A Study on a Ginseng Grade Decision Making Algorithm Using a Pattern Recognition Method

Title & Authors
A Study on a Ginseng Grade Decision Making Algorithm Using a Pattern Recognition Method
Jeong, Seokhoon; Ko, Kuk Won; Kang, Je-Yong; Jang, Suwon; Lee, Sangjoon;

Abstract
This study is a leading research project to develop an automatic grade decision making algorithm of a 6-years-old fresh ginseng. For this work, we developed a Ginseng image acquiring instrument which can take 4-direction`s images of a Ginseng at the same time and obtained 245 jingen images using the instrument. The 12 parameters were extracted for each image by a manual way. Lastly, 4 parameters were selected depending on a Ginseng grade classification criteria of KGC Ginseng research institute and a survey result which a distribution of averaging 12 parameters. A pattern recognition classifier was used as a support vector machine, designed to "k-class classifier" using the OpenCV library which is a open-source platform. We had been surveyed the algorithm performance(Correct Matching Ratio, False Acceptance Ratio, False Reject Ratio) when the training data number was controlled 10 to 20. The result of the correct matching ratio is 94% of the $\small{1^{st}}$ ginseng grade, 98% of the $\small{2^{nd}}$ ginseng grade, 90% of the $\small{3^{rd}}$ ginseng grade, overall, showed high recognition performance with all grades when the number of training data are 10.
Keywords
Pattern Recognition;Ginseng Grade Decision Making;Pattern Classifier;
Language
Korean
Cited by
References
1.
J. Y. Kang, M. G. Lee, and Y. T. Kim, "Automatic Decision-Making on the Grade of 6-Year-Old Fresh Ginseng (Panax ginseng C. A. Meyer ) by and Image Analyzer - I. Shape and Weight Analyses According to the Grade of Fresh Ginseng)," Journal of Ginseng Research, Vol.20, No.1, pp.65-71, 1996.

2.
C. Cortes and V. Vapnik, "Support-vector network," Machine Learning, Vol.20, pp.273-297, 1995.

3.
C. J. C. Burges, "A tutorial on Support Vector Machines for pattern recognition," Data Mining and Knowledge Discovery, Vol.2, No.2, pp.121-167, 1998.