In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σ_b), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (EC_W) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (EC_w) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (ε_a) readings were lower than that of Topp’s permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θ_m) and ε_a^0.5 was strongly linear (0.869 < R² < 0.989), but the linearity of the relation θ_m-ε_a^0.5 decreases with the increase in bulk EC (σ_b) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that ε_a degrades substantially at values of σ_b less than 2.5 dS/m while ε_a returns to near 80 at higher values. Regarding the relation ε_a-σ_b, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σ_p) and the soil type.

Efficiently reporting soil-specific information is of key importance for plant growth but can be quite demanding as well. Indeed, it may require expensive digitizers, subscriptions to services for communication links between each sensor and the cloud, and the incorporation of power-hungry elements. Added to this, soil sensors may vary drastically, e.g., in terms of power characteristics, response times, or interfacing options. The need for improved energy autonomy increases reporting complexity, as it presupposes that the participating components will enter a low-power (sleep) state when not in action. Furthermore, the IoT nodes hosting the sensing instruments should be able to work unattended for long periods under varying environmental conditions. In response to the aforementioned physical and technical challenges, this work highlights the details behind the cooperation of a cost-effective microprocessor equipped with a radio transceiver and some simple and widely available electronic components to form nodes that can host a diverse set of soil sensors and deliver reliable data in satisfactory ranges. The sensitivity and power efficiency of the LoRa protocol make it ideal for rural agri-field use; in the meantime, optimized action/sleep management, along with tiny solar panels, guarantee sustainable operation. The proposed system was tested utilizing various typical soil instruments, and its range coverage, consumption, and measurement quality were thoroughly evaluated under different installation settings, thus providing guidance for similar implementations and indicating its suitability for a wide set of monitoring applications.

This study focuses on investigating the accuracy of the empirical equations proposed by the manufacturing company for the widely used TEROS 10 and TEROS 12 sensors. Specifically, the study examines whether the raw - unprocessed - measurements provided by the two sensors lead to reliable predictions of soil moisture and electrical conductivity values when these particular empirical equations are utilized. For this purpose, a prototype setup was developed, incorporating a lowcost microcontroller along with auxiliary power supply and data acquisition circuits for the sensors. The results highlight the need for specific calibration of the sensors for each soil type and salinity level. Additionally, the behaviour of the sensors operating at low frequency (70 MHz) under increasing salinity conditions is contrary to expectations.

This study sheds light on the performance of the common high-precision electromagnetic sensor TEROS 10 to estimate volumetric soil water content (θ) from dry to saturation across three different substrates, six different soil types having three different levels of electrical conductivity of soil solutions (EC_w), and in liquids with increasing salinity level under laboratory conditions, by using low-cost but accurate experimental IoT hardware arrangements. This performance was evaluated using statistical analysis metrics such as Root Mean Square Error (RMSE). It was found that TEROS 10 performance did not conform to the manufacturer’s specifications throughout the full scale range, although in some cases good water content estimation was provided. Some inconsistencies were identified by applying the manufacturer’s calibration equations, and thus recommendations for improvements are provided, aiming to enhance the sensor’s overall performance. TEROS 10 performance across all six soils and three substrates was improved on average from an RMSE of 0.052 and 0.078 cm³ cm⁻³, respectively, by using factory-derived calibration, to 0.031 and 0.031 cm³ cm⁻³ by using the multipoint calibration method (CAL). Furthermore, a linear calibration formula, using Raw output as the predictor variable, was tested and resulted in an RMSE of 0.026 and 0.046 cm³ cm⁻³ for soils and substrates, respectively.

Agriculture has to be drastically assisted by the new promising technological advancements to alleviate the extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy efficient communication protocols, single board computers, flow and pressure sensors, and in-purpose actuators to form a synergistic platform able to generate and study a diverse set of realistic irrigation scenarios, thus paving the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. Two drip irrigation points were setup, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules were sending data in real-time, via LoRa, to a central node implemented using a Raspberry Pi, for further processing and for making them widely available via common network infrastructures, also provisioning for remote scenario invocation. The exact steps being necessary, for the specific hardware and software components to be combined into a functioning whole, are clearly explained, thus allowing other teams of researchers and/or university educators worldwide to be inspired and benefited from platform replication.