•Redistribution during and after irrigation rises uncertainty in defining temporal stability.•Data obtained during and shortly after irrigation should not be used to detect temporal stability.•The ...measurements period used to evaluate temporal stability affects optimum sensor positioning.•Evapotranspiration prediction depends on the positioning of sensors for data collection.
A recurrent issue in irrigation management refers to the number and optimal positioning of water content sensors in the root zone of a crop. In the field, the definition of the number and location of sensor installation is still arbitrary. How many sensors and where to install them has been studied using the Time Stable Representative Positions (TSRP) concept introduced by Soulis and Elmaloglou (2016). However, the effect of the period of the soil water content data used for the determination of the TSRP is not usually considered. The main objectives of this work are to analyze how the soil water content data collection period within an irrigation cycle affects the determined optimal sensor positioning based on TSRP; and, to evaluate if the sensors positioning affects the prediction of crop evapotranspiration (ETc) by numerical modeling. Over 60,000 soil water content data were obtained during 36 days from 12 TDR probes installed at different monitoring positions in the root zone of an irrigated banana plant. The optimal position of sensors was determined based on the concept of TSRP, considering soil water content data obtained during the entire and during the second part of the irrigation cycles. The SWAP hydrological model was also used to investigate the effect of sensors positioning in prediction of ETc by numerical modeling. It was verified that the use of soil water content data obtained at the beginning of an irrigation cycle - at times when infiltration occurs and there is a high intensity of redistribution of water in the soil - increases the uncertainties regarding the estimation of temporal stability for purposes of irrigation management. The result of the determination of the optimal positioning for sensors installation varies according to the part of the irrigation cycle from which water content measurements are considered. This variation affects the prediction of ETc by numerical modeling. As values of soil water content obtained at the beginning of the irrigation cycle increase the uncertainty of the statistical indicators and are not of practical interest for irrigation management, it is recommended that in the determination of optimal positioning of sensors, only soil water content values obtained after the infiltration and cessation of high rates of irrigation water redistribution are considered.
To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil ...purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.
This paper presents a high precision capacitive moisture sensor for polymers that is part of a compact, fast, and economic moisture analyzer capable of reaching similar precision than commercial ...moisture analyzers based on the Karl Fischer method. The design considerations are described in detail and simulation results are presented showing the relationships between the sensor's geometrical parameters and the sensor's performance. A capacitive moisture sensor prototype was fabricated and experimental results obtained during laboratory tests are reported.
Using evapotranspiration (ET) data for scheduling irrigations on vegetable farms is challenging due to imprecise crop coefficients, time consuming computations, and the need to simultaneously manage ...many fields. Meanwhile, the adoption of soil moisture monitoring in vegetables has historically been limited by sensor accuracy and cost, as well as labor required for installation, removal, and collection of readings. With recent improvements in sensor technology, public weather-station networks, satellite and aerial imaging, wireless communications, and cloud computing, many of the difficulties in using ET data and soil moisture sensors for irrigation scheduling of vegetables can now be addressed. Web and smartphone applications have been developed that automate many of the calculations involved in ET-based irrigation scheduling. Soil moisture sensor data can be collected through wireless networks and accessed using web browser or smartphone apps. Energy balance methods of crop ET estimation, such as eddy covariance and Bowen ratio, provide research options for further developing and evaluating crop coefficient guidelines of vegetables, while recent advancements in surface renewal instrumentation have led to a relatively low-cost tool for monitoring crop water requirement in commercial farms. Remote sensing of crops using satellite, manned aircraft, and UAV platforms may also provide useful tools for vegetable growers to evaluate crop development, plant stress, water consumption, and irrigation system performance.
Wood is an inherently hygroscopic material which tends to absorb moisture from its surrounding. Moisture in wood is a determining factor for the quality of wood being employed in construction, since ...it causes weakening, deformation, rotting, and ultimately leading to failure of the structures resulting in costs to the economy, the environment, and to the safety of residents. Therefore, monitoring moisture in wood during the construction phase and after construction is vital for the future of smart and sustainable buildings. Employing bio‐based materials for the construction of electronics is one way to mitigate the environmental impact of such electronics. Herein, a bio‐graphene sensor for monitoring the moisture inside and around wooden surfaces is fabricated using laser‐induced graphitization of a lignin‐based ink precursor. The bio‐graphene sensors are used to measure humidity in the range of 10% up to 90% at 25 °C. Using laser induced graphitization, conductor resistivity of 18.6 Ω sq−1 is obtained for spruce wood and 57.1 Ω sq−1 for pine wood. The sensitivity of sensors fabricated on spruce and pine wood is 2.6 and 0.74 MΩ per % RH. Surface morphology and degree of graphitization are investigated using scanning electron microscopy, Raman spectroscopy, and thermogravimetric analysis methods.
A humidity and moisture sensor are produced directly on wood using a bio‐based ink in combination with laser‐induced graphitization. Highly sensitive sensors are achieved which can be used to monitor the moisture content inside of the wood, as well as the surrounding air. The devices are demonstrated in controlled environment using wireless readout electronics.
