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Reseach Article

Plant Disease Detection using Image Processing- A Review

by Surender Kumar, Rupinder Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 16
Year of Publication: 2015
Authors: Surender Kumar, Rupinder Kaur
10.5120/ijca2015905789

Surender Kumar, Rupinder Kaur . Plant Disease Detection using Image Processing- A Review. International Journal of Computer Applications. 124, 16 ( August 2015), 6-9. DOI=10.5120/ijca2015905789

@article{ 10.5120/ijca2015905789,
author = { Surender Kumar, Rupinder Kaur },
title = { Plant Disease Detection using Image Processing- A Review },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 16 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number16/22185-2015905789/ },
doi = { 10.5120/ijca2015905789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:34.015947+05:30
%A Surender Kumar
%A Rupinder Kaur
%T Plant Disease Detection using Image Processing- A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 16
%P 6-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper holds a survey on plant leaf diseases classification using image processing. Digital image processing has three basic steps: image processing, analysis and understanding. Image processing contains the preprocessing of the plant leaf as segmentation, color extraction, diseases specific data extraction and filtration of images. Image analysis generally deals with the classification of diseases. Plant leaf can be classified based on their morphological features with the help of various classification techniques such as PCA, SVM, and Neural Network. These classifications can be defined various properties of the plant leaf such as color, intensity, dimensions. Back propagation is most commonly used neural network. It has many learning, training, transfer functions which is used to construct various BP networks. Characteristics features are the performance parameter for image recognition. BP networks shows very good results in classification of the grapes leaf diseases. This paper provides an overview on different image processing techniques along with BP Networks used in leaf disease classification.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Back Propagation Image Processing Artificial Neural Network