Wireless Soil Moisture Sensor Research Articles

Making technological innovations accessible to agricultural water management: Design of a low-cost wireless sensor network for drip irrigation monitoring in Tunisia

Unsustainable use of water resources and climate change will exacerbate the existing tensions surrounding resources, especially in the Mediterranean context. Despite investments in costly modern equipment, the performance of irrigated agriculture remains below expectations, notably because of the lack of available water data and the limited use of decision support tools. Although a variety of soil moisture sensors are available on the market, they are not widely used by the agricultural community because of their high cost and complexity. Access to information at an unprecedented level, via easily accessible low-cost and low-tech sensors, may be a major lever for improved identification of achievable gains in performance, and to guide actors toward efficient water management. To explore this hypothesis, an open source wireless soil moisture sensor, low-energy and economically and technically accessible, was developed. The tool was designed according to water users’ requirements and applied to a Tunisian irrigation scheme subject to major water use efficiency issues. The functioning of the wireless sensor network was tested on pilot plots over a growing season and compared with commercial sensors. A single parameter calibration can be performed in either the laboratory or the field. This low-cost sensor can be used for real-time irrigation monitoring and as a decision-making tool for water management.

Optimization of Applied Irrigation Water for High Marketable Yield, Fruit Quality and Economic Benefits of Processing Tomato Using a Low-Cost Wireless Sensor

Water management is a key factor to optimize fruit quality and yield of processing tomatoes which are site-specific and influenced by environmental conditions e.g., soil, temperature, precipitation. The aim of this study was to evaluate the efficacity of a low-cost wireless soil moisture sensor in determining the irrigation level for optimizing the marketable yield, fruit quality and economic profit of processing tomato. A two-years (2017–2018) trial was conducted in open field, applying nine drip irrigation levels controlled by wireless soil moisture capacitance sensors. The irrigation levels were as follows: 13.2, 16.7, 25.4, 33.3, 50.0, 62.3, 82.5, 100 and 186.8% of water restitution based on soil moisture sensor readings. Because of the crop stress induced by heavy rainfalls occurring in 2018 growing season, total and marketable yields reached higher maximum values in 2017 than 2018. In 2017, total and marketable yields were maximized by supplying 92.8% and 96.2% of irrigation level, respectively. Moreover, 95.6% and 91.2% of irrigation level were necessary in 2018 to maximize total and marketable yield, respectively. In both growing seasons, marketable yield variation was due to changes of both fruit number and fruit mean weight. Total soluble solids of fruit juice linearly decreased by increasing the irrigation level with a more pronounced effect in the driest growing season (2017). Economic analysis demonstrated that 100% of irrigation level should be preferred by the Italian farmers since it maximized the operating margins of processing tomatoes in both years. To conclude, the use of the tested low-cost wireless soil moisture sensor is an effective tool to manage the level of irrigation and optimize the processing tomato yield and economic benefits for farmers.

Precision Irrigation Scheduling Based on Wireless Soil Moisture Sensors to Improve Water Use Efficiency and Yield for Winter Wheat in Sub-Saharan Africa

In Sub-Saharan Africa, where most irrigation systems are manually operated, water allocation and irrigation scheduling are often based on uniform application irrespective of crop needs and growth stages, which results in nonoptimal water use. Recently, a lot of research has been carried out to improve irrigation water use efficiency through automation by employing wireless sensor-based monitoring systems. Further to the improvement of water use efficiency and yield, while reducing costs, a field trial was carried out at a farm in Harare, Zimbabwe, during the 2016, 2017, and 2018 winter seasons to test whether a new approach to the automated irrigation systems, one based on IoT and wirelessly connected soil sensors (called hereafter as WCSS), improves water use efficiency without reducing yield. WCSS method was compared with three widely used conventional irrigation methods, that is, manual scheduling, tensiometer-based scheduling, and weather-based scheduling. Impacts on water savings and yield of winter wheat crops under drip irrigation were evaluated. WCSS saved up to 25% more water compared to typical fixed irrigation schedule rates used by wheat growers during the winter season.

