The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection
Abstract
1. Introduction
- (1)
- Research on an accurate microwave detection method for grain moisture: This study aims to establish an accurate grain moisture detection model, associate microwave signals with two microwave parameters of the material, and realize effective detection of MC.
- (2)
- Detection of abnormal moisture regions: A mobile antenna scanning system was designed to identify and detect abnormal moisture regions in grain samples. Using CST software, a physical model for microwave detection was established and tested, and an effective method for identifying the boundary of moisture abnormal regions was proposed. Combined with the prediction model, the MC of different samples in the one-dimensional mobile detection process was effectively detected.
- (3)
- Based on the two-dimensional mobile scanning detection of the antenna, the effective detection of the positional information of the high-moisture region was realized based on the phase delay analysis imaging method.
2. Materials and Methods
2.1. Principle of Transmissive Microwave Detection
2.2. Design of Microwave MC Detection System
2.3. Sample Preparation and Experimentation
3. Data Analysis and Model Construction
3.1. Preprocessing of Dataset
3.2. Construction of the MC Prediction Model
3.3. CST Modeling of Microwave Detection of Abnormal Moisture Regions
3.4. Phase Delay Analysis Imaging
4. Results and Discussion
4.1. Analysis of Microwave Characteristic Parameters
4.2. Comparative Analysis of MC Prediction Models
4.3. Localization and Analysis of Abnormal Moisture Regions
4.3.1. MC Prediction in Regions with Abnormal Moisture
4.3.2. Analysis of CST Simulation for Abnormal Moisture Regions
4.3.3. Analysis of the Experiment for Localizing the Abnormal MC Region
4.3.4. Two-Dimensional Imaging Results of the Anomalous Region
5. Conclusions
- (1)
- The overall trend of magnitude attenuation and phase shift of wheat hydration positively correlated with microwave frequency. Six regression methods based on selective machine learning (SVR, BP, LightGBM, RF, MLR, and Lasso algorithm) were used to construct an MC prediction model with dual-parameter inputs. Among them, the RF model, with an impressive prediction as R2 = 0.9846, MSE = 0.2768, and MAE = 0.3986, emerged as the accurate and stable model for wheat’s MC prediction. After experimental validation, the RF-based MC prediction model yielded a maximum absolute error between the prediction and the true value of 0.565%, and the maximum relative error was 3.166%.
- (2)
- The feasibility of localizing and visualizing the distribution of wheat’s abnormal (high-MC) regions was assessed in both CST modeling and experimental validation. Additionally, two methods, absolute and gradient methods, were developed and employed for the signal attenuation ΔA. Both results implicitly showed the better performance of the absolute gradient method through its lower prediction error on center position and width. Specifically, the predictive center error was less than 12 mm, while the width error was 1.4 mm for the sample with the normal MC (10.563%) and 30.4 mm for a MC of 15.124%, respectively. Still, the prediction can be further improved by more sampling with the indicated evidence.
- (3)
- Further, this study employed the phase delay analysis method to realize effective imaging and visualization of high-moisture regions. According to the exploration and investigation provided above, this study indicates a bright future and the high possibility of accurately locating and presenting water distribution in order to provide early warnings of high-MC regions in wheat storage.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Sample Group | True MC/% | Average Predicted Value/% | Absolute Error | Relative Error |
---|---|---|---|---|
NO.A | 10.563 | 10.344 | 0.219 | 2.070 |
20.670 | 20.105 | 0.565 | 2.733 | |
NO.B | 15.124 | 14.645 | 0.479 | 3.166 |
20.670 | 20.158 | 0.512 | 2.476 |
LABN/mm | XL Coordinate/mm | XR Coordinate/mm | XC/mm | LLR/mm |
---|---|---|---|---|
125 | 170 | 410 | 290 | 240 |
150 | 150 | 420 | 285 | 270 |
175 | 140 | 440 | 290 | 300 |
200 | 120 | 450 | 285 | 330 |
MC (%) | LABN/mm | X1 Coordinate/mm | X2 Coordinate/mm | XC Coordinate/mm | L12/mm |
---|---|---|---|---|---|
9.007 | 125 | 230 | 370 | 300 | 140 |
150 | 190 | 360 | 275 | 170 | |
175 | 200 | 400 | 300 | 200 | |
200 | 170 | 410 | 290 | 240 | |
11.556 | 125 | 230 | 380 | 305 | 150 |
150 | 190 | 360 | 275 | 170 | |
175 | 200 | 400 | 300 | 200 | |
200 | 170 | 410 | 290 | 240 | |
14.883 | 125 | 220 | 360 | 290 | 140 |
150 | 210 | 370 | 290 | 160 | |
175 | 190 | 380 | 285 | 190 | |
200 | 180 | 400 | 290 | 220 |
Sample Group | MCNOR/% | XL Coordinate/mm | XR Coordinate/mm | XC/mm | LLR/mm |
---|---|---|---|---|---|
NO.A | 10.563% | 170 | 417.3 | 293.7 | 247.3 |
NO.B | 15.124% | 170 | 401.8 | 285.9 | 231.8 |
Sample Group | MCNOR/% | X1 Coordinate/mm | X2 Coordinate/mm | XC/mm | L12/mm |
---|---|---|---|---|---|
NO.A | 10.563% | 216.4 | 340 | 278.2 | 123.6 |
NO.B | 15.124% | 200.9 | 355.5 | 277.8 | 155.4 |
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Dai, D.; Wang, Z.; Huang, H.; Mao, X.; Liu, Y.; Li, H.; Chen, D. The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection. Agriculture 2025, 15, 1649. https://doi.org/10.3390/agriculture15151649
Dai D, Wang Z, Huang H, Mao X, Liu Y, Li H, Chen D. The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection. Agriculture. 2025; 15(15):1649. https://doi.org/10.3390/agriculture15151649
Chicago/Turabian StyleDai, Dong, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li, and Du Chen. 2025. "The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection" Agriculture 15, no. 15: 1649. https://doi.org/10.3390/agriculture15151649
APA StyleDai, D., Wang, Z., Huang, H., Mao, X., Liu, Y., Li, H., & Chen, D. (2025). The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection. Agriculture, 15(15), 1649. https://doi.org/10.3390/agriculture15151649