Machine Vision Sees Contaminants We Can’t

For example, Stephen Delwiche, an agricultural engineer on the team, uses the equipment to test for fungal contamination of wheat kernels, and Lefcourt says a color change in leaves can signal serious nutrient deficiency.

“Machine vision can spot the problem when it’s still minor and is causing slight color changes not visible to the human eye,” he says. “There’s no need to destroy the leaf to diagnose the condition.

The machine, he says, can find common patterns in wholesome agricultural objects, so that any anomalies-diseases, defects or contamination-stand out. Similar machine-vision technology can be applied to detect tumors in chickens, fecal contamination, bruises on apples or a fungus on a kernel of grain.

“Since natural objects are not uniform, we can’t compare one spot on an object to another spot, but we can find common features among objects in the same class,” he adds. “We take pictures of whole objects with spectral signatures at each spot on these objects to detect anomalies and then figure out what the anomalies are.”

The Time is Right

The lab has a cooperative research and development agreement (CRADA) with Stork Gamco, Inc., of Gainesville, Ga.-one of the largest manufacturers of chicken-processing plant equipment in the world-to commercialize the system and move it into use among the nation’s 300-plus poultry processing plants.

Chao says that Stork Gamco will soon test the system in a chicken-processing plant under the most demanding situations-lines that move 140 birds a minute. Chen says the system can handle up to 180 birds a minute.

The new system, he explains, will be contained in a box hung over the beginning of the processing line, right after the point where chickens are killed and de-feathered. Its camera will send spectral images to a computer set up in another room.

Chao along with Sukwon Kang, an agricultural engineer, updated the machine-vision system to its present user-friendly form, ready to leave the research bench for commercial development. The two redesigned the system to use the new camera, instead of multiple cameras that required additional mathematical adjustments to join separate images. They also changed the software from the DOS operating system to function in Windows, where users can easily navigate by clicking on graphic images.

“We recognize that the users, in this case the chicken processing plant employees, must be considered at every design stage,” Chao says.

The new system is ready to market at just the right time, when everything is in place for its success. FSIS is looking at machine vision as a way to help implement its HACCP system, which shifts more inspection responsibility to the processing plant.

“This would free up inspectors so that they have time to take a close, careful look at the birds the machine-vision system’s judges suspect,” Chao says.

Also, the processing industry is moving to high-speed lines in response to a rising demand for poultry. The industry wants the highest feasible speeds for maximum efficiency, and they see machine vision as the way to make it possible while also improving inspection efficacy. Chao says that the high-speed lines separate into two or three more lines after the birds are killed, and more inspectors are added to meet USDA’s requirement of a maximum speed of 35 birds a minute for each inspector.

Don Comis is a member of the USDA’s Agricultural Research Service Information staff. Reach him at [email protected].