Hygrometer Research Articles

Tilting deformation analysis and instability prediction of arch-locked-segment landslides induced by rainfall

The failure of locked-segment landslides is associated with the destruction of locked segments that exhibit an energy accumulation effect. Thus, understanding their failure mode and instability mechanism for landslide hazard prevention and control is critical. In this paper, multiple instruments, such as tilt sensors, pore water pressure gauges, moisture sensors, matrix suction sensors, resistance strain gauges, miniature earth pressure sensors, a three-dimensional (3D) laser scanner, and a camera, were used to conduct the physical model tests on the rainfall-induced arch locked-segment landslide to analyze the resulting tilting deformation and evolution mechanism. The results indicate that the tilting deformation characteristics in the locked segment are consistent with the variation in its strain, stress, hydrodynamic responses, and slope morphology, suggesting that tilting deformation can serve as a novel monitoring approach for landslide instability. Further, the tangent angle method and the tilting rate reciprocal method can be utilized to predict the landslide instability based on the landslide tilting deformation curve. The effectiveness of this method is validated in the Huangzangsi dam area, which provides theoretical foundations for understanding the catastrophic mechanism and instability prediction of arch-locked-segment landslides.

MECANIZAREA LUCRĂRILOR AGRICOLE PE PAJIȘTI PRIN VARIANTE TEHNOLOGICE CU INPUTURI MINIME. REVIEW

Grassland farming plays a vital role in sustainable agricultural systems, providing forage resources for livestock production and contributing to environmental conservation. However, the labor-intensive nature of grassland management requires significant challenges for farmers. The adoption of appropriate mechanization technologies can improve efficiency, reduce labor requirements, and enhance overall productivity. This paper investigates the mechanization of grassland farming through technological variants with minimal inputs. The incorporation of sensor technologies and data analytics facilitates real-time monitoring of grass growth, enabling farmers to make decisions regarding grazing rotations and forage quality. Additionally, the utilization of smart sensors for soil moisture and nutrient content allows for targeted application of inputs, reducing waste and optimizing resource utilization. Overall, this article highlights the potential of mechanization and technological variants with minimal inputs to make efficient the grassland farming, improving productivity, sustainability and the livelihoods of farmers.

Application of the JDL Model for Care and Management of Greenhouse Banana Cultivation

Rational management of scarce water resources is necessary. These resources are not utilised effectively. Therefore, the efficacy of irrigation management at the field level can be enhanced, and the irrigated areas can be expanded through rigorous irrigation management. By estimating water requirements in a straightforward, realistic, precise and feasible manner, achieving optimal water consumption for quality production and profitability is possible. In the context of the development of water resources in tropical and hot climates such as Ghana, estimating water demand assists farmers in planning and adjusting their requirements over time. This study assessed the water requirements of a greenhouse banana during the dry season to assure year-round cultivation, as Ghana has two primary seasons: wet and dry. The estimate was predicated using WSN and the JDL–Mivar data fusion model, which was dependent on the determination of perspiration. The results were contrasted with the existing literature, considering both climatic and biological data and other parameters during the cultivation period due to the model’s ability to fuse datasets. The study determined that the optimal indoor temperature for banana cultivation was 38.1 °C, while the minimum threshold was set at 21 °C. Significant differences and fluctuations in the maximal daily transpiration rates were observed in the water requirements for ‘WN’ values, which ranged from 25 to 50 m3/(ha·J). Banana plants require an intake of 10–20 litres of water per day during their growth season, according to the data collected from the WSN moisture sensor. The banana plants transpired between 100 and 600 kilogrammes of water for every kilogramme of dry matter produced during the humid climate, as indicated by the transpiration ratio, which ranged from 100 to 600. The Leaf Area Index (LAI) fluctuated from 3.3 in June to 4.89 in December. Our proposed method for monitoring bananas in a greenhouse will provide the cultivator with precise information about the bananas that are cultivated within the greenhouse environment. The optimal Leaf Area Index is between 3.6 and 4.5 for bananas to achieve their maximum yield potential. The relative humidity for bananas is typically around 80%, ranging from 65% to 75% during the night and approximately 80% during the day.

