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Related Experiment Video

Updated: May 31, 2025

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

Segment Any Leaf 3D: A Zero-Shot 3D Leaf Instance Segmentation Method Based on Multi-View Images.

Yunlong Wang¹, Zhiyong Zhang¹

¹School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, China.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This study introduces a novel zero-shot 3D leaf segmentation method for plant phenotyping. It accurately characterizes plant morphology using RGB sensors and advanced AI, enabling precise genetic analysis.

Keywords:

RGB sensors instance segmentation multi-view stereo plant phenotyping zero-shot segmentation

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Area of Science:

Plant Science
Computer Vision
Genetics

Background:

Precise plant phenotypic analysis is crucial for understanding genotype-phenotype relationships.
Existing methods struggle with plant morphological diversity and require extensive annotated 3D data.
Generalizability across species and robustness to variations remain significant challenges.

Purpose of the Study:

To develop a zero-shot 3D leaf instance segmentation method for plant phenotyping.
To overcome limitations of current methods in handling plant morphological variability and data requirements.
To enable accurate 3D characterization of plant phenotypes using readily available RGB sensors.

Main Methods:

A multi-view strategy extends the 2D Segment Anything Model (SAM) to 3D.
3D plant point clouds are reconstructed from RGB image sequences using multi-view stereo.
High-Quality SAM (HQ-SAM) performs 2D leaf segmentation, mapped to 3D point clouds.
An incremental fusion method aggregates multi-view segmentations based on confidence scores.

Main Results:

The method achieved point-level precision, recall, and F1 scores exceeding 0.9 on a peanut seedling dataset.
Object-level mean Intersection over Union (mIoU) and precision surpassed 0.75.
Demonstrated state-of-the-art segmentation quality with zero-shot capability and generalizability.

Conclusions:

The proposed method offers a robust and generalizable solution for 3D leaf instance segmentation.
It significantly advances plant phenotyping by enabling precise morphological characterization without extensive annotated data.
The approach holds substantial potential for accelerating genetic studies and crop improvement programs.