Detection of stress cracks in corn kernels using machine vision
Kim, Chul-Soo
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/20995
Description
Title
Detection of stress cracks in corn kernels using machine vision
Author(s)
Kim, Chul-Soo
Issue Date
1991
Doctoral Committee Chair(s)
Reid, John F.
Department of Study
Agricultural and Biological Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Agricultural
Engineering, Electronics and Electrical
Language
eng
Abstract
Maintaining high quality of corn is very important to both corn producers and buyers. The detection of stress cracks remains one of the most important tasks in corn quality inspection. Such an index of quality would be helpful in assessing not only the end-use values of the corn but also the drying method used and the appropriateness of subsequent handling procedures.
For automatic detection of corn stress cracks, a machine vision system was developed, which simulates the processes that the human visual system uses to perceive the stress cracks from the corn kernel in the conventional candling method.
The automatic stress crack detection system consisted of four consecutive stages and was configured in various ways by selecting different image processing algorithms in each stage. Several edge detection algorithms suitable for inclusion in the automatic stress crack detection system were developed and analytically evaluated. From analytical evaluation, it was found that edge detection algorithms had different characteristics in their responses and that proper threshold values should be assigned to each algorithm. The proper threshold values were selected by a statistical design procedure.
A set of performance criteria also was developed to evaluate the automatic stress crack detection system and used to compare the performance of different configuration on several varieties of corn samples against human inspectors. Evaluation results showed that the system configured with the circular band operator, the Duda road operator, and the Hough transform, performed best; with success rates of 78.2% and failure rates of 8.2%. The performance measures of the system with this configuration were superior to that of human inspectors. When the system was used to distinguish cracked-kernels from sound kernels, its accuracy was higher than 90%.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
