@article{(Open Science Index):https://publications.waset.org/pdf/16543,
	  title     = {Matching-Based Cercospora Leaf Spot Detection in Sugar Beet},
	  author    = {Rong Zhou and  Shun’ich Kaneko and  Fumio Tanaka and  Miyuki Kayamori and  Motoshige Shimizu},
	  country	= {},
	  institution	= {},
	  abstract     = {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
site-specific detection of changes in plant disease. We use two-stage
framework for realizing our research purpose; in the first stage,
adaptive OCM was employed which could not only realize the
continuous and site-specific observation of disease development, but
also shows its excellent robustness for non-rigid 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)
xy-color histogram is further utilized for pixel-wise 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.
	    journal   = {International Journal of Nutrition and Food Engineering},
	  volume    = {7},
	  number    = {7},
	  year      = {2013},
	  pages     = {712 - 718},
	  ee        = {https://publications.waset.org/pdf/16543},
	  url   	= {https://publications.waset.org/vol/79},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 79, 2013},