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A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues.

Peter Andrew McAtee¹, Simona Nardozza¹, Annette Richardson²

¹The New Zealand Institute for Plant & Food Research (PFR), Auckland, New Zealand.

Frontiers in Plant Science

|February 14, 2022

Summary

This summary is machine-generated.

A new data-driven method quantifies complex fruit color patterns using perceptually unique colors (PUC). This approach significantly reduces data while preserving color complexity, enabling accurate classification and differentiation of biological images.

Keywords:

colour analysis computer vision fruit growing plant quantification of colour region-growing algorithm

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

Area of Science:

Agricultural science
Computer vision
Color science

Background:

Quantifying fruit color is crucial for plant breeding, postharvest, and consumer quality assessment.
Existing digital color quantification methods struggle with complex patterns, often averaging colors and generating synthetic values.
A need exists for data-driven methodologies to accurately assess color patterning in biological images.

Purpose of the Study:

To present a novel methodology for acquiring and processing digital images of biological samples with complex color gradients.
To develop a data-driven approach for color quantification that avoids synthetic value generation.
To enable accurate classification and differentiation of biological samples based on color patterns.

Main Methods:

Utilized the CIE (Commission Internationale de l'Eclairage/International Commission on Illumination) ΔE2000 formula to identify perceptually unique colors (PUC) in complex fruit images.
Applied a weighted ΔE2000 distance metric for clustering summarized color data.
Developed a procedure for data reduction, achieving an average 98% decrease in unique color values while maintaining a linear relationship with original color complexity.

Main Results:

The methodology successfully summarized complex color data, reducing unique colors by an average of 98%.
Clustering of summarized color data grouped images into their respective binomial families.
The approach demonstrated the ability to detect subtle color differences and differentiate closely related images.

Conclusions:

The presented data-driven methodology offers an accurate and efficient way to quantify complex color patterns in biological samples.
This approach overcomes limitations of existing methods by avoiding synthetic color value generation.
The developed technique and provided image dataset can serve as a benchmark for future colorimetric research and method development.