The effect of mobile camera selection on the capacity to predict water turbidity

Abstract

Recently, cameras of mobile have phones emerged as an alternative for quantifying water turbidity. Most of these studies lack a strategy to determine the water turbidity for new samples, focusing mainly on one particular device. Nevertheless, widespread use of these approaches requires a predictive capacity on out-of-the-sample images acquired in devices of different capabilities. We studied the influence of mobile device camera sensors on the predictive performance of water turbidity for non-previously observed turbid images. For this, a reference database with turbid images acquired for different mobile devices was constructed. A machine learning method based on image quality measures and linear classifiers (least squares and LASSO) was proposed to perform predictions on each mobile device. Relative accuracy and precision were evaluated. Results suggest that these approaches may provide accurate predictions reaching most than 80% of relative accuracy with high test-retest reliability (> 0.99). Nevertheless, our results also indicate that the predictive performance levels dropped in low capacity quality sensors. Therefore, despite the high performance that can be reached using these approaches, widespread use on multiple mobile devices may require further development of low-quality sensors and a better understanding of their operative ranges. HIGHLIGHTS Mobile phone cameras may serve as an alternative for quantification of water turbidity.; We studied mobile cameras’ influence on the predictive performance of water turbidity.; These approaches resulted in high accuracies (>80%) and precisions (>0.99).; Nevertheless, low-quality sensors resulted in low performance.; Widespread use of these approaches requires in low-quality devices.;

Keywords

• camera sensors
• computer vision monitoring
• mobile devices
• predictive capacity
• water quality monitoring
• water turbidity