Smartphone based app development with machine learning using Hibiscus sabdariffa L. extract for pH estimation

Abstract

This study presents a novel approach for pH estimation in buffer solutions using images of solutions prepared with Hibiscus sabdariffa L. as a natural pH indicator. The images of the solutions, each displaying distinctive colours indicative of their pH levels, were transformed into standardized 200x200-pixel images through the application of image processing techniques. Following this, a pH prediction model was constructed using the Adaptive Boosting regressor algorithm. The pH values of the training data used when training the model were distributed irregularly between 0–14. The models were trained with 94 pictures and 1880 experimental values. In addition, a reliable pre-processing part has been placed into the model using image processing techniques, allowing test data to be obtained in any desired environment. The obtained training and test data were separated from noise parameters, affecting the prediction results negatively. A smartphone application based on the model has been developed and made available to everyone. This innovative methodology bridges the gap between traditional pH measurement techniques and computer vision, offering a more accessible and eco-friendly means of pH assessment. The practical applications of this research extend to various fields, including environmental monitoring, agriculture, and educational settings.