Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline
Abstract
:1. Introduction
2. Materials and Methods
2.1. Design and Simulation of Wheat Moisture Content Detection Devices
2.1.1. Structural Design of Wheat Moisture Content Detection Devices
2.1.2. Principle of Dielectric Constant Measurement
2.1.3. Calibration of Dielectric Constant
2.2. Principle of Measurement
2.3. Test Equipment
2.4. Sample Preparation
2.5. Experimental Design
2.6. Prediction Model
2.6.1. Random Forest Algorithm
- n groups of training sample sets are randomly generated via the self-help sampling method, and a decision tree model is constructed based on each group of new samples.
- When selecting attributes in each internal node (non-leaf node), several attributes are randomly selected from all attributes of the sample set to be used as the attribute set of the node; then, the optimal attributes are selected according to the evaluation rules of the CART algorithm and are split until the decision tree is fully grown. As the decision tree grows, no pruning is performed.
- When entering the test sample set, each decision tree is computed to generate a prediction value. Based on all the predicted values, the final results are obtained. For the regression problem, the weighted average of the predicted values of all decision trees is taken as the final result.
2.6.2. Extreme Learning Machine
2.6.3. BP Neural Network
3. Results and Analysis
3.1. Frequency Effect on Wheat Dielectric Constant
3.2. Effect of Temperature on Dielectric Constant of Wheat
3.3. Effect of Volume Density on Dielectric Constant of Wheat
3.4. Effect of Moisture Content on Dielectric Constant of Wheat
3.5. Effects of Variables on Wheat Moisture Content
4. Establishing a Model for Wheat Moisture Content Prediction
Establishment of a Model for Wheat Moisture Content Prediction
5. Conclusions
- (1)
- The stripline detection device was verified via a CST Studio Suite simulation. It has a good signal-to-noise ratio and is able to probe the permittivity of wheat in the 50–200 MHz range.
- (2)
- In the frequency range of 50–200 MHz, the dielectric constant of wheat decreases as the frequency increases, showing a negative correlation. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated.
- (3)
- The RF model, ELM model, and BP neural network model were used to model the dynamics of water content prediction. The determination coefficients R2 and the error analysis of various prediction results revealed that the RF prediction model, which utilizes the frequency, temperature, volume density, and effective dielectric constant A as inputs and the wheat moisture content as the output, exhibited superior predictive performance. Notably, the RF model achieved an exceptional R2 value of 0.99977. The detection method is characterized by its small size and strong anti-jamming capability. By considering the known sample when predicting the moisture content of other samples, the method is simple and can be used for the detection of small grains.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- Wang, Y.; Xin, L.; Hu, Z.; An, X. Analysis of the current situation of wheat production, consumption and trade in China. China’s Agric. Resour. Reg. 2018, 39, 36–45. [Google Scholar]
- Liu, Z.; Zhu, X.; Zhang, W.; Gong, Y. Experimental study on moisture content detection of wheat by dielectric method. J. Sens. Technol. 2017, 30, 1857–1861. [Google Scholar]
- Zhang, Y.; Zhao, J.; Zhao, L.; Cheng, X. Design and test of grain moisture on-line measuring instrument based on dielectric properties. Chin. J. Agric. Mach. Chem. 2020, 41, 105–110. [Google Scholar]
