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    This study presents an automated 3D plant modeling pipeline for phenotyping architectural traits. It reduces bottlenecks and enables non-destructive, accurate plant trait analysis for improved crop yield prediction.

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    Area of Science:

    • Plant Science
    • Computer Vision
    • Agricultural Engineering

    Background:

    • Plant phenotyping is crucial for trait selection and linking genetics to yield.
    • Current phenotyping methods face bottlenecks, especially for architectural traits.
    • Automated, non-destructive 3D modeling can significantly improve phenotyping efficiency.

    Purpose of the Study:

    • To develop an automated active vision pipeline for 3D plant reconstruction.
    • To overcome limitations in current plant phenotyping techniques.
    • To enable non-destructive, non-expert-dependent acquisition of detailed plant architectural data.

    Main Methods:

    • An Active Vision Cell (AVC) with a robot arm and custom software was developed.
    • A novel surface reconstruction algorithm was implemented for 3D model generation.
    • The pipeline automates photometric data acquisition and 3D model recovery.

    Main Results:

    • The pipeline successfully generates accurate and complete 3D plant models.
    • The system is non-intrusive and non-destructive, requiring no botanical expertise.
    • The reconstruction algorithm effectively reduces noise and improves data quality.

    Conclusions:

    • The proposed active vision pipeline offers a robust, flexible, and accurate solution for automated 3D plant reconstruction.
    • This method significantly enhances high-throughput phenotyping capabilities.
    • The framework is extendable and applicable to diverse plant species and forms.