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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/22/7637 |
| _version_ | 1797508470080012288 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:05:21Z |
| format | Article |
| id | doaj.art-c44a53602275414cada2ab422feba60e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:05:21Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c44a53602275414cada2ab422feba60e2023-11-23T01:27:07ZengMDPI AGSensors1424-82202021-11-012122763710.3390/s21227637Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation ReservoirsJavier Rocher0Lorena Parra1Jose M. Jimenez2Jaime Lloret3Daniel A. Basterrechea4Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Grao de Gandía, 46730 Valencia, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Grao de Gandía, 46730 Valencia, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Grao de Gandía, 46730 Valencia, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Grao de Gandía, 46730 Valencia, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Grao de Gandía, 46730 Valencia, SpainIn 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.https://www.mdpi.com/1424-8220/21/22/7637turbiditysedimentalgalight absorptionwater qualityirrigation channel |
| spellingShingle | 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 Sensors turbidity sediment alga light absorption water quality irrigation channel |
| title | 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_short | 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 |
| topic | turbidity sediment alga light absorption water quality irrigation channel |
| url | https://www.mdpi.com/1424-8220/21/22/7637 |
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