论文标题
基于点云数据的盆栽植物的茎和叶的自动分类
Automated classification of stems and leaves of potted plants based on point cloud data
论文作者
论文摘要
植物器官的准确分类是监测植物不断增长的状态和生理学的关键步骤。提出了一种分类方法，以根据植物的点云数据自动对盆栽植物的叶子和茎进行分类，这是一种无损的采集。通过使用三维凸赫尔算法自动提取叶点训练样品，而使用二维投影的点密度提取茎点训练样品。这两个训练集用于利用支持向量机（SVM）算法将所有点分为叶点和茎点。通过使用三个盆栽植物的点云数据测试了所提出的方法，并与另外两种方法进行了比较，这表明所提出的方法可以准确有效地对叶子和茎点进行分类。
The accurate classification of plant organs is a key step in monitoring the growing status and physiology of plants. A classification method was proposed to classify the leaves and stems of potted plants automatically based on the point cloud data of the plants, which is a nondestructive acquisition. The leaf point training samples were automatically extracted by using the three-dimensional convex hull algorithm, while stem point training samples were extracted by using the point density of a two-dimensional projection. The two training sets were used to classify all the points into leaf points and stem points by utilizing the support vector machine (SVM) algorithm. The proposed method was tested by using the point cloud data of three potted plants and compared with two other methods, which showed that the proposed method can classify leaf and stem points accurately and efficiently.
