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.
next to an author's name indicates that all files or directories associated with their ETD are accessible from the Virginia Tech campus network only.
![[VT]](http://scholar.lib.vt.edu/images/ETD-db/restricted.gif)
