On the accuracy of factory-calibrated low-cost soil water content sensors
Domínguez-Niño, Jesús María
Bogena, Heye Reemt
Huisman, Johan Alexander
Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach.
631/635 - Gestió de les explotacions agrícoles
Is part of
Domínguez-Niño, Jesús María, Heye Reemt Bogena, Johan Alexander Huisman, Bernd Schilling, and Jaume Casadesús. 2019. "On The Accuracy Of Factory-Calibrated Low-Cost Soil Water Content Sensors". Sensors 19 (14): 3101. MDPI AG. doi:10.3390/s19143101.
Grant agreement number
MINECO/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RTA2013-00045-C04-01/ES/Incorporación de inteligencia artificial en los algoritmos de reprogramación del riego localizado/
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