# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 3 Results and Discussion
### Sensor Performance Evaluation with Known Dielectric Standards and Intersensor Variability
Standardising the sensor response to \(\varepsilon_{\mathrm{s}}\) offers two key advantages: it reduces the intersensor variability and converts the sensor response to a more accurate \(\varepsilon\) which in turn can be used for more reliable \(\theta\) calculations. Standardisation can be achieved by converting the sensor output \(\theta_{\mathrm{a}}\) (%) to \(\varepsilon_{\mathrm{a}}\) and finding the equation between \(\varepsilon_{\mathrm{a}}\) and \(\varepsilon_{\mathrm{s}}\) or by finding the equation between \(\theta_{\mathrm{a}}\) (%) and \(\varepsilon_{\mathrm{s}}\). The later approach was used as it was found to provide better root-mean-square error values (RMSE) for the 1-32.5 range and R\({}^{2}\) > 0.98 for all nodes. Unit-specific standardisation equations in dielectric permittivity standards were developed for each node and two example are provided in Figure 2.  
It was found that the shorter probes (nodes 1-3) produced consistently higher readings than the longer probes (nodes 4-10), as shown in Table 2. Applying the standardization equations was found to reduce the intersensor variability. An example is shown in Figure 3a,b for a set of samples prepared by incremental addition of water, where intersensor variability is reduced considerably at the extremities of the \(\theta\) range. For these experiments, Equation (1) was used to compute the standardised \(\theta\) from \(\varepsilon_{\mathrm{s}}\). In addition, the standardization corrects for the overestimation of \(\varepsilon_{\mathrm{s}}\) and the standardised \(\theta\) (\(\theta_{\mathrm{SenseCap}}\), Figure 3b) values are much closer to the measured \(\theta\) (R\({}^{2}\) = 0.99, slope = 1.027). Another example to support the response standardisation in shown in Figure 3c,d as time series for 4 weeks of data, with all 10 sensors deployed at approximately 5 cm below the root line. Note the Y axis are identical in both panels to facilitate direct comparison of raw and standardised data. As before, the intersensor variability and \(\theta\) are reduced. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \multirow{2}{*}{**Media**} & \multirow{2}{*}{\(\varepsilon_{\mathrm{s}}\) (\(\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{missingFurthermore, data collected in Table 2 are essential to providing quality control (QC) for when sensors are operating in situ. Sensor drift due to corrosion, or hardware issues, can cause sensor response to deviate from the real value. Often, this response drift is overlooked, and 'bad' data can be taken as reliable, unless QC measures are in place. Identifying sensor  
Figure 3: Raw and standardized response from the Sense Cap units. (**a**) raw sensor response with increasing \(\theta_{\text{m}}\) in prepared soil samples, error bars represent the standard deviation of at least 9 measurements, 3 measurements at 3 different positions in the sample (**b**) standardised sensor response to \(\theta\) and linear relationship between actual and estimated \(\theta\); (**c**) raw response time-series data; (**d**) standardised response time-series data; (**e**) experimental set-up for data in (**c**) showing sensor installation within close proximity to each other, at approximately 5 cm depth. Figure 2: Example of unit-specific standardization equations in \(\varepsilon\) standards. Insets show equation coefficients. nodes that are malfunctioning at an early stage and discarding or correcting the associated data is good practice and cost effective.