CLASSIFICATION OF LEAF DISEASES IN APPLE USING SUPPORT VECTOR MACHINE

sivakamasundari ganesan, Dr.V. Seenivasagam

Abstract


India is an agricultural country, and 70% of the population depends on agriculture, hence heavy crop losses due to plant diseases results in loss of several billion dollars annually. This paper mainly concentrates on detection of leaf diseases. In rural areas and in developed countries, the naked eye observation of agricultural experts to detect the plant diseases is cumbersome. It takes too much of cost and time. This paper aims to provide fast and cheap solution through automatic detection of leaf diseases and thus to improve the yield of the crops by detecting the plant diseases at earlier stage. Using image processing, we can able to identify the type of disease in plant leaf by analyzing the color and texture feature. In our system first, color transformation is done from RGB to HSV and followed by color feature extraction. In color feature analysis, the color distribution of pixels is represented by the mean and standard deviation of the image and local color information is represented by Binary bitmap. For texture feature extraction, first green pixels are masked as they are healthy region. This is followed by segmentation using K means clustering and identification of useful segments. Gray level co-occurrence matrix (GLCM) is obtained from the segmented image from which the texture features are extracted. Using SVM diseased plant is discriminated from the healthy plant and classified based on diseases. The performance of classification is measured using performance measures precision and recall.

Keywords


Color Transformation, Masking, Feature Extraction, Segmentation, Classification

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DOI: https://doi.org/10.26483/ijarcs.v9i1.5124

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