ImaginR

ImaginR standardizes color phenotype quantification for inner shell and pearl coloration in Pinctada margaritifera using HSV (Hue, Saturation, Value) color-space analysis and machine-learning classification.

Key Features:

• Objective Color Quantification: Converts imagery to the HSV (Hue, Saturation, Value) color space to produce standardized, quantitative measures of inner shell color phenotypes.
• Machine Learning Integration: Implements a machine-learning protocol that uses a training dataset to classify individual oysters and pearls into predefined human-based phenotype categories.
• Experimental Validation: Was tested on samples from an experiment assessing donor conditioning depth and detected donor shell color modifications associated with environmental plasticity and subsequent pearl coloration after grafting.
• Industry Standardization Potential: Provides reproducible quantitative color data applicable to standardizing color grading practices in the pearl industry.
• Broader Research Applications: Applies the same HSV-based quantification and classification approach to other biological models where objective color phenotyping is required.

Scientific Applications:

• Pearl industry grading: Standardizes color grading of pearls and inner shells to reduce grader subjectivity and enable consistent phenotype assignment.
• Environmental and genetic studies: Quantifies color variation to investigate genetic and environmental influences, including effects of donor conditioning depth, on phenotypic expression.
• Comparative color phenotyping: Applies objective HSV-based measures and classification to other organisms and studies where color is a measurable phenotype.

Methodology:

Image color data are converted to HSV (Hue, Saturation, Value) space and processed with a machine-learning classification protocol that uses a labeled training dataset to assign samples to predefined phenotype categories.