Liu Wentao, Wang Chenglin, Yan De, Chen Weilin, Luo Lufeng
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China.
Front Plant Sci. 2022 Jul 14;13:885167. doi: 10.3389/fpls.2022.885167. eCollection 2022.
The measurement of grapevine phenotypic parameters is crucial to quantify crop traits. However, individual differences in grape bunches pose challenges in accurately measuring their characteristic parameters. Hence, this study explores a method for estimating grape feature parameters based on point cloud information: segment the grape point cloud by filtering and region growing algorithm, and register the complete grape point cloud model by the improved iterative closest point algorithm. After estimating model phenotypic size characteristics, the grape bunch surface was reconstructed using the Poisson algorithm. Through the comparative analysis with the existing four methods (geometric model, 3D convex hull, 3D alpha-shape, and voxel-based), the estimation results of the algorithm proposed in this study are the closest to the measured parameters. Experimental data show that the coefficient of determination ( ) of the Poisson reconstruction algorithm is 0.9915, which is 0.2306 higher than the coefficient estimated by the existing alpha-shape algorithm ( = 0.7609). Therefore, the method proposed in this study provides a strong basis for the quantification of grape traits.
葡萄表型参数的测量对于量化作物性状至关重要。然而，葡萄串的个体差异给准确测量其特征参数带来了挑战。因此，本研究探索了一种基于点云信息估计葡萄特征参数的方法：通过滤波和区域生长算法分割葡萄点云，并使用改进的迭代最近点算法配准完整的葡萄点云模型。在估计模型表型尺寸特征后，使用泊松算法重建葡萄串表面。通过与现有的四种方法（几何模型、三维凸包、三维α形状和基于体素的方法）进行对比分析，本研究提出的算法估计结果与测量参数最为接近。实验数据表明，泊松重建算法的决定系数（ ）为0.9915，比现有的α形状算法估计的系数（ = 0.7609）高0.2306。因此，本研究提出的方法为葡萄性状的量化提供了有力依据。
