Detection of the Chinese Jujube surface defects by machine vision

Authors

WANG Chunpu, Department of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xian, Shaanxi 710021, China
WEN Huaixing, Department of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xian, Shaanxi 710021, China
WANG Junjie, Department of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xian, Shaanxi 710021, China

Abstract

Based on LingWu jujube as experimental object, used the Halcon12.0 visual processing software by the method of support vector machine (SVM) in IHS color space to extract the mean value and mean variance of H component as the color eigenvalues. Selected the gaussian kernel function by the experiments. When the kernel parameter was 0.2, and the regular constant was 0.005, the accuracy rate was 94.6%, which greatly improved the efficiency of nondestructive on-line detection, decreased the labor cost and labor intensity, and eliminated the scruple on the accuracy of on-line detection for jujube to processors. It has large research significance in fruit grading.

Publication Date

7-28-2019

First Page

168

Last Page

171

DOI

10.13652/j.issn.1003-5788.2019.07.032

Recommended Citation

Chunpu, WANG; Huaixing, WEN; and Junjie, WANG (2019) "Detection of the Chinese Jujube surface defects by machine vision," Food and Machinery: Vol. 35: Iss. 7, Article 32.
DOI: 10.13652/j.issn.1003-5788.2019.07.032
Available at: https://www.ifoodmm.cn/journal/vol35/iss7/32

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