## 3 Results
### Evaluation of the Ensemble Classifier  
In this study, to obtain a more effective classification model, original models and voting algorithms were combined to obtain a better ensemble algorithm. The top-k models with better performance than the individual models were integrated to obtain the final ensemble model. The results after many experiments are shown in Table 8.  
Figure 11 shows that the classification effect of the PB-BV algorithm was best and the PB-TV algorithm was worst in single-model integration. When the single model was classified, the performance of the PB-CDST method was excellent, with its accuracy increased by 0.74% after integration. The performance of the TB-TRST was less than the  
Figure 10: The confusion proportions for each class in (**a**) PB-Conv1D, (**b**) PB-BiLSTM, and (**c**) PB-Transformer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Description** & **k-Value** \\ \hline
**PB-CV** & The top K best-performing PB-Conv1D, PB-CDST, PB-CDSA, and & \\  & PB-CDSTSA models were voting & 5 \\
**PB-BV** & The top K best-performing PB-BiLSTM, PB-BMST, PB-BMSA, and & 7 \\  & PB-BMSTSA models were voting & 3 \\
**PB-TV** & The top K best-performing PB-Transformer, PB-TRST, PB-TRSA, and & 12 \\  & PB-TRSTSA models were voting & 15 \\
**PB-CBT** & The PB-CV, PB-BV, and PB-TV were voting & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Description of integrated model construction. PB-CDST method, with a difference of 1.96%, and the accuracy of the model increased by 2.84% after integrating PB-BiLSTM, which is a considerable improvement, exceeding the PB-CV model. Compared with the best-performing single model ensemble (PB-BV), the PB-CBT model's accuracy improved by 1.21%. The performance of PB-CBT (83.47%) was slightly higher than that of the PB-CB (83.43%) classifier. Although the PB-Transformer model was added to PB-CBT, the accuracy of the PB-CBT did not significantly improve, and the running time of the PB-CBT was greatly increased. This underscores the fact that not integrating all models will improve the classification effect and may even increase the classification time. The PB-CB model can be used as a candidate algorithm for classification studies in subsequent experiments. Figure 12 shows that the PB-TV model obtained inferior classification results for each crop type. Since the PB-Transformer model had the worst discrimination, the integrated learning could not modify the temporal information from the model. Compared with the PB-CDST (Figure 10), PB-CV improved the discrimination of SU. PB-BV enhanced the recognition of WT-OT. PB-CB promoted the identification of RI, WT-OT, WT-RI and WT-SP. Compared to PB-CB, PB-CBT improved the recognition of OT, WT-RI, and WT-SU, illustrating that PB-Transformer can effectively discriminate among three categories. The results for SP and OT were better in a single classifier, indicating that model integration may have decreased the recognition effect of some classes. With the combination of Table A1, the F1 score of SU did not exceed 70% after model integration. It indicated that the models performed weakly for SU. Analyzing Figure 11, the F1 scores obtained by integrating the models were significantly improved and far higher than the highest F1 scores of single models. The PB-CV model was superior to the PB-BV model in identifying SU, RI, and SP. The PB-BV model was more effective in identifying OT and WT-OT than the PB-CV model. For other rotational crops, both were identified effectively, with F1 scores above 80%. The PB-BV model made it easier to confuse RI and SP, and the PB-CV model made it easier to classify WT-OT into SP, WT-RI, and WT-SU, which made the results consistent with those obtained from the analysis of the single model (Figure 10). This indicates that ensemble learning is a combination of results from simple voting and without feature learning.