Design and simulation of an intelligent irrigation system using fuzzy logic

Farmers in developing countries employ various irrigation methods ranging from traditional techniques such as the use of irrigation cans to modern pressurized systems while irrigating short-seasoned crops that require continuous water supply especially during the dry season. Inspite of being cheap and easy to use, farmers find it challenging to optimize farming costs through the application of the neccessary amount of water onto the crops, leading to crop water logging and hence roting. Due to the growing demand for agricultural products, there was need to develop an irrigation system that considers the amount of moisture in the soil, air temperature and light intensity, which are important parameters in plants’ growth. This dissertation therefore presents the design, simulation and implementation of an intelligent irrigation system, utilizing multiple sensors and a fuzzy logic controller to optimize water usage while ensuring high farming yields. The system design integrates light, temperature, and soil moisture sensors to monitor environmental conditions and soil moisture content in real-time. The operation of the irrigation system is governed by a set of one hundred twenty five (125) fuzzy rules that process the inputs from these sensors, determining the appropriate irrigation duration based on the prevailing light intensity, ambient temperature, and soil moisture levels. The fuzzy logic controller employs a combination of membership functions and rule-based inference to handle the relationships between the environmental parameters and the irrigation needs of the soil. By continuously analyzing the data from the sensors, the system dynamically adjusts the water output, ensuring optimal soil moisture levels while minimizing water wastage. Additionally, the system is equipped to send notifications in form of calls to a smartphone connected to the GSM network in case of critical conditions or system malfunctions. Experimental results demonstrate the ability of the intelligent irrigation system to dynamically adjust the amount of water being pumped into the garden basing on the prevailing soil moisture, temperature and light intensity.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

Remotely Operated Video Enhanced Receiver

The "Remotely Operated Video Enhanced Receiver (ROVER) is a versatile system designed for remote environmental monitoring and data acquisition, tailored for challenging terrains, including extraterrestrial surfaces such as the Moon. The project integrates multiple sensors and a real-time video system to provide precise and actionable insights. A soil moisture sensor detects the presence of water or moisture in the substrate, triggering an LED to blink as an indicator. Additionally, a smoke and gas detector identifies harmful gases or smoke in the air, with an LED notification to alert the user to environmental hazards. These features make the system highly effective for monitoring and exploration in remote or hazardous environments. The system also employs an LDR (Light Dependent Resistor) to automatically activate the solar panel, optimizing energy usage by harnessing sunlight when available. A camera module provides real-time video streaming and recording capabilities, enabling visual observation of the surroundings. Designed for remote operation, ROVER offers a practical solution for applications such as planetary exploration, environmental hazard monitoring, and autonomous research operations. The integration of sensors with automated alerts and video recording enhances its usability and reliability for various scientific and industrial purposes.

Sensor-controlled fertigation management for higher yield and quality in greenhouse hydroponic strawberries

Controlled environment agriculture (CEA) for strawberry (Fragaria x ananassa) production has experienced a growth in popularity in recent years, particularly in North America. One of the most common growing systems in CEA strawberry production is the soilless hydroponic system, which uses an inert substrate and nutrient solution to grow the plants. There are several strategies for water management in substrates, and most are based on a rigid schedule rather than variable plant water requirements over time. Comprehensive comparisons among the different strategies are lacking because they are often associated with complicated evapotranspiration models. The use of soil moisture sensors coupled with automated controllers that apply water when the substrate moisture drops below a set threshold has been proven efficient for select ornamental crops and citrus nursery crops but not for strawberries yet. This study aimed to compare various fertigation management strategies and, considering both yield and resource use, determine the optimal strategy for two newly released strawberry cultivars. ‘Florida Brilliance’ and ‘Florida Beauty’ were grown in a greenhouse hydroponic system under six different fertigation management strategies: one timer-based, one leaching fraction-based, and four sensor-based strategies that automatically applied fertilizer solution to maintain a constant volumetric water content threshold (0.36, 0.30, 0.225, or 0.15 m3·m-3). Yield and resource use were quantified during the 129-day experiment, and plants were harvested at the end of the experiment to measure biomass and foliar nutrients. The yield was used to calculate the water and energy use efficiencies for each strategy. Considering yield and resource use efficiencies, the two drier constant volumetric water content thresholds (0.225 and 0.15 m3·m-3) and the leaching fraction-based strategy had optimal performance. The results of this experiment can aid growers in employing more efficient fertigation management strategies to increase crop quality and reduce resource use for CEA strawberry production.

