## 2 Materials and Methods
### The Proposed MSFNet
#### 2.3.1 Multi-Source Fusion Module  
In the task of the regional classification of wheat diseases, reliance solely on RGB imagery fails to capture sufficiently discriminative features. Traditional multi-source data fusion methods in regional classification predominantly employ direct data concatenation or statistical feature selection strategies, which lack the deep modeling of multi-source features and fail to fully exploit the inherent advantages of multi-source data. To overcome these limitations, a novel feature fusion module--the multi-source fusion module (MSFM)--was proposed. The MSFM separates RGB data and multispectral vegetation indices (MS-VIs) into two parallel branches, aiming to enhance the spatial semantics and spectral semantics independently. The enhanced deep features from both branches are then fused to more effectively capture the complementary information of multi-source data, thereby improving the accuracy of disease regional classification. As shown in Figure 4, the MSFM consists of a spatial semantic enhancement module and a spectral semantic enhancement module. In the spatial branch, the input RGB image is processed using a spatial attention mechanism (SAM) that adaptively highlights features corresponding to disease-affected regions. Specifically, the branch applies max pooling and  
Figure 3: The structure of MSFNet. average pooling along the channel dimension to capture key spatial features. The pooled features are concatenated and passed through a convolutional block with a kernel size of 7, which expands the receptive field and enhances local feature extraction. Subsequently, the output undergoes a sigmoid activation function to generate a spatial attention weight map \(W_{r}\), representing the relative importance of different regions in the input image, as shown in Equation (1). Finally, \(W_{r}\) is element-wise multiplied with the original RGB features to generate a spatially enhanced feature map. This enhanced map effectively accentuates disease-induced color variations (e.g., leaf yellowing) and texture changes (e.g., necrosis and tissue degradation), thereby improving the model's focus on affected regions and enhancing the robustness of disease region classification. The multispectral vegetation index (MS-VI) images are processed by the spectral semantic enhancement module, designed to refine the representation of spectral response variations induced by disease stress. Initially, the input MS-VI images undergo a \(1\times 1\) convolution to extract intricate feature relationships between spectral bands and vegetation indices. The resulting feature maps are then subjected to global max pooling and global average pooling. Specifically, global max pooling captures the most prominent spectral responses across channels, generating a salient spectral feature vector that emphasizes critical spectral variations associated with disease stress. In contrast, global average pooling captures the mean spectral response per channel, forming a global spectral descriptor that reflects the overall distribution pattern. These pooled vectors are then passed through a hierarchical linear transformation module (Equation (2)) to extract discriminative spectral semantics. Specifically, \(\text{Linear}_{1}\) projects the input into a latent space to highlight key spectral variations, while the interaction term \(X_{input}\odot\text{Linear}_{1}(X_{input})\) models inter-channel dependencies and selectively emphasizes disease-relevant features. \(\text{Linear}_{2}\) further abstracts these representations, enhancing the model's sensitivity to subtle  
Figure 4: The architecture of the MSFM. spectral cues induced by disease stress. The transformed feature vector is broadcasted and multiplied with the original feature maps along the channel dimension, yielding the final spectral semantic-enhanced features.