# Laboratory Calibration and Performance Evaluation of Low-Cost Capacitive and Very Low-Cost Resistive Soil Moisture Sensors

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## Abstract

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## 1. Introduction

- What is the ability of the capacitive sensors to estimate the refractive index (${\u03f5}_{r}$) of various fluids of known ${\u03f5}_{r}$ values?
- What empirical equation(s) can best explain the relationships between the output of the low- and very low-cost soil moisture sensor instruments tested in the study, and the actual $VWC$, across a variety of soils?
- What is the difference between the respective accuracies of the soil-specific calibration equations developed in-house and the general manufacturer-provided calibration equations?
- What is the accuracy and precision performance of different low- and very low-cost soil moisture sensor instruments tested?
- How is the accuracy and precision of the developed calibration curves affected by variations in (i) temperature and (ii) electrical conductivity, within ranges that are commonly encountered in field conditions?

## 2. Materials and Methods

#### 2.1. Soil Moisture Sensors

#### 2.1.1. Capacitance Based Low-Cost Sensors: Spectrum SM100 and SMEC300

^{−1}, and an operating range of 0.5 °C to 80 °C [36]. The SMEC300 has reported accuracies of $3\%$ for VWC, $\pm 1$ mS·m

^{−1}for EC and 0.6 °C (0.8 °C) for temperatures greater than −30 °C (lesser than −30 °C), and has ranges of operations of 0–1000 mS·m

^{−1}for EC and −50 °C to 85 °C for temperature [37].

#### 2.1.2. Generic Resistance Based Very Low-Cost Sensors: YL100 and YL69

#### 2.1.3. Impedance-Based Sensor: Delta-T ThetaProbe ML3

^{−1}and 0–50% VWC, and soil temperature accuracy of $\pm 0.5$ °C over 0 °C to 40 °C [44]. It is considered to provide a sensitive and precise measurement of VWC and soil temperature [44], and is accepted for surface soil water content measurements [24]. Therefore, it could be justified to be used as a secondary standard [45] for the different experiments conducted in this study.

#### 2.2. Description of the Soils Used

#### 2.3. Sensor Calibration

#### 2.3.1. Calibration of Capacitive Sensors with Fluids

#### 2.3.2. Calibration of Sensors with Repacked Soils

#### 2.4. Performance Measures for the Sensors

#### 2.4.1. Sensor Accuracy

#### 2.4.2. Sensor Precision

#### 2.5. Sensor Sensitivity

#### 2.5.1. Temperature Sensitivity

#### 2.5.2. Salinity Sensitivity

## 3. Results and Discussion

#### 3.1. Sensor Calibration

#### 3.1.1. Performance of Capacitive Sensors with Fluids

#### 3.1.2. Calibration of All Sensors with Repacked Soils

#### Strength of Monotonic Relationship Between Measured ($\widehat{\theta}$) and Actual ($\theta $) VWC

#### Calibration Equations Developed between Measured ($\widehat{\theta}$) and Actual ($\theta $) VWC

#### 3.1.3. Comparison of Manufacturer and In-House Calibration Equations: Capacitive Sensors

#### 3.2. Performance Measures for the Sensors

#### 3.2.1. Sensor Accuracy

#### 3.2.2. Sensor Precision

#### 3.3. Sensor Sensitivity

#### 3.3.1. Temperature Sensitivity

#### 3.3.2. Salinity Sensitivity

#### 3.4. Further Discussion

## 4. Summary and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

A/D | Analog-to-Digital value |

EC | Electrical Conductivity |

EM | Electromagnetic |

FDR | Frequency Domain Reflectometry |

IS | Indian Standard |

LS | Least Squares (estimate) |

MAE | Mean Absolute Error |

RAE | Relative Absolute Error |

RMSE | Root Mean Square Error |

SD | Standard Deviation |

SSR | Sum of Squared Residuals |

TDR | Time Domain Reflectometry |

VWC | Volumetric Water Content |

WSN | Wireless Sensor Network |

## Appendix A

Publication | Sensor Name (Company Name) | Sensor Type | Soils Used | Calibration Curve Details |
---|---|---|---|---|

