Rong Zhou and Shun’ich Kaneko and Fumio Tanaka and Miyuki Kayamori and Motoshige Shimizu
MatchingBased Cercospora Leaf Spot Detection in Sugar Beet
712 - 718
2013
7
7
International Journal of Nutrition and Food Engineering
https://publications.waset.org/pdf/16543
https://publications.waset.org/vol/79
World Academy of Science, Engineering and Technology
In this paper, we propose a robust disease detection
method, called adaptive orientation code matching (Adaptive OCM),
which is developed from a robust image registration algorithm
orientation code matching (OCM), to achieve continuous and
sitespecific detection of changes in plant disease. We use twostage
framework for realizing our research purpose; in the first stage,
adaptive OCM was employed which could not only realize the
continuous and sitespecific observation of disease development, but
also shows its excellent robustness for nonrigid plant object searching
in scene illumination, translation, small rotation and occlusion changes
and then in the second stage, a machine learning method of support
vector machine (SVM) based on a feature of two dimensional (2D)
xycolor histogram is further utilized for pixelwise disease
classification and quantification. The indoor experiment results
demonstrate the feasibility and potential of our proposed algorithm,
which could be implemented in real field situation for better
observation of plant disease development.
Open Science Index 79, 2013