## 1 Introduction  
In the context of continuously changing global environments, abnormal weather has become increasingly frequent, exacerbating the occurrence of plant diseases [1]. During the growth process of Yunnan large-leaf tea trees, the occurrence of diseases not only leads to the loss of nutrients within the plant but also affects the metabolism and the proportion of important substances, thereby resulting in reduced yield, diminished quality, and increased food safety risks [2, 3]. Currently, tea cultivation disease detection primarily relies on on-site inspections by experts and technical personnel; however, this method incurs high training costs, long time investments, and suffers from inefficiencies and high subjectivity [4, 5]. Therefore, how to quickly and accurately obtain tea cultivation disease information has become a critical issue that urgently needs to be addressed in tea production [6]. In recent years, with the rapid development of computer technology, the application of deep learning in the field of agriculture has become more and more extensive, which has brought new opportunities and solutions for the detection of tea diseases. To accurately detect plant diseases, [PERSON] et al. designed an innovative Transformer module for the detection and classification of plant diseases. In this algorithm, Transformer architecture is used to model long-distance features, and soft split token embedding is used to capture local information in surrounding pixels and blocks. Finally, accuracy values of 99.94%, 99.22%, 86.89%, and 77.54% were achieved on VillagePlant, ibean, AI2018, and PlantDoc, respectively [7]. [PERSON] et al. proposed a wheat disease identification model based on EfficientNet architecture to automatically detect major wheat rusts. The test results show that the fine-tuned EfficientNet B4 model achieves 99.35% accuracy on the WheatRust21 dataset [8]. [PERSON] et al. used DenseNet121 as the primary feature extraction network and combined multiple dilated modules with a convolutional block attention module to construct the multi-dilated-CBAM-DenseNet model for the recognition of maize leaf diseases. Tested on the PlantVillage dataset and the authors' self-built dataset, the model achieved an accuracy of 98.84% on small samples [9]. Compared to traditional manual detection methods, these deep learning-based disease detection approaches not only significantly enhance efficiency and reduce costs but also minimize human interference, thereby improving the objectivity and accuracy of the detection results [10]. However, existing technologies still have significant limitations when faced with detection scenarios in tea cultivations where the distribution density is low and disease sizes are small. In particular, there remains further room for optimization in detection efficiency and coping with complex environments. To address the above issues and achieve efficient and flexible tea cultivation disease detection, this study focuses on common diseases such as coal disease, anthracose, and leaf blight. Low-altitude UAV (unmanned aerial vehicle) remote sensing technology was used to collect disease data in tea cultivations, obtaining high-spatial-resolution and high-temporal-resolution disease remote sensing images [11; 12]. To resolve detection errors caused by overlapping and occlusion of diseases, a Shape-IoU is applied to optimize the Bounding Box Regression Loss of the YOLOv10 (You Only Look Once version 10) network. In response to the weak response of traditional convolutions to low-frequency information and the high computational complexity of large-kernel convolutions, the YOLOv10 network's Backbone is optimized using Wavelet Transform Convolution. To simultaneously accommodate local details and global background information, a Histogram Transformer is introduced for network optimization. This study aims to provide an efficient, flexible, and highly adaptable technical solution for the detection of agricultural crop diseases, thereby promoting the development of agricultural monitoring systems.