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Proceedings Paper

A laser-based multispectral imaging system for real-time detection of apple fruit firmness
Author(s): Renfu Lu; Yankun Peng
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Paper Abstract

Recent research showed that spectral scattering is useful for assessing the firmness of apple fruit. This paper reports the development of a laser-based multispectral imaging prototype for real-time detection of apple fruit firmness. The prototype consisted of a common aperture multispectral imaging unit, a multi-laser unit, and a belt conveyor, which was able to capture and process spectral scattering images for up to two fruit/s. The multispectral imaging system was tested for detecting the firmness of 'Golden Delicious' and 'Red Delicious' apples when they were moving on the conveyor belt at an imaging speed of one fruit for every two seconds. The original scattering images were corrected by using the newly developed methods of removing noise pixels and incorporating fruit size into the calculation of the scattering distance and intensity. The corrected scattering images were reduced to one-dimensional scattering profiles by radial averaging. The scattering profiles were fitted with a Lorentzian distribution function of four parameters. Multi-linear regression models were developed using the four Lorentzian parameters for the four wavelengths for each apple cultivar, and the models were then used to predict the firmness of validation apples. The multispectral imaging system achieved good firmness predictions with values for the correlation coefficient of 0.85 for 'Golden Delicious' and 0.86 for 'Red Delicious'. The laser-based multispectral imaging system is fast and relatively easy to implement, and it has the potential to meet the requirement for online sorting and grading of apple fruit.

Paper Details

Date Published: 8 November 2005
PDF: 10 pages
Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 59960F (8 November 2005); doi: 10.1117/12.630814
Show Author Affiliations
Renfu Lu, USDA Agricultural Research Service, Michigan State Univ. (United States)
Yankun Peng, Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 5996:
Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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