Orchard operations such as mechanical pruning and spraying are heavily affected by tree architectures. Quantified inputs (e.g., cutting locations for mechanical pruning, and canopy distribution and density for variable-rate precision spraying) are necessary information for achieving precise control of these orchard operations. Even in planar orchard systems, trees grow differently. Therefore, it is essential to measure the canopy at the individual tree level. A three-dimensional (3D) light detection and ranging (LiDAR) sensor imaging system was developed to estimate the main canopy specifications. The LiDAR sensor was installed on a utility vehicle and driven alongside tree rows in an apple orchard. A total of 1,138 frames of point cloud data were acquired from 69 apple trees in a tall spindle architecture. An algorithm was developed in the MATLAB environment to segment trellis wires, support poles, and tree trunks in these point cloud images. The results indicated that the proposed algorithm achieved overall accuracy values of 88.6%, 82.1%, and 94.7%, respectively, in identifying the corresponding three objects. Furthermore, canopy density and depth maps were created with the distribution of points in the point cloud images. The outcomes from this study provide baseline information for precision orchard operations such as mechanical pruning and precision spraying.

中文翻译：

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基于几何特征的网格状苹果园稀疏3D点云语义分割

机械修剪和喷洒等果园作业受到树木结构的严重影响。量化输入（例如，机械修剪的切割位置，可变速率精确喷洒的冠层分布和密度）是实现对这些果园操作的精确控制的必要信息。即使在平面果园系统中，树木的生长也不同。因此，必须在单个树级别测量冠层。开发了一种三维 (3D) 光探测和测距 (LiDAR) 传感器成像系统来估计主要的冠层规格。LiDAR 传感器安装在一辆多功能车上，沿着苹果园的树行行驶。从 69 棵苹果树中获取了 1,138 帧的点云数据。在 MATLAB 环境中开发了一种算法来分割这些点云图像中的网格线、支撑杆和树干。结果表明，所提出的算法在识别相应的三个对象时的总体准确率分别达到了88.6%、82.1%和94.7%。此外，根据点云图像中的点分布创建冠层密度和深度图。这项研究的结果为精密果园操作（如机械修剪和精密喷洒）提供了基线信息。冠层密度和深度图是根据点云图像中的点分布创建的。这项研究的结果为精密果园操作（如机械修剪和精密喷洒）提供了基线信息。冠层密度和深度图是根据点云图像中的点分布创建的。这项研究的结果为精密果园操作（如机械修剪和精密喷洒）提供了基线信息。