Soil-specific calibration of capacitance sensors considering clay content and bulk density

Parvin, Nargish; Degré, A.

doi:10.1071/sr15036

Cited by 31 publications

(24 citation statements)

References 23 publications

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“…Therefore, any soil property that may vary at this spatial scale would cause the immediate vicinity of a particular sensor to depart from the idealized properties considered in simulations. For instance, sensors would be sensitive to the presence of macropores and stones [ 64 ], microvariations in soil bulk density [ 65 ], uneven distribution of roots [ 66 ], uneven temperature due to contrasts between shaded and sunlit soil surface [ 67 ], etc. Moreover, there may be additional interaction between these factors.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Domínguez-Niño

Oliver-Manera

Arbat

et al. 2020

Sensors

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Among the diverse techniques for monitoring soil moisture, capacitance-type soil moisture sensors are popular because of their low cost, low maintenance requirements, and acceptable performance. However, although in laboratory conditions the accuracy of these sensors is good, when installed in the field they tend to show large sensor-to-sensor differences, especially under drip irrigation. It makes difficult to decide in which positions the sensors are installed and the interpretation of the recorded data. The aim of this paper is to study the variability involved in the measurement of soil moisture by capacitance sensors in a drip-irrigated orchard and, using this information, find ways to optimize their usage to manage irrigation. For this purpose, the study examines the uncertainties in the measurement process plus the natural variability in the actual soil water dynamics. Measurements were collected by 57 sensors, located at 10 combinations of depth and position relative to the dripper Our results showed large sensor-to-sensor differences, even when installed at equivalent depth and coordinates relative to the drippers. In contrast, differences among virtual sensors simulated using a HYDRUS-3D model at those soil locations were one order of magnitude smaller. Our results highlight, as a possible cause for the sensor-to-sensor differences in the measurements by capacitance sensors, the natural variability in size, shape, and centering of the wet area below the drippers, combined with the sharply defined variation in water content at the soil scale perceived by the sensors.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Domínguez-Niño

Oliver-Manera

Arbat

et al. 2020

Sensors

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“…Many researchers have focused on the calibration of low-cost sensors which are used in different sensing techniques, such as capacitance-based sensors [9,10], resistivity-based granular matrix sensors [9,11], and sensors based on a tensiometer technique [9], to measure the soil water content in fields [2,[12][13][14] and to develop low-cost automated irrigation systems [15]. Some studies have considered the effects of the physical and chemical properties of soil on the performances of soil moisture sensors [4,13,14,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Calibration and Validation of a Low-Cost Capacitive Moisture Sensor to Integrate the Automated Soil Moisture Monitoring System

Nagahage

Fujino

2019

Agriculture

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Readily available moisture in the root zone is very important for optimum plant growth. The available techniques to determine soil moisture content have practical limitations owing to their high cost, dependence on labor, and time consumption. We have developed a prototype for automated soil moisture monitoring using a low-cost capacitive soil moisture sensor (SKU:SEN0193) for data acquisition, connected to the internet. A soil-specific calibration was performed to integrate the sensor with the automated soil moisture monitoring system. The accuracy of the soil moisture measurements was compared with those of a gravimetric method and a well-established soil moisture sensor (SM-200, Delta-T Devices Ltd, Cambridge, UK). The root-mean-square error (RMSE) of the soil water contents obtained with the SKU:SEN0193 sensor function, the SM-200 manufacturer’s function, and the SM-200 soil-specific calibration function were 0.09, 0.07, and 0.06 cm3 cm−3, for samples in the dry to saturated range, and 0.05, 0.08, and 0.03 cm3 cm−3, for samples in the field capacity range. The repeatability of the measurements recorded with the developed calibration function support the potential use of the SKU:SEN0193 sensor to minimize the risk of soil moisture stress or excess water application.

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“…Despite the increased use of the soil water content monitoring (SWCM) sensors, however, several studies have shown that the accuracy and precision of these sensors are affected by soil physical properties, e.g., bulk density, compaction, porosity, and temperature [ 4 , 8 , 11 , 12 , 13 , 14 ] and media chemical properties, e.g., electrical conductivity and salinity [ 13 , 15 , 16 , 17 ]. Sensor precision does not always guarantee accuracy; sensor reading could be precise but at the same time inaccurate when sensor readings deviate from the actual values.…”

Section: Introductionmentioning

confidence: 99%

Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions

Fares

Awal

Bayabil

2016

Sensors

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Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions.

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Soil-specific calibration of capacitance sensors considering clay content and bulk density

Cited by 31 publications

References 23 publications

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Analysis of the Variability in Soil Moisture Measurements by Capacitance Sensors in a Drip-Irrigated Orchard

Calibration and Validation of a Low-Cost Capacitive Moisture Sensor to Integrate the Automated Soil Moisture Monitoring System

Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions

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