ColourQuant: A High-Throughput Technique to Extract and Quantify Color Phenotypes from Plant Images

Mao Li; Margaret H Frank; Zoë Migicovsky

doi:10.1007/978-1-0716-2537-8_9

ColourQuant: A High-Throughput Technique to Extract and Quantify Color Phenotypes from Plant Images

Methods Mol Biol. 2022:2539:77-85. doi: 10.1007/978-1-0716-2537-8_9.

Authors

Mao Li¹, Margaret H Frank², Zoë Migicovsky³

Affiliations

¹ Donald Danforth Plant Science Center, St. Louis, MO, USA. mli@danforthcenter.org.
² Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA. mhf47@cornell.edu.
³ Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS, Canada.

PMID: 35895198
DOI: 10.1007/978-1-0716-2537-8_9

Abstract

Color patterning contributes to important plant traits that influence ecological interactions, horticultural breeding, and agricultural performance. High-throughput phenotyping of color is valuable for understanding plant biology and selecting for traits related to color during plant breeding. Here we present ColourQuant, an automated high-throughput pipeline that allows users to extract color phenotypes from images. This pipeline includes methods for color phenotyping using mean pixel values, a Gaussian density estimator of CIELAB color, and the analysis of shape-independent color patterning by circular deformation.

Keywords: Color patterning; Color phenotyping; Continuous color distribution; High-throughput image acquisition; Shape-independent color quantification.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't

MeSH terms

Agriculture
Phenotype
Plant Breeding*
Plants* / genetics