## 3 Results
### Influence of Noise on Model Performance  
To further evaluate the robustness of the proposed model under noisy conditions, we introduced synthetic noise into the test set images to simulate sensor interference and transmission errors. Two common noise types were considered: Gaussian noise and salt-and-pepper noise. Gaussian noise with a standard deviation of 0.2 was added to both RGB and multispectral channels to simulate random sensor disturbances. In addition, salt-and-pepper noise was introduced at a density of 0.1 to mimic image corruption caused by bit errors or hardware faults.  
After generating the noisy dataset, we evaluated both the baseline ResNet50 and the proposed MSFNet models on the same set. As shown in Table 9, MSFNet outperformed ResNet50 across all categories in terms of classification accuracy, with all metrics exceeding 94%.  
Furthermore, when comparing the results to those obtained on the clean test set (see Table 9), we observed that ResNet50 experienced a notable performance drop, with precision and recall decreasing by 2.5% and 3.4%, respectively. In contrast, MSFNet showed much smaller performance degradation under noise, with all metric drops remaining within 1%. These results demonstrate that MSFNet maintains high classification accuracy  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Model** & **Feature for Classification** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{ResNeXt} & Single-Level Feature & 88.1\% & 88.3\% & 88.5\% & 88.4\% \\  & Hierarchical Feature & **91.3\%** \(\uparrow\) 3.2\% & 91.6\% & 91.6\% & 91.6\% \\ MobileNetV3s & Single-Level Feature & 86.6\% & 87.2\% & 87.0\% & 87.1\% \\  & Hierarchical Feature & **88.8\%**\(\uparrow\) 2.2\% & 88.9\% & 89.1\% & 89.0\% \\ SENet & Single-Level Feature & 91.5\% & 91.1\% & 91.7\% & 91.4\% \\  & Hierarchical Feature & **93.8\%**\(\uparrow\) 2.3\% & 94.2\% & 93.8\% & 94.0\% \\ ResNet50 & Single-Level Feature & 88.7\% & 88.6\% & 89.0\% & 88.8\% \\  & Hierarchical Feature & **93.8\%**\(\uparrow\) 5.1\% & 93.9\% & 94.1\% & 94.0\% \\ ResNet101 & Single-Level Feature & 89.2\% & 88.8\% & 89.4\% & 89.1\% \\  & Hierarchical Feature & **94.2\%**\(\uparrow\) 5.0\% & 93.8\% & 94.4\% & 94.1\% \\ \hline \hline \end{tabular}  
* **Note:** Bold values indicate the best performance achieved by the MSFM compared to the baseline model. The \(\uparrow\) symbol denotes the improvement in percentage points over the baseline.  
\end{table}
Table 8: Generalization validation results of the HSSFM across different networks.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Module** & **Metrics** & **Healthy** & **Others** & **Stripe Rust** & **Yellow Dwarf** & **Total** \\ \hline \multirow{4}{*}{ResNet50} & Accuracy & 90.5\% & 82.4\% & 80.5\% & 91.5\% & 86.1\% \\  & Precision & 91.0\% & 83.6\% & 80.8\% & 90.1\% & 86.1\% \\  & Recall & 89.8\% & 80.4\% & 80.5\% & 89.9\% & 84.9\% \\  & _F1-score_ & 90.4\% & 81.9\% & 80.7\% & 90.0\% & 85.4\% \\ \hline \multirow{4}{*}{MSFNet} & Accuracy & 98.9\% & 91.9\% & 91.4\% & 98.7\% & 95.1\% \\  & Precision & 98.3\% & 90.2\% & 93.1\% & 98.5\% & 94.8\% \\ \cline{1-1}  & Recall & 98.8\% & 91.4\% & 90.4\% & 98.7\% & 94.7\% \\ \cline{1-1}  & _F1-score_ & 98.5\% & 90.8\% & 91.7\% & 98.6\% & 94.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance evaluation of MSFNet and ResNet under noisy data conditions.  
even in the presence of noise, indicating its strong robustness and practical applicability in real-world scenarios with imperfect imaging conditions.