Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Principle of the Soil Physical State Identification Method
2.2. Data Acquisition Equipment
2.3. Construction of the Identification Model
2.3.1. Antecedent Network
2.3.2. Consequent Network
2.4. Learning Optimization Algorithm
2.4.1. Clustering Algorithm for the Parameter Identification of the Antecedent Network
- (1)
- Initialization: Using the SCM method to initialize the model input data center value , i = 1, 2, …, c.
- (2)
- Calculate the membership degree of each sample j belonging to the sample data center value , and the constraint condition is .
- (3)
- Recalculate the center value of the model input data:
- (4)
- Calculate the value of the objective function:
- (5)
- After completing a cycle, compare the value of the objective function until the numerical convergence stop iteration.
2.4.2. Hybrid Learning Algorithm for Parameter Optimization
2.5. Overview of Experimental Field
2.6. Test Method
3. Results and Discussion
3.1. Test Platform
3.2. Draft Force and Soil Moisture Content
3.3. Soil Physical State
3.4. Clustering Algorithm
3.5. Perception Model
3.5.1. Datasets
3.5.2. Parameter Settings
3.5.3. Training Environment
3.5.4. Algorithm Comparison
4. Conclusions
- (1)
- Complex and unclear relationships exist between plowing resistance, working parameters, and soil conditions. In this study, the field operation parameters and soil physical parameters were obtained using a data acquisition platform and soil instrument to construct the T–S fuzzy neural network for predicting soil physical state.
- (2)
- We measured soil moisture content corresponding to working parameters during plowing. The average soil moisture content decreased by 4.1% when the plowing depth increased from 16 cm to 24 cm, indicating that the soil moisture content decreased with the increase in paddy soil depth. In addition, at plowing depths of 16 cm and 24 cm, the maximum change values of the adjacent acquisition points were 11.6% and 14.5%, respectively. The change form of the soil moisture content in the horizontal and vertical directions indicates that soil moisture content has spatial differences.
- (3)
- The T–S fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs and the soil’s physical state as output. Our results show that when 280 groups of test data were used to verify the model, 264 groups were accurately predicted, and the Accuracy, Precision, Recall, and F-Score of the fuzzy model are 94.29%, 94.41%, 94.56%, and 93.05%. This indicates that the T–S fuzzy neural network model based on plowing resistance and working parameters can accurately identify the physical state of paddy soil in real-time during plowing, which provides a reliable reference for the selection of the optimal combination of plowing parameters and reduction in the energy consumption, thereby improving the overall efficiency of plowing.
- (4)
- Based on the conclusions of this paper, the optimal combination of operating parameters under different soil physical states can be further studied using DEM.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Clustering Method | Function |
---|---|
Fuzzy C-means Method | According to the membership relationship between soil parameters and the clustering center, the soil physical state category was determined. |
Fuzzy C-means Clustering based on Subtractive Clustering | Identify the structural parameters of the precursor network. |
Algorithm | Index | |||
---|---|---|---|---|
Precision | Recall | F-Score | Response Speed (s) | |
GD | 0.8882 | 0.9079 | 0.8980 | 0.34 s |
Hybrid | 0.9441 | 0.9456 | 0.9305 | 0.50 s |
GA | 0.9586 | 0.9623 | 0.9620 | 0.83 s |
Actual State | Predicted State | ||
---|---|---|---|
Soft | Zero | Hard | |
soft | 86 | 0 | 0 |
zero | 2 | 89 | 9 |
hard | 0 | 5 | 89 |
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Zhao, J.; Zhou, J.; Sun, C.; Wang, X.; Liang, Z.; Qi, Z. Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network. Agriculture 2022, 12, 1367. https://doi.org/10.3390/agriculture12091367
Zhao J, Zhou J, Sun C, Wang X, Liang Z, Qi Z. Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network. Agriculture. 2022; 12(9):1367. https://doi.org/10.3390/agriculture12091367
Chicago/Turabian StyleZhao, Jianlei, Jun Zhou, Chenyang Sun, Xu Wang, Zian Liang, and Zezhong Qi. 2022. "Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network" Agriculture 12, no. 9: 1367. https://doi.org/10.3390/agriculture12091367