Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter


  • Shahad A. Mnati Department of Mechatronics Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad, Baghdad/ Iraq
  • Furat I. Hussein Department of Mechatronics Engineering/ Al-Khwarizmi College of Engineering/ University of Baghdad, Baghdad/ Iraq
  • Ahmed Issa Department, Faculty of Engineering and Information Technology/Mechatronics and Medical Devices Engineering Al- Azhar University – Gaza/ Gaza 1277/ Palestine




Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under detection is one of the results of the proposed classifier. The work demanded the collection of about 5000 color codes which in turn were subjected to algorithms for training and testing. The open-source platform TensorFlow for ML and the open-source neural network library Keras were used to construct the algorithm for the study. The results showed an acceptable efficiency of the built classifier represented by an accuracy of 90% which can be considered applicable, especially after some improvements in the future to makes it more effective as a trusted colorimeter.



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How to Cite

Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter. (2023). Al-Khwarizmi Engineering Journal, 19(4), 67-77.