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" Primary and secondary food
contact requires different food safety
requirements. Robotic companies who
do not have food experience may not
be aware of all of the regulatory and
safety requirements, " Donnelly said.
" This is important when you consider
these two industry forces at play: the
food industry is a growing market
for automation, while automotive is
declining. Those robotics companies
that have serviced the automotive
industry are looking to enter new
markets - such as the food industry -
but lack expertise. "
It's important to take into account
the difference between industrial and
collaborative robots (cobots), Donnelly
added. Handling delicacy and speed come
into play when it comes to fresh produce.
" Collaborative robots are growing
in popularity due to their ease of
deployment and use, " she said. " A
common mistake is not understanding
the differences in speed capability
with collaborative robots, which are
purposefully slower than industrial
robots due to the safety aspects of
collaborative robots. Collaborative
robots also aren't always less expensive
than industrial robots. "
Gripping and picking apparatuses,
such as Soft Robotics' mGrip, should
be able to adapt to size and shape of the
fruit or vegetable to allow for minimal
tooling so the produce does not get
damaged or bruised.
Future capabilities
Computer vision technology can
detect defects in items, even if too small
for the naked eye, in a production when
the items are supposed to be uniform in
shape and size. No two pieces of produce
are exactly the same, however.
" You have to learn
which things you can
do well with robots and
which you can't "
− Rick Brookshire,
Epson Robotics
" In the industrial space, vision systems
are trained to look for something very
specific. Let's say for a pen, the outside of
the pen is always the same, " Brookshire
said. " But if you put five strawberries
down, they all look different. "
Currently, people are needed to pick
out defective produce in a processing
or production line. Brookshire said AI
technology is close to robots be able to
learn what to look for in a defective piece
of produce the same way an experienced
human picker has learned.
" Imagine if you could put 100 good
fruits and 100 bad fruits in front of
a vision system and it could learn
the differences. Slowly, it builds up a
database, " Brookshire said. " It's not exactly
what a good one looks like, but what
the characteristics are of a good one and
what the characteristics are of a bad one.
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You could put a fruit in front of it, and
it would determine, based on what it
knows right now, that's a bad one.
" That's really no different than a
human. If I'm a new person on the line, I
probably won't be able to detect as many
bad ones as someone who has been
doing it for five years. "
Brookshire said such technology
could be available to producers in the
next five years.
" We'll be able to do it in some scope, "
he said. " Will it be perfect? No, but we'll
be able to start doing it. It might not be
in the masses at that point; but in 10
years, it probably will be. "
With any new technology, companies
are reluctant to depend on it until it's
been proven to work in a real production
environment. During an interactive
forum at PACK EXPO, titled " Robotics:
Identifying Applications and Justifying
the Investments, " some show attendees
were openly skeptical of computerized
vision defect detection, even for readyto-ship
packaged products, much less
raw produce.
" Robotics hasn't caught up to detecting
end-of-line defects the way people
can, " said one attendee, who added he's
avoided replacing human packers with
automation for that reason. " If I need
someone to inspect the product, I may as
well have them box it too. "
Brookshire said automation in food
production will likely occur in small
measures, perhaps one trial production
line for larger producers.
" It depends on the company. Some of
the big ones are very quick to take this
kind of thing on, " he said. " For the small
ones, they might look at it as a way to
compete and get their costs down.
" Once it's proven and factories see
other factories doing it, they'll say, 'We
have to do that.' "
Ultimately, there is going to be
trial and error with robotics in food
production.
" Really, it's the same thing that
happened with the automobile industry
30 to 40 years ago, " Brookshire said. " We
tried to do everything with robots, and
we couldn't.
" You have to learn which things you
can do well with robots and which you
can't. " VGN
VGN | MARCH 2020 | 23

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March 2020

Table of Contents for the Digital Edition of March 2020