Innovative Computer Vision System Provides Portion and Process Control for Further Processed Product Lines

Responding to increased demand for ready-to-cook and fully cooked products, poultry processors operate further processing lines that produce a myriad of chicken products, such as the popular breaded breast fillet or nugget. Millions of pounds of these products are produced each week, all of which have a stringent set of customer specifications. For example, some customers require that fillets not deviate from a pre-determined size and shape.

overline screening/sorting prototype system

Parker McGee (left), a co-op student, and Colin Usher (right), a research scientist, perform in-plant tests on the overline screening/sorting prototype system. Developed by Georgia Tech researchers, the innovative computer vision system screens individual meat and poultry portions on-line for both volume and visual quality.

Adherence to such guidelines requires close monitoring by plant quality control personnel. Samples are routinely removed from the processing line to confirm that the product is meeting the desired specifications. However, this manual process can never guarantee that all product shipped meets the customer’s acceptability criteria.

One way to achieve this is through 100 percent inspection and grading. And, of course, that means automation.

Researchers with Georgia Tech’s Food Processing Technology Division, with funding from Georgia’s Traditional Industries Program for Food Processing, have developed an innovative computer vision system for on-line screening of individual meat and poultry portions for both volume and visual quality.

“The requirements being placed on chicken and beef producers to meet the needs of their customers in the further processed and case-ready products areas are challenging to say the least,” comments Wayne Daley, associate division chief of FPTD and project director. “As industry continues to produce more of this product mix, systems such as ours will serve to enhance plant efficiencies and reduce costs.”

According to Daley, the current manual process is labor intensive and does not provide data in real-time to support decision making. An automated system, such as the one developed by his research team, allows processors to optimize the production process while also providing data on conformance to customer specifications.

The prototype system screens individual portions, on-line, for both volume and visual quality. This is used to help ensure that the product meets customer standards while also generating feedback data that can be used to optimize process operations. It employs unique lighting and imaging features that monitor key product characteristics such as size, weight, shape, height, and surface appearance for defects like bruising, tears, and fat coverage, along with a host of other customer specifications. In fact, researchers believe it is the only system they are aware of that is able to sort product not only on weight and dimensions but also on surface quality defects.

Working with industrial partner, Wayne Farms, the research team performed a series of performance tests at its processing plant in College Park, Ga. The system’s design accommodates a parts rate of 100 pieces per minute.

The product’s height is determined by a laser-based structured lighting system, which also drives the acquisition of a visible image of the product. This image is then processed to assess whether or not the product meets the desired specifications. The generated data then passes to a grader that determines which grade to give the product and sends that grade to the sorter which then sorts the product.

During testing, the system demonstrated the ability to monitor the process and to provide real-time feedback and guidance to operators. Such information, explains Daley, allows for fast process adjustments, and if sorting is done based on these parameters, it could be used to guide rework.

A provisional patent has been filed on the use of a dynamic lighting system to provide flexibility in sensing and image acquisition. In addition, discussions are now underway with Gainco, Inc., in regard to licensing the technology.

Daley says in the future, one issue the team plans to address is the loading of the machine. The system is currently manually loaded to obtain the desired presentation for accurate assessment of shape and size.

Additionally, says Daley, the sensing and decisions made by the system lay a solid foundation for developing the next generation of second and further processing machines, “smart machines,” that react to changes in product and processing mix.

The system employs unique lighting and imaging features that monitor key product characteristics such as size, weight, shape, height, and surface appearance for defects like bruising, tears, and fat coverage, along with a host of other customer specifications. The images above show a butterfly fillet that has been processed by the system, highlighting areas of fat coverage.

 

PoultryTech is published by the Agricultural Technology Research Program,
Food Processing Technology Division
of the Georgia Tech Research Institute.
Agricultural Technology Research Program – GTRI/FPTD, Atlanta, GA 30332-0823
Phone: (404) 894-3412 • FAX: (404) 894-8051
Angela Colar - Editor - angela.colar@gtri.gatech.edu