Ultra-Fast Moisture Sensor for Respiratory Cycle Monitoring and Non-Contact Sensing Applications.

As human-machine interface hardware advances, better sensors are required to detect signals from different stimuli. Among numerous technologies, humidity sensors are critical for applications across different sectors, including environmental monitoring, food production, agriculture, and healthcare. Current humidity sensors rely on materials that absorb moisture, which can take some time to equilibrate with the surrounding environment, thus slowing their temporal response and limiting their applications. Here, this challenge is tackled by combining a nanogap electrode (NGE) architecture with chicked egg-derived albumen as the moisture-absorbing component. The sensors offer inexpensive manufacturing, high responsivity, ultra-fast response, and selectivity to humidity within a relative humidity range of 10-70% RH. Specifically, the egg albumen-based sensor showed negligible response to relevant interfering species and remained specific to water moisture with a room-temperature responsivity of 1.15×104. The nm-short interelectrode distance (circa 20nm) of the NGE architecture enables fast temporal response, with rise/fall times of 10/28ms, respectively, making the devices the fastest humidity sensors reported to date based on a biomaterial. By leveraging these features, non-contact moisture sensing and real-time respiratory cycle monitoring suitable for diagnosing chronic diseases such as sleep apnea, asthma, and pulmonary disease are demonstrated.

Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production.

Calibration of Low-Cost Capacitive Soil Moisture Sensors for Irrigation Management Applications.

The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture sensor (SKU:SEN0193, DFRobot, Shanghai, China) in a loamy silt soil typically found in the Puglia region of Italy. The calibration function was derived from a random sample of 12 sensors, with three soil sample replicas per sensor, each of which had one of five gravimetric soil moisture contents, from relatively dry (5%) to full saturation (40%). The study reports the resulting calibration function along with the accuracy achieved with the generalized calibration function. The sensors proved to be accurate, with an R2 value ranging between 0.85 and 0.87 and a root mean square value (RMSE) ranging between 4.5 and 4.9%. The variation between the sensors was also investigated. The results showed that with higher soil moisture contents (above 30%), the sensor-to-sensor variability becomes significant, with a coefficient of variation (CV) ranging between 10 and 16%; meanwhile, in lower soil moisture contents, the CV ranged between 6.5 and 10.3%, implying that it is more consistent in lower moisture content within this soil condition. The resulting calibration function enhances the integration of such low-cost sensors into smart farming solutions. With proper calibration, these affordable capacitive sensors can achieve a high degree of accuracy, making them a viable option for widespread use in cost-effective precision agricultural applications.

Cylindrical Cavity Resonating Sensor for Testing Moisture and Drug Content in Capsule Based on Machine Learning

Cylindrical Cavity Resonating Sensor for Testing Moisture and Drug Content in Capsule Based on Machine Learning

Design of a Low-cost Radiation Weather Station.

