## 3 Results  
### Evaluate a Single Classification Model  
As the PB-Conv1D had the shortest running time of the three models, the best analysis of its hyperparameters, and the best results generated during the procedure. Other models were not repeated. The PB-Conv1D model and PB-Conv1D combine different strategies and have been experimented with several times; the model's parameters are shown in Table 6. Although the foundational structure of the base model remains consistent, the parameters of the models for different strategies are different. Figure 8 shows the accuracy and loss function changes of different methods. The PB-Conv1D model added an early stopping strategy when the loss value of multiple iterations did not decrease during the training process, stopping the model training, and the model was iterated 140 times. The number of iterations for the other methods was fixed. Figure 8(Ac,Bc) shows that PB-CDSA achieved a high accuracy of nearly 95%, with a decreasing loss function on the training set; however, the accuracy was slightly above 80%, and the loss function showed a decreasing and then increasing pattern, which indicated the limited generalization ability of the model. The accuracy function of PB-CDST was cyclical because it used the cycle learning rate, and the learning rate would be relearned from the initial learning rate at the end of each cycle. Combining A and B, the method's accuracy decreased, and the loss value increased in the validation set. However, each model would be saved after 40 iteration cycles, which obtained models with a better generalization ability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Dropout/\%** & **Learning Rate** \\ \hline
**PB-Conv1D** & 40 & 0.0001 \\
**PB-Conv1D combine Snapshot (PB-CDST)** & 40 & 0.0005 \\
**PB-Conv1D combine SWA (PB-CDSA)** & 40 & 0.0005 \\
**PB-Conv1D combine Snapshot and SWA (PB-CDSTSA)** & 10 & 0.0005 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The learning rate and dropout parameters of the optimal model obtained based on the PB-Conv1D. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predict**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline
**Actual** & Positive & True positive (TP) & False negative (FN) \\  & Negative & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix diagram illustrating the predicted and actual classifications. Figure 9 illustrates the effectiveness of the combined strategy compared to the base-model. Among the three models, two achieved optimal results after incorporating the Snapshot strategy, while the third model combined both the Snapshot and SWA strategies to achieve the best results. With the combination of Table A1, the results show that the PB-CDST method had the higher OA among all PB-Conv1D-based models, with an improvement of 0.79%. PB-BMST achieved the best results, resulting in an accuracy improvement of 1.81% compared to the other PB-BiLSTM-based algorithm. The PB-Transformer combined SWA (PB-TRSA) method could not significantly improve the classification accuracy, with an accuracy decrease of 5.99%. Compared with the PB-Transformer method, PB-TRSTSA improved the accuracy by 0.63%. The optimal hyperparameter results for PB-BiLSTM and PB-Transformer are shown in Table 7.  
Comparing the metrics of PB-Conv1D, PB-BiLSTM, and PB-Transformer, the PB-Conv1D model is more effective for crop identification. According to the folded line of Figure 9, it is concluded that the accuracy of the PB-Conv1D model fluctuates less after the integration of the strategy, which means that the parameters of PB-Conv1D change slightly in the Loss function space without causing significant improvement in the model. The folded line of PB-BiLSTM showed that the accuracy fluctuates slightly after adopting the strategy.