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 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

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 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.

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.