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Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the potential to be widely implemented across multiple sites for landslide early warning systems. However, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This research aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determination (R2) ranging from 0.918 to 0.983 and a root mean square error (RMSE) ranging from 1.171 to 2.488. The calibration equation was validated through slope model experiments, with soil samples taken from the models after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 12% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensor has the potential to be used for the development of landslide early warning system. [ABSTRACT FROM AUTHOR]

We present a high-sensitivity fiber optic soil moisture sensor based on side-polished multimode fibers and lossy mode resonance (LMR). The multimode fibers (MMFs), after side-polishing to form a D-shaped structure, are coated with a single-layer SnO2 thin film by electron beam evaporation with ion-assisted deposition technology. The LMR effect can be obtained when the refractive index of the thin film is positive and greater than its extinction coefficient and the real part of the external medium permittivity. The D-shaped fiber optic soil moisture sensor was placed in soil, allowing moisture to penetrate into the thin film microstructure, and it observed the resonance wavelength shift in LMR spectra to measure the relative humidity change in soil. Meanwhile, an Arduino electronic soil moisture sensing module was used as the experimental control group, with soil relative humidity ranging from 10%RH to 90%RH. We found that the D-shaped fiber with a residual thickness of 93 μm and SnO2 thin film thickness of 450 nm had a maximum sensitivity of 2.29 nm/%RH, with relative humidity varying from 10%RH to 90%RH. The D-shaped fiber also demonstrates a fast response time and good reproducibility. [ABSTRACT FROM AUTHOR]

Conventional vegetable watering often presents challenges, particularly in ensuring that plants receive adequate water without excessive manual intervention. This research proposes a solution in the form of an automatic watering system using fuzzy logic, which integrates soil moisture sensors, rain sensors, and an RTC (Real-Time Clock) for scheduling. The system is designed to replace manual watering methods with an automated process, thus improving the efficiency and effectiveness of vegetable cultivation. The developed device uses a soil moisture sensor to monitor soil conditions, a rain sensor to detect rainfall, and an RTC to determine the optimal watering times. The Arduino Uno acts as the main controller that activates the water pump via a relay driver based on data received from the sensors. Test results show that the system operates according to the established criteria, with a satisfactory accuracy level. The system successfully waters the plants at 07:00 WIB and 15:00 WIB, based on dry soil conditions and no rain. The trials showed that the device has an average soil moisture measurement error of 5%, and a time discrepancy of about 22 seconds on the RTC module. Each 1% increase in soil moisture requires approximately 1 second of watering duration. Watering times are adjusted to prevent the plants from drying out or dying, with a soil moisture threshold of below 40% set as the condition for requiring watering.

The soil moisture sensor provides numerical measurements to detect changes in soil moisture using an analog voltage output. This research aims to develop a capacitive sensor based on a statistical model to detect soil moisture for plant watering, leveraging the Internet of Things (IoT). The analysis was conducted using polynomial and linear regression models. The modeling process was based on primary gravimetric test results from dried soil. The best model coefficients, selected based on the highest adjusted R-squared value, were used for sensor recalibration. A watering system was then developed using an Arduino and a model-driven capacitive soil moisture sensor integrated into an internet-connected smart plant pot, enabling remote control via a mobile phone. The research findings indicate that the 8th-order polynomial model, with the highest adjusted R-squared value of 0.9583, is the most accurate. The smart watering system using the model-driven capacitive sensor achieved soil moisture prediction outcomes ranging from 0.08 to 1.01 for 150 to 418 sensor data points. The internet-connected smart plant pot allows precise and real-time control, delivering notifications and enabling actions when plants require watering.

Slope monitoring has become mandatory in opencast mines. Despite various slope monitoring systems, Wireless Sensor Network is faster and more efficient. It is essential to test the sensors before installation in the field to assess their effectiveness and suitability for a particular slope condition. The significant factors affecting the slope's stability are soil moisture and vibration intensity. This study aims to analyze the functions and performance of sensors used in the advanced real-time wireless slope monitoring system. The sensors were validated by comparing the results from the lab experiment with existing methodologies like the Gravimetric method and Geophone method, then installed in a slope model of clay built on a laboratory scale to analyze their performance. The results are drawn and discussed in conclusion. [ABSTRACT FROM AUTHOR]

With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R 2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm−3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions. [ABSTRACT FROM AUTHOR]

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino's built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant. [ABSTRACT FROM AUTHOR]

Various environmental factors not only affect plant growth and physiological responses individually but also interact with each other. To examine the impact of light intensity on the drought responses of sweet basil, plants were subjected to maintenance of two substrate volumetric water contents (VWC) using a sensor-based automated irrigation system under two distinct light intensities. The VWC threshold was set to either a dry (0.2 m3·m−3) or sufficiently wet condition (0.6 m3·m−3) under low (170 μmol·m−2·s−1) or high light intensities (500 μmol·m−2·s−1). The growth and physiological responses of sweet basil (Ocimum basilicum L.) were observed over 21 days in the four treatment groups, where the combination of two environmental factors was analyzed. Under high light intensity, sweet basil showed lower Fv/Fm and quantum yield of PSII, compared to that under low light intensity, regardless of drought treatment. Fourteen days after drought treatment under high light intensity, stomatal conductance and the photosynthetic rate significantly reduced. Whereas plants under low light intensity showed similar stomatal conductance and photosynthetic rates regardless of drought treatment. Assessment of shoot and root dry weights revealed that plant growth decline caused by drought was more pronounced under high light intensity than under low light intensity. Thus, sweet basil showed significant declines in growth and physiological responses owing to drought only under high light intensity; no significant changes were observed under low light intensity. [ABSTRACT FROM AUTHOR]

Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI) is an important technique for determining stress levels in the plant and directing irrigation management techniques. To determine the CWSI for sunflower, a pot-based research trial was carried out in the research field of the Department of Soil and Water Engineering, PAU, Ludhiana (India) during the summer of 2023. The sensor-based irrigation scheduling was carried out on the basis of the depletion of total available soil moisture (TASM). The drip irrigation treatments consist of I1 (full irrigation), I2 (20% depletion of TASM), and I3 (40% depletion of TASM). The results revealed that the highest amount of water applied under I1 was recorded at 484.4 mm, while I2 (387.5 mm) and I3 (290.7 mm) during the growing season of sunflower. The results revealed that for kernel diameter and seed weight, I1 and I2 were statistically non-significant to each other while I1 and I3 were statistically significant to each other. The highest water productivity recorded for I2, followed by I3. The overall findings revealed that an average CWSI value of 0.85 for the sunflower crop falls within the range of lower and upper baselines. The response of physico-chemical properties of sunflower seeds showed a high correlation with the draught condition. HIGHLIGHTS Investigated crop water stress (CWSI) of sunflower under varying soil moisture depletion.; To develop lower and upper limits of baselines for fully irrigated and under stressed conditions.; To analyse physico-chemical changes of sunflower seed and oil.; To develop sensor-based irrigation scheduling for sunflower crops under deficit condition.; To analyse the water productivity of sunflower under various irrigation regimes.;

The automation of agriculture can transform farming from manual to dynamic, resulting in higher profits with less manual management. This article introduces the use of automatic irrigation to monitor and control soil moisture through automatic irrigation. The control unit is implemented by an ATMEGA328P microcontroller on the Uno platform. This device uses a hygrometer to measure actual humidity. This benefit ensures that the system uses water correctly, thus preventing excess/underwater. IoT is used to inform farmers about the status of the water supply. Sensor data are updated regularly via the GSM-GPRS website, and farmers can check whether the water head is open/closed at any time via the website. Sensor readings are transmitted to the object's audio channel to create an image. [ABSTRACT FROM AUTHOR]

Arugula (Eruca sativa Mill.) is a nutritious vegetable, commonly used in salads, known for its high glucosinolate content and various health benefits and flavors. However, arugulas may contain -excessive nitrate levels, potentially harmful to human health. We aimed to examine the effect of substrate moisture levels on the growth and quality of arugula under controlled irrigation conditions to investigate a proper irrigation practice for the quality production of arugula. The plants were cultivated using a sensor-based automated irrigation system to maintain the substrate volumetric water content (VWC) levels at 0.20, 0.30, 0.40, and 0.50 m3·m−3 over three weeks (vegetative stage). The treatment with VWC of 0.20 m3·m−3 resulted in reduced shoot growth, primarily attributed to drought-induced constraints on leaf expansion. Despite the initial reductions in stomatal conductance in arugulas subjected to lower VWC treatments, they eventually recovered and exhibited similar stomatal conductance levels across all VWC treatments 15 days after treatment, indicating acclimation to drought stress. The VWC treatment did not affect the nitrate and total glucosinolate contents of arugula, except for a decrease in glucoerucin content observed in the lowest VWC treatment. Maintaining a VWC level at 0.20 m3·m−3 could impair both the growth and quality of arugula due to severe drought conditions. Alternatively, maintaining the VWC at 0.30 m3·m−3 would ensure a high water use efficiency while securing the growth and quality of arugula. [ABSTRACT FROM AUTHOR]

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in themonitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes' responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes' readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management. [ABSTRACT FROM AUTHOR]

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Frequency domain reflectometry (FDR) sensors can measure moisture content and electrical conductivity (EC) of growing media, thus providing information for effective water and nutrient management of crops. FDR sensors generally provide the bulk EC (σb) value, which is different from the saturated extract EC (σe) values that most growers and practical studies generally use. To properly estimate σe of media, we modified the previous Hilhorst model that calculates pore water EC (σp) from FDR sensor readings. The 16 coir samples with four moisture and four EC levels were prepared, and their changes in the dielectric constant of bulk medium (εb), σb, and medium temperature from the FDR sensors were recorded and used for analysis. As a result, the εσb=0 (εb when σb is 0) of the coir used in this experiment was 1.64, which was much lower than the εσb=0 suggested for mineral soil (4.1), indicating that the medium-specific εσb=0 should be adjusted. Although σe value estimation through the Hilhorst model with adjusted εσb=0 improved, the values were still unreliable to represent the actual σe values (root mean square error, RMSE = 2.280, R2 = 0.30). To convert the σp derived from the Hilhorst model with adjusted εσb=0 to actual σe, the regression equation was applied as σe = 0.5381 × σp + 1.6493. The adjusted Hilhorst model could provide a more reliable σe estimation (RMSE = 0.929, R2 = 0.62). These results indicate that proper adjustment of εσb=0 for the specific medium is necessary, and adjusting the equation for converting σp into σe would be helpful to properly interpret the σb to practically utilize the FDR sensors for effective water and nutrient management.

