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: | , , |
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Format: | Article |
Language: | English English |
Published: |
Institute of Advanced Engineering and Science (IAES)
2018
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Subjects: | |
Online Access: | 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 http://irep.iium.edu.my/64150/ http://iaescore.com/journals/index.php/IJEECS/article/view/10894/8200 |
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Institution: | Universiti Islam Antarabangsa Malaysia |
Language: | English English |
Summary: | 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. |
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