# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 4 Conclusions  
This study demonstrates a low-cost soil moisture sensor network based on laboratory and field measurements with dielectric permittivity standards and soil media. The embedded sensor calibration, in-built within each sensor node, does not accurately predict the \(\varepsilon\) of liquid standards which consequently leads to inaccurate \(\theta\) estimations. Namely, two shortcomings were identified: the sensors overestimate the \(\varepsilon_{\text{s}}\) particularly for values > 32, and a high intersensor variability is present between the two sensor types tested. To normalise the sensor output, the raw response for each unit was standardised to the \(\varepsilon_{\text{s}}\), through unit-specific equations. This approach was found to reduce the intersensor variability and provide robust estimates of \(\theta\) in soil samples with known \(\theta\). Furthermore, when the sensor was tested against a TDR instrument, the two probes were found to be in good agreement throughout the tested range. Although the \(\varepsilon\) was overestimated for the low-middle \(\theta\) range for the heavy clay soil, this seems to be consistent with similar sensors reported in the literature. Sensor drift due to corrosion, or hardware/electronic issues, can cause sensor response to deviate from the ground truth. Identifying sensor nodes that are malfunctioning at an early stage is essential for the collection of robust data. The collected data on liquid standards provide the baseline for QC measurements while sensors are deployed. Sources of errors associated with suboptimal probe installation were identified and discussed. Namely, measurement errors were observed when the times of the probes were partially exposed to air or air gaps and when the tines were deflected from their parallel configuration upon installation. The data communication performance of the network was evaluated in terms of packet drop rate at different ranges and sampling frequencies. It was noticed that the drop rate increased with the distance from the gateway, while sampling frequency had no effect. The range provided by the Sense Cap antenna was found to be small (approximately 500 m) and was significantly improved by upgrading to a 4.5 dBi LoRa Paradar antenna (approximately 1500 m). In summary, the Sense Cap soil moisture sensor network evaluated in this study shows potential for in situ implementation for soil moisture monitoring. The off-the-shelf all-in-one solution provided is low-cost and user-friendly (easy and fast installation which does not require specialised training). Standardisation of sensor units is advised to achieve \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Range** & **40 m** & **100 m** & **300 m** & **460 m** \\ \hline Antenna A & 5.48\% & 1.15\% & 3.83\% & 13.64\% \\ Antenna B & 6.81\% & 1.91\% & 4.91\% & 9.69\% \\ N\({}_{\text{antenna A}}\) & 1569 & 1560 & 1565 & 1547 \\ N\({}_{\text{antenna B}}\) & 2510 & 2511 & 2506 & 2509 \\ \hline \hline \end{tabular}  
* The total number of packages (the maximum number of packages calculated for each node from the sampling frequency and time stamps).  
\end{table} 
Table 3: Communication data drop rate. robust estimates of \(\theta\) and improve the analytical performance. Considering all of the above, the optimal set-up for efficient, accurate and reliable soil moisture networks that can provide both spatial and temporal resolution should be hybrid and encompass multiple low-cost nodes accompanied by at least one TDR profiler for validation purposes. Conceptualization, C.B.-B., F.R. and M.I.A. ; methodology, C.B.-B. and J.Z. ; data curation, C.B.-B. and J.Z. ; writing--original draft preparation, C.B.-B. ; writing--review and editing, J.Z., F.R. and M.I.A. ; supervision, F.R. and M.I.A. ; project administration, C.B.-B., F.R. and M.I.A. ; funding acquisition, F.R. All authors have read and agreed to the published version of the manuscript.