# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 1 Introduction  
Apples are one of the most nutritious fruits, rich in vitamins, micronutrients, and fiber. China is the largest producer and exporter of apples [1], with apple production accounting for one-fourth of the total fruit production in the country and exports accounting for more than one-third of the total fruit exports [2]. Due to its advantageous geographical environment and unique natural climate conditions, Shaanxi Province has been recognized by the Food and Agriculture Organization of the United Nations as the best eugenic area for apples in the world [3]. By the end of 2022, the apple planting area in Shaanxi Province reached 6160 km\({}^{2}\), accounting for 31.45% of the total apple planting area in the country. However, the occurrence of diseases and pests can lead to a decrease in apple yield andquality, causing immeasurable losses [4]. The untimely and inaccurate discovery of diseases and insect pests will lead to the misuse and abuse of pesticides, causing pollution and seriously affecting the healthy and sustainable development of the apple industry in China. When diseases and pests occur in apple orchards, the traditional solution is that fruit growers inspect the orchard on foot or by vehicle, visually judge the types and degree of infection of diseases and pests on fruit trees, and take management measures to curb the development of diseases and pests and reduce the losses caused by them [5]. However, the manual judgment method is time-consuming and laborious and often limited by the height and shade of apple trees, resulting in a lack of sight and omission of diseases and insect pests and a wrong judgment of the type and severity of apple diseases and insect pests. Therefore, realizing fast and efficient diagnosis of apple pests and diseases is crucial to the sustainable development of the apple industry. Due to its excellent algorithmic performance, deep learning has been widely used in various fields, especially in computer vision tasks. For example, deep learning models can autonomously learn to understand image features at different levels, enabling the automated diagnosis of various crop diseases and pests [6; 7; 8; 9; 10; 11; 12; 13]. At the same time, many theories and methods such as MLOPs have been proposed to facilitate the development, deployment, and maintenance of deep learning models, providing assistance for the application of deep learning in agriculture [14]. The structure of convolutional neural networks (CNNs) is capable of adapting to the extraction of spatial features from images and exhibits outstanding performance in the fields of image processing and computer vision. [PERSON] et al. used a CNN to extend transfer learning and ensemble models to identify nine common rice diseases with 98 percent accuracy [7]. [PERSON] et al. designed the Inception structure for feature extraction and used the dense connectivity strategy to realize feature propagation, which improved the accuracy of grape leaf disease recognition [8]. [PERSON] et al. combined the advantages of a VGG network and the Inception block to propose a lightweight CNN model, VGG-ICNN, which can significantly reduce the model parameters while achieving 99.16% classification accuracy on a variety of plant disease datasets. [PERSON] et al. proposed an end-to-end method based on convolutional neural networks (CNNs) and combined it with MLOPs to improve the efficiency of development and deployment, realizing accurate classification of potato fusarium wilt and providing a solution for agricultural disease management [15]. Additionally, CNN networks can also be applied to object detection tasks, accurately locating the positions of disease occurrences while recognizing the types of diseases in images. [PERSON] et al. introduced the Inception-v1 module, Inception-ResNet-v2 module, and Squeeze-and-Excitation module to propose a DR-IACNN for six faster common grape leaf disease detection models, achieving an average accuracy of 81.1% Mean (mAP) and a detection frame rate of 15.01 frames/second (FPS) [9]. [PERSON] et al.