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Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature

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CLCFM3: A 3D Reconstruction Algorithm Based on Photogrammetry for High-Precision Whole Plant Sensing Using All-Around

Atsushi Hayashi^1,2, Nobuo Kochi^1,2,3, Kunihiro Kodama^2,4

¹National Agriculture and Food Research Organization, Tsukuba 305-8518, Ibaraki, Japan.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This study introduces a new photogrammetry technique for high-density 3D plant phenotyping. The closed-loop coarse-to-fine method with multi-masked matching (CLCFM3) overcomes occlusion and errors for accurate 3D plant data.

Keywords:

3D point cloud 3D reconstruction multi-view stereo photogrammetry plant phenotyping structure from motion

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Area of Science:

Plant Science
Computer Vision
Photogrammetry

Background:

Accurate 3D plant phenotyping is crucial for understanding plant growth.
Plant structures with overlapping parts (leaves, stems) pose challenges for 3D reconstruction.
Occlusion and image-matching errors lead to incomplete or inaccurate 3D point clouds.

Purpose of the Study:

To develop a novel technique for acquiring high-density, high-precision 3D point cloud data for plant phenotyping.
To address challenges of occlusion and erroneous points in 3D plant reconstruction.
To enable large-scale comparative analysis of plant phenotypes using 3D information.

Main Methods:

Proposed a closed-loop coarse-to-fine method with multi-masked matching (CLCFM3).
CLCFM3 utilizes repeated local point cloud generation (multi-matching) to suppress occlusion.
Employed masked matching to remove noise points and a closed-loop coarse-to-fine method (CLCFM) for accurate Structure from Motion.

Main Results:

The CLCFM3 method effectively reconstructs high-density, high-precision 3D point clouds for complex plant structures.
Suppression of occlusion and reduction of erroneous points were achieved.
Improved accuracy in Structure from Motion through CLCFM enhances overall 3D data quality.

Conclusions:

The developed photogrammetry technique enables efficient acquisition of detailed 3D plant data.
This approach facilitates comparative analysis of plant phenotypes across various species during growth.
The method is expected to advance plant science research through precise 3D phenotyping.