ArticlePlant phenomics (Washington, D.C.)2022
PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Dynamic sparse point voxel transformer for 3D point cloud instance segmentation of dormant apple trees.Plant phenomics (Washington, D.C.) · 2026Article
- DBGCN: Dual-branch Graph Convolutional Network for organ instance inference on sparsely labeled 3D plant data.Plant phenomics (Washington, D.C.) · 2026Article
- PlantEFRSegnet: A Plant Point Cloud Segmentation Network Based on Edge Point Preservation and Feature Feedback Repair.Sensors (Basel, Switzerland) · 2026Article
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- 3D-OGT: 3D organ growth tracking with minimum segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Pepper-4D: Spatiotemporal 3D Pepper Crop Dataset for Phenotyping.Plants (Basel, Switzerland) · 2026Article
- SPINet: structure-enhanced point cloud instance segmentation network for multi-plant rice phenotyping.Frontiers in plant science · 2026Article
- Structural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning.Plant phenomics (Washington, D.C.) · 2025Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.Plant phenomics (Washington, D.C.) · 2025Article
- 3D-NOD: 3D new organ detection in plant growth by a spatiotemporal point cloud deep segmentation framework.Plant phenomics (Washington, D.C.) · 2025Article
- Segment Any Leaf 3D: A Zero-Shot 3D Leaf Instance Segmentation Method Based on Multi-View Images.Sensors (Basel, Switzerland) · 2025Article
- An improved YOLOv8-seg-based method for key part segmentation of tobacco plants.Frontiers in plant science · 2025Article
- An automated skeleton extraction method for 3D point-cloud phenotyping of Schima Superba seedlings.PloS one · 2025Article
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- Overexpression of the potatoFrontiers in plant science · 2024Article
- Cucumber Seedling Segmentation Network Based on a Multiview Geometric Graph Encoder from 3D Point Clouds.Plant phenomics (Washington, D.C.) · 2024Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- A comparative study on point cloud down-sampling strategies for deep learning-based crop organ segmentation.Plant methods · 2023Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network-PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.
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