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Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

机译：基于点云的三维植物无损检测

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摘要

In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destructive measurement of the height of plants based on machine vision technology is one of the difficulties. We propose a methodology for three-dimensional reconstruction under growing plants by Kinect v2.0 and explored the measure growth parameters based on three-dimensional (3D) point cloud in this paper. The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get the feature boundary lines; finally, calculating the length, width of the leaf by Euclidean distance, and the area of the leaf by surface integral method, measuring the height of plant using the vertical distance technology. The results show that the automatic extraction scheme of plant information is effective and the measurement accuracy meets the need of measurement standard. The established 3D plant model is the key to study the whole plant information, which reduces the inaccuracy of occlusion to the description of leaf shape and conducive to the study of the real plant growth status.
机译：在农业中，有关植物生长空间分布的信息对于应用很有价值。植物特征的定量研究在植物的生长发育研究中起着重要作用，基于机器视觉技术的植物高度的无损检测是其中的难点之一。我们提出了一种通过Kinect v2.0在生长植物下进行三维重建的方法，并探讨了基于三维（3D）点云的量度生长参数。该策略包括三个步骤：首先，预处理3D点云数据，通过点云离群值过滤和表面平滑方法完成3D植物配准;其次，采用局部凸连接斑块方法对植物模型的叶片和茎进行分割。从叶点云中提取特征边界点，并利用轮廓提取算法得到特征边界线。最后，通过欧几里得距离计算出叶子的长度，宽度，并通过表面积分法计算出叶子的面积，使用垂直距离技术测量植物的高度。结果表明，植物信息自动提取方案是有效的，测量精度符合测量标准的要求。建立的3D植物模型是研究整株植物信息的关键，它减少了遮挡叶形描述的不准确性，有利于研究植物的真实生长状况。

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