Measurement of Soil Moisture Content at Microwave Frequencies

Mohan, Ranjini R.; Paul, Binu; Mridula, S.; Mohanan, Pezholil

doi:10.1016/j.procs.2015.01.040

Cited by 23 publications

(16 citation statements)

References 8 publications

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“…Research studies on grain crops have been widely conducted on various aspects such as crops yield prediction [ 7 ], grain quality [ 8 ], soil moisture [ 9 , 10 ], and moisture content detection [ 11 , 12 , 13 , 14 , 15 ]. Numerous research studies have been conducted to find a reliable method to measure the moisture content in seasonal grain crops such as paddy, peanuts, and corn [ 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

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Section: Related Workmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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“…Some researches attempted to determine the effects of ambient conditions, which are created for the preservation of foods and preservation of freshness for a long time in food industry, on the material by the change in the permittivity [14,15]. Successful studies have been conducted to examine the effect of changes in humidity on the freshness of the food [16,17].…”

Section: Electrical and Electronic Properties Of Materialsmentioning

confidence: 99%

Measurement Methods and Extraction Techniques to Obtain the Dielectric Properties of Materials

Öztürk¹,

Guneser²

2019

Electrical and Electronic Properties of Materials

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Material characterization plays an important role in many applications that are called as security, military, communication, bioengineering, medical treatment, food industry, and material processing, since it is useful to identify other properties such as stress-strain relation, bio content, moisture content, materials density, etc. Therefore, the dielectric properties of materials should be achieved with high accuracy using appropriate measurement techniques and extraction techniques. There are many measurement methods to obtain the dielectric properties of materials, which can be divided into two categories: up conversion and down conversion methods. A microwave measurement method can be called as frequency up conversion, while THz time-domain spectroscopy (THz-TDS) system is a frequency down conversion method. The selection of more convenient measurement method depends on some parameters such as frequency range, material phase, and temperature. In this chapter, the measurement methods and extraction techniques will be discussed, and alternative ways will be presented with experimental and simulation results.

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“…The relative dielectric permittivity ( r ) can be converted to the soil moisture according the the empirical relationship derived from the experiment measurements (Behari 2005;Mohan et al 2015). Note that, the relationship between r and moisture is not identical, and may involve some uncertainties.…”

Section: Tomographic Gpr and The Forward Modelmentioning

confidence: 99%

Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

Bao

Hou

Ray

et al. 2018

Stoch Environ Res Risk Assess

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In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a lowdimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)-Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.

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Measurement of Soil Moisture Content at Microwave Frequencies

Cited by 23 publications

References 8 publications

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Measurement Methods and Extraction Techniques to Obtain the Dielectric Properties of Materials

Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

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