A Design Method for a Microwave-Based Moisture Sensing System for Granular Materials in Arbitrarily Shaped Containers

Jiarasuwan, Seksan; Chamnongthai, Kosin; Kittiamornkul, Navapadol

doi:10.1109/jsen.2021.3087414

Cited by 14 publications

(6 citation statements)

References 67 publications

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“…Based on this ARS value, it can be observed that ECT [24], PQCWC [25], MHPS [21], FoS [20], HPCM [6], PHS [17], HF RFID TFS [9], PMMA [15], FFCSM [16], HSAAA [32], PWM [10], FTO [35], CRNS [3] and CM [11] have better overall performance, thus can be used for efficient moisture sensing applications. LEWS LR [1] LR RBM [2] CRNS [3] MWMS [5] GPS [7] HF RFID TFS [9] CM [11] PLMR [12] MSR [14] FFCSM [16] SMAP [18] FoS [20] PRS [22] PQCWC [25] GOFCHS [27] SAR [29] CSMOS [31] SSMDI [33] FTO [35] PBG [38] MSOCCML [40] CRNS [42] Accuracy of moisture sensing models…”

Section: Discussionmentioning

confidence: 99%

“…While highly accurate sensing interfaces are costly, but can be used for high-speed moisture sensing applications. Specifically, DBN RBM, CRNS, SMAP LEWS LR [1] DBN RBM [2] LR RBM [2] BP RBM [2] CRNS [3] FBG [4] MWMS [5] HPCM [6] GPS [7] UHF RFID [8] HF RFID TFS [9] PWM [10] CM [11] PPMR [12] PLMR [12] RFID UHF [13] MSR [14] PMMA [15] FFCSM [16] PHS [17] SMAP [18] SMAP RF DN [19] FoS [20] MHPS [21] PRS [22] ECT [24] PQCWC [25] HDES [26] GOFCHS [27] TDR [28] SAR [29] SMI MODIS [30] CSMOS [31] HSAAA [32] SSMDI [33] P Band & L Band [34] FTO [35] eSMAP [36] PBG [38] MSNs [39] MSOCCML [40] SMAP TFC [41] CRNS [42] kCRNS [43] Computational RF DN, GOFCHS, TDR, and P Band & L Band models outperform other models; thus, they can be used for highly accurate moisture detection applications. While, HPCM, HF RFID TFS, PWM, PMMA, FFCSM, MHPS, ECT, PQCWC, ...…”

Section: Discussionmentioning

confidence: 99%

“…The model was applied to transformers & other electric equipments, but can be extended to other applications with modifications in component compositions. Another highly efficient model for moisture estimation is discussed in [5], wherein Microwave-based moisture sensing (MWMS) is performed for granular materials via design of arbitrary shaped containers. The model is able to improve accuracy of moisture detection by 7.8% when compared with antenna array-based approach.…”

Section: Literature Reviewmentioning

confidence: 99%

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Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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“…Online measurement of moisture has become an urgent problem to be solved in the eld of food production [1,2]. e development of moisture sensors based on the microwave free-space method is presently a popular research direction.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Lens Antenna Design for Microwave Moisture Detection

Zhang

Yin

et al. 2022

International Journal of Antennas and Propagation

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In this study, a novel Vivaldi antenna with dimensions of 100 mm × 85 mm × 1.6 mm, designed for a moisture measurement system, is built to enhance the gain of conventional Vivaldi antennas in the low-frequency band to suit the needs of moisture detection. The fence structure and choke slot are modified to enhance the antenna’s radiation properties in the low-frequency band, and simulation is performed to determine how different structural parameters affect the antenna’s performance. The results show that in the frequency band of 5-6 GHz, the voltage standing-wave ratio (VSWR) of the antenna is less than 2, and the gain at 5.8 GHz reaches 16.2 dBi after installing the lens. Compared with conventional unmodified Vivaldi antennas, the gain at 5.8 GHz increases by approximately 6.11 dBi. The antenna is then processed and measured, and the measured results are in good agreement with the simulated results; hence, the antenna can be widely used in the field of moisture detection.

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“…In the recent era, microwave sensing approaches are mainly used for various purposes such as moisture tissue detection [1,2], oil quality control [3], humidity sensing, breast cancer detection [4], brain stroke detection [5], organic vapour sensing, flow rate measurement, and acetone-ethanolmethanol concentration detection [6]. Microwave sensing systems have many advantages such as real-time, noninvasive, flexibility, and high adaptation rate for many applications [7].…”

Section: Introductionmentioning

confidence: 99%

Microwave-Based Electrochemical Sensor Design by SRR Approach for ISM Sensing Applications

et al. 2022

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This paper presents a transmission line and a split ring resonator (SRR) based sensor structure with a high sensitivity capacity to discriminate various methanol mixtures. The height of the dielectric layer and thickness of copper are assigned as 1.6 mm and 0.035 mm, respectively. The overall dimensions of the sensor structure are defined as 16 mm × 16 mm × 1.6 mm . The operating frequency is selected for the ISM bands, especially around 2.45 GHz. Different methanol-water mixtures are prepared at various ratios, and then, the complex permittivity values are measured. Different sensor structures are modelled and investigated using a two-port transmission line approach. Various types of SRR based sensors are designed, and an optimum design is proposed for methanol mixture detection applications. The observed quality factor of the proposed sensor is 16.5. The resonance shifts of the transmission value ( S 21 ) are used for sensing capability around 2.45 GHz at -45 dB and 90 MHz resonance shifts. The sensitivity of the sensor has been evaluated as 1 MHz. Finally, the electric field distributions of the proposed SRR integrated transmission line are investigated. The novelty of the proposed design is to exactly sense the ratio of methanol in water with a very simple design. The proposed sensor structure can be used for methanol detection applications in medical, military, and chemical research.

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A Design Method for a Microwave-Based Moisture Sensing System for Granular Materials in Arbitrarily Shaped Containers

Cited by 14 publications

References 67 publications

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Low-Cost Lens Antenna Design for Microwave Moisture Detection

Microwave-Based Electrochemical Sensor Design by SRR Approach for ISM Sensing Applications

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