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.

中文翻译：

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基于点云数据的盆栽植物茎叶自动分类

植物器官的准确分类是监测植物生长状态和生理的关键步骤。提出了一种基于植物点云数据对盆栽植物叶茎进行自动分类的分类方法，是一种无损采集。利用三维凸包算法自动提取叶点训练样本，利用二维投影的点密度提取茎点训练样本。利用支持向量机（SVM）算法，利用两个训练集将所有点分类为叶点和茎点。通过使用三种盆栽植物的点云数据对所提出的方法进行了测试，并与其他两种方法进行了比较，