# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 4 Conclusions  
A regional classification method of apple diseases and insect pests based on multi-source image fusion is proposed. Firstly, the RGB images and multispectral canopy images of an apple orchard are collected by UAV. After regional division and labeling, a multi-source apply canopy image dataset of diseases and pests is constructed. Secondly, a vegetation indices selection method based on salient attention is proposed, which calculates the vegetation index with a strong correlation between the physiological information of 22 apples and optimizes the selection of the vegetation indices. Finally, a multi-label classification model of the apple disease and pest areas based on RGB and multispectral image fusion is proposed, which can effectively combine the texture information of RGB images and the apple physiological information reflected by multispectral images. The experiments show that the subset accuracy of the model proposed in this paper, tested on the apple pest and disease canopy multi-source image dataset, is 8.93% and 10.9% higher than prediction methods based on single-source RGB images or multispectral images, reaching 93.92%. It is worth mentioning that the classification accuracy for individual apple pest and disease regions is higher than 97%. Therefore, this model meets the accuracy requirements of practical production, providing technical support for coarse-grained localization of apple pests and diseases and ensuring the healthy development of the apple industry. At the same time, in order to extend the proposed method to other plant species to improve the universality of disease and insect pest diagnosis, how to develop spectral feature extraction methods suitable for more plant species to avoid differences in spectral features between species, data quality and quantity, and the influence of changes in spectral characteristics under different conditions is an important direction for further research in the future. Conceptualization, H.L. ([PERSON]) and H.L. ([PERSON]); methodology, L.S. and H.L. ([PERSON]); software, L.S. and H.L. ([PERSON]); validation, L.S. and B.T. ; formal analysis, H.L. ([PERSON]) and B.T. ; investigation, B.T. ; resources, B.L. ; data curation, B.T. and L.S. ; writing--original draft preparation, B.T. ; writing--review and editing, H.L. ([PERSON]) and H.Z. ; visualization, B.T. ; supervision, H.Z. and H.L. ([PERSON]); project administration, B.L. ; funding acquisition, B.L. All the authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Contribution Aspect** & **This Work** & **Literature References** \\ \hline Attention mechanism & Applied channel attention to re-weight fused features, improving model performance by 2.72\%. & Used different attention mechanisms [5], which did not achieve comparable improvements. \\  & Evaluated the effectiveness of different input data combinations on various model architectures, demonstrating the superiority of the vegetation indices combination. & Compared single-source input methods without exploring the impact of different input data combinations on model performance [4; 5; 17]. \\ Accuracy & Achieved a subset accuracy of 92.92\%, outperforming single-source methods by 8.93\% for RGB and 10.9\% for multispectral images. & Reported subset accuracies of 83.99\% for RGB images and 82.02\% for multispectral images [5]. \\ \hline \hline \end{tabular}
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Table 14: Cont. **Funding:** This research was funded by the National Natural Science Foundation of China (No. 62376226), by Shaanxi's Key Research and Development Program (No. 2024 NC-YBXM-191), by the Key Industry Chain of Shaanxi Provincial Key Research and Development Program (No. 2023-ZDLNY-63), by Xianyang's Key Research and Development Program (No. L2022-ZDYF-NY-019), by Technology Innovation Leading Program of Shaanxi (No. 2024 QCY-KJ-094), by Key technology projects of key agricultural industry chain in Xi'an (NO.