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Reseach Article

Disease Detection and Diagnosis on Plant using Image Processing - A Review

by Khushal Khairnar, Rahul Dagade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 13
Year of Publication: 2014
Authors: Khushal Khairnar, Rahul Dagade
10.5120/18973-0445

Khushal Khairnar, Rahul Dagade . Disease Detection and Diagnosis on Plant using Image Processing - A Review. International Journal of Computer Applications. 108, 13 ( December 2014), 36-38. DOI=10.5120/18973-0445

@article{ 10.5120/18973-0445,
author = { Khushal Khairnar, Rahul Dagade },
title = { Disease Detection and Diagnosis on Plant using Image Processing - A Review },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 13 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 36-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume108/number13/18973-0445/ },
doi = { 10.5120/18973-0445 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:42:54.664615+05:30
%A Khushal Khairnar
%A Rahul Dagade
%T Disease Detection and Diagnosis on Plant using Image Processing - A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 13
%P 36-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diseases decrease the productivity of plant. Which restrict the growth of plant and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the diseases. In which initially the infected region is found then different features are extracted such as color, texture and shape. Finally classification technique is used for detecting the diseases. There are different feature extraction techniques for extracting the color, texture and edge features such as color space, color histogram, grey level co-occurrence matrix (CCM), Gabor filter, Canny and Sobel edge detector. There are also different classification techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Backpropagation (BP) Network, Probabilistic Neural Network (PNN), Radial Basis Function (RBF) Neural Network.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Classification Support Vector Machine Neural Network.