A Comparative Analysis of Leaf Disease Detection using Image Processing Technique

C. M. Samiha*, S. P. Pavan Kumar **
* PG Scholar, Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.
** Assistant Professor, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
Periodicity:July - September'2018
DOI : https://doi.org/10.26634/jip.5.3.15044

Abstract

The essential part of any ecosystem is plant. All the organisms get energy from plants directly or indirectly. It is important to identify the disease in plant parts like leaf, stem, and fruit. Leaf diseases are caused by virus, bacteria, etc. Normally, a farmer identifies the leaf disease by observing spots, color, and shape of the leaf, but sometimes they take help from the experts to detect diseased leaf or crops. The manual detection of disease is less accurate and complex. Image processing techniques help farmers for timely detection of the diseases. K-Nearest Neighbor (KNN), K-Means clustering, Support Vector Machine (SVM), Artificial Neural Network (ANN), and various segmentation algorithm and classifiers are used for detection and classification of leaf diseases. In this paper, various diseases that occur in parts of the plant and identification of leaf diseases were discussed.

Keywords

ANN Classifier, KNN Classifier, K-Means Clustering Segmentation, GLCM.

How to Cite this Article?

Samiha, C.M., and Kumar, P.S.P., (2018). A Comparative Analysis of Leaf Disease Detection Using Image Processing Technique. i-manager’s Journal on Image Processing, 5(3), 40-46. https://doi.org/10.26634/jip.5.3.15044

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