Low-cost automatic irrigation systems require quality calibrated soil moisture sensors. The sensor is an indirect method of soil moisture measurement. The sensor works based on the change in the dielectric constant. So, it requires to be calibrated in terms of the soil water content. Polynomial and linear models are frequently used to calibrate soil moisture sensor data in the gravimetric test method. However, computational effort is required. This study aims to obtain a sensor calibration application that can provide the best model of the available models for model-based capacitive soil moisture sensor. This research was conducted using primary data from gravimetric test experiment on Internet of things (IoT) based soil moisture sensor. Web-based re-calibration application produced best model based on adjusted R Squared. Finally, model-based capacitive soil moisture sensor set up using best model coefficient. The results show that the web-based re-calibration application can provide the best model for model-based capacitive soil moisture sensor. Based on gravimetric test experiments and web applications, the best model is a polynomial regression model order 3 with 0.945 adjusted R Squared. The model predicted value for soil moisture is in the range 0 – 1.2 for raw sensor data values of 100 – 530. When the model coefficient configured in capacitive soil moisture sensor and Blynk application, soil moisture measurement can be done via mobile phone in real time.

Objective Soil moisture information crucial for various applications, such as natural ecological restoration, farmland irrigation management, and soil engineering construction. One of the main sensors used to obtain this information is the capacitive soil moisture sensor. Methods To accurately calibrate the soil water content observation data of the 5TE capacitive soil water sensor, soil dielectric permittivity, electric conductivity and temperature observation experiments were carried out under different temperature, salt content and soil water content conditions. A soil water content estimation model based on the random forest machine learning method was established. Results The results showed that: ① The soil dielectric permittivity was significantly affected by varying salinity and temperature with constant soil water content. The traditional soil water content estimation model based only on soil dielectric permittivity became invalid, ② The soil water content estimation model based on the random forest method could effectively improve the soil water content estimation with the soil dielectric permittivity, electric conductivity and temperature data as input. Random forest method obtained soil moisture estimation with RMSE=0.05 m3/m3 and R2=0.77, while RMSE=0.07 m3/m3 and R2=0.54 were obtained by the modified Topp equation, and ③ The soil electric conductivity was the most important factor for soil water content estimation, followed by the dielectric permittivity and temperature. Nevertheless, the importance of the dielectric permittivity and temperature did not reach a negligible level. Conclusion This study provides a way to support the successful application of capacitive soil moisture sensors in areas with variable temperature and salinity.

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

Watering and fertilizing are very important to maintain the quality of the oil palm seeds. If the oil palm seeds are not watered regularly the soil will dry out while the oil palm seeds generally need to maintain adequate soil moisture and require a loose textured soil medium. So far, farmers have not optimally fulfilled the needs for water and fertilizer for oil palm seeds because they still use manual and traditional methods. Technological developments have made it possible to create irrigation and fertilization techniques automatically. In this study, a prototype of an automatic watering and fertilizing system was designed and realized for oil palm seedlings using the NodeMCU ESP8266 as the main controller in addition to RTC, humidity sensors, relays, water pumps, LCDs, and smartphones. RTC is used for fertilization scheduling, humidity sensors to sense soil moisture levels, relays as automatic switches that turn on and off pumps, water pumps to pump water and liquid fertilizer, LCD to display information, and smartphones to monitor conditions and processes of watering and fertilizing coconut seedlings palm. The test results show that the prototype tool can work well. When the soil moisture is read by the humidity sensor less than 30% (dry soil condition) then pump one is activated and when the soil moisture is more than 60% (wet soil condition) then pump one turns off. Furthermore, fertilization can take place according to a predetermined schedule where the second pump will automatically activate with a duration of 20 seconds for each fertilization period.

Measuring the soil water content (SWC) is a fundamental component of the sustainable management of water resources, soil preservation, and high irrigation efficiency. Non-destructive SWC measurements using soil moisture sensors (SMSs) enables timely irrigation and reduces overirrigation and water stress. Within this context, the performance of four commercial single-point soil moisture sensors (Watermark and tensiometer (Irrometer Company, Inc., Riverside, CA, USA), SM150 (Delta-T Devices, Cambridge, UK)), FieldScout TDR300 (Spectrum Technologies, Aurora, IL, USA) and one soil profile PR2 probe (Delta-T Devices, Cambridge, UK) were tested under anthropogenic eutric cambisol with a silty clay loamy texture (20, 30, and 40 cm) to evaluate accuracy and sensitivity to changes in the SWC in an irrigated apple orchard. The Watermark and tensiometer were additionally tested in the laboratory to convert soil water tension (kPa) to the volumetric soil water content (%vol.). In general, all tested SMSs responded to changes in the SWC, with sensor-to-sensor differences. The Watermark and tensiometer underestimated the SWC, while the TDR overestimated the SWC. The SM150 and PR2 showed high accuracy, i.e., SM150—RMSE-2.24 (20 cm), 2.18 (30 cm) and 2.34 (40 cm), MSE—5.02 (20 cm), 2.93 (30 cm) and 1.89 (40 cm), and PR2—RMSE-1.8 (20 cm), 1.3 (30 cm) and 1.55 (40 cm), MSE-3.23 (20 cm), 1.7 (30 cm) and 2.39 (40 cm) at all observed soil depths.