Paltineanu and Starr (1997) [55] | Multisensor Capacitance probe: MCAP (Enviroscan) | Capacitance sensor | Mattaplex silt loam (fine-silty, mixed, mesic, Aquic Hapludult) | Scaled frequency |

Baumhardt et al. (2000) [69] | Multisensor Capacitance probe: MCAP (Enviroscan) | Capacitance sensor | 2 soil materials: Surface and calcic horizons of an Olton soil | Scaled frequency |

Czarnomski et al. (2005) [35] | ECH2O (Decagon), CT 1502C (Tektronix Inc.), WCR CS615 Campbell Scientific) | Capacitance sensors | Alluvial soils of volcanic origin (sandy loam to sandy clay loam) | Linear (for capacitance sensor) |

Sakaki et al. (2008) [70] | ECH2O (Decagon) | Capacitance sensor | 4 sands | Linear, quadratic, 2-point alpha mixing model |

Kargas and Soulis (2012) [2] | 10HS (Decagon Devices) | Capacitance sensor | Liquids and porous media of known dielectric permittivity | 2-point calibration equation |

Matula et al. (2016) [24] | ThetaProbe ML2x (Delta-T Devices Ltd.), ECH2O EC10 (Decagon), ECH2O EC 20 (Decagon), ECH2O EC5 (Decagon), ECH2O TE (Decagon) | Impedance sensors, FDR sensors | Silica sand and loess | Comparison between manufacturer and developed linear calibration equations |

Kargas and Soulis (2019) [49] | CS655 (Campbell Scientific) | Water Content Reflectometer | Liquids of known dielectric permittivity and 10 soils (sand, sandy-loam, sandy-clay-loam, loam, clay-loam, clay) | 2-point, multi-point calibration equations; calibration equation for non-conductive soils using Kelleners’ method [71] |

González-Teruel et al. (2019) [33] | Self-developed soil moisture sensor with SDI-12 communication | Capacitance based | 3 soils (clay-loams and sand) | Exponential equations |

Category | Relevant publications |
---|---|

Sensor accuracy | Czarnomski et al. (2005) [35], Kargas and Soulis (2012) [2], González-Teruel et al. (2019) [33] |

Sensor precision | Czarnomski et al. (2005) [35] |

Sensor-to-sensor variability | Sakaki et al. (2008) [70], Rosenbaum et al. (2010) [72], Kargas and Soulis (2012) [2], Bogena et al. (2017) [3], González-Teruel et al. (2019) [33] |

Temperature effects | Paltineanu and Starr (1997) [55], Baumhardt et al. (2000) [69], Czarnomski (2005) [35], Chanzy (2012) [58], Kargas and Soulis (2012) [2], Fares et al. (2016) [73], Bello et al. (2019) [56], Szypłowska et al. (2019) [57], Zhu et al. (2019) [74] |

Salinity effects | Baumhardt et al. (2000) [69], Kargas and Soulis (2012) [2], Matula et al. (2016) [24], Kargas and Soulis (2019) [49] |

Volume of influence/sensitivity | Paltineanu and Starr (1997) [55], Sakaki et al. (2008) [70], Sun et al. (2012) [75] |

**Table A3.**Coefficients of the calibration equations for repacked soil samples, of the form indicated in Equation (1). The segment limits indicate the $[lower,upper]$ limits of the fitted piecewise linear segments.

