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Density-independent moisture calibration function (ψ) for unshelled pod peanuts (ψ p ) and for shelled peanut kernels (ψ k ) at 8 GHz and 24°C.
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The importance of moisture measurement in grain and seed is discussed, and a brief history of the development of electrical moisture sensing instruments, based on sensing the electrical properties of these materials, is presented. Data are presented graphically on the permittivities or dielectric properties of grain and seed, showing their variatio...
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Context 1
... measurements on unshelled peanuts (pod peanuts) and shelled peanuts (peanut kernels) have demon- strated that the moisture content of the peanut kernels, which is required, in practice, can be determined from rapid measurements on the unshelled peanuts (pod peanuts) ). The density-independent moisture calibration function (eq. 3) was determined separately for unshelled and shelled peanuts, as shown in figure 8, where separate lines of nearly the same slope are shown for the unshelled peanuts (pod peanuts) of moisture content M p and the shelled kernels of moisture content M k . ...
Context 2
... measurements on unshelled peanuts (pod peanuts) and shelled peanuts (peanut kernels) have demon- strated that the moisture content of the peanut kernels, which is required, in practice, can be determined from rapid measurements on the unshelled peanuts (pod peanuts) ). The density-independent moisture calibration function (eq. 3) was determined separately for unshelled and shelled peanuts, as shown in figure 8, where separate lines of nearly the same slope are shown for the unshelled peanuts (pod peanuts) of moisture content M p and the shelled kernels of moisture content M k . ...
Similar publications
The importance of moisture measurement in grain and seed is discussed, and a brief history of the development of electrical moisture sensing instruments, based on sensing the electrical properties of these materials, is presented. Data are presented graphically on the permittivities or dielectric properties of grain and seed, showing their variatio...
Citations
... Over the past decades, [8] has presented the dielectric properties of grains and seeds as a function of the applied frequency, their percentage of moisture content, as well as the temperature and density of these materials. They are practically expressed as the complex permittivity relative to free space, [9]. ...
Inefficient storage of paddy and rice grains can lead to grain deterioration, resulting in post-harvest losses ranging from 10% to 30%. The quality of grains cannot be improved throughout the storage period. Therefore, following the mechanisation of agricultural industries, air dryers have been developed to control the crops’ moisture level by blowing ambient or heated air into the silo to improve the aeration and allow the grains to be preserved with minimal loss of quality until the appropriate time for managing and marketing processes. However, the conventional sampling method used to measure the moisture level is inefficient because it is very localised and only represents part of the moisture distribution inside the bulk grains. Additionally, incorporating advanced technologies can be a significant cost limitation for small-scale industries. Thus, to address the issue, this research study developed a radio tomographic imaging (RTI) system in a silo-scale prototype using 20 sensor nodes operating at 2.4 GHz to localise and monitor the moisture level constructively. The RTI system reconstructs the cross-sectional images across the rice silo by measuring radio frequency attenuation, in terms of received signal strength (RSS) quality, caused by the rice moisture phantoms within the wireless sensor network (WSN) area. A total of five phantoms’ profiles having a percentage of moisture content (MC) of 15%, 20% and 25% were reconstructed using four image reconstruction algorithms, Linear Back Projection (LBP), Filtered Back Projection (FBP), Newton’s One-step Error Reconstruction (NOSER) and Tikhonov Regularisation.
... Meanwhile, waveguide structures have been further developed and applied in the measurement of liquid permittivity. Microwave measurement technology has the advantages of non-invasiveness, high sensitivity, high precision and real-time monitoring, and has become the mainstream technology in the field of liquid permittivity measurement [28]. The permittivity of whole blood was affected by the polarization of water in microwave frequency band. ...
