@inproceedings{07ce3658ad694e17b85d2902fe28d2e6,
title = "Feature extraction for identification of sugarcane rust disease",
abstract = "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.",
keywords = "classification, feature extraction, leaf image, rust disease, sugarcane",
author = "Dewi, {Ratih Kartika} and Ginardi, {R. V.Hari}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Information, Communication Technology and System, ICTS 2014 ; Conference date: 24-09-2014",
year = "2014",
doi = "10.1109/ICTS.2014.7010565",
language = "English",
series = "Proceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "99--103",
booktitle = "Proceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014",
address = "United States",
}
