Calibration method to address influences of temperature and electrical conductivity for a low-cost soil water content sensor in the agricultural field

Low-cost, accurate soil water sensors combined with wireless communication in an internet of things (IoT) framework can be harnessed to enhance the benefits of precision irrigation. However, the accuracy of low-cost sensors (e.g., based on resistivity or capacitance) can be affected by many factors, including salinity, temperature, and soil structure. Recent developments in wireless sensor networks offer new possibilities for field-scale monitoring of soil water content (SWC) at high spatiotemporal scales, but to install many sensors in the network, the cost of the sensors must be low, and the mechanism of operation needs to be robust, simple, and consume low energy for the technology to be practically relevant. This study evaluated the performance of a resistivity–capacitance-based wireless sensor (Sensoterra BV, 1018LE Amsterdam, Netherlands) under different salinity levels, temperature, and soil types in a laboratory. The sensors were evaluated in glass beads, Oso Flaco sand, Columbia loam, and Yolo clay loam soils. A nonlinear relationship was exhibited between the sensor measured resistance (Ω) and volumetric soil water content (θ). The Ω–θ relationship differed by soil type and was affected by soil solution salinity. The sensor was extremely sensitive at higher water contents with high uncertainty, and insensitive at low soil water content accompanied by low uncertainty. The soil solution salinity effects on the Ω–θ relationship were found to be reduced from sand to sandy loam to clay loam. In clay soils, surface electrical conductivity (ECs) of soil particles had a more dominant effect on sensor performance compared to the effect of solution electrical conductivity (ECw). The effect of temperature on sensor performance was minimal, but sensor-to-sensor variability was substantial. The relationship between bulk electrical conductivity (ECb) and volumetric soil water content was also characterized in this study. The results of this study reveal that if the sensor is properly calibrated, this low-cost wireless soil water sensor has the potential of improving soil water monitoring for precision irrigation and other applications at high spatiotemporal scales, due to the ease of integration into IoT frameworks.

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Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor

Sensors ◽

10.3390/s19235272 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5272 ◽

Cited By ~ 1

Author(s):

Zemni ◽

Bouksila ◽

Persson ◽

Slama ◽

Berndtsson ◽

...

Keyword(s):

Electrical Conductivity ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Low Cost ◽

Low Frequency ◽

Field Conditions ◽

Soil Electrical Conductivity ◽

Application Range ◽

Laboratory Calibration

Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m3 m−3) as compared to laboratory experiments (RMSE = 0.06 m3 m−3). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K’ reading, failed to improve the Hilhorst model for ECp > 3 dS m−1 for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K0, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

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Temporal stability of electrical conductivity in a sandy soil

International Agrophysics ◽

10.1515/intag-2016-0005 ◽

2016 ◽

Vol 30 (3) ◽

pp. 349-357 ◽

Cited By ~ 12

Author(s):

Aura Pedrera-Parrilla ◽

Eric C. Brevik ◽

Juan V. Giráldez ◽

Karl Vanderlinden

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Precision Agriculture ◽

Temporal Stability ◽

Principal Component ◽

Model Parameters ◽

Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Using Multivariate Geostatistics to Assess Patterns of Spatial Dependence of Apparent Soil Electrical Conductivity and Selected Soil Properties

The Scientific World JOURNAL ◽

10.1155/2014/712403 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Glécio Machado Siqueira ◽

Jorge Dafonte Dafonte ◽

Montserrat Valcárcel Armesto ◽

Ênio Farias França e Silva

Keyword(s):

Electrical Conductivity ◽

Soil Properties ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Clay Content ◽

Data Sets ◽

Soil Electrical Conductivity ◽

Multivariate Geostatistics ◽

Northwestern Spain

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECadata sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECaand gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECaand clay content (ranging from 0.197 to 0.495, when different ECarecording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECadata sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECaas secondary variable with respect to the use of ordinary kriging.

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