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Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm

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Main Authors: A. F. A., Ahmad Effendi, M. N., Md Isa, M. I., Ahmad, M. F., Che Husin, S. Z., Md Naziri
Other Authors: nazrin@unimap.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
GLCM
Image processing
K-Mean clustering
MATLAB
Paddy disease
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75087
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spelling my.unimap-750872022-05-09T00:40:15Z Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm A. F. A., Ahmad Effendi M. N., Md Isa M. I., Ahmad M. F., Che Husin S. Z., Md Naziri nazrin@unimap.edu.my GLCM Image processing K-Mean clustering MATLAB Paddy disease Link to publisher's homepage at http://ijneam.unimap.edu.my The traditional human-based visual quality inspection approach in agriculture is unreliable and uneven due to various variables, including human errors. In addition to the lengthy processing durations, the traditional method necessitates plant disease diagnostic experts. On the other hand, existing image processing approaches in agriculture produce low-quality output images despite having a faster computation time. As a result, a more comprehensive set of image processing algorithms was used to improve plant disease detection. This research aims to develop an efficient method for detecting leaf diseases using image processing techniques. In this work, identifying paddy diseases based on their leaves involved a number of image-processing stages, including image pre-processing, image segmentation, feature extraction, and eventually paddy leaf disease classification. The proposed work targeted the segmentation step, whereby an input image is segmented using the K-Means clustering with image scaling and colour conversion technique in the pre-processing stage. In addition, the Gray Level Co-occurrence Matrix technique (GLCM) is used to extract the features of the segmented images, which are used to compare the images for classification. The experiment is implemented in MATLAB software and PC hardware to process infected paddy leaf images. Results have shown that K-Means Clustering and GLCM are capable without using the hybrid algorithm on each image processing phase and are suitable for paddy disease detection. 2022-05-09T00:40:15Z 2022-05-09T00:40:15Z 2021-12 Article International Journal of Nanoelectronics and Materials, vol.14 (Special Issue), 2021, pages 253-263 1985-5761 (Printed) 1997-4434 (Online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75087 http://ijneam.unimap.edu.my en Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic GLCM
Image processing
K-Mean clustering
MATLAB
Paddy disease
spellingShingle GLCM
Image processing
K-Mean clustering
MATLAB
Paddy disease
A. F. A., Ahmad Effendi
M. N., Md Isa
M. I., Ahmad
M. F., Che Husin
S. Z., Md Naziri
Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
description Link to publisher's homepage at http://ijneam.unimap.edu.my
author2 nazrin@unimap.edu.my
author_facet nazrin@unimap.edu.my
A. F. A., Ahmad Effendi
M. N., Md Isa
M. I., Ahmad
M. F., Che Husin
S. Z., Md Naziri
format Article
author A. F. A., Ahmad Effendi
M. N., Md Isa
M. I., Ahmad
M. F., Che Husin
S. Z., Md Naziri
author_sort A. F. A., Ahmad Effendi
title Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
title_short Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
title_full Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
title_fullStr Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
title_full_unstemmed Image processing for paddy disease detection using K-Means Clustering and GLCM Algorithm
title_sort image processing for paddy disease detection using k-means clustering and glcm algorithm
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75087
_version_ 1738511704529043456
score 13.214268

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