Automated rice leaf disease detection using color image analysis

Reinald Adrian D. L. Pugoy; Vladimir Y. Mariano

doi:10.1117/12.896494

8 July 2011 Automated rice leaf disease detection using color image analysis

Reinald Adrian D. L. Pugoy, Vladimir Y. Mariano

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80090F (2011) https://doi.org/10.1117/12.896494
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

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.

Citation Download Citation

Reinald Adrian D. L. Pugoy and Vladimir Y. Mariano "Automated rice leaf disease detection using color image analysis", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090F (8 July 2011); https://doi.org/10.1117/12.896494

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