## 2 Methodology and System Specifications  
### Methods and Equipment for Soil Measurements  
The accurate estimation of volumetric soil water content (\(\theta\)), or VWC or soil moisture, assists in efficient irrigation management to achieve high crop production and to rationalize water consumption. In this regard, as soil characteristics are crucial for plant growth, the physiology (i.e., the principles of operation and the powering and interfacing options they provide) of the sensors involved in the measuring process should be carefully studied for fluent data harvesting and monitoring. From a scientific perspective, the methods used to determine soil moisture can be classified as direct and indirect; the direct methods are those that quantify soil moisture by weighing (e.g., gravimetric), and indirect methods are those that work through reflectance,neutron moderation, and soil stress (e.g., frequency domain reflectometry--FDR, time domain reflectometry--TDR, and tensiometers) [10]. According to the recent analysis in [11], dielectric sensors constitute the most preferable choice as they provide simplicity and efficient data handling, estimating soil characteristics indirectly via radiofrequency properties (i.e., capacitor charging time, travel time, oscillation frequency, and attenuation). Indeed, dielectric methods do not disorder the ground and thus are suitable for automation and remote sensing. Among the dielectric sensors, TDR, FDR, and capacitance-based are the most popular ones for monitoring soil moisture [21]. Modern dielectric capacitance sensors, which are characterized by a low operating frequency (\(f\)), usually less than 100 MHz, can provide continuous recording of \(\theta\). Almost all soil sensors available today use the apparent dielectric permittivity (\(\epsilon_{\alpha}\)) as a basis for determining (\(\theta\)), benefiting from the fact that water exhibits high apparent dielectric permittivity (\(\epsilon_{\alpha}\) = 80) in contrast to soil solids (\(\epsilon_{\alpha}\) = 2-5) or air (\(\epsilon_{\alpha}\) = 1) [22]. Due to the low operating frequency, the determination of the apparent dielectric permittivity is affected from bulk soil electrical conductivity (\(\sigma_{\text{b}}\)), and thus the estimation of \(\theta\) is not always accurate [22; 23]. Thus, the accurate measurement of \(\theta\) presupposes the special calibration of the device, i.e., the correlation of the soil water content (\(\theta\)) and the apparent dielectric permittivity (\(\epsilon_{\alpha}\)) with the sensor measurements [20], typically expressed via proper exponential/polynomial equations. Further improvement of \(\theta\) estimation can be achieved by taking into account the effect of (\(\sigma_{\text{b}}\)) on apparent dielectric permittivity [24]. Consequently, many sensors estimate additional soil properties like \(\sigma_{\text{b}}\) or soil temperature [25]. With regard to the technical perspective, the powering specifications of the sensing instruments connected to an IoT node may vary, as well as the data extraction method/protocol they support. Capacitance-based units exhibit in general reduced power requirements, and this is an additional reason for their popularity for in situ monitoring deployment [26]. Furthermore, several instruments require a warm-up and preparation time prior to make available reliable measurement readings, while different protocols may correspond to the same interface line, according to the time elapsed since powering the instrument up. These specific operating characteristics will become more apparent after the presentation of the units adopted and utilized in this study. Specifically, the IoT platform being presented was designed to be compared as a host and tested against several well-known commercial soil sensor instruments, namely, ThetaProbe ML2, Decagon 10 HS and 5 TE, and METER TEROS 10 and TEROS 12 units.