## 5 Results  
### Ablation Experiment Results  
In the ablation experiments, we tested the complete PVM model, the PV model with the PAM module removed, the VM model with the MTAF module removed, and the baseline V model without any modules on the HN-NG Set and CCD Set datasets. Some ofthe experimental visualization results are shown in Figure 8. As shown in Table 3, on the HN-NG Set, the PVM model exhibited high performance across all metrics, with Precision at 82.16%, Recall at 80.04%, F1-score reaching 74.84%, IoU at 61.38%, and mPA at 70.46%. In terms of the impact of adding or removing modules, introducing the PAM module (PV compared to V) resulted in an 8.3% increase in IoU, indicating that the phenology-aware mechanism significantly enhanced the model's ability to identify key growth stages. Additionally, introducing the MTAF module (VM compared to V) led to a 6.2% increase in F1-score, highlighting the importance of multi-source fusion of optical and radar data for modeling complex temporal features. To further validate the model's generalization ability, we tested the model on the CCD Set. As shown in Table 4, the overall accuracy on the CCD Set is slightly lower than that on the HN-NG Set. The F1-score of the PVM model is 71.93%, which is approximately 2.91% lower than the former. Nevertheless, the contribution trend of the modules remains consistent. After removing the PAM module (VM model), the IoU drops to 52.37%, which further validates the important role of phenology modeling in enhancing time-series recognition  
Figure 8: Ablation experiment results on the Hunan dataset. From left to right, the four regions are: Original Image, VM, PV and V. Among them, VM, PM, and V refer to removing the PAM module, VIT module, and PAM and MTAF modules, respectively, in order to verify the validity of each module. This study primarily focuses on identifying farmland plots. Although the figure includes tea garden pixels, due to geographical constraints, tea trees are primarily cultivated in hilly regions, resulting in a very small number of tea garden pixels in the figure that are nearly invisible. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**HN-NG Set**} \\  & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** & **IoU (\%)** & **mPA (\%)** \\ \hline PVM & 82.16 & 80.04 & 74.84 & 61.38 & 70.46 \\ VM & 80.15 & 76.84 & 70.19 & 55.22 & 67.73 \\ PV & 76.47 & 74.18 & 66.27 & 52.52 & 63.33 \\ V & 72.36 & 65.69 & 60.71 & 45.28 & 58.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment results of PVM on the HN-NG Set dataset. capabilities. After removing the MTAF module (PV model), the Recall significantly decreased by 8.91% (from 75.65% to 66.74%). Furthermore, the baseline model (V) performed the worst on both datasets, with an F1-score of only 56.82%. Both sets of experiments collectively confirm that the PAM and MTAF modules play a critical role in improving the model's ability to perceive the spatial structure and temporal variation features of non-food crops. To further validate that the PAM module's temporal modeling mechanism authentically captures each crop's key growth stages, we compared the model's learned attention-weight distributions against the actual phenological periods of rapeseed, cotton, and tea. As shown in Figure 9, the PAM-equipped model demonstrates strong sensitivity to critical phenological windows, with attention peaks closely aligning with the Gantt-chart-style phenology calendar in Figure 3. For instance, rapeseed exhibits heightened attention during the full-bloom and maturity phases (March-June), and cotton shows focused attention during the maturity and harvest stages (July-October). Tea presents a more sustained attention pattern peaking after mid-season, consistent with its prolonged growth and summer development characteristics. It is worth noting that all crops are modeled using a unified set of 15 phenological stages to maintain structural consistency across input sequences.