Correlation of UAV and satellite-derived vegetation indices with cotton physiological parameters and their use as a tool for scheduling variable rate irrigation in cotton

Current irrigation management zones (IMZs) for variable rate irrigation (VRI) systems are static. They are delineated in the beginning of the season and used thereafter. However, recent research has shown that IMZ boundaries are transient and change with time during the growing season. The primary goal of this study was to explore the potential of using vegetation indices (VIs) developed from unmanned aerial vehicle (UAV) and satellite images to predict cotton physiological parameters that can be used to delineate in-season boundaries of IMZs. A 2 year study was conducted in a 38 ha commercial cotton field in southwestern Georgia, USA. Throughout the two growing seasons, VIs were calculated from UAV, PlanetScope, and Sentinel-2 images. Predawn leaf water potential (LWPPD) and plant height were measured at 37 locations in the field on the same day as the flights and correlated with UAV and satellite based-VIs. GNDVI (Green normalized difference vegetation index) was the best predictor of plant height with correlation values of 0.72 (p < .0001) and 0.84 (p < .0001) for 2019 and 2020, respectively. A secondary goal was to compare the performance of dynamic VRI (DVRI) to conventional irrigation. The field was divided into alternating parallel conventional, and DVRI strips to compare the two scheduling methods. The conventional strips were irrigated using the farmer’s standard method and individual IMZs within the DVRI strips were irrigated based on soil water tension (SWT) measured with a wireless soil moisture sensor network. LWP and SWT measurements correlated well. IMZs were initially delineated using soil texture, apparent soil electrical conductivity (ECa), and yield maps and satellite images from previous years and were modified in-season to reflect patterns observed in the plant height maps. In 2020, the DVRI system prescribed an average irrigation amount of 50.8 mm while conventional irrigation applied an average of 58.4 mm. Average yields for DVRI and conventional were 1248 and 1191 kg ha−1, respectively. The DVRI system resulted in average yield 4.6% higher than conventional irrigation, while applying 14.0% less water. Despite the lower water application by the DRVI system, the performance comparison between the DRVI and the conventional irrigation was not conclusive.

Evaluation of irrigation scheduling approaches within sand‐capped turfgrass systems

AbstractIn an effort to improve performance of turfgrass irrigated with poor‐quality water, the practice of sand capping is increasing. Given current strains on water supplies, evaluation of various methods of irrigation scheduling approaches for these systems is needed. The objectives of this 2‐yr field study were to evaluate turfgrass performance, temporal and spatial soil moisture and salinity dynamics, and comparative water use among four irrigation scheduling approaches including (a) wireless soil moisture sensor (SMS), (b) on‐site reference evapotranspiration (ETo), (c) National Oceanic and Atmospheric Administration forecasted reference evapotranspiration, and (d) visual‐wilt‐based treatment. The turfgrass used was ‘Latitude 36’ hybrid bermudagrass [Cynodon dactylon (L.) Pers. ×C. transvaalensis Burtt‐Davy] planted atop a 17.8‐cm medium‐coarse textured sand cap. Results demonstrated that all approaches produced similar levels of acceptable turfgrass quality and percentage green cover with no apparent differences in root development. Forecasted reference evapotranspiration was found to be a good predictor of on‐site ETo (R2 = .97) when comparing daily values across two growing seasons. Under wilt‐based irrigation, the volumetric water content (7.6 cm sand‐cap depth) at which wilt occurred was highest mid‐summer (4.1–4.7%) but declined during early and late summer months (1.8–2.2%), suggesting different thresholds may be needed throughout the season when using SMS‐based scheduling. Finally, seasonal water use was 23% lower for the on‐site ETo–based approach compared with SMS‐based scheduling, although this did not result in elevated electrical conductivity within the sand cap. The results provide important information to guide adoption of data‐driven approaches to irrigation scheduling.

Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept

A reasonable estimation of crop water uptake depth (WUD) provides vital prerequisites for determining scientific irrigation scheduling. However, real-time acquisition of crop WUD to guide irrigation has not been reported. In the current study, a smart irrigation system based on real-time soil moisture data was developed, in which the dynamic crop WUD was estimated using the spatiotemporal characteristics of soil moisture distributions. Subsequently, the data of crop WUD were acquired using a central irrigation controller to achieve a precise irrigation depth at each irrigation event. A drip irrigation experiment for tomato (Lycopersicum esculentum) planting was conducted in a greenhouse in northern China. Wireless soil moisture sensors were installed to collect real-time soil moisture data from a 0–100 cm soil profile. Soil moisture exhibited a ladder trend for 0–60 cm and a stable trend for 60–100 cm. A regression equation (WUDi = −0.0119d2 + 1.9387d - 6.5795, R2 = 0.89) quantifying the dynamic depth of crop water uptake was successfully obtained based on the characteristics of soil moisture distribution, and a water-saving scheme (i.e., irrigation to real-time crop WUD) was determined to guide every irrigation event via a remote automatic irrigation system. Tomato evapotranspiration (ET) calculated using soil moisture data was consistent with that calculated using indoor meteorological data, confirming the reliability of real-time soil moisture data for estimating the tomato WUD. The tomato irrigation water use efficiency (IWUE) of the established system reached 41.23 kg/m3, which was an evident improvement compared with that obtained using a traditional irrigation scheme (31.58 kg/m3). In addition, the IWUE of tomato in the established system was almost approached that of the previously published results. The findings of this study highlight the importance of reasonable water-saving irrigation scheduling and provide insights into the development of an efficient and automatic irrigation system.

Retraction: A wireless soil moisture sensor powered by solar energy.

Retraction: A wireless soil moisture sensor powered by solar energy.

A wireless soil moisture sensor powered by solar energy.

In a variety of agricultural activities, such as irrigation scheduling and nutrient management, soil water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring soil water dynamics at field scale, this study presents a wireless soil moisture sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure soil water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring moisture in large-scale farmland using solar power and wireless communication.

Irrigation of organic greenhouse cucumber with a low cost wireless soil moisture sensor

Irrigation of organic greenhouse cucumber with a low cost wireless soil moisture sensor

Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem

Mapping of soil water content (SWC) by electromagnetic induction (EMI) is an established method to obtain field-scale SWC information. However, the relationship between SWC and the apparent electrical conductivity (ECa) measured with EMI is complex and affected by several confounding factors at the catchment scale such as variable porosity (ϕ) and pore water electrical conductivity (σw). In this study, we investigated these confounding factors using a time-lapse EMI data set obtained in a forest ecosystem with soils of low ECa and catchment-wide SWC data provided by a wireless soil moisture sensor network. To assess the impact of variable ϕ on the accuracy of SWC estimates, we compared three different models to relate SWC and ECa: (i) a linear regression model and two nonlinear models based on Archie’s equation with (ii) constant ϕ and (iii) variable ϕ. The linear model reached a prediction accuracy of RMSE = 5.83 vol%, while the Archie models increased the accuracy to RMSE = 4.55 vol% (constant ϕ) and RMSE = 4.20 vol% (variable ϕ). Although we found strong spatial similarities between SWC and ECa maps, the temporal trends in SWC and ECa were inconsistent. This was attributed to temporal variations in σw due to seasonal changes in ion concentrations of the soil pore water. To support this hypothesis, σw was calculated from the measured ECa and the known soil saturation from SoilNet. The resulting σw maps showed highly structured and consistent patterns. We thus conclude that in addition to variation in SWC and ϕ, spatiotemporal variations of σw affected the ECa measured with EMI. These potentially confounding factors in the interpretation of EMI measurements in terms of SWC have not been sufficiently recognized in the literature so far, and the results presented in this study indicate a range of limitations for the use of EMI to monitor spatiotemporal changes in SWC at test sites with low ECa.

Addressing Groundwater Declines with Precision Agriculture: An Economic Comparison of Monitoring Methods for Variable-Rate Irrigation

Irrigated row-crop agriculture is contributing to declining groundwater in areas such as the Mississippi Delta region of eastern Arkansas. There is a need to move toward sustainable levels of groundwater withdrawal. Recent improvements in remote monitoring technologies such as wireless soil moisture sensors and unmanned aerial vehicles offer the potential for farmers to effectively practice site-specific variable-rate irrigation management for the purpose of applying water more efficiently, reducing pumping costs, and retaining groundwater. Soil moisture sensors and unmanned aerial vehicles are compared here in terms of their net returns per acre-foot and cost-effectiveness of aquifer retention. Soil moisture sensors ($9.09 per acre-foot) offer slightly more net returns to producers than unmanned aerial vehicles ($7.69 per acre-foot), though costs associated with unmanned aerial vehicles continue to drop as more manufacturers enter the market and regulations become clear.

