Please use this identifier to cite or link to this item:
|標題:||Rice quality classification using an automatic grain quality inspection system||作者:||Wan, Y.N.
|關鍵字:||automatic quality inspection;rice;machine vision;image processing;digital image-analysis;computer vision;software separation;touching;grains;corn kernels;discrimination||Project:||Transactions of the Asae||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.
|Appears in Collections:||期刊論文|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.