Capacitive Sensors Based on Recycled Carbon Fibre (rCF) Composites.

Recycled carbon fibre (rCF) composites are increasingly being explored for applications such as strain sensing, manufacturing of automobile parts, assistive technologies, and structural health monitoring due to their properties and economic and environmental benefits. The high conductivity of carbon and its wide application for sensing makes rCF very attractive for integrating sensing into passive structures. In this paper, capacitive sensors have been fabricated using rCF composites of varying compositions. First, we investigated the suitability of recycled carbon fibre polymer composites for different sensing applications. As a proof of concept, we fabricated five touch/proximity sensors and three soil moisture sensors, using recycled carbon fibre composites and their performances compared. The soil moisture sensors were realised using rCF as electrodes. This makes them corrosion-resistant and more environmental-friendly, compared to conventional soil moisture sensors realised using metallic electrodes. The results of the touch/proximity sensing show an average change in capacitance (ΔC/C~34) for 20 mm and (ΔC/C~5) for 100 mm, distances of a hand from the active sensing region. The results of the soil moisture sensors show a stable and repeatable response, with a high sensitivity of ~116 pF/mL of water in the linear region. These results demonstrate their respective potential for touch/proximity sensing, as well as smart and sustainable agriculture.

Assessment of Low-Cost and Higher-End Soil Moisture Sensors across Various Moisture Ranges and Soil Textures.

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.

Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels.

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 R2 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.

Wireless Underground Sensor Communication Using Acoustic Technology.

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin-Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance.

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability. This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters. This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Performance analysis of capacitive soil moisture, temperature sensors and their applications at farmer's field.

This study aims to assess the effectiveness of PCB-based capacitive soil moisture sensors for local field conditions. The electrical scheme of designed sensors has been presented in this study. The PCB-based capacitive soil moisture sensors are calibrated using a linear equation developed between analog values of capacitive sensors and soil moisture content measured from the gravimetric method. The performance of the designed soil moisture sensors was assessed at five different locations at varying depths (i.e., 15 cm, 30 cm, and 45 cm). The calibration results indicated a positive correlation between the soil moisture content and measurement frequency of the sensor for wheat crop, with R2 values of 0.72, 0.83, and 0.83 for 15 cm, 30 cm, and 45 cm depths, respectively. Results reveal that 85% of the sensors accurately detected the patterns in soil moisture fluctuations during the cropping period. The designed capacitive sensors demonstrated a maximum relative error of 5.87% for 45 cm depth. However, the relative error remained below 5% for the 15 cm and 30 cm soil depths. For the sugarcane crop, R2 values vary from 0.66 to 0.82, with the highest relative error of 5.22% at a 15 cm depth. These sensors offer a highly cost-effective solution for farmers, with the entire wireless sensor network system including one sensor node, three soil moisture sensors, and one soil temperature sensor, which is priced at approximately $150, making it a practical and affordable option for widespread adoption.

Rapid Manufacturing Approach of an Ultrathin Moisture Sensor for Health Monitoring.

This study developed a rapid manufacturing approach for a moisture sensor based on contactless jet printing technology. A compact measurement system with ultrathin and flexure sensor electrodes was fabricated. The proposed sensor system focuses on continuous urine measurement, which can provide timely information on subjects to ensure efficient diagnosis and treatment. The obtained results verify that the proposed sensor system can exhibit a typical responsivity of up to -7.76 mV/%RH in the high-sensitivity range of 50-80 %RH. A preliminary field experiment was conducted on a hairless rat, and the effectiveness of the proposed ultrathin moisture sensor was verified. This ultrathin sensor electrode can be fabricated in the micrometer range, and its application does not affect the comfort of the user. The ultrathin electrode sensors can be printed directly on the diaper or undergarment of the user for in situ urine health monitoring, particularly of infants and the elderly.

A Portable Pull-Out Soil Profile Moisture Sensor Based on High-Frequency Capacitance.

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R2) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m3/m3, the mean bias error (MBE) was ±0.009 m3/m3, and the maximum error was ±0.039 m3/m3. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture.

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning.

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.

Thioxanthylium Cations: Highly Reversible Hydrochromic Mate-rials with Tunable Color and Moisture Sensitivity.

Hydrochromic materials, which change color in response to moisture, are highly sought-after due to some unique applications such as moisture detection, humidity monitoring, anti-counterfeiting, and sweat-pore mapping. We herein report a new thioxanthene-9-ols/thioxanthylium cations hydrochromic system. The hydrochromic thioxanthylium cations can be generated/regenerated by exposing a chemically stable thioxanthene-9-ol to trifluoroacetic acid (TFA). The intensely colored thioxanthylium cations can be chemically reverted to the colorless thioxanthene-9-ol. Interestingly, the conversion between thioxanthene-9-ol and its cation species was found to be highly reversible in most cases (up to 50 activation-deactivation cycles). The color and moisture-sensitivity of thioxanthylium cations were also validated to be tunable via functionalization of the thioxanthene core with different aliphatic and aromatic groups at its 9 or 2,7-positions. Finally, paper probes were prepared using solution-immersion and inkjet-printing techniques to demonstrate the system's potential applications in humidity sensing and anti-counterfeiting.