The automation of agriculture can transform farming from manual to dynamic, resulting in higher profits with less manual management. This article introduces the use of automatic irrigation to monitor and control soil moisture through automatic irrigation. The control unit is implemented by an ATMEGA328P microcontroller on the Uno platform. This device uses a hygrometer to measure actual humidity. This benefit ensures that the system uses water correctly, thus preventing excess/underwater. IoT is used to inform farmers about the status of the water supply. Sensor data are updated regularly via the GSM-GPRS website, and farmers can check whether the water head is open/closed at any time via the website. Sensor readings are transmitted to the object’s audio channel to create an image.

Arugula (Eruca sativa Mill.) is a nutritious vegetable, commonly used in salads, known for its high glucosinolate content and various health benefits and flavors. However, arugulas may contain -excessive nitrate levels, potentially harmful to human health. We aimed to examine the effect of substrate moisture levels on the growth and quality of arugula under controlled irrigation conditions to investigate a proper irrigation practice for the quality production of arugula. The plants were cultivated using a sensor-based automated irrigation system to maintain the substrate volumetric water content (VWC) levels at 0.20, 0.30, 0.40, and 0.50 m3·m−3 over three weeks (vegetative stage). The treatment with VWC of 0.20 m3·m−3 resulted in reduced shoot growth, primarily attributed to drought-induced constraints on leaf expansion. Despite the initial reductions in stomatal conductance in arugulas subjected to lower VWC treatments, they eventually recovered and exhibited similar stomatal conductance levels across all VWC treatments 15 days after treatment, indicating acclimation to drought stress. The VWC treatment did not affect the nitrate and total glucosinolate contents of arugula, except for a decrease in glucoerucin content observed in the lowest VWC treatment. Maintaining a VWC level at 0.20 m3·m−3 could impair both the growth and quality of arugula due to severe drought conditions. Alternatively, maintaining the VWC at 0.30 m3·m−3 would ensure a high water use efficiency while securing the growth and quality of arugula.

This publication addresses the influence of irrigation scheduling on water use, plant growth, and development for young pongamia trees (Millettia pinnata (L.) Panigrahi). Written by Daniel Palacios, Sandra M. Guzmán, Amir Rezazadeh, Liliana M. Cano, Lorenzo Rossi, and Alan Wright, and published by the UF/IFAS Department of Agricultural and Biological Engineering, November 2023.

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The Internet of Things (IoT) is transforming the agriculture industry and enabling farmers to contend with the enormous challenges they face. Livestock monitoring, conservation monitoring, and plant & soil monitoring are the challenges for which IoT can provide solutions. Innovative IoT applications address agricultural issues, enhancing the quality, quantity, sustainability, and cost-effectiveness of production. Today's large and local farms can leverage IoT to remotely monitor sensors detecting soil moisture, crop growth, pests, and control smart connected harvesters and irrigation equipment. This paper aims to monitor soil parameters such as moisture, temperature, and electrical conductivity, and automate the irrigation process. Decision-making is executed through a microcontroller. Users are notified via android apps when there are deviations from expected values, ensuring system health. Greenhouses are controlled environments for plant growth. The main aim of this paper is to design a simple, low-cost, IoT-based system to continuously monitor and control environmental parameters to achieve optimal plant growth and yield. [ABSTRACT FROM AUTHOR]

Aim: Efficient fog water utilization under soil drought conditions is important for improving the water status and growth of forest trees. However, importance of fog water retention for trees during drought is yet to be completely examined. The aim of this study was to examine changes in fog water retention in different organs of Japanese black pine (Pinus thunbergii) saplings under well-watered and drought conditions. Methods : P. thunbergii saplings were grown under well-watered and drought conditions in a glasshouse for 19 days and exposed to 2 H-enriched water vapor under a high relative humidity (~ 95%) for 1 h in a growth chamber under a uniform light condition. The exposed trees were harvested 48 h after the fog exposure experiment and the 2 H concentration of water in current needles, old needles, current branches, old branches, roots, and soil was determined. Results: Absorbed fog water was detected in the needles, branches, and roots 48 h after exposure to fog. The amount of water in the current needles, old needles, and current branches contributed by fog water was significantly higher in drought-stressed trees than in well-irrigated trees. Small amount of absorbed fog water was redistributed to the soil, irrespective of soil water conditions. Conclusion: We found that larger amount of absorbed fog water was retained in drought-stressed trees than that in well-watered trees, which may improve the water status of plants under drought. Our results also suggest that hydraulic redistribution from the roots to the soil may occur irrespective of soil drought conditions. [ABSTRACT FROM AUTHOR]

