Classification of leaf disease from image processing technique

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Kamal M.M.; Masazhar A.N.I.; Rahman F.A.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040936799&doi=10.11591%2fijeecs.v10.i1.pp191-200&partnerID=40&md5=1bedc2e41d94e24449c0451a1ee1ba99

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. © 2018 Institute of Advanced Engineering and Science. All rights reserved.