## 5 Conclusions  
This paper proposes MSFNet, an ultra-efficient model tailored for the precise diagnosis of wheat disease-affected regions. Given the scarcity of publicly available remote sensing datasets for wheat disease regional classification, this study leverages a multispectral UAV platform to acquire both RGB and multispectral images of wheat canopies, constructing a high-quality multispectral wheat disease dataset named MSWDD2024. To effectively integrate multimodal data, a dual-branch multi-source fusion module is designed, incorporating a parallel spatial-spectral semantic enhancement branch to deeply extract spatial texture features from RGB images and spectral response characteristics from MS-VI images, thereby fully utilizing the complementary information of multi-source data. Furthermore, to enhance the model's capability in perceiving spectral information at different levels, a novel hierarchical spectral semantic fusion module is proposed. By integrating attention mechanisms and a pyramid structure, this module effectively fuses spectral semantics across multiple levels, expanding the receptive field and improving spectral feature extraction. Experimental results on the MSWDD2024 dataset demonstrate that MSFNet achieves state-of-the-art performance, attaining an accuracy of 95.4%, a precision of 95.6%, a recall of 95.6%, and an _F1-score_ of 95.6%. Notably, MSFNet outperforms single-source RGB-based methods by 12.7% in accuracy and 13.0% in precision, surpasses MS-VI-based models by 15.0% in accuracy and 18.3% in precision, and exceeds the ResNet18 baseline by 6.0% inaccuracy, highlighting its robust multi-source fusion capabilities. These results strongly validate the effectiveness of MSFNet in wheat disease regional diagnosis, providing technical support for precision agriculture disease monitoring. Moreover, to extend the applicability of this approach to disease diagnosis in other crops and to enhance the generalizability of remote sensing-based plant disease detection, future research will focus on improving the model's generalization ability and mitigating the noise caused by spectral and spatial variations across different plant species.  
Conceptualization, W.J.; Data curation, Z.G.; Formal analysis, W.J.; Funding acquisition, B.L.; Methodology, W.J.; Project administration, H.Z. and B.L.; Supervision, H.Z.; Validation, W.Z.; Visualization, W.J.; Writing--original draft, W.J., Z.G. and W.Z.; Writing--review and editing, H.Z. and B.L. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the National Natural Science Foundation of China (No.62376226), the National Natural Science Foundation of China Youth Project (No.62406254), Shaanxi's Key Research and Development Program (No. 2024 NC-ZBCM-191), Shaanxi's Key Research and Development Program (No. 2024 NC-ZDCMY-05-05), Shaanxi's Key Research and Development Program (No. 2023-ZDLNY-63), Xianyang's Key Research and Development Program (No. L2022-ZDYF-NY-019), the Technology Innovation Leading Program of Shaanxi (No. 2024 QCY-KXJ-094), Xi'an Key Technology Research Projects for Key Agricultural Industry Chains (No. 2024 JH-NYZD-0027), and the Chinese College Students' Innovative Entrepreneurial Training Plan Program (No. 202410712188).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.