Here, I will be writing details about how to approach a new algorithm for image-based color detection. How they will differ from the previously used approaches. How can this method be compared to other methods -- using what evaluation parameters. In general, it will include ideas and approaches in a structured manner.
Some methods to try: linear regression, random forest, SVM, neural network.
But, preprocessing is equally important to reduce variance due to illumination change. (need to explore)
Some steps:
1st start with the image background and foreground separation.
2nd localize the two circular zones with a contour and abstract the color values for RGB channels.

A Simple Linear Regression Using RGB values:

Using the food color dataset (144 rows with dilution factor and RGB values), I am building a simple linear regression model. I am using python sklearn LinearRegression for this purpose.