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Image Processing and Pattern Classification Technique in a Machine Vision System that Identifies and Classifies the Plant Diseases Based on the Visual Symptoms
by
Krishna, Balamurali
, Kamalakannan, J
, Manoharan, Prabukumar
in
Accuracy
/ Algorithms
/ Back propagation
/ Classification
/ Fourier transforms
/ Humidity
/ Information technology
/ Methods
/ Neural networks
/ Pattern recognition systems
/ Plant diseases
/ Support vector machines
/ Vision systems
/ Wavelet transforms
2010
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Image Processing and Pattern Classification Technique in a Machine Vision System that Identifies and Classifies the Plant Diseases Based on the Visual Symptoms
by
Krishna, Balamurali
, Kamalakannan, J
, Manoharan, Prabukumar
in
Accuracy
/ Algorithms
/ Back propagation
/ Classification
/ Fourier transforms
/ Humidity
/ Information technology
/ Methods
/ Neural networks
/ Pattern recognition systems
/ Plant diseases
/ Support vector machines
/ Vision systems
/ Wavelet transforms
2010
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Do you wish to request the book?
Image Processing and Pattern Classification Technique in a Machine Vision System that Identifies and Classifies the Plant Diseases Based on the Visual Symptoms
by
Krishna, Balamurali
, Kamalakannan, J
, Manoharan, Prabukumar
in
Accuracy
/ Algorithms
/ Back propagation
/ Classification
/ Fourier transforms
/ Humidity
/ Information technology
/ Methods
/ Neural networks
/ Pattern recognition systems
/ Plant diseases
/ Support vector machines
/ Vision systems
/ Wavelet transforms
2010
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Image Processing and Pattern Classification Technique in a Machine Vision System that Identifies and Classifies the Plant Diseases Based on the Visual Symptoms
Journal Article
Image Processing and Pattern Classification Technique in a Machine Vision System that Identifies and Classifies the Plant Diseases Based on the Visual Symptoms
2010
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Overview
The proposed method in this paper is to perform the classification using SVM classifier by considering the input features from discrete wavelet transform, to identify disease in the plant by using visual symptoms. Testing has been done by using 40 images of banana leaf. The classification accuracy of the proposed method is 97% which is better compare with 86% of accuracy, produced by Back-propagation Neural Network (BPNN) method.
Publisher
International Journal of Advanced Research in Computer Science
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