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    This study introduces an automated 3D machine vision system for high-throughput plant phenotyping. The system accurately analyzes plant growth, revealing rhythmic patterns in crops like Arabidopsis and barley.

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    Area of Science:

    • Agricultural Science
    • Computer Vision
    • Plant Biology

    Background:

    • 2D plant analysis methods have limitations.
    • 3D plant analysis is advancing phenotyping technologies.
    • High-throughput phenotyping is crucial for agriculture and crop science.

    Purpose of the Study:

    • To present an automated 3D system for analyzing plant growth in indoor conditions.
    • To enable high-throughput plant phenotyping throughout the plant's life cycle.
    • To demonstrate the system's capability in capturing rhythmic growth patterns.

    Main Methods:

    • Utilized a gantry robot system with a 3D laser scanner for automated plant scanning.
    • Captured surface point cloud data from multiple views during light/dark cycles.
    • Employed 3D reconstruction algorithms for mesh generation, surface area, and volume computation.

    Main Results:

    • Successfully monitored plant growth from vegetative to reproductive stages.
    • Quantified rhythmic growth patterns in Arabidopsis thaliana and Hordeum vulgare.
    • Demonstrated a fully automated workflow from scanning to growth analysis.

    Conclusions:

    • The automated 3D system offers a user-friendly solution for advanced plant phenotyping.
    • The technology facilitates detailed analysis of plant growth dynamics.
    • This approach supports high-throughput crop science and agricultural research.