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Humidity Sensors for Industrial and Agricultural Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 30560

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Special Issue Editors

Dr. Maria Vesna Nikolic

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Guest Editor

Institute for Multidisciplinary Research, University of Belgrade
Interests: sensing materials; sensor properties; metal oxide nanomaterials; humidity sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Zoran Stamenkovic

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Guest Editor

1. Institute for Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
2. IHP-Leibniz-Institut fur Innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt Oder, Germany
Interests: precision agriculture; machine learning; sensors; microelectronics
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Tariq Saeed Chani

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Guest Editor

Center of Excellence for Advanced Materials Research, King Abdulaziz University, Jeddah, Saudi Arabia
Interests: humidity sensors; Temperature sensors

Special Issue Information

Dear Colleagues,

Humidity sensors are used for measuring relative humidity, absolute humidity, and the dew point. Moisture monitoring together with temperature monitoring are crucial in many industrial and agricultural applications. In industry, humidity monitoring is needed in manufacturing, such as wafer processing, chemical gas purification, paper and textile production, food processing, refrigeration, automated industrial processes, etc. Agricultural applications include humidity sensors in greenhouses, plantation protection (dew protection) and in monitoring the soil moisture content. In striving to achieve high accuracy, good repeatability, interchangeability, long-term stability, ability to recover from condensation, resistance to chemical and physical contaminants, small size, functional packaging, and cost effectiveness, a wide range of different humidity sensing materials can be used. The operating principle of these sensors is capacitive, resistive, calorimetric, optical or surface-acoustic.

This Special Issue focuses on humidity sensors for industrial and agricultural applications. This issue will accept high-quality scientific contributions in the form of review papers or original scientific papers on humidity sensing materials, humidity sensor properties, humidity sensor design concepts, humidity sensor fabrication, and incorporation of humidity sensors in sensor systems.

Dr. Maria Vesna Nikolic
Dr. Zoran Stamenkovic
Dr. Muhammad Tariq Saeed Chani
Guest Editors

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Keywords

humidity sensors
humidity sensing materials
humidity sensor properties
humidity sensor design
humidity sensor fabrication
humidity sensing systems and control
humidity monitoring
moisture monitoring

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Published Papers (7 papers)

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Research

17 pages, 3428 KiB

Open AccessArticle

Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

by Maedeh Farokhi, Farid Faridani, Rosa Lasaponara, Hossein Ansari and Alireza Faridhosseini

Sensors 2021, 21(15), 5211; https://doi.org/10.3390/s21155211 - 31 Jul 2021

Cited by 12 | Viewed by 2816

Abstract

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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13 pages, 823 KiB

Open AccessArticle

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

by Dušan Marković, Dejan Vujičić, Snežana Tanasković, Borislav Đorđević, Siniša Ranđić and Zoran Stamenković

Sensors 2021, 21(14), 4846; https://doi.org/10.3390/s21144846 - 16 Jul 2021

Cited by 46 | Viewed by 5867

Abstract

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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13 pages, 6425 KiB

Open AccessArticle

Electrospun Nickel Manganite (NiMn₂O₄) Nanocrystalline Fibers for Humidity and Temperature Sensing

by Milena P. Dojcinovic, Zorka Z. Vasiljevic, Jugoslav B. Krstic, Jelena D. Vujancevic, Smilja Markovic, Nenad B. Tadic and Maria Vesna Nikolic

Sensors 2021, 21(13), 4357; https://doi.org/10.3390/s21134357 - 25 Jun 2021

Cited by 9 | Viewed by 3127

Abstract

Nickel manganite nanocrystalline fibers were obtained by electrospinning and subsequent calcination at 400 °C. As-spun fibers were characterized by TG/DTA, Scanning Electron Microscopy and FT-IR spectroscopy analysis. X-ray diffraction and FT-IR spectroscopy analysis confirmed the formation of nickel manganite with a cubic spinel structure, while N₂ physisorption at 77 K enabled determination of the BET specific surface area as 25.3 m²/g and (BJH) mesopore volume as 21.5 m²/g. The material constant (B) of the nanocrystalline nickel manganite fibers applied by drop-casting on test interdigitated electrodes on alumina substrate, dried at room temperature, was determined as 4379 K in the 20–50 °C temperature range and a temperature sensitivity of −4.95%/K at room temperature (25 °C). The change of impedance with relative humidity was monitored at 25 and 50 °C for a relative humidity (RH) change of 40 to 90% in the 42 Hzπ1 MHz frequency range. At 100 Hz and 25 °C, the sensitivity of 327.36 ± 80.12 kΩ/%RH was determined, showing that nickel manganite obtained by electrospinning has potential as a multifunctional material for combined humidity and temperature sensing. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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24 pages, 2554 KiB

Open AccessArticle

Trustworthy Wireless Sensor Networks for Monitoring Humidity and Moisture Environments

by Radomir Prodanović, Sohail Sarang, Dejan Rančić, Ivan Vulić, Goran M. Stojanović, Stevan Stankovski, Gordana Ostojić, Igor Baranovski and Dušan Maksović

