In this paper we have proposed a novel segmentation method for classification of diseased and healthy maize and paddy leaves using Opposite Color Local Binary Pattern (OCLBP). The proposed works have been done on the maize and paddy leaves, the dataset has the diseased and healthy leaves, diseased leaves have the yellowish brown patches. Disease in maize and paddy leaves may be due to biotic causes. Generally, leaves spotted with yellow at initial stage and appear bronzed brown color at end stage at its disease levels. The diseased spots are all having color transition from yellow to Bronzed brown color. This yellow to bronzed brown color transition is appeared in between red and green colors of RGB color cube. This color transition motivated us to use OCLBP as a segmentation tool. The OCLBP textured image is the image of segmented diseased part which helps in extract the features. So here considered red color channel against green color channels to get the OCLBP textured image. SVM is used for diseased and heathy leaves classification. We have attempted to introduce the best segmentation, feature selection and dimensionality approaches for image texture which support fast and accurate pattern recognition and object identification.

Feature Selection, Local Binary Pattern, Gabor features, OCLBP

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