Wang Yawei, Chen Yifei
College of Information and Electrical Engineering, China Agricultural University, Qinghuadonglu No. 17, HaiDian District, Beijing 100083, China.
Engineering Practice Innovation Center, China Agricultural University, Qinghuadonglu No. 17, HaiDian District, Beijing 100083, China.
Plants (Basel). 2020 Apr 29;9(5):571. doi: 10.3390/plants9050571.
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.
在农业中，植物生长空间分布的信息对于实际应用具有重要价值。植物特征的定量研究在植物生长发育研究中起着重要作用，而基于机器视觉技术对植物高度进行无损测量是其中的难点之一。本文提出了一种利用Kinect v2.0对生长中的植物进行三维重建的方法，并基于三维（3D）点云探索了测量生长参数的方法。该策略包括三个步骤：首先，对三维点云数据进行预处理，通过点云离群值滤波和表面平滑方法完成三维植物配准；其次，使用局部凸连通补丁方法从植物模型中分割出叶子和茎干；从叶点云中提取特征边界点，并使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叶片的长度、宽度，利用表面积分法计算叶片面积，使用垂直距离技术测量植物高度。结果表明，植物信息自动提取方案有效，测量精度满足测量标准要求。所建立的三维植物模型是研究植物整体信息的关键，减少了遮挡对叶片形状描述的不准确性，有利于研究真实的植物生长状况。
