Size is an essential metric for the postharvest grading of Vidalia sweet onions. Currently, the size of Vidalia onions is mainly measured by machine vision systems using 2-D imaging or mechanical sizers. This work investigated the potential of using an 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. A 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 images were quite robust to changes in onion orientation.
Figure 1. The schematic for the configuration of the RGB-depth sensor based imaging system.
Figure 2. Flow chart for calculating the maximum diameter of the onion using the point cloud image
Figure 3. Demonstration of the voxel image of the onion based on the transformed point cloud
Figure 4. Mathematical description of the volume of the voxel image of the onion