# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
## 3 Results and Discussion
### Cross-Comparison with TDR Sensor for Varying \(\theta\)  
There are two common methods used for laboratory and field calibration of soil moisture sensors [23; 26]. The more commonly used method, the mixed-cell method or the disturbed calibration method, uses measurements made in cells containing soil mixed with different known amounts of water to provide distinct points describing the relationship between the \(\varepsilon\) and \(\theta\)[26]. The second method is known as the undisturbed calibration method, or the infiltration-addition method, and was described previously [27]. The main difference between the two is the soil structure which is removed when using the first method through soil sieving, grinding and subsequent mixing with water. Most sensor manufacturers recommend that calibration is undertaken on soil in which the structure has been removed, although it is argued that ideally the structure should be maintained to limit uncertainty associated with the pore size distribution and the small volume of influence of some probes. Due to the size of the TDR instrument used (Figure 4c), the mixed-cell method was used in this study although it is in general more laborious and results in variable bulk densities. Two types of soil substrates were used for the cross-comparison study: garden soil substrate (clay loam soil) and potting soil substrate (peat moss soil). None of the soils tested showed a good fit with the [PERSON]'s equation, although polynomial third equation models could be fitted to the experimental data to provide R\({}^{2}\) > 0.99 (Figure 4a,b; see insets for coefficients). Such soil-specific calibration curves are generally developed for field application with soil samples from the site, and it has been shown previously that both these types of soils tend produce high RMSD values when fitted with the Topp equation. For example, results are in agreement with previous results on clay and rockwood [23]. The potting soil substrate (peat moss soil) used here can be classified as organic soil, for which the response seems to be best described by [PERSON]'s equation for organic forest soils [28] with a similar response reported previously [12]. The offset from [PERSON]'s equation for these types of soils is driven by the lower density and higher porosity of the solid phase [29]. The garden soil substrate used can be classified as clay-rich soil, for which deviation from [PERSON]'s equation were reported before with increasing clay content [12; 20]. It is considered that this deviation is due to the particle shape, clay mineralogy and high surface area (bound water) which in turn alter the \(\varepsilon_{\text{a}}\)[12]. Another reason proposed is the nonrigid structure of many clay minerals and their ability to shrink and swell, which could maintain connectivity between interaggregate pores at low water contents [30]. In turn, this effect produces lower observed \(\varepsilon\) at low \(\theta\) and higher \(\varepsilon\) at high \(\theta\) in relation to the Topp's function, as noticed here (Figure 4a). In this experiment, the main aim was to compare the low-cost sensor's response as dielectric permittivity with the TDR instrument. For both soils, the Sense Cap probes overestimate \(\varepsilon\) by comparison with the \(\varepsilon_{\text{TDR}}\) (Figure 4a,b,d). This offset is minimal in the potting soil substrate, with slightly higher \(\varepsilon_{\text{SenseCap}}\) throughout the tested range but in good agreement with the \(\varepsilon_{\text{TDR}}\). On the other hand, there is a significant overestimation in the garden soil substrate, particularly the low-middle range. These differences can be attributed to differences in the measurement frequency and operation mode (capacitance vs. TDR) [12], or variations in sensor characteristics including probe geometry, printed circuit board design, and sensor head sensitivity [31].