An automatic method for identifying different variety of rice seeds using machine vision technology will be investigated and its system, consisting of an automatic inspection machine and an image-processing unit, was also developed. The system could continually present matrix-positioned rice seed to CCD cameras, singularize each rice seed image from the background. The inspection machine had scattering and positioning devices, a photographing station, a parallel discharging device, and a continuous conveyer belt with carrying holes for the rice seed. The rice seeds' image was achieved continuously by single chip controlled device. The line was stopped every one second for one second by the device. The camera took an image of simple seed when it stopped. Image analysis was carried out programmed by Visual C++ 6.0. Color features in RGB (red, green, blue) and color spaces were computed. A back-forward neural network was trained to identify rice seeds. Almost all 86.65% rice seeds were correctly identified. The correct classification rates for five rice varieties were: No.5 'Xiannong' of 99.99%, 'Jinyougui' of 99.93%,'You166' of 98.89%, No. 3 'Xiannong' of 82.82% and 'Medium you' 463 of 86.65%, respectively. Based on the results, it was concluded that the system was enough to use for inspection of varieties of different rice seed based on its appearance characters of seeds.

Date Published: 8 November 2005
PDF: 10 pages
Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 59961H (8 November 2005); doi: 10.1117/12.631004

Published in SPIE Proceedings Vol. 5996:
Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)