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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #341130

Research Project: Nondestructive Quality Assessment and Grading of Fruits and Vegetables

Location: Sugarbeet and Bean Research

Title: Structured-illumination reflectance imaging coupled with phase analysis techniques for surface profiling of apples

item LU, YUZHEN - Michigan State University
item Lu, Renfu

Submitted to: Journal of Food Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2018
Publication Date: 3/21/2018
Citation: Lu, Y., Lu, R. 2018. Structured-illumination reflectance imaging coupled with phase analysis techniques for surface profiling of apples. Journal of Food Engineering. 232:11-20.

Interpretive Summary: Three-dimensional (3-D) shape information is valuable for fruit quality evaluation. For instance, in machine vision inspection of surface defects of apples, the stem and calyx regions are often misclassified as defects due to their low reflectance. With the 3-D shape information of apples, the stem and calyx regions can be easily discriminated from the defective regions. This research was therefore aimed at developing image processing algorithms for reconstruction of the 3-D surface of fruit from the images acquired by a structured-illumination reflectance image (SIRI) system recently developed in our lab. SIRI acquires reflectance images from objects under illumination of special patterns, as opposed to uniform, diffuse illumination commonly used in conventional imaging technique. In this research, image processing procedures were developed to extract a set of special images from each acquired SIRI image. These images displayed different characteristics or values for detecting surface or subsurface tissue defects, which, otherwise, cannot be ascertained by conventional imaging technique, as well as for reconstructing 3-D shape of objects. 3-D reconstruction algorithms were developed and validated using two reference blocks of special shape as well as apple fruit. Results showed that the algorithms were effective for reconstructing the shape of apples, and they can be used to differentiate the stem/calyx region from defects. The research has expanded the capability of SIRI for food quality detection.

Technical Abstract: Three-dimensional (3-D) geometry information is valuable for fruit quality evaluation. This study was aimed at exploring an emerging structured-illumination reflectance imaging (SIRI) system, coupled with phase analysis, for reconstructing surface profiles of fruit surface. Phase-shifted sinusoidal patterns, distorted by the fruit geometry, were acquired and processed through phase demodulation, phase unwrapping and other post-processing procedures to obtain phase difference maps relative to the phase of a reference plane. The phase maps were then transformed into height profiles based on phase-to-height calibrations. A reference plane-based approach, in conjunction with the curve fitting technique using polynomials of order 3 or higher, was utilized for phase-to-height calibrations, which achieved superior accuracies with the root-mean-squared errors (RMSEs) of 0.027-0.033 mm for a height measurement range of 0-91 mm. The 3rd-order polynomial curve fitting technique was further tested on two reference blocks with known heights, resulting in relative errors of 3.75% and 4.16%. Tests of the calibrated system for reconstructing the surface of apple samples showed that surface concavities (i.e., stem/calyx regions) could be readily discriminated from bruising defects from both the phase difference images and reconstructed height profiles. This study has laid a foundation for using SIRI for reconstructing the 3-D geometry, and thus expanded the capability of the technique for quality evaluation of horticultural products. Further research is needed to utilize the phase analysis techniques for detecting surface concavities of apples, and optimize the phase demodulation and unwrapping algorithms for faster and more reliable detection.