Ratih Kartika Dewi, R. V. Hari Ginardi
This research propose an image pattern classification to identify rust disease in sugarcane leaf with a combination of texture and color feature extraction. The purpose of this research is to find appropriate features that can identify sugarcane rust disease. Firstly, normal and diseased images are collected and pre-processed. Then, features of shape, color and texture are extracted from these images. After that, these images are classified by support vector machine classifier. A combination of several features are used to evaluate the appropriate features to find distinctive features for identification of rust disease. When a single feature is used, shape feature has the lowest accuracy of 51% and texture feature has the highest accuracy of 96.5%. A combination of texture and color feature extraction results a highest classification accuracy of 97.5%. A combination of texture and color feature extraction with polynomial kernel results in 98.5 % classification accuracy. © 2014 IEEE.
Information Technology and Computer Science Program, Brawijaya University, Malang, Indonesia; Dept. of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
