Purdue News
Purdue News
April 1995
"The system can improve food quality when quality depends upon separation relative to exterior appearance," said Purdue agricultural engineering Professor Gary Krutz. "It could work for grading meat, pretzels, chips, peas, corn, carrots, beans and other produce."
On current sorting and packing lines, people stand elbow to elbow, watching the produce or meat cuts slide by, grabbing for blemished radishes, carrots or rib-eye steaks. Hours pass. Their eyes tire. Their minds wander. A few moldy carrots or a few fatty steaks slip past.
Machines don't tire. They don't get bored by repetition. In fact, it's what they do best. And with current technology, they can be programmed to sort by color and color patterns. They can "learn" to weed out bad produce and to sort good quality fruits, vegetables or meat cuts into uniform batches.
Krutz has run beef steaks through a prototype machine-vision separation system and has been able to sort them by percent fat and brightness of their red color. The machine could be programmed to sort by amount of marbling or bone content, he said.
"We could use it to tell consumers the percent fat in their meat," Krutz said. "And this could dramatically improve exports. At Japanese meat counters, for example, every piece is uniform in color, size and shape. You'd think they'd cloned them. We could greatly increase Indiana's export markets if we could supply what the Japanese want."
Krutz said he also sees applications to the Indiana furniture industry.
"Indiana is about fifth in the nation in furniture production based on the number of large furniture manufacturing plants located here," said Daniel Cassens, a forestry professor involved in the machine-vision project.
The same machine that sorted steaks can sort lumber by color and grain of wood. Furniture made from sorted, well-matched pieces of wood could bring Indiana manufacturers better prices and increased opportunity for sales abroad.
To correctly identify and sort a wood piece, the prototype machine needs only a sixth of a second, according to Harry Gibson, associate professor of forestry and leader of the wood products project. On a production line, that translates into 200 feet per minute, more than six times faster than human sorters. And its memory for color and grain detail exceeds that of human counterparts.
Krutz estimated the price of the hardware needed for a scaled-up version of the wood sorter at $20,000, a cost he said could be quickly recouped by increased sales.
"If an Indiana furniture manufacturer added one of these systems to a plant, they would increase their yield of better-quality products by 5 percent," Krutz said.
That would increase their ability to sell furniture in Europe and Japan, where savvy consumers demand pieces made of well-matched wood, Krutz said.
A similar machine-vision system already is measuring the amount of trash found in seed corn for Pioneer Hybrid International in Johnston, Iowa.
Seed corn must be shelled (removed from the cob) after sale to seed companies, because the mechanical shellers farmers use for field corn would break seed corn kernels and kill the embryo inside. Some husks and trash are left on the unshelled corn. Seed corn companies take one or more samples from a load brought to them for sale, measure the amount of trash in the samples, then pay the farmer according to the cleanliness of his seed.
Current sampling procedures leave room for an error of as much as $2,000 per load,
according to Krutz. A machine-vision system designed by Krutz and his colleagues
has cut the margin of error to a few dollars by enabling seed corn buyers to quickly
check an entire load, measure the trash by machine vision, and pay the producer accordingly.
Sources: Gary Krutz, (765) 494-1179; Internet, krutz@ecn.purdue.edu
Harry Gibson, (765) 494-1190; Internet, gibson@ecn.purdue.edu
Dan Cassens, (765) 494-3644
Writer: Rebecca J. Goetz (765) 494-0461; Internet, rjg@aes.purdue.edu
Purdue News Service: (765) 494-2096; e-mail, purduenews@purdue.edu
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