Computer Vision Estimation of the Volume and Weight of Apples by Using 3D Reconstruction and Noncontact Measuring Methods

Abstract

A computer vision system for the estimation of apple volume and weight by using 3D reconstruction and noncontact measuring methods was investigated. The 3D surface of the apples could be reconstructed by using a single multispectral camera and near-infrared linear-array structured light. Both the traditional image feature and height information were extracted from the height maps. Two different type height features (Type I and II) were extracted, and both of them were fused with a projection area to form combination features (Combination Feature I and II). Partial least squares analysis and least squares-support vector machine were implemented for calibration models with projection area and combination features as inputs. Grid-Search Technique and Leave-One-Out Cross-Validation were also investigated to find out the optimal parameter values of the RBF kernel. The optimal LS-SVM models with Combination Feature II outperformed PLS models. The coefficient and root mean square error of prediction for the best prediction by LS-SVM were 0.9032 and 10.1155 for volume, whereas 0.8602 and 9.9556 for weight, respectively. The overall results indicated that height information can improve the prediction performance, and the proposed system could be applied as an alternative to the traditional methods for noncontract measurement of the volume and weight of apple fruits.