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Proceedings Paper

Automated rice leaf disease detection using color image analysis
Author(s): Reinald Adrian D. L. Pugoy; Vladimir Y. Mariano
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Paper 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.

Paper Details

Date Published: 8 July 2011
PDF: 7 pages
Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090F (8 July 2011); doi: 10.1117/12.896494
Show Author Affiliations
Reinald Adrian D. L. Pugoy, Univ. of the Philippines Los Baños (Philippines)
Vladimir Y. Mariano, Univ. of the Philippines Los Baños (Philippines)

Published in SPIE Proceedings Vol. 8009:
Third International Conference on Digital Image Processing (ICDIP 2011)
Ting Zhang, Editor(s)

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