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Related Experiment Video

Updated: Jun 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.

Sifan Dong¹, Xueyan Fan¹, Xiuhua Li^1,2

¹State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Plant Phenomics (Washington, D.C.)

|December 19, 2025

Summary

This summary is machine-generated.

A novel two-stage method using PointNeXt and Quickshift++ achieves accurate organ instance segmentation for diverse plant types, advancing plant phenotyping research.

Keywords:

Organ segmentation Plant phenotyping Point cloud PointNeXt Quickshift++

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Area of Science:

Computer Vision
Plant Science
Agricultural Technology

Background:

Accurate organ instance segmentation of 3D plant point clouds is essential for plant phenotyping.
Existing methods often lack generalization across different crop types (monocotyledonous vs. dicotyledonous).

Purpose of the Study:

To develop a generalized two-stage method for single-plant organ instance segmentation.
To improve the accuracy and applicability of plant organ segmentation across diverse species.

Main Methods:

A two-stage approach combining an improved PointNeXt for semantic segmentation (stems, leaves) and Quickshift++ for instance segmentation.
Training and validation on diverse datasets including sugarcane, maize, and tomato point clouds.

Main Results:

Achieved high semantic segmentation accuracy (mOA 96.96%, mIoU 87.15%).
Outperformed state-of-the-art methods in instance segmentation (mPrec 93.32%, mRec 85.60%, mF1 87.94%, mIoU 81.46%).
Demonstrated strong generalization across different plant species and early growth stages.

Conclusions:

The proposed method offers superior generalization for organ instance segmentation in 3D plant point clouds.
This approach provides a robust tool for advancing plant phenotyping research across various crops.