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Estimating the diameter and volume of Vidalia sweet onions using the consumer-grade RGB-depth camera

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  131593519,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: @2013
Authors:   Weilin Wang, Changying Li
Keywords:   Machine vision 3D depth RGB-D size volume.

Abstract. Size is an essential metric for the postharvest grading of Vidalia sweet onions. Currently, the size of the Vidalia onion is mainly measured by machine vision systems using 2-D imaging or mechanical sizers. This work investigated the potential of using the RGB-depth sensor to improve the accuracy and efficiency of quantifying the size features of onions (the maximum diameter and volume). In the study, the color and depth images of onions in various postures were collected from different viewpoints. The maximum diameter of the onion was calculated in 2-D and 3-D Euclidean space using its color and depth image, respectively. The depth image of the onion was converted to the voxel image for calculating the volume. Linear regression model was developed to predict the onion volume based on the total volume of the onion voxel image. The results showed that the proposed approaches accurately measured the maximum diameter using either the color image (RMSE=3.1 mm) or the depth image (RMSE=1.5 mm). The onion diameter estimation using the depth image showed a higher accuracy and robustness than the method using the color image. The proposed method of measuring the onion volume showed a RMSEP of 16 cm3 and an accuracy of 96.7%. Results showed that both the onion maximum diameter and volume can be estimated by using a single depth image of the onion. The proposed methods of measuring the onion diameter and volume based on depth image were quite robust to the change of the onion orientation.

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