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NonDestructive Testing of Fruit Firmness with RealTime constraints

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... 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 ... – PowerPoint PPT presentation

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Title: NonDestructive Testing of Fruit Firmness with RealTime constraints


1
Non-Destructive Testing of Fruit Firmness with
Real-Time constraints
  • Christopher Mills
  • Supervisors Dr. Andrew Paplinski
  • Mr Charles Greif

2
Contents
  • Fruit Firmness
  • Non-destructive testing (NDT)
  • Research Plan aims, methods, work to date.
  • Conclusions

3
Fruit Firmness
  • Measurement of Fruit Firmness is important
    because
  • 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
  • Feel/look
  • Response to preparation/cooking
  • The feeling as fruit is consumed

4
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
5
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.

6
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 cameras)
  • We will concentrate on ultrasonic methods to
    measure fruit firmness (most other methods are
    destructive)

7
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?)

8
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
  • 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

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.
9
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.

10
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.

11
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

12
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.
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