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A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis.

Ronghao Wang1, Yumou Qiu2, Yuzhen Zhou1

  • 1Department of Statistics, University of Nebraska-Lincoln, Lincoln 68503, USA.

Plant Phenomics (Washington, D.C.)
|December 14, 2020
PubMed
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The "implant" R package integrates plant feature extraction and statistical analysis for high-throughput phenotyping. It enables robust image analysis and functional data analysis for plant growth dynamics.

Area of Science:

  • Plant Science
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput phenotyping systems are increasingly vital in plant science research.
  • Current data analysis separates image processing and statistical analysis across different platforms.
  • This fragmentation hinders efficient analysis of plant growth dynamics.

Purpose of the Study:

  • To develop an integrated R package named "implant" for plant phenotyping data analysis.
  • To provide robust image processing tools for plant feature extraction.
  • To enable advanced functional data analysis for plant growth studies.

Main Methods:

  • The "implant" package utilizes image processing techniques such as thresholding, Hidden Markov Random Field models, and morphological operations.

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  • It offers nonparametric curve fitting with confidence regions for analyzing plant growth trajectories.
  • Functional ANOVA is implemented to assess treatment and genotype effects on growth dynamics.

    • Main Results:

    • The "implant" package successfully integrates feature extraction and functional data analysis within a single R environment.
    • It provides robust methods for extracting key plant features from images.
    • The package facilitates sophisticated statistical analysis of plant growth patterns, including treatment and genotype effects.

    Conclusions:

    • The "implant" R package offers a unified solution for the data analysis pipeline in high-throughput plant phenotyping.
    • This integration streamlines research by combining image processing and advanced statistical modeling.
    • It enhances the ability to study plant growth dynamics and understand genetic and environmental influences.