Graph convolutional network for labeling organs on plant point clouds

Yudongfeng Zhang; Zhaoyi Zhou; Mingbo Zhao; Dawei Li

doi:10.1117/12.3037053

22 October 2024 Graph convolutional network for labeling organs on plant point clouds

Yudongfeng Zhang, Zhaoyi Zhou, Mingbo Zhao, Dawei Li

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740W (2024) https://doi.org/10.1117/12.3037053
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yudongfeng Zhang, Zhaoyi Zhou, Mingbo Zhao, and Dawei Li "Graph convolutional network for labeling organs on plant point clouds", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740W (22 October 2024); https://doi.org/10.1117/12.3037053

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