## 3 Results
### Comparison with SOTA Methods  
To further assess the effectiveness of the proposed method, we conducted comparative experiments on MSWD2024 using various state-of-the-art (SOTA) image classification models, including Transformer-based models (Vision Transformer [38] and Swin Transformer [39]) and CNN-based models (MobileNetV3-s [40], ShuffleNetV2 [41], SENet [42], ResNet [5], ResNeXt [43], and EfficientNetV2 [44]). All inference speed (FPS) measurements were conducted on the same hardware platform described in Section 3.1 to ensure consistency. As shown in Table 5, the proposed MSF-ResNet outperformed all baseline models across all evaluation metrics, demonstrating its superior performance and validating its effectiveness in wheat disease region classification. Among the baseline models, the Transformer-based models (e.g., ViT and Swin Transformer) achieve lower accuracy, possibly due to their lack of explicit channel modeling and reliance on larger input sizes, which may limit their ability to capture fine-grained spectral-spatial patterns in small 64 x 64 patches. Among the CNN-based baseline models, SENet achieves the best performance, with an accuracy of 91.5%, primarily due to its channel attention mechanism, which effectively enhances the model's ability to focus on discriminative spectral features. EfficientNetV2 also demonstrates strong classification performance (90.1%), benefiting from its compound scaling strategy, which strikes a balance between network depth, width, and resolution. Traditional ResNet models, while stable, slightly lag behind the aforementioned models, with ResNet18, ResNet50, and ResNet101 achieving accuracies of 89.4%, 88.7%, and 89.2%, respectively. Although deeper networks, such as ResNet101, extract richer feature representations, the improvement is marginal, likely due to the increased model complexity failing to effectively integrate refined spectral-spatial feature expressions, thus limiting performance gains. Notably, the proposed MSFNet significantly outperforms all baseline models across all evaluation metrics, achieving the best performance, with an accuracy of 95.7%, a precision of 96.4%, a recall of 95.2%, and an _F1-score_ of 95.8%. Moreover, MSFNet achieves a high inference speed of 62.18 FPS, which is only slightly lower than that of the fastest baseline ResNet18 (79.54 FPS) and significantly higher than that of most other models, including Transformer-based architectures. In addition, MSFNet contains only 14.2 M parameters, indicating lower memory and storage demands than the other methods. These results demonstrate that MSFNet not only delivers superior accuracy but also meets the computational and memory constraints required for real-time UAV deployment. The superior performance of MSFNet is primarily attributed to its efficient data fusion strategy and multi-level spectral feature extraction mechanism. Specifically, the MSFM effectively uncovers complementary features between RGB and MS-VIs, enhancing the synergy \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Params** & **FPS** \\ \hline Vision Transformer & 84.6\% & 85.5\% & 86.1\% & 85.8\% & 48.6 M & 51.96 \\ Swin Transformer & 85.6\% & 86.2\% & 86.8\% & 86.5\% & 87.8 M & 26.91 \\ MobileNetV3 & 86.6\% & 87.2\% & 87.0\% & 87.1\% & 41.5 M & 48.22 \\ ResNeXt50 & 88.1\% & 88.3\% & 88.5\% & 88.4\% & 25.0 M & 38.19 \\ ShuffleNetV2-s & 88.2\% & 88.4\% & 88.6\% & 88.5\% & 14.5 M & 48.58 \\ EfficientNetV2-s & 90.1\% & 90.4\% & 90.4\% & 90.4\% & 21.4 M & 23.49 \\ SENet50 & 91.5\% & 91.1\% & 91.7\% & 91.4\% & 26.1 M & 18.16 \\ ResNet18 & 89.4\% & 89.6\% & 89.8\% & 89.7\% & 13.1 M & 79.54 \\ ResNet50 & 88.7\% & 88.6\% & 89.0\% & 88.8\% & 25.6 M & 61.16 \\ ResNet101 & 89.2\% & 88.8\% & 89.4\% & 89.1\% & 44.6 M & 57.62 \\ MSFNet & 95.4\% & 95.6\% & 95.6\% & 95.6\% & 14.2 M & 62.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiment results. of multi-source information and improving the effectiveness of data fusion.