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The accurate classification of plant organs is a key step in monitoring thegrowing status and physiology of plants. A classification method was proposedto classify the leaves and stems of potted plants automatically based on thepoint cloud data of the plants, which is a nondestructive acquisition. The leafpoint training samples were automatically extracted by using thethree-dimensional convex hull algorithm, while stem point training samples wereextracted by using the point density of a two-dimensional projection. The twotraining sets were used to classify all the points into leaf points and stempoints by utilizing the support vector machine (SVM) algorithm. The proposedmethod was tested by using the point cloud data of three potted plants andcompared with two other methods, which showed that the proposed method canclassify leaf and stem points accurately and efficiently.

Automated classification of stems and leaves of potted plants based on point cloud data