Water-pipeline leakage is one of the most common problems that depletes water supplies. Countries like Jordan, which are really experiencing a water deficit, are particularly concerned about this issue. The lack of monitoring tools makes the underground water-pipeline leakage a challenge since the pipelines are invisible. Besides, reducing the amount of time needed to precisely detect and locate the leak is another challenge. If not reduced, the aforementioned element has an effect on cost. A small broken water distribution line costs $64,000 per year. In Jordan, water leakage costs $1.7 million. This expense can be significantly decreased using an effective early water leak detection system. In this paper, we proposed an efficient internet of things system for detecting water-pipeline leakage based on a shielded pipeline, a NodeMCU, a soil moisture sensor, and the Firebase database. We created a baseline system, and then we tested and evaluated the proposed system when various types of soil are used. Furthermore, this paper compared several strategies offered for detecting water-pipeline leaking including the proposed system. The results showed that the proposed system reduced the time required for detecting water-pipeline leakage by 70% and the system hardware cost by 83% compared with the earlier work. It was difficult to compare the total cost of the proposed system with the total cost of previous works since the total cost is not calculated in their systems. Besides, in this paper, we proposed an IoT system for securing the underground water pipelines from adversaries. [ABSTRACT FROM AUTHOR]

This study investigated how the substrate volumetric water content (VWC) and fluctuating irrigation affect the growth and performance of variegated Ficus pumila 'Variegata' indoors using a soil moisture sensor based-automated irrigation system. The irrigation treatments consisted of three treatments maintaining a constant VWC (at 0.20, 0.40, and 0.60 m3·m-3) and one treatment with a fluctuating VWC (threshold, 0.20 m3·m-3; 0.20 F). Plant shoot growth, chlorophyll content, leaf variegation, and coloration parameters were measured at four weeks after treatment. The 0.20 and 0.20 F treatment groups had lower shoot fresh weight than the groups maintained at higher VWC thresholds. Although the 0.40 and 0.20 F treatments had similar average VWCs during the experiment, the 0.20 F group had less shoot growth than the 0.40 group, resulting in a lower irrigation amount. Physiological responses, such as leaf relative water content and Fv/Fm, were not affected by irrigation treatment. Similarly, leaf variegation and coloration were not influenced by the irrigation treatments. A higher irrigation amount was required to maintain the 0.60 treatment group compared to the 0.40 treatment group, even though there were no significant differences in growth parameters between the groups. In contrast, plants in the 20 F treatment group had high water use efficiency, as they had proper growth with low irrigation amounts. Overall, maintaining a low water content or providing intermittent irrigation at 0.20 m3·m-3 provided a slow growth of F. pumila 'Variegata' indoors, resulting in adequate plant performance and ease of management for house plants. [ABSTRACT FROM AUTHOR]

The aim of research is to reduce the utilization of water by irrigating the agricultural fields that have low moisture level through the implementation of Wireless Sensor Network (WSN). The ARM-based irrigation solution consists of a Solar Tracking System, Wireless Information Unit (WIU), WSN and Remote Access. The Solar Tracking System utilizes maximum solar energy by using Light Dependent Resistor(LDR) to track the sun. The electric energy produced is stored in the battery which powers the ARM processor. Wireless Information Unit will collect the sensor information from Wireless Sensor Network through the use of Wi-Fi. The system monitors soil-moisture and controls the flow of water using a solenoid valve depending upon the set threshold. In the tank, Ultra Sonic sensor identifies the water level controlled by the WIU. An Android application helps the farmer to monitor the water flow and soil moisture remotely. The proposed water management methodologies for agriculture optimizes the water usage and such practices maintain the crop health, keep yield intact, and avoids human intervention. In this proposed system, soil moisture is maintained appropriately to ensure that a good harvest is obtained which in turn preserves different types of nutrients.

Technology in agriculture has been widely and massively applied. One of them is automation technology and the use of big data through the Internet of Things (IoT). The use of IoT allows a process to run automatically without human intervention. Extreme weather changes and narrow land use are one of the main problems in agriculture. The development of IoT devices has been widely developed regarding this subject. One of them is a soil moisture detection system. This study aims to build an IoT soil moisture detection system. The system will use a sensor as input which is then processed in a microcontroller device and the prediction results are sent to the IoT cloud platform. Prediction results are obtained using a time series model and then its performance is evaluated using RMSE. This model was chosen because the structure of the observed soil moisture data is based on time. The results of this study indicate that the soil moisture IoT system can work well. This is supported by the results of the prediction evaluation value of the RMSE = 1.175682x10-5 model which is very small.

In this article, the Moroccan climate is currently undergoing several changes that have negatively affected agricultural activities, especially in the field of irrigation. That's why we relied on internet of things (IoT) technology to overcome these problems. In this article, we present the design and realization of an intelligent irrigation system via a solar submersible pump. We used the ESP32 microcontroller which reads the temperature and humidity values measured by the soil moisture sensor. The communication between the blocks is ensured by the radiofrequency module. We have implemented a Web server to monitor the measured quantities. The graphical representation of the data will be ensured using the ThingSpeak platform which makes it possible to store and collect the data coming from the sensors via the hypertext transfer protocol (HTTP). Our achievement was executed and tested without any problems detected, which shows that our smart irrigation study was very successful. [ABSTRACT FROM AUTHOR]