Sensors 2021, 21(11), 3636; https://doi.org/10.3390/s21113636 - 24 May 2021

Cited by 10 | Viewed by 4673

Abstract

Wireless sensors networks (WSNs) are characterized by flexibility and scalability in any environment. These networks are increasingly used in agricultural and industrial environments and have a dual role in data collection from sensors and transmission to a monitoring system, as well as enabling the management of the monitored environment. Environment management depends on trust in the data collected from the surrounding environment, including the time of data creation. This paper proposes a trust model for monitoring humidity and moisture in agricultural and industrial environments. The proposed model uses a digital signature and public key infrastructure (PKI) to establish trust in the data source, i.e., the trust in the sensor. Trust in data generation is essential for real-time environmental monitoring and subsequent analyzes, thus timestamp technology is implemented here to further ensure that gathered data are not created or changed after the assigned time. Model validation is performed using the Castalia network simulator by testing energy consumption at the receiver and sender nodes and the delay incurred by creating or validating a trust token. In addition, validation is also performed using the Ascertia TSA Crusher application for the time consumed to obtain a timestamp from the free TSA. The results show that by applying different digital signs and timestamps, the trust entity of the WSN improved significantly with an increase in power consumption of the sender node by up to 9.3% and receiver node by up to 126.3% for a higher number of nodes, along with a packet delay of up to 15.6% and an average total time consumed up to 1.186 s to obtain the timestamp from the best chosen TSA, which was as expected. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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13 pages, 5518 KiB

Open AccessFeature PaperArticle

Full-Self-Powered Humidity Sensor Based on Electrochemical Aluminum–Water Reaction

by Marko V. Bošković, Biljana Šljukić, Dana Vasiljević Radović, Katarina Radulović, Milena Rašljić Rafajilović, Miloš Frantlović and Milija Sarajlić

Sensors 2021, 21(10), 3486; https://doi.org/10.3390/s21103486 - 17 May 2021

Cited by 8 | Viewed by 3246

Abstract

A detailed examination of the principle of operation behind the functioning of the full-self-powered humidity sensor is presented. The sensor has been realized as a structure consisting of an interdigitated capacitor with aluminum thin-film digits. In this work, the details of its fabrication and activation are described in detail. The performed XRD, FTIR, SEM, AFM, and EIS analyses, as well as noise measurements, revealed that the dominant process of electricity generation is the electrochemical reaction between the sensor’s aluminum electrodes and the water from humid air in the presence of oxygen, which was the main goal of this work. The response of the sensor to human breath is also presented as a demonstration of its possible practical application. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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11 pages, 2751 KiB

Open AccessCommunication

Capacitive Humidity Sensor Based on Carbon Black/Polyimide Composites

by Jihong Kim, Jang-Hoon Cho, Hyung-Man Lee and Sung-Min Hong

Sensors 2021, 21(6), 1974; https://doi.org/10.3390/s21061974 - 11 Mar 2021

Cited by 31 | Viewed by 5111

Abstract

A novel capacitive humidity sensor based on carbon black/polyimide composites is presented in this paper. The details of the fabrication, sensor characteristics, and effect of the carbon black additive are described. It was confirmed that the polyimide composite filled with a tiny amount of carbon black was suitable for a humidity sensing dielectric. The humidity sensors with three different dielectrics, which were pure polyimide, 0.01 wt% carbon black/polyimide, and 0.05 wt% carbon black/polyimide, were fabricated by a micro-electro-mechanical-system (MEMS) process. As the amount of the carbon black additive increased, the sensitivity of the humidity sensor increased. The humidity sensor with 0.05 wt% of carbon black had a much higher sensitivity of 15.21% (20–80% RH, 0.2535%/% RH) than that of the sensor with pure polyimide, which was 9.73% (0.1622%/% RH). The addition of carbon black also led to an enhancement in the hysteresis and response speed. The hysteresis of the humidity sensor decreased from 2.17 to 1.80% when increasing the amount of the carbon black additive. The response speed of the humidity sensor with 0.05 wt% of carbon black was measured to be ~10% faster than that of the sensor with pure polyimide. The long-term stability of the humidity sensors was demonstrated as well. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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20 pages, 617 KiB

Open AccessArticle

Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process

by Oliwia Krauze, Dariusz Buchczik and Sebastian Budzan

Sensors 2021, 21(2), 667; https://doi.org/10.3390/s21020667 - 19 Jan 2021

Cited by 8 | Viewed by 3373

Abstract

Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates mutual relations between material moisture and particle classification process in a grinding installation. The experimental setup involves an inertial-impingement classifier and cyclone being part of dry grinding circuit with electromagnetic mill and recycle of coarse particles. The tested granular material is copper ore of particle size 0–1.25 mm and relative moisture content 0.5–5%, fed to the installation at various rates. Higher moisture of input material is found to change the operation of the classifier. Computed correlation coefficients show increased content of fine particles in lower product of classification. Additionally, drying of lower and upper classification products with respect to moisture of input material is modelled. Straight line models with and without saturation are estimated with recursive least squares method accounting for measurement errors in both predictor and response variables. These simple models are intended for use in automatic control system of the grinding installation. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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