Sensor Name | Soil Type | Equation Characteristics | Segment 1 | Segment 2 |
---|---|---|---|---|

SMEC300 | Soil-1 | Segment limits | [1135, 1280) | [1280, 1792) |

Slope (${\beta}_{1}$) | 0.13 | 0.03 | ||

Intercept (${\beta}_{0}$) | −152.65 | −23.21 | ||

Soil-2 | Segment limits | [1200, 1451) | 1451, 1707) | |

Slope (${\beta}_{1}$) | 0.07 | 0.04 | ||

Intercept (${\beta}_{0}$) | −85.91 | −34.23 | ||

Soil-3 | Segment limits | [1231, 1402) | [1402, 1899) | |

Slope (${\beta}_{1}$) | 0.08 | 0.02 | ||

Intercept (${\beta}_{0}$) | −94.19 | −19.71 | ||

Soil-4 | Segment limits | [1275, 1525) | [1525, 1685) | |

Slope (${\beta}_{1}$) | 0.09 | 0.00 | ||

Intercept (${\beta}_{0}$) | −112.50 | 23.58 | ||

SM100 | Soil-1 | Segment limits | [1200, 1238) | [1238, 1812) |

Slope (${\beta}_{1}$) | 0.25 | 0.04 | ||

Intercept (${\beta}_{0}$) | −303.95 | −42.88 | ||

Soil-2 | Segment limits | [1200, 1464) | [1464, 1728) | |

Slope (${\beta}_{1}$) | 0.07 | 0.03 | ||

Intercept (${\beta}_{0}$) | −87.61 | −32.15 | ||

Soil-3 | Segment limits | [1263, 1578) | [1578, 1895) | |

Slope (${\beta}_{1}$) | 0.06 | 0.02 | ||

Intercept (${\beta}_{0}$) | −78.57 | −14.80 | ||

Soil-4 | Segment limits | [1319, 1630) | [1630, 1833) | |

Slope (${\beta}_{1}$) | 0.06 | 0.01 | ||

Intercept (${\beta}_{0}$) | −81.29 | −3.56 | ||

YL100 | Soil-1 | Segment limits | [2, 467.5) | [467.5, 763) |

Slope (${\beta}_{1}$) | 0.04 | 0.03 | ||

Intercept (${\beta}_{0}$) | −0.80 | 5.01 | ||

Soil-2 | Segment limits | [6, 615.5) | [615.5, 826) | |

Slope (${\beta}_{1}$) | 0.03 | 0.09 | ||

Intercept (${\beta}_{0}$) | −0.81 | −32.32 | ||

Soil-3 | Segment limits | [5, 333.5) | [333.5, 709) | |

Slope (${\beta}_{1}$) | 0.02 | 0.08 | ||

Intercept (${\beta}_{0}$) | −0.17 | −20.81 | ||

Soil-4 | Segment limits | [6, 418.5) | [418.5, 705) | |

Slope (${\beta}_{1}$) | 0.02 | 0.07 | ||

Intercept (${\beta}_{0}$) | −1.08 | −21.08 | ||

YL69 | Soil-1 | Segment limits | [11, 134) | [134, 724) |

Slope (${\beta}_{1}$) | 0.07 | 0.04 | ||

Intercept (${\beta}_{0}$) | −1.35 | 3.24 | ||

Soil-2 | Segment limits | [7, 722] | ||

Slope (${\beta}_{1}$) | 0.05 | |||

Intercept (${\beta}_{0}$) | −0.87 | |||

Soil-3 | Segment limits | [18, 838] | ||

Slope (${\beta}_{1}$) | 0.03 | |||

Intercept (${\beta}_{0}$) | 1.48 | |||

Soil-4 | Segment limits | [14, 824) | ||

Slope (${\beta}_{1}$) | 0.03 | |||

Intercept (${\beta}_{0}$) | −0.71 |

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**Figure 1.**The four soil moisture sensors investigated in the study; from left to right (in the order of ascending cost): YL69, YL100, SM100, and SMEC300. The rightmost sensor is the secondary standard sensor, ThetaProbe.

