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Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis.

John Stamford1, Seher Bahar Aciksoz2, Tracy Lawson3

  • 1School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, UK.

Methods in Molecular Biology (Clifton, N.J.)
|April 22, 2024
PubMed
Summary
This summary is machine-generated.

This study details hyperspectral imaging (HSI) setup and data collection for noninvasive plant trait measurement. It provides practical code examples for developing predictive models from HSI data, making advanced techniques accessible to beginners.

Keywords:
HyperspectralMachine learningModelingPLSPartial least squaresPhenotypingTraits

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Area of Science:

  • Plant Science
  • Remote Sensing
  • Spectroscopy

Background:

  • Hyperspectral imaging (HSI) offers a non-invasive method for assessing plant traits.
  • Accurate data acquisition and calibration are crucial for reliable HSI analysis.
  • Developing predictive models from HSI data can enhance our understanding of plant physiology.

Purpose of the Study:

  • To provide a comprehensive guide on hyperspectral imaging procedures for plant trait analysis.
  • To demonstrate the development of predictive models using partial least-squares regression.
  • To offer practical, implementable code examples for beginners in the field.

Main Methods:

  • Detailed protocols for camera setup, scanning, and calibration in hyperspectral imaging.
  • Acquisition of essential black and white reference materials for image correction.
  • Application of partial least-squares regression for quantitative trait prediction from spectral data.

Main Results:

  • Established procedures for acquiring high-quality hyperspectral imagery of plants.
  • Demonstrated the efficacy of partial least-squares regression in predicting plant traits using both small and large datasets.
  • Provided functional code examples for immediate application of HSI techniques.

Conclusions:

  • Hyperspectral imaging is a powerful tool for non-invasive plant trait measurement.
  • The outlined methods and provided code facilitate the practical implementation of HSI for researchers.
  • This work lowers the barrier to entry for beginners utilizing hyperspectral data analysis.