Combining a traditional weather station with radiation monitors draws the public's attention to the magnitude of background radiation and its typical variation while providing early indications of unplanned radiological releases, such as nuclear power plant accidents or terrorist acts. Several networks of combined weather and radiation monitoring sensors exist, but these fail to be affordable for broad distribution. This work involves creating an affordable system to accumulate data from multiple locations into a single open-source database. The data collected should thus serve as a friendly database for high school students. The system is designed around an inexpensive sensor package featuring a cup anemometer, wind direction vane, and tip bucket rain gauge. A Raspberry Pi 4 microcomputer interfaces through RJ11 and RJ45 connectors to these and other sensors. Custom-designed circuits were implemented on printed circuit boards supporting sensor chips for temperature, pressure, humidity, and air electrical resistance. The outdoor board communicates with ultraviolet light, soil moisture, and temperature sensors, relaying data using wired connections indoors where a Raspberry Pi 4 and indoor circuit board are located. The indoor board employs wireless internet protocol to communicate with a homemade Geiger-Mueller counter and a consumer-grade temporal radon monitor. The system employs an internet connection to transfer data to a cloud-based storage system. This enables a website with continuously updated pages dedicated to each established system to display collected data. Weatherproofed fused filament fabricated indoor and outdoor cases were designed. Sensor functions were tested for functionality and accuracy.

Highly sensitivity and wide-range flexible humidity sensor based on LiCl/cellulose nanofiber membrane by one-step electrospinning

Cellulose is considered an ideal material for flexible electronic humidity sensors due to its green renewable nature, rich surface groups and strong hydrophilicity. However, the traditional cellulose-based humidity sensor cannot simultaneously have wide detection range, high sensitivity and fast response time, which seriously hinder their development and application. Here, an ultrathin Lithium chloride (LiCl)/cellulose nanofiber membrane as moisture sensitive materials was prepared by one-step electrospinning and used to assemble a high-performance humidity sensor. N, N-Dimethylacetamide/Lithium chloride (DMAc/LiCl) solvent system was used to dissolve the cellulose, and DMAc volatilized into the air while LiCl remained in the cellulose nanofibers during the electrospinning process. LiCl as a highly moisture-sensitive inorganic salt has strong hydrophilicity and enhances the moisture sensing detection limit of cellulose fiber (especially under low humidity conditions), thus giving the cellulose moisture sensor excellent sensitivity (up to 4191 %) and a wide detection range (5–98 % RH). The nanometer size of cellulose fibers, the extremely low thickness and large pores of cellulose membranes accelerate the mass exchange of moisture between the air and the membrane, thus giving cellulose humidity sensor a fast response/recovery time (99/110 s) and low hysteresis (2.9 %). Moreover, the performances of humidity sensors didn’t degrade even after being subjected to long-term application (>30 days), high/low temperatures (73.6/0.1 ℃) and thousands of bending cycles. The proposed LiCl/cellulose nanofiber humidity sensor brings innovative solutions for the design of cellulose based sensor with great potential for use in non-contact humidity detection, respiratory monitoring and sleep apnea detection applications.

Intelligent Precision Farming with an Eco-Conscious Smart Irrigation System

India’s agricultural sector plays a pivotal role in supporting the livelihoods of over half of the country’s population. However, with the rapidly growing demand for food, this sector is under immense pressure to ensure sustainability while enhancing productivity. Agriculture accounts for 83% of the nation’s water consumption, yet a significant portion of this water is wasted due to outdated and inefficient irrigation practices. This research introduces an advanced smart irrigation system utilizing Internet of Things (IoT) technology to enhance irrigation efficiency and ensure optimal water utilization. This system is designed around the Node MCU microcontroller and integrates advanced sensors, including soil moisture, temperature, and humidity sensors, to enable real-time monitoring and automated decision-making. This approach integrates advanced technology with sustainable methods, promoting water conservation while improving crop productivity and establishing a standard for contemporary agricultural practices.

Bokashi-Based Home Composter for Fertigation System

Organic waste management is a huge problem coming from residential homes throughout the country. Improper decomposition of waste generates methane, which contributes significantly to the environment in terms of greenhouse effects. The proposed solution is the Bokashi-Based Home Composter for Fertigation System as a medium of Internet of Things (IoT). This system is designed to help users initiate composting through Bokashi composting and automated fertigation. Bokashi composting involves using airtight containers, Bokashi bran, and organic waste, which undergoes anaerobic fermentation to produce Bokashi Tea - a liquid by-product used as a fertilizer. The system architecture features an ESP32 microcontroller, soil moisture, humidity, and temperature sensors, as well as pumps for dosing and irrigation, all controlled through an application dashboard. The final product assembly successfully achieved its objectives to produce Bokashi Tea in less than 14 days and utilize it by dosing it to water with ratio of 1:100 with flow a flow rate of 1.6 ml/s and main pump flow rate of 32.5 ml/s for fertigation purposes in residential homes. The system is complemented by an application dashboard that provides IoT functionality, where the hardware parameters can be monitored and controlled by the application through Wi-Fi connectivity. This paper explores the process of product design.

