Transcript Non-Destructive Testing of Fruit Firmness with Real-Time constraints Christopher Mills

Non-Destructive Testing of Fruit
Firmness with Real-Time constraints
Christopher Mills
Supervisors: Dr. Andrew Paplinski
Mr Charles Greif
Contents
• Fruit Firmness
• Non-destructive testing (NDT)
• Research Plan: aims, methods,
work to date.
• Conclusions
Fruit Firmness
Measurement of Fruit Firmness is important because
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Firmness affects the perception of enjoyment of food.
Perception of firmness is linked to freshness and the ripeness of fruit
Such perception may be of greater importance for the preparation of fruit for
later consumption
Humans decide fruit firmness in a variety of ways
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Feel/look
Response to preparation/cooking
The feeling as fruit is consumed
Fruit Firmness (cont)
• Biological factors of Fruit Firmness
– Cell size/shape
– Cell water content
– Cell organization
• Firmness varies with
– Fruit type (apple, orange)
– Fruit Age (under ripe, over ripe)
– Conditions during maturation and storage
Image of apple cells at 100x magnification
Fruit Firmness (cont)
Fruit firmness testing is critical to
industries involved in the sorting
and grading of fruit. As sorting can
be done based on fruit firmness
measures.
• For the duration of this project, a
company called Colour Vision
Systems (CVS) will be providing
support for this project.
– CVS build large scale fruit sorting
machines, so their interest in such
a system is obvious.
Non-Destructive Testing
• NDT includes any methods of testing that do not cause damage to
the target eg
– Ultrasound used to find impurities in steel
• Various modalities of NDT exist, such as
– Sound methods (ultrasound, acoustic, etc)
– Wave energy response (laser, infrared, x-ray)
– Vision (Video camera’s)
• We will concentrate on ultrasonic methods to measure fruit firmness
(most other methods are destructive)
Project Aims
• With our background research in Ultrasonic imaging, the aim is to
produce a simple system that will grade fruit firmness using NDT
• Ensure that the system could be used in an industrial setting, i.e.
testing fruit on a rapidly moving conveyer belt.
– Work within hard real time constraints (ie 10 fruit/sec)
– Be able to test fruit without actual contact with the skin of fruit (is this
possible?)
Method
• Empirically determine response of the cellular structure of fruit to
ultrasound
• Simulate response using a software package called Field 2, which
can produce images based on simulation values or real readings
from an ultrasonic system
Field 2 can create images or
simple signal over time
graphs, here is an example of
field 2 taking a source image
and simulating how it would
look through ultrasonic testing.
The same could be done with
a mock up of fruit internals.
• However, we do not require images, just an overall characterization
of fruit firmness
• Devise a Neural Network or other type of system that is capable of
determining fruit firmness (e.g. statistical methods) based on the
results of experimentation
Method (cont)
Possible Final system
• Use Ultrasonic methods on fruit via non-contact transducers
• Attempt to use information from external systems (if possible)
– such as a vision system to detect blemishes (Some blemishes are
caused by fruit diseases that would effect firmness also)
– Weight and volume information (fruit density could prove useful in
determining fruit firmness)
• Process all available information via a neural network that will
require training for each available fruit type.
Work to Date
• Research into Non-Contact Ultrasound (NCU)
– The conclusion is that NCU could possibly resolve the problem of using
contact ultrasound, but finding sources for NCU transducers is proving
to be difficult
• Classification system
– At this stage, a neural network is the most likely system to use for
classification of Fruit Firmness
– Other systems have been considered, such as pattern recognition
methods including statistical analysis.
Work to Date (cont)
Work to date (cont)
• Hardware Prototype
– Due to the difficulty with sourcing NCU, I have yet to begin the
prototype. A full NCU implementation may be postponed and some work
could be done using a dry-contact system
• Physical arrangement of system
– Some ideas have been discussed, such as the angle between the
emitter and receiver(s)
– Angles of transducers to fruit surface
Conclusions
At this point, I can see no reason why the system I propose would not
work. I expect that I will have at least a functioning prototype and
algorithm development with off-the-shelf equipment.