Background: Adequate hemodialysis requires not only sufficient removal of excess toxins, but also removal of water retained for the dialysis patient. There is no accurate detection technique for setting the amount of ultrafiltration during the hemodialysis. The purpose of this study was to detect changes of water in the blood during hemodialysis. Method: The changes in the permittivity of blood can be used to solve this problem very well. On the basis of the traditional metal waveguide, the stepped structure is proposed to measure the permittivity of blood. The scattering parameters of waveguide sensor is measured using vector network analyzer and the corresponding permittivity is deduced by full-wave de-embedding algorithm and transmission line method. The change in the water content of the blood during dialysis were measured for five hemodialysis volunteers. The changes of the water content in the blood were derived through changes of the permittivity. Results: The water content of each time period in the dialysis process is detected. The experiment showed that the water content gradually decreased and the water content showed a small rebound in the fourth hour. The water content of blood after dialysis is reduced by about 19–26% compared with that before dialysis. The error between the experimental and the theoretical value is less than 5.85%. Conclusions: The change of water content in the blood was obtained by measuring the permittivity of the blood during hemodialysis. The technique is proven for detecting changes in water content in the blood. The sensor structure and detection method have great clinical application value.
... Another traditional technique is oven drying [4]. Researchers have also developed moisture detection techniques based on the electrical and dielectric characteristics of the grains [8,9]. For non-invasive estimation, generalized growing degree days-based models are used to determine grain moisture and dry down periods (GDDs) [1]. ...
... However, this approach provides minimum accountability of localized soil factors, crop varieties, crop management practices, and tillage practices that may impact CGM at spatiotemporal scales. Nonetheless, all these methods are either destructive, time consuming, spatially inaccurate, subjective, or expensive, therefore they are prone to errors or limitations [6][7][8]10]. Given these limitations, there is a great need for techniques that not only determine CGM non-destructively but are high-throughput in nature as well as account for spatial variability. ...
Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive , spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68-0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train-test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (r train : 0.97, rRMSE train : 1.17%, r entire : 0.95, rRMSE entire : 1.37%). However, when validated for the test dataset, an increase in the train-test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train-test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.
... Owing to their specific sizes, aspect ratios, moisture content, starch, protein and fat reserves, seeds present electrical conductivity and permittivity parameters, enabling their modeling by resistor-conductor dipoles [21]. Since the values of these parameters can significantly differ from one type of seed to another one [22], their packing into a DBD reactor is expected to change plasma through its electrical (and therefore physicochemical) properties, even if identical setpoint parameters are imposed (gas flow rate, power, gap distance, etc.). One of the key parameters, which is also an unknown, is the capacitance of the seeds' bed. ...
Seeds have been packed in a dielectric barrier device where cold atmospheric plasma has been generated to improve their germinative properties. A special attention has been paid on understanding the resulting plasma electrical properties through an equivalent electrical model whose experimental validity has been demonstrated here. In this model, the interelectrode gap is subdivided into 4 types of elementary domains, according to whether they contain electric charges (or not) and according to their type of medium (gas, seed or insulator). The model enables to study the influence of seeds on the plasma electrical properties by measuring and deducing several parameters (charge per filament, gas capacitance, plasma power, ...) either in no-bed configuration (i.e. no seed in the reactor) or in packed-bed configuration (seeds in the reactor). In that second case, we have investigated how seeds can influence the plasma electrical parameters considering six specimens of seeds (beans, radishes, corianders, lentils, sunflowers and corns). The influence of molecular oxygen (0-100 sccm) mixed with a continuous flow rate of helium (2 slm) is also investigated, especially through filaments breakdown voltages, charge per filament and plasma power. It is demonstrated that such bed-packing drives to an increase in the gas capacitance, to a decrease in the beta-parameter and to variations of the filaments' breakdown voltages in a seed-dependent manner. Finally, we show how the equivalent electrical model can be used to assess the total volume of the contact points, the capacitance of the seeds in the packed-bed configuration and we demonstrate that germinative effects can be induced by plasma on four of the six agronomical specimens.
... Since the early 1950s, a great deal of new measurement methods and techniques have been developed and applied in these fields, such as the transmission/reflection methods, resonator perturbation method, and planar circuit method [2]. More recently, the physical properties of biological materials such as normal and diseased human breast tissue [3] and the dielectric properties of animal tissues at radio and microwave frequencies [4] have been identified by using the measurement of permittivity. ...
