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A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields
The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500614/ https://www.ncbi.nlm.nih.gov/pubmed/36146185 http://dx.doi.org/10.3390/s22186840 |
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author | López, Emiliano Vionnet, Carlos Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Contini, Guillermo Prodolliet, Jorge Maiztegui, José |
author_facet | López, Emiliano Vionnet, Carlos Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Contini, Guillermo Prodolliet, Jorge Maiztegui, José |
author_sort | López, Emiliano |
collection | PubMed |
description | The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193). |
format | Online Article Text |
id | pubmed-9500614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95006142022-09-24 A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields López, Emiliano Vionnet, Carlos Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Contini, Guillermo Prodolliet, Jorge Maiztegui, José Sensors (Basel) Article The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193). MDPI 2022-09-09 /pmc/articles/PMC9500614/ /pubmed/36146185 http://dx.doi.org/10.3390/s22186840 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article López, Emiliano Vionnet, Carlos Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Contini, Guillermo Prodolliet, Jorge Maiztegui, José A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title | A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title_full | A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title_fullStr | A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title_full_unstemmed | A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title_short | A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields |
title_sort | low-power iot device for measuring water table levels and soil moisture to ease increased crop yields |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500614/ https://www.ncbi.nlm.nih.gov/pubmed/36146185 http://dx.doi.org/10.3390/s22186840 |
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