Date of Award

5-1-2012

Degree Name

Master of Science

Department

Biomedical Engineering

First Advisor

Qin, Jun

Abstract

A fast corn grading system can replace the traditional method in unofficial corn grading locations. The initial design of the system proved that it can classify corn kernels with a high success rate. This study tested the robustness of the system against samples from different locations with different moisture contents. The experimental results were compared with the official grading results for 3 out of the 6 samples. This study also tested the limitations of the segmentation algorithm. The results showed that 60 to 70 kernels in a 100 cm2 could be correctly segmented in a relatively short running time. Classification accuracy would improve with modifications to the system, including increased training samples of damaged kernels, uniform illumination, color calibration, and improved weight approximation of the kernels.

Share

COinS
 

Access

This thesis is only available for download to the SIUC community. Others should
contact the interlibrary loan department of your local library.