The objective of this study was to develop an inexpensive automated device for grading raw oyster meats. The automation technique chosen was digital imaging. Typically, a computer vision system contains a microcomputer and a digital camera. An inexpensive digital camera connected to a personal computer was used to measure the projected area of the oyster meats. Physical characteristics of the oyster meats were important in designing a computer vision grading system and the necessary data were not found in the literature. Selected physical characteristics of oyster meats, including the projected area, weight, height, and volume were measured by independent methods. The digital image areas were found to be highly correlated to oyster meat volumes and weights. Currently oysters are marketed on the basis of volume. The results from this study indicated that the relationship between the oyster meat area as measured by computer vision and volume can be used as a grading criterion. The oysters ranged in volume from 3.5 cm³ to 19.4 cm³ A three dimensional image was not required because the height was not important. Tests showed that the system was consistent and successfully graded 5 oysters per second. The system was calibrated, and the prediction equation was validated with an estimated measurement error of ± 3.04 cm³ at a 95% confidence level. The development of automated graders using digital imaging techniques could help improve the quality and consistency of the graded oyster meats. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/43742 |
| Date | 15 July 2010 |
| Creators | Awa, Teck Wah |
| Contributors | Agricultural Engineering, Byler, Richard K., Diehl, Kenneth C., Shanholtz, Vernon O. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis, Text |
| Format | x, 145 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 18523919, LD5655.V855_1988.A842.pdf |
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