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Proceedings Paper

Detection of fresh bruises in apples by structured-illumination reflectance imaging
Author(s): Yuzhen Lu; Richard Li; Renfu Lu
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Paper Abstract

Detection of fresh bruises in apples remains a challenging task due to the absence of visual symptoms and significant chemical alterations of fruit tissues during the initial stage after the fruit have been bruised. This paper reports on a new structured-illumination reflectance imaging (SIRI) technique for enhanced detection of fresh bruises in apples. Using a digital light projector engine, sinusoidally-modulated illumination at the spatial frequencies of 50, 100, 150 and 200 cycles/m was generated. A digital camera was then used to capture the reflectance images from ‘Gala’ and ‘Jonagold’ apples, immediately after they had been subjected to two levels of bruising by impact tests. A conventional three-phase demodulation (TPD) scheme was applied to the acquired images for obtaining the planar (direct component or DC) and amplitude (alternating component or AC) images. Bruises were identified in the amplitude images with varying image contrasts, depending on spatial frequency. The bruise visibility was further enhanced through post-processing of the amplitude images. Furthermore, three spiral phase transform (SPT)-based demodulation methods, using single and two images and two phase-shifted images, were proposed for obtaining AC images. Results showed that the demodulation methods greatly enhanced the contrast and spatial resolution of the AC images, making it feasible to detect the fresh bruises that, otherwise, could not be achieved by conventional imaging technique with planar or uniform illumination. The effectiveness of image enhancement, however, varied with spatial frequency. Both 2-image and 2-phase SPT methods achieved the performance similar to that by conventional TPD. SIRI technique has demonstrated the capability of detecting fresh bruises in apples, and it has the potential as a new imaging modality for enhancing food quality and safety detection.

Paper Details

Date Published: 17 May 2016
PDF: 12 pages
Proc. SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII, 986406 (17 May 2016); doi: 10.1117/12.2225148
Show Author Affiliations
Yuzhen Lu, Michigan State Univ. (United States)
Richard Li, Michigan State Univ. (United States)
Renfu Lu, USDA Agricultural Research Service (United States)
Michigan State Univ. (United States)

Published in SPIE Proceedings Vol. 9864:
Sensing for Agriculture and Food Quality and Safety VIII
Moon S. Kim; Kuanglin Chao; Bryan A. Chin, Editor(s)

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