Graph convolutional network for labeling organs on plant point clouds

Current plant organ analysis algorithms are mostly based on two-dimensional images. There is an issue of overlapping in plant organs in the 2D space, and only specific species of plants can be well handled in 2D. Therefore, we propose a method for propagating labels of plant organs by using a modified Graph Neural Network on three-dimensional plant point clouds. We first convert the three-dimensional point cloud files of plants into a dataset with a graph data structure, and then input it into an improved GCNII for training. Only a few sampled points of the plant are needed for label propagation, then it can lift the efficiency of plant organ segmentation and classification. The accuracy of label propagation results for maize, sorghum, tomato, and tobacco can reach over 95%. The research is of great significance to reducing the amount of manual labeling work load for 3D plant phenotyping.