@inproceedings{eb4a63eddba545f1844b2dcae30e9cbb,
title = "Sugarcane leaf disease detection and severity estimation based on segmented spots image",
abstract = "About 15\% of sugarcane leaf is defective because of diseases, it reduces the quantity and quality of sugarcane production significantly. Early detection and estimation of plant disease is a way to control these diseases and minimize the severe infection. This paper proposes a model to identify the severity of certain spot disease which appear on leaves based on segmented spot. The segmented spot is obtained by thresholding a component of L∗a∗b∗color space. Diseases spots are extracted with maximum standard deviation of segmented spot that use for detection the type of disease using classification techniques. The classifier is a Support Vector Machine (SVM) which uses L∗a∗b∗color space for its color features and Gray Level Co-Occurrence Matrix (GLCM) as its texture features. This proposed model capable to determine the types of spot diseases with accuracy of 80\% and 5.73 error severity estimation average.",
keywords = "gray level co-occurrence matrix, L∗a∗b∗, sugarcane spot disease, support vector machine, thresholding",
author = "Ratnasari, \{Evy Kamilah\} and Mustika Mentari and 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.2014.7010564",
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 = "93--98",
booktitle = "Proceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014",
}