**Figure 3.**Response of the capacitive soil moisture sensors (SMEC300 and SM100) and secondary standard (impedance-based ThetaProbe) to fluids of known ${\u03f5}_{r}$ at 25 °C. The X- and Y-axis depict the actual and measured refractive indices ($\sqrt[]{{\u03f5}_{r}}$), respectively. Although the ThetaProbe measures $\sqrt[]{{\u03f5}_{r}}$ directly, the $VWC$ values of SM100 and SMEC300 were converted to the corresponding $\sqrt[]{{\u03f5}_{r}}$ values based on the literature [10]. n is the total number of measurements in the experiment of a fluid, and the error bar shows the mean and standard error of the estimated values.

**Figure 4.**Calibration of capacitive sensors (

**a**) SMEC300 and (

**b**) SM100, and resistive sensors (

**c**) YL100 and (

**d**) YL69, in repacked soil using piecewise linear equations. The raw values correspond to either the raw readings from the Spectrum’s FieldScout reader (for capacitive sensors), or the raw outputs generated using the Arduino setup developed in-house (for resistive sensors). The coefficient of determination, R

^{2}, for each soil, is illustrated adjacent to the corresponding line.

**Figure 5.**Comparison of manufacturer and in-house calibration equations for capacitive sensors (

**a**) SMEC300 and (

**b**) SM100 for the four different experimental soils.

**Figure 6.**Accuracy (primary and secondary) and precision of different soil moisture sensors (SMEC300, SM100, YL69, YL100), in 4 different soils (corresponding to four quadrants). Overall accuracy, ${\sigma}_{eff}$ (Table 5), is the Euclidean distance of the bubble cross-hairs from the origin. The closer the bubble is to the origin, the more accurate the sensor is. Precision is indicated by the size of the bubbles ($radius=100\times {s}_{r,p}$); the smaller the bubble, the more precise the sensor. ‘n’ is the number of sensor units per sensor used in the experiment.

**Figure 7.**Temperature sensitivity of estimated VWC for different sensors: (

**a**) capacitive SMEC300, (

**b**) capacitive SM100, (

**c**) resistive YL100, and (

**d**) resistive YL69. The horizontal lines represent the actual VWC according to the legend. The hollow circular and solid square markers, along with their error bars, represent the average and standard deviations of the calibrated/estimated sensor readings corresponding to the fixed lower and higher actual VWC values, respectively. Positive temperature effects are seen to different extents in all sensors, with the resistive sensors’ performance being limited by relatively lower accuracy and precision.

**Figure 8.**Effect of water of different electrical conductivity (EC) values on VWC measured (${\hat{\theta}}_{i}$) by different sensors: (

**a**) capacitive SMEC300, (

**b**) capacitive SM100, (

**c**) resistive YL100, and (

**d**) resistive YL69. (

**e**) shows the relationship between the median values of the bulk soil EC measured by SMEC300 and the EC of water (with the corresponding best-fit line).

Measurement Technique | Soil Moisture Sensor (Company) | Price (Quotation) | Nomenclature Used in Study |
---|---|---|---|

Capacitance based | SMEC300 Soil Moisture, Temperature and EC sensor (Spectrum Technologies) | $219.00 | Low-cost *. |

SM100 Soil Moisture sensor (Spectrum Technologies) | $89.00 | Low-cost. | |

Resistance based | YL100 Soil Hygrometer Detection Module soil moisture sensor (Electronicfans) | $3.89 | Very Low-cost. |

YL69 Generic Soil Moisture Sensor Module (Kitsguru) | $2.11 | Very Low-cost. | |

Impedance based | ThetaProbe ML3 Soil Moisture sensor (Delta-T Devices) | $516.33 | High-cost, ‘true’ secondary standard sensor. |

**Table 2.**Description of physical properties of the 4 soils used in the study [47].

Nomenclature Used in Study | Soil Description | Bulk Density [g/cc] | Soil Texture Classification |
---|---|---|---|

