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.

Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-98
Number of pages6
ISBN (Electronic)9781479968572
DOIs
Publication statusPublished - 2014
Event2014 International Conference on Information, Communication Technology and System, ICTS 2014 - Surabaya, Indonesia
Duration: 24 Sept 2014 → …

Publication series

NameProceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014

Conference

Conference2014 International Conference on Information, Communication Technology and System, ICTS 2014
Country/TerritoryIndonesia
CitySurabaya
Period24/09/14 → …

Keywords

  • L∗a∗b∗
  • gray level co-occurrence matrix
  • sugarcane spot disease
  • support vector machine
  • thresholding

Fingerprint

Dive into the research topics of 'Sugarcane leaf disease detection and severity estimation based on segmented spots image'. Together they form a unique fingerprint.

Cite this