Automated rice leaf disease detection using color image analysis
Issue Date
7-2011
Abstract
In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Source or Periodical Title
Proceedings of SPIE - The International Society for Optical Engineering
ISSN
0277-786X
Volume
8009
Document Type
Article
Language
English
Subject
brown spot, histogram intersection, K-means clustering, leaf scald, outlier, rice, threshold
Recommended Citation
Pugoy, R.A.D.L., Mariano, V.Y. (2011). Automated Rice Leaf Disease Detection Using Color Image Analysis. Proceedings of SPIE - The International Society for Optical Engineering, 8009. doi:10.1117/12.896494.
Identifier
doi:10.1117/12.896494.
Digital Copy
yes