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Proceedings Paper

Machine vision system for inspecting characteristics of hybrid rice seed
Author(s): Fang Cheng; Yibin Ying
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Paper Abstract

Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

Paper Details

Date Published: 30 March 2004
PDF: 8 pages
Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.516048
Show Author Affiliations
Fang Cheng, Zhejiang Univ. (China)
Yibin Ying, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 5271:
Monitoring Food Safety, Agriculture, and Plant Health
George E. Meyer; Yud-Ren Chen; Shu-I Tu; Bent S. Bennedsen; Andre G. Senecal, Editor(s)

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