Full
Outline
Accurate detection of the drive wheel slip in tractor ploughing operations is fundamental to achieving slip control, which helps improve the traction efficiency and ploughing quality of a tractor. To address the issue of ensuring the accuracy and real-time performance of slip rate detection under heavy load ploughing conditions, a slip rate detection method based on multisensor data fusion was proposed. A real-time data collection platform for tractor ploughing operations was established. Field experiments were conducted to obtain tillage speed, forward acceleration, tractor pitch angle, roll angle, traction resistance, tillage depth and corresponding slip rate under different operating conditions. A datasets was constructed and subjected to statistical analysis. Four machine learning algorithms—random forest (RF), support vector regression (SVR), radial basis function neural network (RBFNN), and artificial neural network (ANN)—were employed to construct the slip rate detection models. The hyperparameters of these models were optimized by using grid search and particle swarm optimization (PSO). The performance of the models was evaluated via three metrics: the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The results indicated that, except for the RF model, which exhibited overfitting, the other three models achieved satisfactory slip rate predictions (R2>0.9). Among them, the ANN model optimized with particle swarm optimization (PSO) for initial threshold weight optimization outperformed both the SVR model with an R2 of 0.918, an RMSE of 2.6% and a MAPE of 8.9%, and the RBFNN model with an R2 of 0.903, an RMSE of 3.0%, and a MAPE of 8.8% on the test dataset, achieving an R2 of 0.937, an RMSE of 1.0%, and a MAPE of 7.6%. This method enabled reliable and accurate detection of tractor drive wheel slip rate, offering an approach for precise online detection and control of slip rate.
231
101
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
