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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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From image processing to computer vision: plant imaging grows up.

Hannah Dee¹, Andrew French²

¹Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK.

Functional Plant Biology : FPB

|June 3, 2020

Summary

Image analysis advances plant science by automating image capture and analysis. This bridges the genotype-phenotype gap, enabling objective phenotyping for large datasets.

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Area of Science:

Plant Science
Computer Vision
Genetics

Background:

The genotype-phenotype gap limits understanding of plant traits.
Automated image capture and analysis are increasingly vital in plant research.
Objective, accurate phenotyping of large datasets is a growing need.

Purpose of the Study:

To present recent advances in plant image analysis.
To showcase applications of image processing and computer vision in plant sciences.
To highlight the importance of phenotyping in modern plant research.

Main Methods:

Automated image capture techniques.
Advanced image processing algorithms.
Computer vision methodologies for biological data.

Main Results:

Demonstration of novel image analysis techniques.
Examples of objective and accurate phenotyping from large image datasets.
Increased focus on phenotype in plant science research.

Conclusions:

Image analysis is crucial for bridging the genotype-phenotype gap.
Automated methods enhance the objectivity and accuracy of plant phenotyping.
Computer vision is becoming indispensable in plant science research.