Soil 1 | Grade I sand (1–2 mm) | 1.82 | Sand |

Soil 2 | Grade III sand (0.09–0.5 mm) | 1.59 | Sand |

Soil 3 | Field soil from experimental site at IIT Kanpur (Kanpur, India) | 1.23 | Silty-Loam |

Soil 4 | Graded Silty-Loam | 1.20 | Silty-Loam |

Fluid | ${\mathit{\u03f5}}_{\mathit{r}}$ at T = 25 °C [2] |
---|---|

Air | 1.0 |

Butanol | 16.8 |

Ethanol | 24.3 |

Ethylene-glycol | 37.0 |

De-ionized water (Water) | 81.0 |

**Table 4.**Electrical conductivities ($EC$) of the water samples and corresponding $VWC$ measurements of the soil samples investigated in the salinity experiment.

EC of the Water Added [mS/cm] | Actual VWC [%] | Symbolic Representation in Figure 8 |
---|---|---|

1.7 | 17.8 | Circle (○) |

1.7 | 32.3 | |

1.7 | 48.81 | |

3.02 | 20.08 | Triangle(△) |

3.02 | 31.12 | |

3.02 | 47.32 | |

6.32 | 34.09 | Square(□) |

6.32 | 38.5 | |

6.32 | 49.53 | |

9.69 | 17.59 | Pentagon(⬠) |

9.69 | 34.8 | |

9.69 | 43.53 |

**Table 5.**The list of performance measures used in the study: ${\theta}_{i}$ denotes an actual $VWC$ value; $\widehat{{\theta}_{i}}$ represents a raw value measured by the sensor; $\overline{\theta}$ is the average of the actual $VWC$ values; $\overline{\widehat{\theta}}$ is the average of the raw values measured by the sensor; $R\left(x\right)$ is the rank of x and n is the number of data points used in the computation. k, ${n}_{k}$, m and ${s}_{k}$ are the index of the current series, number of measurements in series k, total number of series, and corresponding standard deviation of the series, respectively, and are used to compute ${s}_{r,p}$.

Performance Metric | Description/Equation | Range (Ideal Value) |
---|---|---|

Coefficient of Determination (${R}^{2}$) [59] | ${R}^{2}=\frac{\left(n{\sum}_{i=1}^{n}\widehat{{\theta}_{i}}{\theta}_{i}\right)-\left({\sum}_{i=1}^{n}\widehat{{\theta}_{i}}\right)\left({\sum}_{i=1}^{n}{\theta}_{i}\right)}{\sqrt{n{\sum}_{i=1}^{n}{(\hat{{\theta}_{i}})}^{2}-{({\sum}_{i=1}^{n}\widehat{{\theta}_{i}})}^{2}}\sqrt{n{\sum}_{i=1}^{n}{\left({\theta}_{i}\right)}^{2}-{\left({\sum}_{i=1}^{n}{\theta}_{i}\right)}^{2}}}$ | 0 to 1 (1) |

Mean Absolute Error ($MAE$) [60] | $MAE=({\sum}_{i=1}^{n}|{\theta}_{i}-\widehat{{\theta}_{i}}|)/n$ | 0 to ∞ (0) |

Pooled relative standard deviation (${s}_{r,p}$) [54] | ${s}_{r,p}=\sqrt{\frac{{\sum}_{k=1}^{m}({n}_{k}-1){s}_{k}^{2}(1/{\overline{\hat{{\theta}_{k}}}}^{2})}{{\sum}_{k=1}^{m}({n}_{k}-1)}}$ | 0 to ∞ (0) |

Relative Absolute Error ($RAE$) [60] | $RAE={\sum}_{i=1}^{n}|{\theta}_{i}-\widehat{{\theta}_{i}}|/{\sum}_{i=1}^{n}\left|\widehat{{\theta}_{i}}-\overline{\theta}\right|$ | 0 to ∞ (0) |

Root Mean Squared Error ($RMSE$) [60] | $RMSE=\sqrt{({\sum}_{i=1}^{n}{({\theta}_{i}-\widehat{{\theta}_{i}})}^{2})/n}$ | 0 to ∞ (0) |

