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Updated: Sep 8, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.

Dawei Li^1,2, Jinsheng Li³, Shiyu Xiang³

¹State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.

Plant Phenomics (Washington, D.C.)

|June 13, 2022

Summary

This study introduces Voxelized Farthest Point Sampling (VFPS) and PSegNet for plant 3D point cloud segmentation. PSegNet achieves superior semantic and instance segmentation for plant phenotyping, advancing intelligent agriculture.

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Plant phenotyping is crucial for understanding genetic traits and advancing modern breeding and intelligent agriculture.
3D point cloud segmentation of plant organs aids automatic growth monitoring and stress assessment.

Purpose of the Study:

To develop a novel point cloud downsampling strategy (VFPS) and a deep learning network (PSegNet) for accurate plant organ segmentation.
To enable simultaneous semantic and leaf instance segmentation for multiple plant species.

Main Methods:

Proposed Voxelized Farthest Point Sampling (VFPS) for dataset preparation.
Designed PSegNet with Double-Neighborhood Feature Extraction Block (DNFEB), Double-Granularity Feature Fusion Module (DGFFM), and Attention Module (AM).
Trained and evaluated PSegNet on a plant point cloud dataset.

Main Results:

PSegNet achieved superior quantitative and qualitative segmentation results compared to mainstream networks.
Achieved high mean Precision (95.23%), Recall (93.85%), F1 (94.52%), and IoU (89.90%) in semantic segmentation.
Achieved high mPrec (88.13%), mRec (79.28%), mCov (83.35%), and mWCov (89.54%) in instance segmentation.

Conclusions:

PSegNet effectively performs semantic and instance segmentation of plant point clouds.
The proposed VFPS and PSegNet contribute to improved plant phenotyping and intelligent agriculture.
This work advances automated growth monitoring and stress analysis in plants.