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Proceedings Paper

Noninvasive maturity detection of citrus with computer vision
Author(s): Yibin Ying; Zhenggang Xu; Xiaping Fu; Yande Liu
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Paper Abstract

A computer vision system was established to explore a method for citrus maturity detection. The surface color information and the ratio of total soluble solid to titratable acid (TSS/TA) were used as maturity indexes of citrus. The spectral reflectance properties with different color were measured by UV-240 ultraviolet and visible spectrophotometer. The biggest discrepancy of gray levels between citrus pixels and background pixels was in blue component image by image background segmentation. Dynamic threshold method for background segmentation had best result in blue component image. Methods for citrus image color description were studied. The citrus spectral reflectance experiments showed that green surface and saffron surface of citrus were of highest spectral reflectance at the wavelength of 700nm, the difference between them reached to maximum, about 53%, and the image acquired at this wavelength was of more color information for maturity detection. A triple-layer feed forward network was established to map citrus maturity from the hue frequency sequence by the mean of artificial neural network. After training, the network mapper was used to detect the maturity of the test sample set, which was composed of 252 Weizhang citrus with different maturity. The identification accuracy of mature citrus reached 79.1%, that of immature citrus was 63.6%, and the mean identification accuracy was 77.8%. This study suggested that it is feasible to detect citrus maturity non-invasively by using the computer vision system and hue frequency sequence method.

Paper Details

Date Published: 30 March 2004
PDF: 11 pages
Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.516052
Show Author Affiliations
Yibin Ying, Zhejiang Univ. (China)
Zhenggang Xu, Zhejiang Univ. (China)
Xiaping Fu, Zhejiang Univ. (China)
Yande Liu, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 5271:
Monitoring Food Safety, Agriculture, and Plant Health
George E. Meyer; Yud-Ren Chen; Shu-I Tu; Bent S. Bennedsen; Andre G. Senecal, Editor(s)

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