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  • 1Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany.

Plant Methods
|September 10, 2019
PubMed
Summary
This summary is machine-generated.

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3D sensing offers advanced plant phenotyping by measuring traits like size and volume. This review details 3D techniques, data processing, and applications in plant research, including drought response and genetic analysis.

Area of Science:

  • * Plant Science and Agricultural Technology
  • * Computational Biology and Data Science

Background:

  • * 3D sensing technologies have become increasingly important for plant phenotyping.
  • * Traditional methods are being complemented by advanced 3D measurement techniques for greater accuracy and detail.

Purpose of the Study:

  • * To provide a comprehensive overview of 3D traits, measurement techniques, and biological applications in plant phenotyping.
  • * To compare the accuracy of high-resolution 3D devices with traditional tools like leaf meters.
  • * To outline processing workflows for deriving plant parameters from 3D data.

Main Methods:

  • * Review of various 3D measuring techniques: laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning, and structured light.
  • * Comparison of measurement accuracy between a high-resolution 3D device and a leaf meter.
Keywords:
3D plant scanningParameterizationPlant modelPlant traitsPoint cloud

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  • * Development of a generalized 3D point cloud processing workflow.
  • * Introduction of sensor fusion (3D and spectral data) for multi-dimensional plant modeling.

    • Main Results:

    • * Diverse plant traits (leaf width/length, plant size, volume, development) can be assessed using different 3D techniques.
    • * Applications include trait and growth analysis, water budget studies, drought response evaluation, and quantitative trait loci (QTL) analysis.
    • * Processing pipelines enable trait derivation at plant and organ levels, including time-series analysis and machine learning approaches.
    • * Sensor fusion enhances plant modeling by accounting for geometric influences on radiometric measurements.

    Conclusions:

    • * 3D sensing is a powerful tool for detailed plant phenotyping across various scales, from organs to fields.
    • * Future research should focus on high-resolution 3D data acquisition at field scales and automated organ trait extraction.
    • * Integrating 3D data with other sensor types (e.g., spectral) is crucial for comprehensive, multi-dimensional plant modeling.