Spatio-temporal validation of long-term 3D hydrological simulations of a forested catchment using empirical orthogonal functions and wavelet coherence analysis

Soil moisture plays a key role in the water and energy balance in soil, vegetation and atmosphere systems. According to Wood et al. (2011) there is a grand need to increase global-scale hyper-resolution water–energy–biogeochemistry land surface modelling capabilities. These modelling capabilities should also recognize epistemic uncertainties, as well as the nonlinearity and hysteresis in its dynamics. Unfortunately, it is not clear how to parameterize hydrological processes as a function of scale, and how to test deterministic models with regard to epistemic uncertainties. In this study, high resolution long-term simulations were conducted in the highly instrumented TERENO hydrological observatory of the Wüstebach catchment. Soil hydraulic parameters were derived using inverse modelling with the Hydrus-1D model using the global optimization scheme SCE-UA and soil moisture data from a wireless soil moisture sensor network. The estimated parameters were then used for 3D simulations of water transport using the integrated parallel simulation platform ParFlow-CLM. The simulated soil moisture dynamics, as well as evapotranspiration (ET) and runoff, were compared with long-term field observations to illustrate how well the model was able to reproduce the water budget dynamics. We investigated different anisotropies of hydraulic conductivity to analyze how fast lateral flow processes above the underlying bedrock affect the simulation results. For a detail investigation of the model results we applied the empirical orthogonal function (EOF) and wavelet coherence methods. The EOF analysis of temporal–spatial patterns of simulated and observed soil moisture revealed that introduction of heterogeneity in the soil porosity effectively improves estimates of soil moisture patterns. Our wavelet coherence analysis indicates that wet and dry seasons have significant effect on temporal correlation between observed and simulated soil moisture and ET. Our study demonstrates the usefulness of the EOF and wavelet coherence methods for a more in-depth validation of spatially highly resolved hydrological 3D models.

Low-Temperature Co-fired Ceramic (LTCC) Technology for Development of Sensors for Emerging Applications

LTCC technology continues to be an attractive option for fabrication of sensors, sensor packages and complex micro-systems. The objective of this work is to present LTCC sensors developed by CSEM Brasil with a focus on capacitive type wireless soil moisture sensor (SMS) for precision farming that is capable to measure soil moisture in real time and transmit the data to 80 m distance (line of sight). The sensor is constructed from 4 functional blocks: PCB (printed circuit board) base including the capacitive electrodes, LTCC block carrying the electronics and soldered on PCB, voltage regulator and wireless unit. Tests conducted with 28 prototypes reveal that the sensor response is over 1.00 V at full humidity and reproducible whilst the battery consumption is around 200 mW, permitting a battery life of 5 years at a measurement frequency of 1 sampling per minute.

A wireless application of drip irrigation automation supported by soil moisture sensors

Irrigation by help of freshwater resources in agricultural areas has a crucial importance. Because of highly increasing demand for freshwater, optimal usage of water resources has been provided with greater extent by automation technology and its apparatus such as solar power, drip irrigation, sensors and remote control. Traditional instrumentation based on discrete and wired solutions, presents many difficulties on measuring and control systems especially over the large geographical areas. This paper describes an application of a wireless sensor network for low-cost wireless controlled irrigation solution and real time monitoring of water content of soil. Data acquisition is performed by using solar powered wireless acquisition stations for the purpose of control of valves for irrigation. The designed system has 3 units namely: base station unit (BSU), valve unit (VU) and sensor unit (SU). The obtained irrigation system not only prevents the moisture stress of trees and salification, but also provides an efficient use of fresh water resource. In addition, the developed irrigation method removes the need for workmanship for flooding irrigation. The designed system was applied to an area of 8 decares in a venue located in central Anatolia for controlling drip irrigation of dwarf cherry trees. Key words: Wireless soil moisture sensor, irrigation, real time monitoring, drip irrigation, automation.