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Automatic Produce Grading System

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Abstract

The development of a machine vision system for automated high-speed produce grading is described. Image processing techniques were used to obtain an estimate of the volume of each item, which was then related to the weight through a closed-loop calibration. Accurate weight estimation led to more accurate and better control over the spread of package weights. This reduced the average package weight by approximately 20%, with a standard deviation of 2.5 g for a nominal 100 g package. Improved processing efficiencies doubled the throughput and significantly increased the profitability of the packinghouse.

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References

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Acknowledgements

A large project such as this is never a one-man effort. My role in the project related to the image and signal processing aspects. The other members of the development team were: Ralph Ball and Harvey Barraclough, who focussed on the mechanical engineering aspects of the project; Ken Mercer, who developed the intelligent actuators; Colin Plaw, who developed the LabVIEW based control system; Andrew Gilman, who investigated high-speed weighing using load cells; and Geoff Lewis from the packinghouse, who provided the test system and copious quantities of asparagus for testing.

We also acknowledge funding for this project from Technology New Zealand through a Technology for Business Growth Grant.

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Correspondence to Donald Bailey .

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© 2012 Springer-Verlag London Ltd.

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Bailey, D. (2012). Automatic Produce Grading System. In: Batchelor, B.G. (eds) Machine Vision Handbook. Springer, London. https://doi.org/10.1007/978-1-84996-169-1_37

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