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50 MACHINERY UPDATE JULY/AUGUST 2018 www.machineryupdate.co.uk
Feature: Automation, robotics and vision
Machine vision is
a great way to ‘see’
defects in produce
Many parameters and
defects need to be
considered in grading
fruit and vegetables,
especially when
automating the inspection process.
These include discolouration,
ripeness, size, shape, bruising,
mould, pests and diseases.
Colour imaging, 3D imaging and
image processing are important in
measuring external characteristics
of the produce such as colour,
size and shape, but NIR imaging
and the newer technique of
hyperspectral imaging can be used
to identify sub-surface defects
such as bruising or diseases at an
early stage before they manifest
themselves on the surface, says
Rob Webb of Stemmer Imaging.
TARGET COLOUR RANGES
High-end supermarkets favour
produce with target colour ranges/
shades and are willing to pay more
for it. Line scan colour imaging,
where an image is built up line
by line as the produce passes in
front of the camera, can help to
automatically detect the produce’s
colour and sort it at very high
speeds. Colour measurement
can also be used in ripeness
assessment. Supermarkets also
set size requirements and by only
harvesting at the right times,
farmers can ensure that they have
the highest yield. 3D laser scanning
of produce in the field helps locate
the position and size of the produce
to determine what should be picked
(and when), passing the location
of each acceptable item onto an
automated robotic picking system.
Separating ‘misshapen’ fruit and
vegetables is also important.
All produce has some natural
variation in shape and the vision
system needs to take this into
account with its grading process.
Stemmer’s Common Vision Blox
image processing software has
a versatile pattern recognition
tool developed from research into
artificial intelligence that can
Detecting a
fruit defect
The NIR image
removes colour
information
making it easier
to see any cuts.
Meanwhile, the
hyperspectral
image shows
a bruise (red)
and the 3D
image allows
size and
volume to
be measured
be used for this. By using all the
information from multiple image
planes and sources (which can
include 3D point cloud data from
the 3D laser scanner), it is able to
identify the key features that help
grade the produce.
EARLY SIGNS OF DECAY
NIR imaging at wavelengths
from around 750nm to 1µm can
be used to identify early signs of
decay, mechanical bruising and
pest damage below the surface of
produce. Mould can grow on living
and decomposing produce and
Demand for high quality
fruit and vegetables is driving
the use of automated inspection
can spread quickly. By the time
its presence has been detected by
the trained eye or colour vision
systems, large colonies have
already formed. Early NIR detection
can help reduce contamination
(and save any resulting costs)
further down the line.
It is also important to
recognise early signs of pests
and diseases in crops to be able
to deal with the problem quickly.
Hyperspectral imaging combines
NIR imaging spectroscopy to
allow the identification of defects
on the basis of their chemical
composition. This can be useful
to identify problems such as sugar
end defects in potatoes, or assess
the ripeness of food that doesn’t
change colour when it ripens,
such as the avocado.
T 01252 780000
W www.stemmer-imaging.co.uk