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AN EVALUATIVE MODEL FOR LEAF DISEASE DETECTION IN AGRICULTURE BASED ON CBIR
1 Dr. Jasmine Samraj, 2Ms. V. Soumiya

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-5, pp.140-154, Oct 2017
DPI :-> 16.10089.JARAS.2017.V4I5.140154.2097



Abstract
In Agriculture field plant leaf diseases are the major cause of significant reduction in both quality and quantity of crops in agriculture. It leads to economic loss. Farmers are facing problem rising from various types of plant diseases. Sometimes Farmers are unable to diagnosis the disease, results in lack of identification of right type of disease and this leads to crop damage if not taken proper care of it at right time. Farmers need information about fertilizers, pesticides, information of atmosphere, etc. Today this information is available in scatteredmanner, and it does not diagnosis about various diseases. Image analysis by using Content Based Image Retrieval (CBIR) is an important method for research used widely in image processing. This paper discusses an approach to detect the diseased plant leaf image and to identify the disease of affected leaf using feature extraction techniques. The method used in this research feature extraction is divided into two major phases. First phase concerns with color space, gray-level co-occurrence matrix (GLCM) and canny edge detection. Second phase concerns with color histogram, histogram of oriented gradient (HOG) and c-means clustering. The neural network classification is used for similarity matching for featured query image and database images. The main aim of this system is to provide different feature extraction techniques for effective required image retrieval.

Key-Words / Index Term
Content Based Image Retrieval (CBIR), Color Space, Gray-level Co-occurrence Matrix (GLCM), Canny Edge Detection, Color Histogram, Histogram of Oriented Gradient (HOG), C-Means Clustering.

How to cite this article
1 Dr. Jasmine Samraj, 2Ms. V. Soumiya , “AN EVALUATIVE MODEL FOR LEAF DISEASE DETECTION IN AGRICULTURE BASED ON CBIR”, Journal for Advance Research in Applied Sciences, 4, Issue-5, pp.140-154, Oct 2017. DPI:16.10089.JARAS.V4.I5.2097