Determination and modelling of dielectric properties of the cherry leaves of varying moisture content over 3.30–7.05 GHz frequency range

Doğan, Habib; Başyiğit, İbrahim Bahadır; Genç, Abdullah

doi:10.1080/08327823.2020.1794724

Cited by 24 publications

(16 citation statements)

References 17 publications

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“…It is worth to note that these steps are conducted according to some criterions. 17 In addition, S 11 and S 21 parameters for different f and MC of the samples are measured by VNA. True reflect line method (TRLM), which is a well-known calibration method to give accurate results, is selected to eliminate systematic errors in the measurements.…”

Section: The Sample Generationmentioning

confidence: 99%

“…In each drying steps, Suofei SF‐718 micro weighing for the gram scale is used in Figure 1D and MC s are calculated depending on the different weights of the samples. It is worth to note that these steps are conducted according to some criterions 17 . In addition, S 11 and S 21 parameters for different f and MC of the samples are measured by VNA.…”

Section: The Sample Generationmentioning

confidence: 99%

“…14 Some researchers dwell upon the measuring dielectric parameters of thin Monstera leaves, 15 and also complex dielectric permittivity for magnolia and rubber depending on water content. 16 In our previous works, dielectric characterizations of sweet/sour cherry leaves for 3.30 to 7.05 GHz, 17 orange/ lemon leaves at F-band 18 are measured and modeled for microwave applications. In addition, the dielectric properties of fig and mulberry leaves depending on the moisture content (MC) and frequency (f) are measured by the waveguide transmission line technique (WTLT) at C and S-bands for various technologies.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous works, dielectric characterizations of sweet/sour cherry leaves for 3.30 to 7.05 GHz, 17 orange/lemon leaves at F ‐band 18 are measured and modeled for microwave applications. In addition, the dielectric properties of fig and mulberry leaves depending on the moisture content ( MC ) and frequency ( f ) are measured by the waveguide transmission line technique (WTLT) at C and S ‐bands for various technologies 19 .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model

Metlek

Kayaalp

Başyiğit

et al. 2020

Int J RF Microw Comput Aided Eng

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In this paper, dielectric properties of citrus leaves are predicted with long shortterm memory (LSTM) which is one of the well-known deep neural network (DNN) models and real-time measurements for any moisture content (MC) values in the range of 4.90 to 7.05 GHz at a fixed temperature of 24 C for microwave applications, as a novelty. Firstly, S-parameters of samples are measured with WR-159 waveguide and Waveguide Transmission Line Method. In addition, the MCs of samples depending on their weights are calculated. Thus, the dataset depending on various MC and frequency is obtained with the measurement results to both training and testing the DNN model. Secondly, a total of 4000 datasets are obtained, 80% of which is used for training, and 20% for testing. The proposed DNN model consists of four inputs (f, MC, S 11 , and S 21) and two outputs (ε 0 and ε 00). Finally, the dielectric parameters for the desired MC and f are displayed with the graphical user interface in real-time. Success criteria for the prediction such as mean absolute error, root mean squared error, mean absolute percentage error, and R-squared are calculated. The results indicated that there is good agreement between the measured and predicted ones. R-squared are calculated as 0.962 and 0.968 for ε 0 and ε 00 , respectively.

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Section: The Sample Generationmentioning

confidence: 99%

Section: The Sample Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model

Metlek

Kayaalp

Başyiğit

et al. 2020

Int J RF Microw Comput Aided Eng

Self Cite

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“…[23][24][25] Depending on the design algorithm, these methods are different. Different methods of design in the resonance style depend on whether the sample type is solid, [26][27][28][29] liquid, [30][31][32][33][34][35][36][37] or gas. 38 Gas sensors are able to detect their presence and type, and they are very useful in industries.…”

Section: Introductionmentioning

confidence: 99%

Measurement of low‐loss aqueous solutions permittivity with high detection accuracy by a contact and free‐label resonance microwave sensor

Navaei

Rezaei

Kiani

2022

Int J Communication

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SummaryIn this paper, a microwave sensor is designed and built to measure the permittivity of aqueous solutions. The samples used in this experiment are different purities of ethanol mixed with water. Ethanol solution with different purities is a widely used material in industry. Structures of meander, ladder, and T‐structure were used to design the sensor. Then the designed sensor is simulated in CST software in range of relative dielectric from 10 to 80. After obtaining the desired answer in the simulation, the proposed sensor is built on the Rogers 4003 substrate, the built‐in sensor measured ethanol solution with a purity of 5% to 35% (equivalent to permittivity of 76 to 58), and is showed a sensitivity of 4% and a Q‐factor of 3500. The results of simulation and measurement are consistent with each other, and they indicated that the proposed sensor beyond the fluids can be supported and determine their relative dielectrics.

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Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

Shojaeyan,

Banakar,

Hashjin

et al. 2023

Agrosystems Geosci & Env

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Dried figs are a garden produce that must be graded after harvesting. Moisture levels and contaminated blank are two of the most critical effective elements on the marketability of dried figs, and they are highly related to fig quality. In the present research, an intelligent system was employed to classify dried figs based on moisture content levels and infected blank fruits. Capacitance characteristics, average diameter, and fruit area were all taken into account in this study. The dried fig dielectric constant was measured at six different frequency levels: 12, 22, 32, 42, 52, and 62 MHz. The best frequency was then chosen using the improved distance evaluation feature selection approach. Image processing was also used to determine the average diameter and area of the figures. Following that, the dielectric constant of the most effective frequency, the average diameter, and the area of the fruit were used as input parameters in the artificial neural network classification model to classify and describe the moisture and porosity level of the dried fig. The most essential dielectric constant information relating to moisture and porosity level was at frequencies of 22 and 52 MHz, respectively. Finally, classification accuracy of 95.7% for moisture and 91.3% for porosity level was attained. The results demonstrated the excellent performance and capabilities of the proposed approach for rating the internal quality of dried figs.

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Determination and modelling of dielectric properties of the cherry leaves of varying moisture content over 3.30–7.05 GHz frequency range

Cited by 24 publications

References 17 publications

The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model

The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model

Measurement of low‐loss aqueous solutions permittivity with high detection accuracy by a contact and free‐label resonance microwave sensor

Determination of moisture content and contaminated blank dried figs (Ficus carica L.) using dielectric property and artificial neural network

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