Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and countershave a major influence on the interior d ´ecor which is essential to set the moodand ambience of a house. A system is needed to help the end users differentiatebetween granites, which enhance the grandeur of their house and also check thefrauds of different color granite being sent by the merchant as compared to whatwas selected by the end user. Several models have been developed for this causeusing CNN and other image processing techniques. However, a solution for thispurpose must be precise and computationally efficient.
Methods: For this purpose,researchers in this work developed a machine learning based granite classifier us-ing Edge Computing and a website to help users in choosing which granite wouldgo well with their d ´ecor is also built. The developed system consists of a colorsensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGBcontrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock intodifferent granite types from a granite dealer and yield the category prediction. Re-sults: The proposed system yields a result of 94% accuracy when classified usingRandom Forest Algorithm.
Conclusion: Thus, this system provides an upper handfor the end users in differentiating between different types of granites.
Keywords: ESP8266; Edge Computing; Granite; Machine Leaning; Random Forest; SVM; TCS3200.
Copyright: © 2022 Karanam M et al.