- Peng, X.; Yan, J. Coaxial Resonant Cavity Measurement of Dielectric Constant of Sheet Dielectric Materials. Electron. Meas. Technol. 2022, 45, 1–6. [Google Scholar]
- Xu, X.; Sun, Y.; Yin, Y.Y.; Xue, Y.W.; Ma, F.Y.; Song, C.; Yin, H.; Zhao, L.Q. A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying. J. Food Qual. 2022, 2022, 9620349. [Google Scholar] [CrossRef]
- Ma, F.Y.; Wang, D.W.; Yin, Y.Y.; Yin, H.; Song, C.; Xu, X.; Sun, Y.; Xue, Y.W.; Zhao, L.Q. Determining peanut moisture content by scattering coefficient. J. Food Eng. 2023, 344, 111398. [Google Scholar] [CrossRef]
- Yin, H.; Ma, F.Y.; Wang, D.W.; He, X.N.; Yin, Y.Y.; Song, C.; Zhao, L.Q. Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient. Agriculture 2023, 13, 1136. [Google Scholar] [CrossRef]
- Guo, J.; Duan, K.; Guo, W. Detection method of wheat moisture content based on microwave free space measurement. J. Agric. Mach. 2019, 50, 338–343. [Google Scholar]
- Cai, Z.; Liu, Z.; Zhang, G.; Yang, T.; Jin, C. Research progress of grain moisture content measurement technology. China Agric. Mach. Chem. 2021, 42, 99–109. [Google Scholar]
- Zhan, P.; Che, G.; Zhang, Y.; Yang, X. Research on Intelligent Grain Moisture Detector. Mod. Agric. 2021, 502, 71–72. [Google Scholar]
- Zhao, J.; Huan, C.; Li, B. Summary of grain moisture content detection methods. Agric. Sci. Technol. Inf. 2018, 549, 46–49. [Google Scholar]
- An, X.; Dai, J.; Luo, C.; Meng, Z.; Li, L.; Zhang, A. Research on wheat moisture content detection device of combine harvester based on dielectric properties. J. Agric. Mach. 2022, 53, 185–190. [Google Scholar]
- Liu, J.; Qiu, S.; Zhou, J.; Wei, Z. Development and application of portable grain moisture content detection device based on microstrip microwave sensor. J. Food Saf. Qual. Insp. 2022, 13, 5485–5494. [Google Scholar]
- Karpenko, M.; Skačkauskas, P.; Prentkovskis, O. Methodology for the Composite Tire Numerical Simulation Based on the Frequency Response Analysis. Eksploat. I Niezawodn. Maint. Reliab. 2023, 25, 1–11. [Google Scholar] [CrossRef]
- Taheri, S.; Brodie, G.; Gupta, D. Optimised ANN and SVR models for online prediction of moisture content and temperature of lentil seeds in a microwave fluidised bed dryer. Comput. Electron. Agric. 2021, 182, 106003. [Google Scholar] [CrossRef]
- Guo, W.; Wang, J.; Liu, C. Moisture content detection method of adlay based on dielectric properties. J. Agric. Mach. 2012, 43, 113–117. [Google Scholar]
- Li, J. Study on Nondestructive Detection of Soybean Quality Based on Hyperspectral Image Technology. Master’s Thesis, Sichuan Agricultural University, Yaan, China, 2023. [Google Scholar]
- Zhou, X. Microwave Technology and Antennas, 3rd ed.; Nanjing Southeast University Press: Nanjing, China, 2009; p. 568. [Google Scholar]
- Ren, J.J.; Sheng, M.J.; Zhou, Z.Y. An Effective Method for Electromagnetic Parameter Measurement of Flexible Materials Based on Air Coaxial Line. Int. J. Antennas Propag. 2022, 2022, 1667251. [Google Scholar] [CrossRef]
- Pozar, D.M. Microwave Engineering; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
- Ladniak, L. Application of CST Software for Calculation and Analysis of Electric Field Distribution. Prz. Elektrotechniczny 2012, 88, 226–228. [Google Scholar]
- Zhang, P.; Tao, Z.; Guo, Y.; Xiao, L.; Zhu, X. Application of CST Microwave Studio in the Experimental Teaching of High Gain Planar Reflectarray Antennas. Lab. Res. Explor. 2023, 42, 204–208. [Google Scholar]
- Duan, D.Y.; Ma, F.Y.; Zhao, L.Q.; Yin, Y.Y.; Zheng, Y.H.; Xu, X.; Sun, Y.; Xue, Y.W. Variation law and prediction model to determine the moisture content in tea during hot air drying. J. Food Process Eng. 2022, 45, e13966. [Google Scholar] [CrossRef]