Development of an Automated Drip Irrigation System using Arduino Microcontroller for Sweet Corn

This study proposes an automated irrigation system using an Arduino microcontroller that is cost-effective and reliable for use in planting. The proposed drip irrigation system was developed to automatically irrigate the sweet corn seedlings from 0–4 weeks of age by detecting water insufficiency based on the moisture percentage of the soil used. The automated irrigation system was a fully functional prototype where the crop was irrigated automatically when the soil moisture sensor sensed a soil moisture value below 40%. The system consists of Arduino microcontroller, sensors to monitor soil moisture, ambient temperature and humidity; an LCD display to show the sensors readings; a relay module that is used to control the on and off switch of the water pump. The performance of the automated system development was then evaluated to ensure proper irrigation. The relay module was found switched on the water pump automatically to start the watering process when the soil moisture value below 40%. The data on soil moisture percentage (%), ambient temperature (°C), and ambient humidity (%) was displayed on an LCD screen that allowed real-time monitoring. The range of temperature and humidity were recorded around 310C to 400C and 62% to 80% respectively affecting the reduction of soil moisture percentage from 80% to 44%. In conclusion, an Arduino Uno-based automated plant watering system has been successfully installed and performed.

Successful PLC Based Solar Powered Automated Irrigation System Prototyping for Water-Strained Agricultural Nations Like Pakistan

Water scarcity poses a critical challenge to agricultural sustainability in nations like Pakistan, despite abundant solar energy potential offering a sustainable solution for year-round productivity enhancement. This research focuses on designing and prototyping a PLC-based solar-powered automated irrigation system tailored for water-strained agricultural nations. The primary goal of this research is to design, prototype, and evaluate a PLC-based solar-powered automated irrigation system. The study utilizes PLC technology (Siemens SIMATIC S7-1200) for automation, incorporating capacitive moisture sensors for closed-loop control based on soil moisture and environmental data for data-driven decision making and precision agriculture. HMI is employed for real-time monitoring and control interface. Results from prototype development show promising outcomes in optimizing water use efficiency and reducing reliance on unreliable electricity sources. This paper underscores the potential of such technologies in fostering sustainable AgriTech development and addressing the pressing water challenges faced by agricultural sectors in developing countries. Prototype development and testing have shown significant improvements in water efficiency compared to traditional irrigation methods. Automated control based on real-time sensor feedback has demonstrated precise water management, enhancing crop yield potential without increasing water consumption.

A Review on Detection of Nutrients of Soil Using NPK and Moisture Sensors

With an emphasis on the combination of NPK (nitrogen, phosphorus, and potassium) and moisture sensors, this review paper explores the critical role that contemporary sensor technology plays in identifying soil nutrients and moisture levels. Precision agriculture, which seeks to optimize irrigation and fertilization techniques based on real-time data, depends on accurate measurement of these parameters. The study looks at different kinds of moisture and NPK sensors, describing how they operate and how they are used in agricultural settings. There are various advantages to incorporating these sensors into farming operations. One of the biggest benefits is increased agricultural yields since optimal plant growth is ensured by accurate fertilizer and moisture management. Another important advantage is cost effectiveness; farmers can save input costs and boost profitability by eliminating excessive fertilization and irrigation. Additionally, efficient resource usage promotes sustainable farming methods, which improve soil health and lessen environmental deterioration. The content of vital nutrients in the soil can be determined using NPK sensors. These sensors allow farmers to customize their fertilization plans to match the unique requirements of their crops by giving them real-time information on the levels of nitrogen, phosphorus, and potassium. This accuracy guarantees that plants get the appropriate quantity of nutrients at the right time, leading to improved crop yields and reduced environmental impact. This paper identifies the need for developing superior systems for soil monitoring to strengthen precision agriculture, crop production, and sustainable farming.