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With India's population growing at a rapid pace, traditional agriculture will have a tough time meeting future food demands. Water availability and conservation are major concerns for farmers. This paper aims to discuss the aspects related to designing and fabricating an automatic irrigation system using the Internet of Things (IoT) which will save the farmer's time and money significantly. Human intervention in fields will be reduced. Changes in soil moisture are detected by soil moisture sensors and irrigation is automated using IoT. The proposed system is most economical for underdeveloped places because it is very costeffective. Based on the soil moisture content, the sensor detects and sends signals to the node MCU, which activates the motor. When the plants receive enough water, the motor automatically shuts off. The user will be alerted about the soil's moisture content through his mobile phone. The proposed smart irrigation system is implemented at our campus which conserves energy and water. [ABSTRACT FROM AUTHOR]

This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33 % , a virtual sensor of 8.67 % , and an augmented sensor of 1.84 % , which improves down to 1.53 % if filtered in post-processing. [ABSTRACT FROM AUTHOR]

The aim of research is to reduce the utilization of water by irrigating the agricultural fields that have low moisture level through the implementation of Wireless Sensor Network (WSN). The ARM-based irrigation solution consists of a Solar Tracking System, Wireless Information Unit (WIU), WSN and Remote Access. The Solar Tracking System utilizes maximum solar energy by using Light Dependent Resistor(LDR) to track the sun. The electric energy produced is stored in the battery which powers the ARM processor. Wireless Information Unit will collect the sensor information from Wireless Sensor Network through the use of Wi-Fi. The system monitors soil-moisture and controls the flow of water using a solenoid valve depending upon the set threshold. In the tank, Ultra Sonic sensor identifies the water level controlled by the WIU. An Android application helps the farmer to monitor the water flow and soil moisture remotely. The proposed water management methodologies for agriculture optimizes the water usage and such practices maintain the crop health, keep yield intact, and avoids human intervention. In this proposed system, soil moisture is maintained appropriately to ensure that a good harvest is obtained which in turn preserves different types of nutrients. [ABSTRACT FROM AUTHOR]

Central Sulawesi Province in Indonesia has great potential for horticultural commodities, namely local red onion Palu. In the current climate change, local farmers are still watering plants in the conventional way. The automatic watering system simplifies the work of local farmers. This device uses a soil moisture sensor as a soil moisture detector and Arduino as a program brain. This study aims to determine the position of soil moisture sensor, the optimal length of watering time and analyze the quality of data stored. The experiment was carried out using a Completely Randomized Design (CRD). The position of the soil moisture sensor was analyzed by Profile Analysis. The optimal length of watering time was determined by Analysis of Variance (ANOVA) and Least Significant Difference (LSD). The quality of data stored was determined by a number of missing values and frequency of watering. The results showed that in soil planting media the position of soil moisture sensor had no significant effect, while in others planting media (water and combination of water and soil) the position of the sensor had a significant effect. The optimal watering time was 3 seconds. The stored data has low quality in terms of missing values and lack of consistency.

Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters. It also gives accurate and precise measurements when compared to traditional measurement techniques. This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature, moisture and humidity of their field. Real-time data on temperature, moisture, and humidity enables farmers to make informed decisions about irrigation and crop protection. Furthermore, the use of smart monitoring ensures accurate and precise measurements, surpassing the limitations of traditional techniques. [ABSTRACT FROM AUTHOR]

A more accurate monitoring system needs to be developed by calculating the infiltration process of rainwater into the soil. The development of a digital infiltration profiler (DIP) is expected to be able to improve the accuracy of the early warning system, as well as to open a more considerable opportunity to study the landslide mechanism related to the infiltration process and the increase of pore-water pressure in the soil at both field-scale and laboratory scale. The performance and reliability of developed DIP was tested through laboratory and field ponding simulation. The field testing was demonstrated through a real installment procedures to be applied in the actual case. The field testing also consisted of water ponding and data reading. Numerical simulation using SEEP/W software of Geostudio packages was conducted to be compared to the field-testing data. From the laboratory ponding experiment, the sensor readings correspond well to the wetting front observation. The field ponding observation results showed good response of the DIP in detecting the wetting front. The results also implied the similarity of reducing wetting front rate that was simulated in SEEP/W and previous studies. The coupled numerical simulation and slope stability analysis of the conceptual slope model suggested the importance of multi-depth saturation measurement is necessary. The near-surface observation needs to confirm the saturation of the soil surface. The deeper sensor can continuously monitor soil saturation change in prolonged rainfall to predict the safety factor of the slope. [ABSTRACT FROM AUTHOR]

Irrigation is one of the largest consumers of fresh water in the agricultural sector. Surface irrigation is one the most widely adopted method of irrigation due to its simplicity and low energy requirement. Although, it is mostly associated with poor irrigation application efficiency mainly due to deep percolation and uneven distribution of irrigation water. A study was conducted in closed-end level basin layout in loamy soil to establish and test an Internet of Things (IoT) based system through a wireless connection between the automatic check gate and soil moisture sensors which can be operated remotely based on real-time soil moisture status. An attempt had been made to identify the suitable location of the sensor in the basin layout for improving irrigation application efficiency. An aluminium automatic-check gate fitted in an iron frame was placed in the concrete water supply channel to control the water flow. Three capacitance-based soil moisture sensors were placed at 25%, 50% and 75% of field length (i.e., 15 m, 30 m and 45 m) at 37.5, 15 cm and 7.5 cm depth, respectively. A wireless communication network was established between capacitance-based soil moisture sensors and an automatic check gate through the LoRa module and GSM module. The software used for automatic control of the check gate in this study was Croplytics® by Agsmartic. The system was tested for nine irrigation events under bare soil conditions. Three different operational schedules based on different soil moisture sensor placement had been tested under 40%, 30% and 20% soil moisture deficit conditions. The results suggested that the best location of sensors for closing the system would be at 37.5 cm depth placed at 25% length from the inlet in higher soil moisture deficit condition and at 7.5 cm depth placed at 75% length in low moisture deficit condition. Overall, the irrigation application efficiency was improved up to 86.6% with automation.

Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped. [ABSTRACT FROM AUTHOR]

The process of manual fertilization and irrigation of plants has disadvantages such as it requires human labor and is inefficient in the use of fertilizers and water sources. Therefore, the purpose of this research is to design and develop an automatic fertigation system. The method used in this research is a drip fertigation technique where the fertigation liquid that has been stirred is placed in a tank with 100 cm height from ground. With the gravitational force, the fertigation liquid is distributed through pipes, hoses and emitters around the plant. While the control system and automation of fertigation distribution to plants is carried out based on a microcontroller that need the condition of soil moisture values around the plant. If the watering time and humidity values are met, the electronic valve will open so that the fertigation liquid flows from the tank to the plants. Furthermore, in this study the tests were carried out to determine the uniformity coefficient and debit of drip fertigation. From the results, it shows that the emitter’s debit influenced by the liquid level in the tank. The uniformity coefficient obtained in the two tests is greater than 90%. From the two tests carried out from the moisture sensor 1 and 2, it shows the mean percentage error for both sensor is less than 1.7%, while the percentage error of soil moisture sensor 1 is 1.6% and the percentage error of soil moisture sensor 2 is 1%, respectively. Meanwhile, from the ultrasonic sensor testing it was obtained 0% of error. It also shows from the testing conducted that the fertigation system works very well and successfully.

Eucheuma cottonii is a type of seaweed that is cultivated in the coastal area of Nagari Sungai Pinang, Koto XI Tarusan District, Pesisir Selatan District, West Sumatra Province. Before being sold, seaweed farmers must dry their seaweed first, because what can be sold is dried seaweed. Drying the seaweed takes two to three days depending on the weather conditions. In this research a seaweed drying system has been designed with a tool that rotates the seaweed in the drying chamber. In this rotary drying system, the soil moisture sensor is used which functions to detect the moisture content of the seaweed, if the moisture content of the seaweed is still read > 30% then the microcontroller turns on the relay which forwards instructions to the heater and the DC motor starts rotating the player container. The DS18B20 sensor is used to detect the temperature of the drying chamber, if the total moisture content of the seaweed is 30%, then the state of dry seaweed has been reached. Tests have been carried out to determine the difference in time required between the seaweed drying process using the system that has been built in this study and the seaweed drying process that utilizes direct sunlight. To achieve a moisture content of 30% in the seaweed drying process with the system designed in this study it takes an average of ±50 minutes, while using direct sunlight it takes ±9 hours to dry the seaweed.

ABSTRACT Capacitive methods for determining soil moisture are extremely practical and easily applied in the agricultural environment, but are influenced by temperature, salinity and the type of soil. In this study, the aim was to verify the performance of the Capacitive Moisture Sensor for Porous Media when subjected to saline media. The sensor under evaluation was developed at the Federal University of Ceará, and provides measurements and electrical frequencies related to soil moisture. The capacitive sensor for porous media was subjected to saline solutions with different concentrations of potassium chloride (KCl), when it was seen that the sensor electrodes appear sensitive to salinity when inserted in saline aqueous solutions with an electrical conductivity of up to 4 dS/m. Adjustments to the RC (resistor-capacitor) circuit were tested as an alternative to lessen the effect of salinity on the measurements, and it was possible to generate readings related to soil moisture in electrical frequencies ranging from kHz to MHz. Tests were then carried out in a sandy soil under different conditions of moisture and salinity. No significant differences were seen when using different resistors in the RC circuit that was constructed to lessen the effects of salinity on the measurements taken by the sensor, whether in solution or in the soil.

• Cost-effective, self-sufficient soil moisture monitoring systems are crucial for sustainable agriculture. • Frequently, low-cost soil moisture systems uses Arduino microcontroller and radio frequency communication devices. • Exploring in-situ alternative energy sources is crucial for remote areas that primarily depend on solar energy and batteries for power. • Need to explore other biodegradable polymers for the design of eco-friendly soil moisture sensors. • A cost-effective, off-grid biodegradable soil moisture sensor is not yet available in the market. Soil moisture monitoring is essential for a variety of applications including agriculture, forestry, and environmental monitoring. However, soil moisture sensors may be expensive and require batteries or other energy sources, making them unsuitable for remote or off-grid locations and farmers. Improper e-waste management of short-lived sensing components can reveal the contradictions of solutions aimed at environmental sustainability, which also degrade environmental health. Therefore, the development of low-cost, off-grid, biodegradable in-situ soil moisture sensing system (SMSS) is necessary for these regions. This article provides an overview of the current state-of-the-art in low-cost, off-grid, and biodegradable in-situ soil moisture sensing. It highlights low-cost SMSS components including hardware (microcontrollers and communication modules), software, and off-grid ambient energy sources. It also highlights the current research in biodegradable polymers used for moisture sensing. The challenges in combining low-cost, off-grid, and biodegradable soil moisture sensing are identified as a research gap. Finally, the underlining question of the "perfect" choice of SMSS is explored based on the trade-offs of performance, operational feasibility, and the newly proposed aspect of biodegradability, consequently suggesting context-specific decisions by consciously managing these tradeoffs. [ABSTRACT FROM AUTHOR]

