Finding poultry defects before processing - Screening

USA Today (Society for the Advancement of Education), June, 2003

Researchers believe they are on the edge of a breakthrough, but they don't want to count their chickens before they're processed. At Gold Kist's poultry processing plant in Carrollton, Ga., a machine vision system developed at the Georgia Tech Research Institute (GTRI), Atlanta, is undergoing field testing. If it results in the success researchers expect, it could open the door for automating many visual-inspection tasks in the industry.

The technology is called a systemic screener. Installed near the front end of the chicken-processing line, cameras look for defects such as improperly bled birds and those afflicted by systemic diseases, like septicemia and toxemia. Unique software and algorithms provide the intelligence for translating visual data from the system's cameras into the appropriate mechanical commands for dispensation of each chicken. Those that pass the screening proceed to the next step, while unfit chickens are quickly and automatically removed from the processing line.

"It's a vision-based, closed-loop inspection and removal system--one of the first of its kind." notes Craig Wyvill, chief of the Food Processing Technology Division in the Electro-Optics, Environment and Materials Laboratory of GTRI. By removing unacceptable birds early in the operation, the systemic screener allows subsequent areas of the plant to have "higher utilization of the processing line," he notes.

While poultry processing is already highly automated, it still depends heavily on manual processes, many of which are visually based. "We're looking at applications that span the whole gamut of the processing operation," says Wayne Daley, a senior research engineer at GTRI and head of the development team. "From beginning to end, live bird to the shrink-wrapped package, there are places where visual input is required to properly process the product. We're looking at where we can apply machine vision technology, what would be required, and how we can modify our system to run tests and see how it functions."

Another important visual screening task that could be handled by computers entails identification of cosmetic defects such as tears, bruises, or missing limbs. Inspectors use that information to route the product accurately to the appropriate handling station.

In later processing stages such as cut-up, deboning, breading, and marinating, machine vision software can help make machinery more intelligent. Whereas most assembly-line applications of machine vision involve single objects of consistent size and shape, chicken parts vary considerably in those characteristics. "For this automation to work properly, the machines need to be able to adjust for product variability," Daley points out. "You need detailed visual information that tells you about the specific part you're working on." Carrying that point further, GTRI researchers are investigating ways to combine computer vision with X-ray imaging to improve the accuracy and thoroughness of product-screening processes after deboning.