A field-deployable water quality monitoring with machine learning-based smartphone colorimetry

Abstract

Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.