Detection of Unhealthy Region of Plant Leaves and Classification of Plant Leaf Diseases using Texture Based Clustering Features

The recent development of digital camera and growth of data storage has led to a huge amount of image databases. Only a few were proposed for specified databases such as satellite images, leaf sets, maps, faces, fingers and so on. Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and classification of plant leaf diseases. The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%. Experimental results on a database of about 500 plant leaves confirm the robustness of the proposed approach. This paper provides various methods used to study of leaf disease detection using clustering process. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, and this RGB is converted to HSI because RGB is for color generation and his for color descriptor. Then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed from the SGDM (Spatial Gray-level dependence) Matrices, finally the presence of diseases on the plant leaf is evaluated.