Deep Segmentation of Point Clouds of Wheat

Morteza Ghahremani*, Kevin Williams, Fiona M.K. Corke, Bernard Tiddeman, Yonghuai Liu, John H. Doonan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
131 Downloads (Pure)


The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.

Original languageEnglish
Article number608732
Number of pages15
JournalFrontiers in Plant Science
Publication statusPublished - 24 Mar 2021


  • 3D analysis
  • convolutional neural network
  • deep learning
  • pattern
  • point cloud
  • segmentation
  • wheat


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