## 3 Results and Analysis
### Path-Tracking Deviation Pattern Identification Test
#### 3.1.2 Performance Testing of Models  
1. DBN performance tests The performance test mainly includes: (1) Comparing the feature learning ability of the three models in the face of different data samples through a single training; (2) Observing the prediction performance of the three models on different datasets after multiple training and testing by using a 10-fold cross-validation test. Test 1: After 100 epochs of pre-training and 50 epochs of reverse fine-tuning, the Loss (CCE), MAE, and Accuracy for the training, validation, and testing of Models I, II, and III are shown in Figure 12. Test 2: After a 10-fold cross-validation test, the final average test results for Models I, II, and III are shown in Tables 11-13. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Description** \\ \hline \(L_{input}\) & 9 & Number of nodes in the input layer, corresponds to the number of features \\ \(L_{s1}\) & 100 & Number of nodes in the hidden layer 1 \\ \(L_{s2}\) & 100 & Number of nodes in the hidden layer 2 \\ \(B_{pre}\) & 10 & Batch size of pre-training \\ \(LR_{pre}\) & 0.35 & Learning rate of pre-training \\ \(E_{pre}\) & 100 & Epoch of pre-training \\ \(LS_{pre}\) & 0.999 & Learning rate scale factor of pre-training \\ \(B_{ret}\) & 10 & Batch size of reverse fine-tuning \\ \(LR_{ret}\) & 0.50 & Learning rate of reverse fine-tuning \\ \(E_{ret}\) & 50 & Epoch of reverse fine-tuning \\ \(LS_{ret}\) & 0.999 & Learning rate scale factor of reverse fine-tuning \\ \(L_{output}\) & 3 & Number of nodes in the output layer corresponds to the number of categories \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameters and description of DBN model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **MAE** & **Macro-F1/\%** & **Accuracy/\%** & **Time/s** \\ \hline Training & 0.0193 & 98.38 & 98.84 & 0.00809 \\ Validation & 0.0156 & 98.67 & 99.02 & 0.00208 \\ Testing & 0.0330 & 97.29 & 98.13 & 0.00151 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Mean of the test results for Model I. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **MAE** & **Macro-F1/\%** & **Accuracy/\%** & **Time/s** \\ \hline Training & 0.0026 & 99.93 & 99.74 & 0.00869 \\ Validation & 0.0056 & 100 & 99.44 & 0.00266 \\ Testing & 0.0035 & 100 & 99.65 & 0.00242 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Mean of the test results for Model III. Figure 12: Loss, MAE and Macro-F1 of DBN: (**a**) Model I; (**b**) Model II; (**c**) Model III. Analyzed by the above data, each model shows excellent performance in different deviation pattern recognition tests. Convergence speed: Analyzed by the Loss curve in Figure 12, Models I and II can converge stably, while Model III has more obvious fluctuation changes during the training process, and the overall convergence speed is slower. Prediction speed: In Tables 11-13, time is the sum of prediction time for all data in the dataset. The average time taken for each data sample in the training of Models I, II, and III is 3.33 \(\upmu\)s, 3.58 \(\upmu\)s, and 2.64 \(\upmu\)s; the average time taken for each sample of data in validation is 7.7 \(\upmu\)s, 9.85 \(\upmu\)s, and 7.19 \(\upmu\)s; the average time taken for each sample of data in testing is 5.03 \(\upmu\)s, 8.06 \(\upmu\)s, and 5.93 \(\upmu\)s. The time consumed, as mentioned above, will not affect the normal operation of the system in comparison with the high-frequency data of the rice transplanter navigation system, and the deviation pattern can be identified very quickly. Generalization ability: As analyzed by the curves in Figure 12, with the increase in the epoch, the changing trend of the training, validation, and testing is gradually consistent. Combined with the data analysis in Tables 11-13, the models have good stability, accuracy, and very fast prediction speed, showing strong generalization ability.