Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/71531
標題: Rice quality classification using an automatic grain quality inspection system
作者: Wan, Y.N.
Lin, C.M.
Chiou, J.F.
關鍵字: automatic quality inspection
rice
machine vision
image processing
digital image-analysis
computer vision
software separation
touching
grains
corn kernels
discrimination
期刊/報告no:: Transactions of the Asae, Volume 45, Issue 2, Page(s) 379-387.
摘要: In this article, we examine the peformance of an automatic inspection system for rice quality classification. Sorting of rice into sound, cracked, chalky, immature, dead., broken, damaged, and off-type kernels was performed by the system. Specific rice quality inspection software was developed to prepare sorting parameters and to refine sorting precision and machine operation. A range-selection algorithm was implemented as a series of parameter range tables. The software was developed in the Windows environment to provide a graphical and user-friendly interface. Results show that the automated inspection system could correctly categorize over 90% of rice kernels based on comparison with human inspection. Results for sound, chalky,, and cracked kernels indicated high accuracy in each quality category, around 95%, 92%, and 87%, respectively. The average processing speed for online rice quality, inspection was over 1200 kernels/min. This prototype grain quality inspection system demonstrated performance comparable to subjective human inspection.
URI: http://hdl.handle.net/11455/71531
ISSN: 0001-2351
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