Journal
SENSORS
Volume 22, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/s22186840
Keywords
open-source hardware; crop productivity; hydro-environmental monitoring; machine-learning calibration
Funding
- Universidad Nacional del Litoral [CAID 50520190100249LI, CAAT 03012020]
- Secretaria d'Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu [PID2019-107910RB-I00, 2017SGR-990]
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This paper describes the design of a datalogger device based on open-source hardware platforms to measure water table levels and soil moisture data. Open-source IoT hardware platforms are emerging as an attractive alternative to commercial instruments, offering flexibility and low cost.
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).
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