Sensor Based Classification and Evaluation Methods using Machine Learning Algorithm for the Evaluation of Indian Traditional Medicine (Siddha)

The present work analyses sensor based classification and evaluation methods for the evaluation of churna. The churna is a powdered form of Siddha medicine. The churna is evaluated based on organoleptic and physicochemical parameters. The organoleptic parameters such as color and physicochemical parameters such as moisture content value and pH value are analysed in this work. The proposed methodology facilitates the development and integration of hardware and software modules for churna identification and classification. The proposed hardware setup comprises Raspberry pi camera, color sensor, moisture sensor and pH sensor interfaced with raspberry pi 3b.  Churnas are discriminated by classifying the color values using machine learning algorithms such as the Support Vector Machine (SVM) and Random Forest (RF) classifiers separately. The experimental results depict that the performance of the RF Classifier excels the SVM Classifier in churna name identification with greater accuracy, sensitivity and specificity.