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

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

Published on: March 28, 2025

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

Malia A Gehan¹, Noah Fahlgren¹, Arash Abbasi¹

¹Donald Danforth Plant Science Center, St. Louis, MO, United States of America.

|December 7, 2017

Summary

The Plant Computer Vision (PlantCV) software package offers advanced, open-source tools for non-destructive plant phenotyping. This release enhances image analysis with new features for improved plant trait measurement and machine learning applications.

Keywords:

Computer vision Image analysis Machine learning Morphometrics Plant phenotyping

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Area of Science:

Plant science
Computer vision
Bioinformatics

Background:

Non-destructive plant phenotyping requires advanced computational tools.
Existing tools may lack flexibility for diverse plant imaging challenges.
The Plant Computer Vision (PlantCV) software package was developed to address these needs.

Purpose of the Study:

To present major developments in the second release of the PlantCV software package.
To introduce new functionalities for enhanced plant image analysis.
To foster a community-driven, open-source approach to plant phenotyping tools.

Main Methods:

Development of modular and reusable image processing tools.
Implementation of new image processing and normalization techniques.
Integration of machine learning modules for advanced analysis.

Main Results:

PlantCV v2 includes improved project organization and new functionalities.
New tools support multi-plant analysis, leaf segmentation, and morphometric landmark identification.
Enhanced capabilities for non-destructive plant phenotyping using computer vision.

Conclusions:

The latest release of PlantCV provides a more robust and versatile toolkit for plant image analysis.
These advancements facilitate higher-resolution, non-destructive measurement of plant phenotypes.
The open-source, community-driven nature of PlantCV promotes wider adoption and development.