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Proceedings Paper

Tissue reflectance and machine vision for automated sweet cherry sorting
Author(s): Daniel E. Guyer; Patchrawat Uthaisombut; George C. Stockman
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Paper Abstract

This study describes machine vision procedures which are able to classify defective cherries from non-defective cherries. Defects can be divided into bruises, dry cracks, and wet cracks. Bandpass filters that enhance the intensity contrast between bruised and unbruised cherries are determined. An optimum combination of two wavelengths is identified at 750 nm (near-infrared range) and 500 nm (green range). An optimum single wavelength is identified at 750 nm. The image acquisition using these filters is described. Four detection methods using single view infrared images were studied. One method performed well in classifying cherries with bruises and wet cracks from non-defective cherries. One detection method using single view green images is studied. It performs well in classifying cherries with dry cracks from non-defective cherries. One detection method using infrared images and another using green images are used in combination to perform the detection on the entire surface of cherries. Two images, infrared and green, are taken from each of 6 orthogonal directions from the cherries. The integrated classifier misclassified 13% of non-defective cherries, 16% of bruised cherries, 0% of cherries with wet cracks, and 10% of cherries with dry cracks.

Paper Details

Date Published: 18 December 1996
PDF: 14 pages
Proc. SPIE 2907, Optics in Agriculture, Forestry, and Biological Processing II, (18 December 1996); doi: 10.1117/12.262855
Show Author Affiliations
Daniel E. Guyer, Michigan State Univ. (United States)
Patchrawat Uthaisombut, Michigan State Univ. (United States)
George C. Stockman, Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 2907:
Optics in Agriculture, Forestry, and Biological Processing II
George E. Meyer; James A. DeShazer, Editor(s)

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