${\sigma}_{effective}$ | ${\sigma}_{eff}=\sqrt{{\left({\sigma}_{primary}\right)}^{2}+{\left({\sigma}_{secondary}\right)}^{2}}$ | 0 to ∞ (0) |

${\sigma}_{primary}$ | $RAE$ between in-house calibrated and actual $VWC$ value | 0 to ∞ (0) |

${\sigma}_{secondary}$ | $RAE$ between in-house calibrated and ThetaProbe $VWC$ value | 0 to ∞ (0) |

Spearman’s Rank Correlation Coefficient (${r}_{s}$) [61] | ${r}_{s}=\frac{\frac{1}{n}{\sum}_{i=1}^{n}(R(\widehat{{\theta}_{i}})-R(\overline{\widehat{\theta}}))(R({\theta}_{i})-R(\overline{\theta}))}{\sqrt{\left(\frac{1}{n}{\sum}_{i=1}^{n}{(R(\widehat{{\theta}_{i}})-R(\overline{\widehat{\theta}}))}^{2}\right){(\frac{1}{n}{\sum}_{i=1}^{n}R({\theta}_{i})-R(\overline{\theta}))}^{2}}}$ | −1 to 1 (−1 or 1) |

**Table 6.**Performance metrics of the capacitive (SMEC300 and SM100) and secondary standard (impedance-based ThetaProbe) sensors, in measuring refractive indices ($\sqrt[]{{\u03f5}_{r}}$) of fluids of known ${\u03f5}_{r}$ at 25 °C.

SMEC300 | SM100 | ThetaProbe | |
---|---|---|---|

$MAE$ | 0.87 | 0.55 | 0.48 |

$RAE$ | 0.22 | 0.27 | 0.24 |

$RMSE$ | 1.08 | 0.74 | 0.75 |

${s}_{r,p}$ | 0.0062 | 0.0062 | 0.0405 |

**Table 7.**Spearman’s Rank Correlation Coefficient ${r}_{s}$ between the sensor readings and the actual soil volumetric water content (VWC) ($\theta $) across the different soils. All the values are significant at $\alpha =5\%$.

Low-Cost | Very Low-Cost | |||
---|---|---|---|---|

Capacitive Sensors | Resistive Sensors | |||

SMEC300 | SM100 | YL100 | YL69 | |

Soil 1 | 0.93 | 0.92 | 0.78 | 0.91 |

Soil 2 | 0.96 | 0.97 | 0.89 | 0.94 |

Soil 3 | 0.84 | 0.94 | 0.94 | 0.73 |

Soil 4 | 0.95 | 0.92 | 0.94 | 0.85 |

Average | 0.92 | 0.94 | 0.89 | 0.86 |

**Table 8.**Accuracy performance indicators of the tested sensors, with in-house calibration and manufacturer calibration (applicable only to capacitive sensors): Mean Absolute Error ($MAE$, in % $VWC$), Root Mean Squared Error ($RMSE$, in % $VWC$), and Relative Absolute Error ($RAE$ or ${\sigma}_{primary}$, dimensionless). The same performance indicators are provided for the secondary standard sensor (for which no calibration equations were developed).

Low-Cost Capacitive Sensors | Very Low-Cost Resistive Sensors | Secondary Standard | |||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

SMEC300 | SM100 | YL100 | YL69 | ThetaProbe | |||||||||||||||||

Manufacturer Calibration | In-house Calibration | Manufacturer Calibration | In-house Calibration | In-house Calibration | In-house Calibration | Manufacturer Calibration | |||||||||||||||

MAE | RMSE | RAE | MAE | RMSE | RAE | MAE | RMSE | RAE | MAE | RMSE | RAE | MAE | RMSE | RAE | MAE | RMSE | RAE | MAE | RMSE | RAE | |

