# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 1 Introduction
refactored the lead backbone of YOLOv5 using the bneck module of MobileNetV3 and reduced the size of the model through pruning and quantification methods while achieving a detection efficiency of 26.5 FPS and a 93% mAP, realizing the real-time accurate detection of tomato diseases [10]. [PERSON] et al. combined the efficient channel attention (ECA) mechanism and the Transformer encoder to improve the YOLO model and proposed a real-time detection model, Pest-YOLO, which achieved 73.4% on multi-class pest detection tasks [11]. [PERSON] et al. proposed an early apple leaf disease detection model RE-RCNN [13] by introducing a small spot feature enhancement branch and an improved SCMLoss to balance the differences between similar and large spots. However, in apple orchards, relying solely on leaf-scale disease and pest identification and detection methods is limited by perspective and field of view, making it difficult to quickly capture disease and pest information across the entire orchard. Thus, there is still a significant gap before practical application can occur in production. In comparison, the canopy-scale approach has significant advantages. Canopy-scale models can observe orchards from a broader perspective, capturing more spatial information, which is beneficial for comprehensively understanding the overall health of the orchard. Therefore, research on automatic classification methods for apple diseases and pest areas using canopy-scale spectral images has great significance. As an important component of remote sensing tools, unmanned aerial vehicles (UAVs) have the advantages of low manufacturing cost, portability, and ease of operation, bringing new opportunities to the area of precision agriculture. They have a wide range of applications in crop growth monitoring [16; 17; 18; 19; 20], yield estimation [21; 22; 23; 24], disease diagnosis [25; 26; 27; 28], and pesticide spraying [29; 30; 31]. Due to their close-to-earth flight characteristics, UAVs can quickly and flexibly complete monitoring tasks and obtain data with ultra-high spatio-temporal resolution under suitable weather conditions [32]. In addition, the multispectral images collected by the multispectral cameras carried by UAVs can reflect specific pest information in each band and then can be targeted for the detection of the corresponding pest. For example, near-infrared spectroscopy images can detect corn pathogens [26] and peanut bacterial wilt [33], visible light (red, green, and blue) images can detect wheat leaf rust [34] and grape yellow flower disease [35], etc. These studies have confirmed the feasibility of using UAVs equipped with multispectral cameras to diagnose various crop diseases and pests. However, manually discovering and verifying the correlation between multispectral data and crop diseases and pests is inefficient, requires high professional expertise, and often lacks universality. Vegetation indices (VIs), due to their ability to significantly reflect crop conditions and their ease of calculation, have received widespread attention from researchers studying crop diseases and pests. Due to the differences in the composition of background substances such as plants and soil, as well as the changes in the material structure of plants in the process of crop erosion by diseases and insect pests, the differences and changes in spectral wavelengths at the corresponding wavelengths will occur. Therefore, the VIs can be used to distinguish between soil background, healthy plants, and infected plants. [PERSON] et al. screened seven VIs related to apple fire blight through the clustering method and proposed an image segmentation method for diagnosing apple fire blight based on canopy multispectral images taken by drone [5]. [PERSON] et al. proposed a recognition method for the gray mold of strawberry leaves by exploring the band and texture and calculated VIs of hyperspectral images of strawberry leaves, combining these various characteristics with existing machine learning models [36].