NaCl Ionization-Based Moisture Sensor Prepared by Aerosol Deposition for Monitoring Respiratory Patterns.

A highly polarizable moisture sensor with multimodal sensing capabilities has great advantages for healthcare applications such as human respiration monitoring. We introduce an ionically polarizable moisture sensor based on NaCl/BaTiO3 composite films fabricated using a facile aerosol deposition (AD) process. The proposed sensing model operates based on an enormous NaCl ionization effect in addition to natural moisture polarization, whereas all previous sensors are based only on the latter. We obtained an optimal sensing performance in a 0.5 µm-thick layer containing NaCl-37.5 wt% by manipulating the sensing layer thickness and weight fraction of NaCl. The NaCl/BaTiO3 sensing layer exhibits outstanding sensitivity over a wide humidity range and a fast response/recovery time of 2/2 s; these results were obtained by performing the one-step AD process at room temperature without using any auxiliary methods. Further, we present a human respiration monitoring system using a sensing device that provides favorable and stable electrical signals under diverse respiratory scenarios.

Moisture sensor for exudative wounds - A pilot study.

BACKGROUND: Exudative wounds cause discomfort for patients. Introduction of a moisture sensor to dressings could facilitate change of dressings only when needed. The aim of this pilot study was to evaluate the ability of a newly developed moisture sensor to detect moisture in relation to the absorbing capacity of the dressing. MATERIALS AND METHODS: In five patients, with one leg ulcer each, three dressing changes per patient were observed. Interval of dressing change was according to clinical need and healthcare professional's decision. Sensor activation, dressing weight and complications were registered. To investigate the effect of dressing on sensor activation, half of the observations were made without an extra layer of non-woven between the dressing and sensor (Variant A), and half with (Variant B). RESULTS: The sensor indicated time for dressing change in six out of fifteen observations. Variants A and B did not differ regarding activation or the timing of the activation. CONCLUSIONS: The addition of a moisture sensor for facilitating management of exudative wounds is promising. We recommend future larger studies evaluating the potential clinical benefits and risks of the addition of a moisture sensor. We also recommend evaluation of potential home monitoring of wounds by a moisture sensor.

Sensor Node Network for Remote Moisture Measurement in Timber Based on Bluetooth Low Energy and Web-Based Monitoring System.

This paper proposes an IoT system based on wireless BLE connectivity to monitor the moisture content of wood, using a compact and low-cost moisture device that relies on a resistance measurement method valid for an ultra-wide range of resistance values. This device is digitally controlled with a BLE-incorporated micro-controller characterized by its small size and low power consumption, providing long-life battery. The proposed system consists of two main parts: first, the BLE moisture device including the moisture content measurement and wireless capability (BLE); second, the cloud-based monitoring platform, providing remote visualization and control for all the sensor nodes of the network. The complete infrastructure shows how multiple nodes can read and transmit moisture content of timber in buildings using small and unattended devices, with data saved in a central database and monitored by multiple commercial devices such as PC, smartphone, tablet, etc. The proposed system is innovative, scalable and low cost, and it can be deployed in wooden buildings and the wood industry, providing a practical solution that will help to avoid rot and other damaging effects caused by the moisture content.

Embroidered Textile Antennas: Influence of Moisture in Communication and Sensor Applications.

Moisture causes detuning and increased losses in textile antennas, and it affects resonant and wideband textile antennas differently. In this work, we studied the effect of moisture on a resonant textile planar inverted-F antenna (PIFA) and a wideband textile monopole antenna. Both antennas were manufactured by embroidering conductive yarn in denim textile. The input reflection coefficient, antenna gain, and gain patterns were measured on both antennas for different moisture contents. The results show that wideband antennas are less affected by moisture in comparison with resonant antennas. For communications applications, large moisture content in the textile antenna should be avoided; therefore a flexible, textile-based waterproofing antenna cover was proposed, manufactured, and tested. On the other hand, the effect of antenna detuning by moisture can be used for moisture-sensing application. This concept was demonstrated on the resonant textile PIFA in transmission and reflection setups, showing that the reflection setup gives better results.

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors.

A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or open-source software libraries. The whole system was implemented to demonstrate the feasibility of a modular system built with cheap off-the-shelf components, including sensors. The experimental outputs were collected and stored in a database managed by a virtual machine running in a cloud service. The collected data can be visualized in real time by the user with a graphical interface. The reliability of the whole system was proven during a continued experiment with two natural soils, Loamy Sand and Silty Loam. Regarding soil parameters, the system performance has been compared with that of a reference sensor from Sentek. Measurements highlighted a good agreement for the temperature within the supposed accuracy of the adopted sensors and a non-constant sensitivity for the low-cost volumetric water contents (VWC) sensor. Finally, for the low-cost VWC sensor we implemented a novel procedure to optimize the parameters of the non-linear fitting equation correlating its analog voltage output with the reference VWC.

