Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs

In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the...

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Autores principales:	Javier Rocher, Lorena Parra, Jose M. Jimenez, Jaime Lloret, Daniel A. Basterrechea
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	turbidity sediment alga light absorption water quality irrigation channel Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/c44a53602275414cada2ab422feba60e
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spelling	oai:doaj.org-article:c44a53602275414cada2ab422feba60e2021-11-25T18:58:06ZDevelopment of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs10.3390/s212276371424-8220https://doaj.org/article/c44a53602275414cada2ab422feba60e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7637https://doaj.org/toc/1424-8220In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the concentration of algae in irrigation ponds. The sensor is composed of 5 LEDs with different wavelengths and light-dependent resistances as photoreceptors. Data are gathered for the calibration of the prototype, including two turbidity sources, sediment and algae, including pure samples and mixed samples. Samples were measured at a different concentration from 15 mg/L to 4000 mg/L. Multiple regression models and artificial neural networks, with a training and validation phase, are compared as two alternative methods to classify the tested samples. Our results indicate that using multiple regression models, it is possible to estimate the concentration of alga with an average absolute error of 32.0 mg/L and an average relative error of 11.0%. On the other hand, it is possible to classify up to 100% of the samples in the validation phase with the artificial neural network. Thus, a novel prototype capable of distinguishing turbidity sources and two classification methodologies, which can be adapted to different node features, are proposed for the operation of the developed prototype.Javier RocherLorena ParraJose M. JimenezJaime LloretDaniel A. BasterrecheaMDPI AGarticleturbiditysedimentalgalight absorptionwater qualityirrigation channelChemical technologyTP1-1185ENSensors, Vol 21, Iss 7637, p 7637 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	turbidity sediment alga light absorption water quality irrigation channel Chemical technology TP1-1185
spellingShingle	turbidity sediment alga light absorption water quality irrigation channel Chemical technology TP1-1185 Javier Rocher Lorena Parra Jose M. Jimenez Jaime Lloret Daniel A. Basterrechea Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
description	In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the concentration of algae in irrigation ponds. The sensor is composed of 5 LEDs with different wavelengths and light-dependent resistances as photoreceptors. Data are gathered for the calibration of the prototype, including two turbidity sources, sediment and algae, including pure samples and mixed samples. Samples were measured at a different concentration from 15 mg/L to 4000 mg/L. Multiple regression models and artificial neural networks, with a training and validation phase, are compared as two alternative methods to classify the tested samples. Our results indicate that using multiple regression models, it is possible to estimate the concentration of alga with an average absolute error of 32.0 mg/L and an average relative error of 11.0%. On the other hand, it is possible to classify up to 100% of the samples in the validation phase with the artificial neural network. Thus, a novel prototype capable of distinguishing turbidity sources and two classification methodologies, which can be adapted to different node features, are proposed for the operation of the developed prototype.
format	article
author	Javier Rocher Lorena Parra Jose M. Jimenez Jaime Lloret Daniel A. Basterrechea
author_facet	Javier Rocher Lorena Parra Jose M. Jimenez Jaime Lloret Daniel A. Basterrechea
author_sort	Javier Rocher
title	Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
title_short	Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
title_full	Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
title_fullStr	Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
title_full_unstemmed	Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs
title_sort	development of a low-cost optical sensor to detect eutrophication in irrigation reservoirs
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c44a53602275414cada2ab422feba60e
work_keys_str_mv	AT javierrocher developmentofalowcostopticalsensortodetecteutrophicationinirrigationreservoirs AT lorenaparra developmentofalowcostopticalsensortodetecteutrophicationinirrigationreservoirs AT josemjimenez developmentofalowcostopticalsensortodetecteutrophicationinirrigationreservoirs AT jaimelloret developmentofalowcostopticalsensortodetecteutrophicationinirrigationreservoirs AT danielabasterrechea developmentofalowcostopticalsensortodetecteutrophicationinirrigationreservoirs
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Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs

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