${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | ${\sigma}_{\mathit{primary}}$ | |||||||||||||||

Soil 1 | 9.63 | 11.76 | 1.01 | 2.28 | 3.34 | 0.24 | 8.17 | 10.22 | 0.84 | 2.27 | 2.97 | 0.23 | 4.31 | 5.88 | 0.47 | 2.58 | 3.53 | 0.28 | 3.79 | 4.84 | 0.40 |

Soil 2 | 7.13 | 8.63 | 0.89 | 0.96 | 1.39 | 0.12 | 6.75 | 8.23 | 0.87 | 1.12 | 1.63 | 0.14 | 3.42 | 4.54 | 0.35 | 2.95 | 3.90 | 0.29 | 2.88 | 4.46 | 0.34 |

Soil 3 | 7.17 | 9.99 | 1.00 | 3.33 | 4.20 | 0.47 | 5.82 | 7.74 | 0.80 | 1.54 | 2.55 | 0.21 | 3.41 | 5.99 | 0.35 | 6.38 | 8.09 | 0.61 | 2.98 | 4.29 | 0.39 |

Soil 4 | 6.44 | 7.90 | 0.96 | 1.90 | 2.61 | 0.28 | 4.18 | 5.27 | 0.63 | 1.74 | 2.27 | 0.26 | 2.90 | 4.45 | 0.31 | 4.60 | 6.65 | 0.46 | 3.07 | 4.23 | 0.42 |

Average | 7.59 | 9.57 | 0.97 | 2.12 | 2.88 | 0.28 | 6.23 | 7.86 | 0.78 | 1.67 | 2.36 | 0.21 | 3.51 | 5.21 | 0.37 | 4.13 | 5.54 | 0.41 | 3.18 | 4.45 | 0.39 |

**Table 9.**Comparison of precision performance of the tested sensors, based on pooled relative standard deviation, ${s}_{r,p}$ (% VWC). In-house calibration equations were used for the capacitive and resistive sensors, and Manufacturer calibration was used for the secondary standard sensor (for which no calibration equations were developed).

Low-Cost | Very Low-Cost | Secondary | |||
---|---|---|---|---|---|

Capacitive Sensors | Resistive Sensors | Standard | |||

SMEC300 | SM100 | YL100 | YL69 | ThetaProbe | |

Soil 1 | 0.51 | 0.55 | 1.11 | 0.81 | 0.47 |

Soil 2 | 0.05 | 0.44 | 1.13 | 0.63 | 0.30 |

Soil 3 | 0.48 | 0.30 | 0.74 | 0.40 | 0.24 |

Soil 4 | 0.28 | 0.35 | 0.78 | 0.72 | 0.24 |

Average | 0.33 | 0.41 | 0.94 | 0.64 | 0.31 |

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**MDPI and ACS Style**

Adla, S.; Rai, N.K.; Karumanchi, S.H.; Tripathi, S.; Disse, M.; Pande, S. Laboratory Calibration and Performance Evaluation of Low-Cost Capacitive and Very Low-Cost Resistive Soil Moisture Sensors. *Sensors* **2020**, *20*, 363.
https://doi.org/10.3390/s20020363

**AMA Style**

Adla S, Rai NK, Karumanchi SH, Tripathi S, Disse M, Pande S. Laboratory Calibration and Performance Evaluation of Low-Cost Capacitive and Very Low-Cost Resistive Soil Moisture Sensors. *Sensors*. 2020; 20(2):363.
https://doi.org/10.3390/s20020363

**Chicago/Turabian Style**

Adla, Soham, Neeraj Kumar Rai, Sri Harsha Karumanchi, Shivam Tripathi, Markus Disse, and Saket Pande. 2020. "Laboratory Calibration and Performance Evaluation of Low-Cost Capacitive and Very Low-Cost Resistive Soil Moisture Sensors" *Sensors* 20, no. 2: 363.
https://doi.org/10.3390/s20020363