- Zhou, X.; Zhang, P.; Qin, N.; Huo, D.; Qiao, J. Effect of soil firmness on soybean yield in black soil area based on GA-RF model. J. Northeast Agric. Univ. 2022, 53, 67–75. [Google Scholar]
- Wang, W.; Cui, X.; Qi, Y.; Liang, R.; Jia, B.; Xue, K. Regression analysis model of coal spontaneous combustion temperature in goaf based on SSA-RF. Chin. J. Saf. Sci. 2023, 33, 136–141. [Google Scholar]
- Wang, J.; Bi, H. An extreme learning machine based on particle swarm optimization. J. Zhengzhou Univ. (Sci. Ed.) 2013, 45, 100–104. [Google Scholar]
- He, N.; Xi, K.; Gao, F.; Liu, Y. Predictive control parameter tuning based on FCM-ELM-BBPS. J. Hunan Univ. (Nat. Sci. Ed.) 2023, 50, 168–177. [Google Scholar]
- Lei, D.; Fu, Y.; Jin, H.; Li, D.; Yang, Y.; Ju, J.; Ren, S. Research on BP neural network in prediction of corn drying moisture content. Grain Process. 2022, 47, 45–48. [Google Scholar]
- Wang, L.; Zhong, K.; Hu, S.; Xiao, B. Study on the prediction of agricultural mechanization of dried tangerine peel moisture content based on BP neural network. J. Agric. Mech. Res. 2024, 46, 215–222. [Google Scholar]
- Deptuła, A.; Augustynowicz, A.; Stosiak, M.; Towarnicki, K.; Karpenko, M. The Concept of Using an Expert System and Multi-Valued Logic Trees to Assess the Energy Consumption of an Electric Car in Selected Driving Cycles. Energies 2022, 15, 4631. [Google Scholar] [CrossRef]
- Funk, D.B.; Gillay, Z.; Meszaros, P. Unified moisture algorithm for improved RF dielectric grain moisture measurement. Meas. Sci. Technol. 2007, 18, 1004–1015. [Google Scholar] [CrossRef]
- Zou, H.; Shen, S.; Lan, T.; Sheng, X.; Zan, J.; Jiang, Y.; Du, Q.; Yuan, H. Prediction Method of the Moisture Content of Black Tea during Processing Based on the Miniaturized Near-Infrared Spectrometer. Horticulturae 2022, 8, 1170. [Google Scholar] [CrossRef]
Serial Number | Parameter | Numerical Value |
---|---|---|
1 | W | 76 mm |
2 | t | 4 mm |
3 | 58 mm | |
4 | 1.00053 |
Serial Number | Name | Numerical Value |
---|---|---|
1 | Source impedance | 50 |
2 | Coaxial connector impedance () | 50.1059 |
3 | Impedance of the connection section between the coaxial line and the central plate | 222.674 |
4 | Impedance of air filling section ) | 50.0514 |
5 | Impedance of polytetrafluoroethylene filling section | 28.2927 |
6 | The attenuation caused by the loss mechanism of the transmission line itself | 0.001 |
Feature | Correlation Coefficient | Significance |
---|---|---|
Frequency | 0 | 1 |
Dielectric constant | 0.698 | 0.000 |
Temperature | 0.009 | 0.02 |
Bulk density | −0.271 | 0.000 |
Input Parameter | Model | R2 | MAE | MSE | RMSE |
---|---|---|---|---|---|
RF | 0.96198 | 0.73596 | 0.87905 | 0.93758 | |
ELM | 0.94482 | 0.65408 | 0.86697 | 0.93111 | |
BP | 0.95603 | 0.63757 | 0.69833 | 0.83566 | |
Frequency, Temperature, | RF | 0.99977 | 0.044386 | 0.0053011 | 0.072809 |
ELM | 0.92938 | 0.81584 | 01.114 | 1.0554 | |
BP | 0.97275 | 0.51944 | 0.4299 | 0.65567 |
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Song, C.; Zhang, X.; Ma, F.; Yin, Y.; Yin, H.; Wang, S.; Zhao, L. Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline. Agriculture 2024, 14, 471. https://doi.org/10.3390/agriculture14030471
Song C, Zhang X, Ma F, Yin Y, Yin H, Wang S, Zhao L. Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline. Agriculture. 2024; 14(3):471. https://doi.org/10.3390/agriculture14030471
Chicago/Turabian StyleSong, Chao, Xinpei Zhang, Fangyan Ma, Yuanyuan Yin, Hang Yin, Shuhao Wang, and Liqing Zhao. 2024. "Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline" Agriculture 14, no. 3: 471. https://doi.org/10.3390/agriculture14030471