NDLI: Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Rong Zhou Kaneko, S. Tanaka, F. Kayamori, M. Shimizu, M.
Copyright Year	2013
Description	Author affiliation: Graduation Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan (Rong Zhou; Kaneko, S.) \|\| Central Agric. Exp. Station, Hokkaido Res. Organ., Naganuma, Japan (Tanaka, F.; Kayamori, M.; Shimizu, M.)
Abstract	This paper present a novel method for robust and early Cercospora leaf spot detection in sugar beet using hybrid algorithms of template matching and support vector machine. We adopt three-stage framework to achieve our research target: first, a plant segmentation index of G-R is introduced to distinguish leaf parts from soil-contained background for automatic selection of initial sub templates, second, we adopt a robust template matching method called orientation code matching (OCM), which could not only realize the continuous and site-specific observation of disease development, but also shows its excellent robustness for nonrigid plant object searching in scene illumination, foliar translation and small rotation, afterward, we employ a machine learning method of support vector machine (SVM) for robust and early disease classification by a color-based feature named two dimensional (2D) xy-color histogram, which has stable ability to classify disease against various illumination changes. The indoor experiment results demonstrate the robust performance of our proposed method for early disease detection against complex changes of external environment.
Starting Page	300
Ending Page	304
File Size	833127
Page Count	5
File Format	PDF
e-ISBN	9781479927968
DOI	10.1109/CANDAR.2013.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machine (SVM) Image processing Disease detection Sugar industry Diseases Support vector machines Histograms Quantization (signal) Image color analysis Orientation code matching (OCM) Cercospora leaf spot (CLS) Sugar beet Robustness
Content Type	Text
Resource Type	Article

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