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Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation
Martina Zorić1, Sandra Cvejić2, Emina Mladenović3
1Institute of Lowland Forestry and Environment, University of Novi Sad, Novi Sad, Serbia.
Frontiers in Plant Science
|November 26, 2020
Summary
A new software, Flower Color Image Analysis (FloCIA), objectively classifies sunflower ray floret color, improving upon subjective human evaluations. This digital UPOV (dUPOV) method enhances accuracy and repeatability in sunflower genotype development.
Area of Science:
- Plant breeding and genetics
- Agricultural technology
- Color science
Background:
- Sunflower ray floret color is a key esthetic trait influencing genotype development and market value.
- Current International Union for the Protection of New Varieties of Plants (UPOV) sunflower evaluation methods are subjective and require expert evaluators.
- Human color perception can be inconsistent, impacting the reliability of traditional assessment.
Purpose of the Study:
- To develop and validate a novel, objective methodology for sunflower ray floret color classification.
- To address the subjectivity and expertise limitations of existing UPOV sunflower evaluation guidelines.
- To create a software tool that aids in accurate and repeatable color assessment for sunflower breeding.
Main Methods:
- Evaluation of six commercial sunflower genotypes by 100 agriculture experts using UPOV guidelines.
- Development of Flower Color Image Analysis (FloCIA) software for digital image segmentation and classification.
- Comparison of FloCIA software's automated classification accuracy against expert evaluations on 153 F2 genotype images.
Main Results:
- The developed Flower Color Image Analysis (FloCIA) software achieved 91.50% accuracy in unsupervised classification of sunflower ray floret color.
- Precision in color determination was higher between digital UPOV (dUPOV) expert evaluation and software evaluation than between two UPOV-based expert evaluations.
- FloCIA provides visualizations of image segmentation, dominant color clusters, and color space representation (CIE L*a*b*).
Conclusions:
- The digital UPOV (dUPOV) methodology using FloCIA software offers a more objective and repeatable alternative to traditional sunflower ray floret color evaluation.
- FloCIA software assists evaluators in determining dominant colors and identifying multiple significant colors in sunflower genotypes.
- This advancement supports the development of new sunflower varieties with improved and consistent market-valued traits.
