• Journal of Ginseng Research
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• v.20 no.1
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• pp.65-71
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• 1996

This study was undertaken to evaluate the automatic decision-making on the grading of 6-year-old fresh ginseng (Panax ginseng C.A. Meyer) by an image analyzer. The best input method for the 6-year-old fresh ginseng was under condition of a low resolution (128u 128 pixel) and illumination direction from bottom to up (light box). It was possible to identify the main root, lateral root, and rhizome of fresh ginseng by application of OPEN process in a function of an image analyzer. Finally, we developed the grade decision-making programs, GinP-1. The fitness rates for the fresh ginseng standards which were classified by experts were 94.6, 80.6, 81.5, and 100.0% for 1st, 2nd, 3rd, and 4th grade of fresh ginseng, respectively, and the total time of decision-making was about 4.3 seconds per one root. The decision-making time was reduced to 0.8 seconds per one root by enhancemeat of the Image analyzer, which was tested by the technical company of the image analyzer,'Carl Zeiss (Germany). As a result of this study, the automatic decision-making on the grade of fresh gin send by image analyzer seems to have high possibility.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.327-332
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• 2016

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 $1^{st}$ ginseng grade, 98% of the $2^{nd}$ ginseng grade, 90% of the $3^{rd}$ ginseng grade, overall, showed high recognition performance with all grades when the number of training data are 10.

• Journal of Ginseng Research
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• v.26 no.1
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• pp.6-9
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• 2002

This study was undertaken to evaluate the automatic decision-making on the rusty root of fresh ginseng (Panax ginseng C.A. Meyer) by an image analyzer. Critical value of rusty root of ginseng by image analyzing was the percentage of grey value 0∼148 area (G 148) to the total area of grey value 0∼255. And the discriminant formula of rusty root of ginseng as follows; rusty root of ginseng : 6.68$\times$G(148) +3.74, normal ginseng : 2.86$\times$G(148) +9.96, and fitness rates of this formula were 89.8%. Also, we developed the automatic rusty root of decision-making program. As the result of this study, the automatic decision-making on the rusty root of fresh ginseng by an image analyzer seems to have high possibility.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.333-338
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• 2016

This study is to estimate proximity without direct measurement of the weight of fresh ginseng. For this work, we developed a ginseng image acquiring instrument and obtained 126 ginseng images using the instrument. Image analysis and parameter extraction process was used C language based Labwindows/CVI development tools and open source library OpenCV. Estimation formula is made by weighing the sample with image analysis of fresh ginseng. We analyzed the correlation between the pixel number and the weight of ginseng using a linear regression approach. It was obtained a strong positive correlation coefficient of 0.9162 with a linearity value.