Paper Title:
Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique
  Abstract

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

  Info
Periodical
Key Engineering Materials (Volumes 321-323)
Edited by
Seung-Seok Lee, Joon Hyun Lee, Ik Keun Park, Sung-Jin Song, Man Yong Choi
Pages
1225-1228
DOI
10.4028/www.scientific.net/KEM.321-323.1225
Citation
S. M. Kim, C. S. Kim, C. H. Lee, M. H. Kim, S. J. Park, "Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique", Key Engineering Materials, Vols. 321-323, pp. 1225-1228, 2006
Online since
October 2006
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Price
$32.00
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