Classification of leaf disease from image processing technique

Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for cl...

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Main Authors:	Md Kamal, Mahanijah, Masazhar, Ahmad Nor Ikhwan, Abdul Rahman, Farah Diyana
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES) 2018
Subjects:	SB Plant culture TN275 Practical mining operations. Safety measures TR287 Photographic processing. Darkroom technique
Online Access:	http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/1/64150_Classification%20of%20Leaf%20Disease%20from%20Image_article.pdf http://irep.iium.edu.my/64150/2/64150_Classification%20of%20Leaf%20Disease%20from%20Image_scopus.pdf

id	iium-64150
recordtype	eprints
spelling	iium-641502019-03-04T04:19:31Z http://irep.iium.edu.my/64150/ Classification of leaf disease from image processing technique Md Kamal, Mahanijah Masazhar, Ahmad Nor Ikhwan Abdul Rahman, Farah Diyana SB Plant culture TN275 Practical mining operations. Safety measures TR287 Photographic processing. Darkroom technique Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose. Institute of Advanced Engineering and Science (IAES) 2018-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64150/1/64150_Classification%20of%20Leaf%20Disease%20from%20Image_article.pdf application/pdf en http://irep.iium.edu.my/64150/2/64150_Classification%20of%20Leaf%20Disease%20from%20Image_scopus.pdf Md Kamal, Mahanijah and Masazhar, Ahmad Nor Ikhwan and Abdul Rahman, Farah Diyana (2018) Classification of leaf disease from image processing technique. Indonesian Journal of Electrical Engineering and Computer Science, 10 (1). pp. 191-200. ISSN 2502-4752 E-ISSN 2502-4760 http://iaescore.com/journals/index.php/IJEECS/article/view/10894/8200 10.11591/ijeecs.v10.i1.pp191-200
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	SB Plant culture TN275 Practical mining operations. Safety measures TR287 Photographic processing. Darkroom technique
spellingShingle	SB Plant culture TN275 Practical mining operations. Safety measures TR287 Photographic processing. Darkroom technique Md Kamal, Mahanijah Masazhar, Ahmad Nor Ikhwan Abdul Rahman, Farah Diyana Classification of leaf disease from image processing technique
description	Disease in palm oil sector is one of the major concerns because it affects the production and economy losses to Malaysia. Diseases appear as spots on the leaf and if not treated on time, cause the growth of the palm oil tree. This work presents the use of digital image processing technique for classification oil palm leaf disease sympthoms. Chimaera and Anthracnose is the most common symtoms infected the oil palm leaf in nursery stage. Here, support vector machine (SVM) acts as a classifier where there are four stages involved. The stages are image acquisition, image enhancement, clustering and classification. The classification shows that SVM achieves accuracy of 97% for Chimaera and 95% for Anthracnose.
format	Article
author	Md Kamal, Mahanijah Masazhar, Ahmad Nor Ikhwan Abdul Rahman, Farah Diyana
author_facet	Md Kamal, Mahanijah Masazhar, Ahmad Nor Ikhwan Abdul Rahman, Farah Diyana
author_sort	Md Kamal, Mahanijah
title	Classification of leaf disease from image processing technique
title_short	Classification of leaf disease from image processing technique
title_full	Classification of leaf disease from image processing technique
title_fullStr	Classification of leaf disease from image processing technique
title_full_unstemmed	Classification of leaf disease from image processing technique
title_sort	classification of leaf disease from image processing technique
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2018
url	http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/ http://irep.iium.edu.my/64150/1/64150_Classification%20of%20Leaf%20Disease%20from%20Image_article.pdf http://irep.iium.edu.my/64150/2/64150_Classification%20of%20Leaf%20Disease%20from%20Image_scopus.pdf
first_indexed	2023-09-18T21:30:59Z
last_indexed	2023-09-18T21:30:59Z
_version_	1777412508516089856

Classification of leaf disease from image processing technique

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