Background: The spot, streak and rust are the most common diseases in maize, all of
which require effective methods to recognize, diagnose and handle. This paper presents a novel image
classification approach to the high accuracy recognition of these maize diseases.
Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence
of image noise and irrelevant background, so that the area of maize diseases could be effectively
extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix
(GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining
their texture, shape and color features. Finally, support vector machine (SVM) classification method
is used to identify three diseases.
Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed
Keywords: Maize Diseases, Image Processing, Color Segmentation, Gray Level Co-occurrence Matrix, Feature Extraction, Support Vector Machine
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