In southern Europe, irrigation is the major water user and thus, development of operational tools that support decisions aiming to improve irrigation management, is of great importance. In this ...study, a web-based participatory decision support system for irrigation management (DSS), based on the principles of UN FAO’s paper 56, without requirement for any special monitoring hardware to be installed in each field, is evaluated for the case of a commercial wine grapevine (Vitis vinifera ‘Vertzami’) located at Epirus (northwest Greece), for two successive years (2021 and 2022). The soil moisture time series that were generated by the DSS’s model were compared to those measured by soil moisture sensors. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) ranged between 2.98–3.22% and 3.63–4.06%, respectively, under various irrigation practices and goals. Irrigation resulted very high yields and Crop Water Productivity (WPC) was 20–44% improved when following the DSS’s recommendations. The results also confirm potential pitfalls of sensor-based soil moisture monitoring and rainfall estimations using mathematical models. Finally, the value of water meters as practical sensors, which could support efficient irrigation management, is underlined. In every case, mindful application of decision support systems that require minimum or no hardware to be installed in each field, could extensively support growers and agronomic consultants to test, document and disseminate good practices and calculate environmental indices.
AbstractA study was conducted in Cary, North Carolina, in the spring and summer of 2009 with the purpose of evaluating the effectiveness of two “smart irrigation” controllers based on the amount of ...irrigation applied and resulting turf quality in residential settings. Twenty-four residential sites were selected, in clusters of four, representing six geographical areas within the town. Each geographical cluster included one site of each treatment. The treatments were standard irrigation controller with an add-on soil moisture sensor system (SMS); standard irrigation controller with an add-on evapotranspiration-based adjustment system (ET); standard irrigation controller using seasonal runtimes based on historical climate data (ED); and a control group which used a standard irrigation controller with no intervention (CON). Weekly water usage was obtained from irrigation meter readings and turf quality was characterized using a visual rating and a normalized difference vegetation index (NDVI) meter. Maximum water savings were achieved by the SMS treatment (42% less than CON), followed by ET and ED treatments. No statistical difference in average weekly water use was found between the ET group and the ED group provided with controller run-time guidance. The mean weekly visual turf quality index was highest for the SMS treatment, but only statistically different from the ED group. Average weekly NDVI was greatest for the ED group, although average NDVI values were not statistically different among any of the groups. Although water use was less during the 2009 study period contrasted against the three previous years for those receiving some form of intervention (ED, ET, and SMS), the same trend in water use was found by the CON group, rendering any findings in change in behavior inconclusive. Variability in water application by cooperator groups receiving an intervention decreased in the study period compared to the three previous years, suggesting an impact.
In this work, laboratory based virtual instrumentation (VI) system for recalibration of various smart soil moisture sensors is implemented along with modelling and performance analysis of these ...sensors. This semiautomatic VI system integrates the standard gravimetric method with wireless data acquisition device to ascertain gravimetric water content in the field soil and online acquisition and analysis of the corresponding voltage samples of the sensors. The proposed VI system, is effectively used to test and record the recalibration data of commercially available analogue groove resistive sensor and capacitive (V1.0) soil moisture sensors and devise efficient characteristics models for the same. Both least-square linear-fit and polynomial-fit inverse models reveal that non-linear model of capacitive soil-moisture sensor has high accuracy (R2 = 0.98) as compared to resistive soil moisture sensor (R2 = 0.96). Further, to enhance measurement accuracy of capacitive soil moisture sensor, 3-layer neural network is modelled using back propagation algorithm to describe the output-input relationship of capacitive senor providing highest value of accuracy (R2 = 0.99). Easy to use and operate with remote connectivity, the VI tool offers ready to use on-site system for recalibration, modelling and comparison of many smart soil moisture sensors for reliable use in IoT based irrigation and water management.
The use of soil moisture sensors is a practice applied to improve irrigation water management. ECH2O-5TE sensors are increasingly being used to estimate the volumetric water content (VWC). In view of ...the importance of the efficient use of these devices, six main factors affecting the accuracy of sensor measurements were studied: soil moisture levels, soil salinity, temperature, organic matter, soil texture, and bulk density. The study showed that the electrical conductivity of the soil and the temperature independently affect the measurements, while the influence of other factors interferes with that of salinity. This study found that the sensor measurements of the VWC were closest to the actual VWC at the soil ECe and temperatures of 2.42 dS m−1 and 25 °C, with root-mean-square errors (RMSE) of 0.003 and 0.004 m3 m−3. Otherwise, the measured VWC values of these sensor readouts significantly overestimated the actual VWC, with an increasing soil ECe and/or producing temperatures higher than the stated values, and vice versa. Given the importance of these sensors for obtaining accurate measurements for water management, a simplified empirical equation was derived using the data collected from a wide range of measurements to correct the influences of electrical conductivity and temperature on the measurement accuracy of the sensors, while considering the influence of the soil’s texture. Thus, the following equation was proposed: ϴva = θvsaECe2+bECe+c+dT2+eT+f−1. The results concerning the measurement of different VWC levels via these sensors and the proposed L&O correction equation were compared with the corresponding actual VWC values determined by gravimetric methods. It was found that this empirical equation reduced the differences in the RMSE between the sensor readings for the VWC and the actual VWC from 0.072 and 0.252 to 0.030 and 0.030 m3 m−3 for 1 and 5 dS m−1, respectively, with respect to the EC’s influence at 25 °C and reduced the RMSE from 0.053 and 0.098 to 0.007 and 0.011 at 3 and 50 °C, respectively, regarding the effect of the temperature at EC 2.42 dS m−1 at different levels of the actual VWC values.
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