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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field

Joshua Chopin1, Pankaj Kumar1, Stanley J Miklavcic1

  • 1Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, 5095 Australia.

Plant Methods
|June 1, 2018
PubMed
Summary

This study introduces a color correction method using a standard color checker to accurately measure plant color in field images despite changing light conditions. This improves the reliability of phenotyping for plant traits.

Keywords:
ColourNDVIPhenomicsWheat

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Area of Science:

  • Agricultural Science
  • Plant Science
  • Computer Vision

Background:

  • Illumination variability is a major challenge in image-based field phenotyping.
  • Changing light conditions can obscure true plant color changes, impacting trait measurement.
  • Accurate color measurement is crucial for automated phenotyping of plant traits.

Purpose of the Study:

  • To develop and validate a robust method for correcting color variations in field images caused by fluctuating illumination.
  • To ensure reliable and consistent measurement of plant color as a phenotypic trait over time and across different imaging sessions.

Main Methods:

  • Utilized an industry-standard color checker present in all images as a ground truth.
  • Employed a least squares approach to fit a quadratic model for RGB value correction.
  • Applied the correction method to images from a four-month field trial.

Main Results:

  • Successfully reduced the error between observed and reference color checker tile values.
  • Significantly decreased the standard deviation of mean canopy color across multiple days post-correction.
  • Demonstrated the method's effectiveness in improving the accuracy of phenotypic trait analysis.

Conclusions:

  • The proposed color correction technique effectively addresses illumination variability in field phenotyping.
  • Accurate color measurements enhance the analysis of plant phenotypic traits and variation.
  • This method provides a reliable foundation for automated plant research using image analysis.