A Comparative Study in Kernel-Based Support Vector Machine of Oil Palm Leaves Nutrient Disease
Investigation of the nutrient disease in oil palm motivates the need for a programmed detection system. Automated detection using vision system and pattern recognition are implemented to detect the symptoms of nutrient diseases and also to classify the disease group. In this paper, Support Vector Ma...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Online Access: | View Fulltext in Publisher |
| Summary: | Investigation of the nutrient disease in oil palm motivates the need for a programmed detection system. Automated detection using vision system and pattern recognition are implemented to detect the symptoms of nutrient diseases and also to classify the disease group. In this paper, Support Vector Machine (SVM) is evaluated as classifier with three different kernels namely linear kernel, polynomial kernel with soft margin and polynomial kernel with hard margin. Initial results show that the recognition of oil palm leaves is possible to be performed by SVM classifier. Based on the best performance result, polynomial kernel with soft margin is capable of classifying nutrient diseases accurately in the oil palm leaves with accuracy of 95% of correct classification. (C) 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Centre of Humanoid Robots and Bio-Sensor (HuRoBs), Faculty of Mechanical Engineering. Universiti Teknologi MARA. |
|---|---|
| ISBN: | 1877-7058 |
| DOI: | 10.1016/j.proeng.2012.07.321 |