In this paper, we performed and designed a new rectangular cavity to identify and analyze the complex permittivity of two cancer cells (Breast-MDA231, Uveal melanoma) that have a high dielectric constant and dielectric loss. The rectangular cavity device is based on the improved cavity perturbation technology. The sample of the improved cavity perturbation device is placed at the position of a/n close to the wall of the cavity, where a is the wide side of the cavity and n is the positive even number. For high-loss biological materials, the improved cavity perturbation method has higher accuracy than the traditional cavity perturbation method. The results present that the relative dielectric constants of a single cell at Ka-band (26.5–40 GHz) are in the range 8–15, and the relative dielectric loss is 24–31. The information of the cancer cells at Ka-band waves can be helpful for further cancer detection and clinical treatment.
... Owing to their specific sizes, aspect ratios, moisture content, starch, protein and fat reserves, seeds present electrical conductivity and permittivity parameters, enabling their modeling by resistor-conductor dipoles [21]. Since the values of these parameters can significantly differ from one type of seed to another one [22], their packing into a DBD reactor is expected to change plasma through its electrical (and therefore physicochemical) properties, even if identical setpoint parameters are imposed (gas flow rate, power, gap distance, etc.). One of the key parameters, which is also an unknown, is the capacitance of the seeds' bed. ...
Seeds have been packed in a dielectric barrier device where cold atmospheric plasma has been generated to improve their germinative properties. A special attention has been paid on understanding the resulting plasma electrical properties through an equivalent electrical model whose experimental validity has been demonstrated here. In this model, the interelectrode gap is subdivided into 4 types of elementary domains, according to whether they contain electric charges (or not) and according to their type of medium (gas, seed or insulator). The model enables to study the influence of seeds on the plasma electrical properties by measuring and deducing several parameters (charge per filament, gas capacitance, plasma power, …) either in no-bed configuration (i.e. no seed in the reactor) or in packed-bed configuration (seeds in the reactor). In that second case, we have investigated how seeds can influence the plasma electrical parameters considering six specimens of seeds (beans, radishes, corianders, lentils, sunflowers and corns). The influence of molecular oxygen (0-100 sccm) mixed with a continuous flow rate of helium (2 slm) is also investigated, especially through filaments breakdown voltages, charge per filament and plasma power. It is demonstrated that such bed-packing drives to an increase in the gas capacitance (ξOFF), to a decrease in the β-parameter and to variations of the filaments' breakdown voltages in a seed-dependent manner. Finally, we show how the equivalent electrical model can be used to assess the total volume of the contact points, the capacitance of the seeds in the packed-bed configuration and we demonstrate that germinative effects can be induced by plasma on four of the six agronomical specimens.
... Since the reference method involves tedious procedures, time consuming and not suitable for online and on-site measurement, a secondary method which is simpler, low cost, rapid and non-destructive have been developed. This method works on the principle of sensing the clear relationship between moisture content and the dielectric properties of the grain [6][7] [8]. Over the past decades, [9][10][11][12][13] presented that the grain dielectric properties is a function of moisture content, applied frequency, temperature of the grain and the material density. ...
A brief review on the dielectric properties of grain and their use in sensing moisture content is presented. Dielectric constant and loss factor were defined generally, and their correlation with grain permittivity from historical studies is presented. References are cited on dielectric measurement techniques using various frequency ranges from radio frequency through microwave frequencies. The described techniques including from the past decades until current studies.
... properties. In cereals, dielectric properties have mostly been used in moisture content determination (Nelson and Trabelsi, 2012a). Other applications of dielectric properties in cereals include dielectric drying (Zhu et al., 2012), bulk density measurement (Trabelsi et al., 1998), prediction of grain damage levels (Al-Mahasneh et al., 2001), and the control of pest in stored grains (Jiao et al., 2011). ...