Soil moisture sensors are valuable tools in irrigation management, but due to soil spatial variability it is challenging for point-based sensors to characterize the entire soil water profile. Universal multiple linear regression (uMLR) is a simple machine learning tool that analyzes the relationship between all possible subsets of observations and the prediction of interest. This study used uMLR and volumetric water content data (VWC) taken from maize field experiments at the Limited Irrigation Research Farm near Greeley, Colorado, to optimize the number and placement of soil moisture sensors to characterize the entire soil water deficit (SWD) in the root zone. The uMLR analysis quantified the accuracy of using one or two VWC measurements to represent SWD. We evaluated seven field seasons, with 12 treatments and four replicates of each, where volumetric VWC was typically measured one to three times weekly. Results indicate that the best single sensor depth is at 30 cm, and the best combination of two sensors is at 0 to 15 cm and 60 cm. To validate the experiment, a continuous VWC sensor was used at 30 cm to predict SWD results with acceptable accuracy (RMSE = 4.60 mm). This study suggests that in the sandy loam site evaluated and with varying irrigation levels, soil moisture sensor placement near the soil surface along with knowledge of soil texture can be used to characterize SWD. This technique may be transferable to other locations and soils and shows potential for optimizing irrigation management via soil moisture sensor networks with continuous SWD data. [ABSTRACT FROM AUTHOR]

This paper proposed a novel framework for soil humidity control and watering management based on the Internet of Things (IoT) and ARM. The framework is designed to minimize manual field process and transfer information to a cloud server that is accessible through a web application. The proposed scheme has advantages in reducing costs, restricting water waste as well as minimizing the physical interference. It also advocates low maintenance and environmentally sustainable irrigation. The sound, humidity and temperature sensors are combined with ARM single board computer (called the Raspberry Pi). A relay module is placed to regulate the stream of water. The sensor obtained values are stored on the cloud server and the necessary values and recommendation are provided via the web application. When the ground temperature is high and soil humidity is low, the automatic irrigation system is triggered and user notification can be performed with email. The model showed expected impacts at the various levels of humidity. [ABSTRACT FROM AUTHOR]

Increasing interest in studying the variability of soil water content and its spatial scale dependency necessitates the development of new techniques to accurately monitor soil water over a wide range of spatial scales. Distributed Temperature Sensing (DTS) techniques offer unprecedented opportunities to measure temperature with a spatial resolution of a few centimeters over several kilometers, which can be used to measure soil moisture. This study is the first of its kind that investigates under field conditions the feasibility of combining the Dual‐Probe Heat‐Pulse (DPHP) technique with the DTS technology to measure soil thermal properties and variation in soil moisture. A field experiment was conducted over a 30 m transect in the Lake Wheeler Field laboratory in Raleigh, NC. Three different DPHP sensors were constructed from combinations of different fiber optic cables and heating elements and were tested to assess their performance and the effect of their construction characteristics on their accuracy. Measurements were taken over different soil moisture conditions and the system performance was compared against independent soil water content sensors. The system was able to track changes in soil water content with a mean RMSE of 0.02 m3 m−3 using the optimal DPHP sensor. The key advantage of the tested system is that it does not need any site‐specific calibrations typically required for other DTS‐based systems. The findings of this study provide some practical information and measures that need to be taken for successful DTS‐DPHP construction and application under field conditions. Plain Language Summary: High‐resolution and accurate soil moisture measurements that can cover different spatial scales are required to support many applications including irrigation systems optimization. However, available techniques to measure soil moisture over scales that range from meters to several kilometers are lacking. This study is the first of its kind that investigates under field conditions a novel technique to measure changes in soil moisture that can bridge the gap in the spatial scales covered by the existing techniques. The novel technique utilizes the high‐resolution temperature measurements obtained using the Distributed Temperature Sensing (DTS) technology along a fiber‐optic (FO) cable and a heating element to measure the soil thermal properties and changes in soil moisture over the length of the FO cable. Three DTS‐based sensors varying in their properties were tested. The optimal combination resulted in measurements of the changes in soil moisture with a mean RMSE of 0.02 m3 m−3. The key advantage of the novel technique is that it does not need any additional information about the soil properties to give accurate measurements of changes in soil moisture. Key Points: This study is the first attempt to apply the novel Distributed Temperature Sensing‐Dual‐Probe Heat‐Pulse soil moisture sensing system under field conditionsThe technique can measure soil thermal properties and changes in soil moisture without any soil‐specific calibrationsThe system can measure changes in soil moisture with a mean Root Mean Square Error of 0.02 m3 m−3 and a mean maximum error of 0.05 m3 m−3 [ABSTRACT FROM AUTHOR]