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.

中文翻译：

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通过3D重构和非接触式测量方法对苹果体积和重量进行计算机视觉估计

研究了通过3D重建和非接触式测量方法估算苹果体积和重量的计算机视觉系统。苹果的3D表面可以通过使用单个多光谱相机和近红外线性阵列结构光进行重建。传统图像特征和高度信息都从高度图中提取。提取两个不同的类型高度特征（类型I和II），并将它们与投影区域融合以形成组合特征（组合特征I和II）。对于投影面积和组合特征作为输入的校准模型，实施了偏最小二乘分析和最小二乘支持向量机。还研究了网格搜索技术和留一法交叉验证，以找出RBF内核的最佳参数值。具有组合功能II的最佳LS-SVM模型的性能优于PLS模型。对于LS-SVM进行最佳预测的预测系数和均方根误差分别为体积的0.9032和10.1155，而重量的分别为0.8602和9.9556。总体结果表明，高度信息可以提高预测性能，所提出的系统可以替代传统的非合约式苹果果实体积和重量测量方法。体积分别为1155，而重量分别为0.8602和9.9556。总体结果表明，高度信息可以提高预测性能，所提出的系统可以替代传统的非合约式苹果果实体积和重量测量方法。体积分别为1155，而重量分别为0.8602和9.9556。总体结果表明，高度信息可以提高预测性能，所提出的系统可以替代传统的非合约式苹果果实体积和重量测量方法。