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

Area of Science:

• Plant biology
• Agricultural science
• Horticultural science

Background:

• Plant color patterning influences ecological interactions, horticultural breeding, and agricultural performance.
• High-throughput phenotyping of color is vital for understanding plant biology and trait selection in breeding programs.

Purpose of the Study:

• To present ColourQuant, an automated high-throughput pipeline for extracting plant color phenotypes from images.
• To provide researchers with a tool for efficient and accurate color trait analysis.

Main Methods:

• Utilizes mean pixel values for color phenotyping.
• Employs a Gaussian density estimator for CIELAB color analysis.
• Incorporates circular deformation for shape-independent color patterning analysis.

Main Results:

• ColourQuant enables automated extraction of detailed color phenotypes.
• The pipeline offers robust analysis of color traits from digital images.
• Shape-independent analysis of color patterns is achieved through circular deformation.

Conclusions:

• ColourQuant is an effective tool for high-throughput color phenotyping in plants.
• This pipeline facilitates advancements in plant biology research and breeding.
• Automated color analysis supports selection for desirable plant traits.