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

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...

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Autores principales:	Jorge Villamil, Jorge Victorino, Francisco Gómez
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	camera sensors computer vision monitoring mobile devices predictive capacity water quality monitoring water turbidity Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/950dc3ab0d9a44ff9de0405ac599d5d1
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spelling	oai:doaj.org-article:950dc3ab0d9a44ff9de0405ac599d5d12021-12-02T07:40:14ZThe effect of mobile camera selection on the capacity to predict water turbidity0273-12231996-973210.2166/wst.2021.238https://doaj.org/article/950dc3ab0d9a44ff9de0405ac599d5d12021-11-01T00:00:00Zhttp://wst.iwaponline.com/content/84/10-11/2749https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732Recently, 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.;Jorge VillamilJorge VictorinoFrancisco GómezIWA Publishingarticlecamera sensorscomputer vision monitoringmobile devicespredictive capacitywater quality monitoringwater turbidityEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 10-11, Pp 2749-2759 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	camera sensors computer vision monitoring mobile devices predictive capacity water quality monitoring water turbidity Environmental technology. Sanitary engineering TD1-1066
spellingShingle	camera sensors computer vision monitoring mobile devices predictive capacity water quality monitoring water turbidity Environmental technology. Sanitary engineering TD1-1066 Jorge Villamil Jorge Victorino Francisco Gómez The effect of mobile camera selection on the capacity to predict water turbidity
description	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.;
format	article
author	Jorge Villamil Jorge Victorino Francisco Gómez
author_facet	Jorge Villamil Jorge Victorino Francisco Gómez
author_sort	Jorge Villamil
title	The effect of mobile camera selection on the capacity to predict water turbidity
title_short	The effect of mobile camera selection on the capacity to predict water turbidity
title_full	The effect of mobile camera selection on the capacity to predict water turbidity
title_fullStr	The effect of mobile camera selection on the capacity to predict water turbidity
title_full_unstemmed	The effect of mobile camera selection on the capacity to predict water turbidity
title_sort	effect of mobile camera selection on the capacity to predict water turbidity
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/950dc3ab0d9a44ff9de0405ac599d5d1
work_keys_str_mv	AT jorgevillamil theeffectofmobilecameraselectiononthecapacitytopredictwaterturbidity AT jorgevictorino theeffectofmobilecameraselectiononthecapacitytopredictwaterturbidity AT franciscogomez theeffectofmobilecameraselectiononthecapacitytopredictwaterturbidity AT jorgevillamil effectofmobilecameraselectiononthecapacitytopredictwaterturbidity AT jorgevictorino effectofmobilecameraselectiononthecapacitytopredictwaterturbidity AT franciscogomez effectofmobilecameraselectiononthecapacitytopredictwaterturbidity
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The effect of mobile camera selection on the capacity to predict water turbidity

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