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Automatic Produce Grading System

  • Donald Bailey
Reference work entry

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.

Keywords

Specular Reflection Machine Vision System Central View Target Weight Image Processing Operation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

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