Characterization of Low-Cost Capacitive Soil Moisture Sensors for IoT Networks.

The rapid development and wide application of the IoT (Internet of Things) has pushed toward the improvement of current practices in greenhouse technology and agriculture in general, through automation and informatization. The experimental and accurate determination of soil moisture is a matter of great importance in different scientific fields, such as agronomy, soil physics, geology, hydraulics, and soil mechanics. This paper focuses on the experimental characterization of a commercial low-cost "capacitive" coplanar soil moisture sensor that can be housed in distributed nodes for IoT applications. It is shown that at least for a well-defined type of soil with a constant solid matter to volume ratio, this type of capacitive sensor yields a reliable relationship between output voltage and gravimetric water content.

Boxer Underwear Incorporating Textile Moisture Sensor to Prevent Nocturnal Enuresis.

Around 15% of children still wet their bed after five years old. Although bedwetting alarms have proven to be effective to achieve nighttime dryness, they are cumbersome so children could be reluctant to use them. Therefore, the moisture sensor and wire were made unobtrusive by seamlessly integrated them into fully textile underwear by using conductive yarns. Consequently, the alarm acceptability should be enhanced by improving children's comfort. Three conductive textile metallic yarns, made of silver or stainless steel, were considered to fabricate the urine leakage sensor. Silver-plated-nylon yarn, which showed the highest electrical conductivity, outperformed the stainless-steel yarns regarding its ability to detect urine leakage as well as its detection speed. Furthermore, it was proven to withstand multiple urine soakings and the following machine-washings, even at high temperature (60 °C). However, the electrical current, necessary to detect the leakage, tends to corrode the silver. Therefore, the detection circuit was adapted. Eventually, the designed leakage sensor was seamlessly integrated into a child's trunk underwear, into which a miniaturized alarm can be plugged. The resulting textile underwear aims at replacing the rigid alarm system currently available, hence improving the quality of life of enuretic children and help them achieving nighttime dryness.

Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors.

Commercial soil moisture sensors have been widely applied into the measurement of soil moisture content. However, the accuracy of such sensors varies due to the employed techniques and working conditions. In this study, the temperature impact on the soil moisture sensor reading was firstly analyzed. Next, a pioneer study on the data-driven calibration of soil moisture sensor was investigated considering the impacts of temperature. Different data-driven models including the multivariate adaptive regression splines and the Gaussian process regression were applied into the development of the calibration method. To verify the efficacy of the proposed methods, tests on four commercial soil moisture sensors were conducted; these sensors belong to the frequency domain reflection (FDR) type. The numerical results demonstrate that the proposed methods can greatly improve the measurement accuracy for the investigated sensors.

Source Separation Using Sensor's Frequency Response: Theory and Practice on Carbon Nanotubes Sensors.

Nowadays, there is an increased demand in integrated sensors for electronic devices. Multi-functional sensors provide the same amount of data using fewer sensors. Carbon nanotubes are non-selectively sensitive to temperature, gas and strain. Thus, carbon nanotubes are perfect candidates to design multi-functional sensors. In our study, we are interested in a dual humidity-temperature sensor. Here, we present a novel method to differentiate at least two sources using the sensor's frequency responses based on multiwall carbon nanotubes sensors. The experimental results demonstrate that there are temperature- or moisture-invariant frequencies of the impedance magnitude, and their values depend on the sensor's geometry. The proposed measurement model shows that source-invariant frequencies of the phase can be also determined. In addition, the source separation method is generalized to other materials or sources enabling multi-functional sensors for environment monitoring.

The Implementation of a Low Power Environmental Monitoring and Soil Moisture Measurement System Based on UHF RFID.

A smart sensor label based on the integration of ultra high frequency (UHF) radio frequency identification (RFID) technology and sensors is presented. The label is composed of a semi-active system that measures temperature, light, relative humidity and gravimetric water content (GWC) in the soil. The deployed system provides a simple, cost effective solution to monitor and control the growing of plants in modern agriculture and is intended be a part of a smart wireless sensor network (WSN) for agricultural monitoring. This paper is focused on analysis and development of a moisture sensor to measure GWC. It is based on a capacitance measurement solution, the accuracy of which is enhanced using several sensor driving frequencies. Thanks to the cancellation of supply voltage variations, the modeling of the GWC sensor and readout circuit was correct. The results we measured were close to modeled values. The maximum measurement resolution of the capacitive moisture sensor was 0.07 pF. To get the GWC from measured capacitance, a scale was used to weigh the mass of water in the soil. The comparison between capacitance measurement and calculated soil GWC is presented. The RFID measurement system has energy harvesting capabilities and an ultra-low power microcontroller, which uses embedded software to control the measurement properties. The microcontroller has to choose the appropriate model depending on the measured amplitude and chosen frequency to calculate the actual voltage on the sensing capacitor.