The purpose of this study was to investigate the effect of aflatoxin contamination on the dielectric constant of maize kernels. A factorial experiment comprising of three levels of moisture content (13.3%, 15.3%, and 16.4%), three frequencies (25, 50, and 100 kHz), and nine levels of aflatoxin contamination (0, 1.5, 2.6, 10, 50, 100, 150, 172, and 230 μg kg-1) was used. The maize kernels were poured into a custom-built sample holder comprising a shielded parallel plate capacitor. An ISO-TECH LCR-821 meter was used to measure the capacitance, from which the dielectric constant was computed. The results indicated that moisture content and frequency significantly (p≤0.05) affected the dielectric constant. The dielectric constant increased with increase in moisture content and decreased with increasing frequency. However, aflatoxin contamination level had no significant (p>0.05) effect on the dielectric constant of maize kernels. The coefficient of determination (R 2) of dielectric constant and aflatoxin contamination levels was low (R 2 = 0.2687), indicating a poor correlation between the aflatoxin levels and the dielectric constant of maize kernels. Based on the findings, the dielectric constant is unsuitable for predicting the level of aflatoxin contamination in maize kernels within the 20-200 kHz frequency range.
... properties. In cereals, dielectric properties have mostly been used in moisture content determination (Nelson and Trabelsi, 2012a). Other applications of dielectric properties in cereals include dielectric drying (Zhu et al., 2012), bulk density measurement (Trabelsi et al., 1998), prediction of grain damage levels (Al-Mahasneh et al., 2001), and the control of pest in stored grains (Jiao et al., 2011). ...
The purpose of this study was to investigate the effect of aflatoxin contamination on the dielectric constant of maize kernels. A factorial experiment comprising of three levels of moisture content (13.3%, 15.3%, and 16.4%), three frequencies (25, 50, and 100 kHz), and nine levels of aflatoxin contamination (0, 1.5, 2.6, 10, 50, 100, 150, 172, and 230 μg kg-1) was used. The maize kernels were poured into a custom-built sample holder comprising a shielded parallel plate capacitor. An ISO-TECH LCR-821 meter was used to measure the capacitance, from which the dielectric constant was computed. The results indicated that moisture content and frequency significantly (p≤0.05) affected the dielectric constant. The dielectric constant increased with increase in moisture content and decreased with increasing frequency. However, aflatoxin contamination level had no significant (p>0.05) effect on the dielectric constant of maize kernels. The coefficient of determination (R 2) of dielectric constant and aflatoxin contamination levels was low (R 2 = 0.2687), indicating a poor correlation between the aflatoxin levels and the dielectric constant of maize kernels. Based on the findings, the dielectric constant is unsuitable for predicting the level of aflatoxin contamination in maize kernels within the 20-200 kHz frequency range.
... Since then, rapid grain moisture measurement methods based on electrical and dielectric property measurements have been developed. Over the last century, numerous researchers have developed different moisture detection methods based on the electrical and dielectric properties of specific varieties of grain [6][7][8][9][10]. For corn ear moisture content measurement, Kang et al. [11] proposed a type of electronic probe instrument that was used to detect the moisture content of the corn ear directly to determine the moisture content of the corn kernels. ...
Moisture content is an important factor in corn breeding and cultivation. A corn breed with low moisture at harvest is beneficial for mechanical operations, reduces drying and storage costs after harvesting and, thus, reduces energy consumption. Nondestructive measurement of kernel moisture in an intact corn ear allows us to select corn varieties with seeds that have high dehydration speeds in the mature period. We designed a sensor using a ring electrode pair for nondestructive measurement of the kernel moisture in a corn ear based on a high-frequency detection circuit. Through experiments using the effective scope of the electrodes’ electric field, we confirmed that the moisture in the corn cob has little effect on corn kernel moisture measurement. Before the sensor was applied in practice, we investigated temperature and conductivity effects on the output impedance. Results showed that the temperature was linearly related to the output impedance (both real and imaginary parts) of the measurement electrodes and the detection circuit’s output voltage. However, the conductivity has a non-monotonic dependence on the output impedance (both real and imaginary parts) of the measurement electrodes and the output voltage of the high-frequency detection circuit. Therefore, we reduced the effect of conductivity on the measurement results through measurement frequency selection. Corn moisture measurement results showed a quadric regression between corn ear moisture and the imaginary part of the output impedance, and there is also a quadric regression between corn kernel moisture and the high-frequency detection circuit output voltage at 100 MHz. In this study, two corn breeds were measured using our sensor and gave R2 values for the quadric regression equation of 0.7853 and 0.8496.
