ArticleFrontiers in plant science2021
Deep Segmentation of Point Clouds of Wheat.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Quantifying wheat spike morphology by high resolution 3D surface scanning.Plant methods · 2026Article
- Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow.Plant methods · 2026Article
- From 3DGS scenes to plant traits: a scalable extraction and segmentation framework for muskmelon phenotyping.Frontiers in plant science · 2026Article
- ELGCot3D: a lightweight 3D cotton point cloud segmentation model based on EdgeConv-Local Attention-GCN and semantic feature enhancement.Frontiers in plant science · 2026Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- A Method for Sesame (Plants (Basel, Switzerland) · 2025Article
- HALF: Histogram of Angles in Linked Features for 3D Point Cloud Data Segmentation of Plants for Robust Sensing.Sensors (Basel, Switzerland) · 2025Article
- 3D-NOD: 3D new organ detection in plant growth by a spatiotemporal point cloud deep segmentation framework.Plant phenomics (Washington, D.C.) · 2025Article
- Article
- Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.AoB PLANTS · 2024Article
- A comparative study on point cloud down-sampling strategies for deep learning-based crop organ segmentation.Plant methods · 2023Article
- Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning.Plant methods · 2023Article
- Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks.Plant methods · 2023Article
- Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field.Frontiers in plant science · 2023Article
- Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation.Plant phenomics (Washington, D.C.) · 2023Article
- Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images.Plant phenomics (Washington, D.C.) · 2023Article
- Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.Frontiers in plant science · 2023Article
- Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping.Frontiers in plant science · 2023Article
- Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX.Frontiers in plant science · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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