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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]