease was used. Experimental results indicate that the classification performance of Support Vector Machine is better than that of neural networks. Recognition rate of grape disease based on SVM of shape and texture feature is better than that of only using the shape or texture feature, recognition rate of grape disease based on SVM of color and texture feature is higher than that of only using the color or texture feature.