A Review on Plant Monitoring System using ESP8266

Every nation has engaged in agriculture for a very long time. The science and skill of growing plants is called agriculture. The main factor in the rise of sedentary human civilization was agriculture. Agriculture has always been done by hand. Since the world is moving toward new technology and applications, it is imperative that agriculture follow suit. However, there are currently obstacles in agriculture as a result of rural-to-urban migration. Therefore, we have suggested an IOT and smart agriculture solution to address this issue. Agriculture, which is considered a science and a skill of plant cultivation, has been practiced throughout history in every nation. Technology is evolving in today’s world, and agriculture must likewise keep up with the times. A key component of smart agriculture is IoT. Sensors from the Internet of Things (IoT) are utilized to supply the data that is required for agricultural areas. Using wireless sensor networks to monitor agriculture and gather data from multiple sensors placed at different locations and transmitted over wireless protocols is the primary function of the Internet of Things. Node MCU powers smart agriculture through the use of IoT systems. The increasing demand for effective water management in agriculture in recent years has drawn a lot of attention to smart farming systems. Water use, crop output, and sustainability have all increased as a result of the integration of Internet of Things (IoT) technologies with farming systems, which has created new opportunities for real-time monitoring and control. The farming system uses the PIR (Passive Infrared Sensor) to identify the presence of animals nearby. Determining the soil’s water content is mostly dependent on the soil and moisture sensor. By keeping an eye on the outside temperature, the temperature sensor offers important information about the surrounding environment. IoTbased smart agriculture is a cutting-edge method of farming that makes use of technology to maximize agricultural productivity. Manual labor and antiquated methods, which were frequently labor-intensive and ineffective, were key components of the conventional agricultural approach.

Quantifying the Impact of Soil Moisture Sensor Measurements in Determining Green Stormwater Infrastructure Performance

The ability to track moisture content using soil moisture sensors in green stormwater infrastructure (GSI) systems allows us to understand the system’s water management capacity and recovery. Soil moisture sensors have been used to quantify infiltration and evapotranspiration in GSI practices both preceding, during, and following storm events. Although useful, soil-specific calibration is often needed for soil moisture sensors, as small measurement variations can result in misinterpretation of the water budget and associated GSI performance. The purpose of this research is to quantify the uncertainties that cause discrepancies between default (factory general) sensor soil moisture measurements versus calibrated sensor soil moisture measurements within a subsurface layer of GSI systems. The study uses time domain reflectometry soil moisture sensors based on the ambient soil’s dielectric properties under different soil setups in the laboratory and field. The default ‘loam’ calibration was compared to soil-specific (loamy sand) calibrations developed based on laboratory and GSI field data. The soil-specific calibration equations used a correlation between dielectric properties (real dielectric: εr, and apparent dielectric: Ka) and the volumetric water content from gravimetric samples. A paired t-test was conducted to understand any statistical significance within the datasets. Between laboratory and field calibrations, it was found that field calibration was preferred, as there was less variation in the factory general soil moisture reading compared to gravimetric soil moisture tests. Real dielectric permittivity (εr) and apparent permittivity (Ka) were explored as calibration options and were found to have very similar calibrations, with the largest differences at saturation. The εr produced a 6% difference while the Ka calibration produced a 3% difference in soil moisture measurement at saturation. Ka was chosen over εr as it provided an adequate representation of the soil and is more widely used in soil sensor technology. With the implemented field calibration, the average desaturation time of the GSI was faster by an hour, and the recovery time was quicker by a day. GSI recovery typically takes place within 1–4 days, such that an extension of a day in recovery could result in the conclusion that the system is underperforming, rather than it being the result of a limitation of the soil moisture sensors’ default calibrations.