## 2 Materials and Methods  
### Dataset Construction  
The data collection sites of this study were tea cultivations in Yunxian and Fengqing counties of Lincang City (99\({}^{\circ}\)E, 24\({}^{\circ}\)N), Yunnan Province, and organic tea cultivations in Xishuangbanna (100\({}^{\circ}\)E, 21\({}^{\circ}\)N). Xishuangbanna organic tea cultivations are located in southwest China, belonging to the tropical monsoon climate area. The region is located in the southwest monsoon climate zone. The average daily temperature is 22 \({}^{\circ}\)C, the annual average relative humidity is 79%, the annual average rainfall is 1665 mm, and the annual runoff is 700 mm [13]. Lincang City is one of the renowned tea-producing regions in Yunnan Province, with tea cultivations distributed across mountainous areas characterized by a humid climate and fertile soil that are conducive to tea tree growth. Its tea is famed for its unique flavor and high quality, particularly its black tea, which is highly popular among tea consumers.  
To ensure the clarity of the original images, the image acquisition device used was the DJI AIR 2S unmanned aerial vehicle, equipped with a 1-inch 20-megapixel Exmor R CMOS sensor. The collected images' wavelength range is visible light, with the UAV flying at a speed of 2 m per second, capturing images every 0.5 s. To ensure the generalizability of the final model, image data were collected between 7 and 9 in the morning, 12 and 14 at noon, and 16 and 18 in the afternoon. The data collection method is illustrated in Figure 1. For the tea trees, data were acquired from three angles: aerial view, left-side view, and right-side view. Given that the subjects have a height ranging from 1 to 1.3 m, the unmanned aerial vehicle was required to fly at an altitude of 1.5 m for the top-down view and at an altitude of 0.75 m for the left and right views [14].  
A total of 1358 original diseased samples were collected in this study, including coal disease, anthracnose, and leaf blight, under different light intensities. To enhance the generalization ability of the model under complex environments and low-light conditions, and to better extract the characteristics of tea plant diseases at different distances and perspectives, data enhancement technology was used to expand the training set. The image brightness and contrast were randomly adjusted to 0.5 to 1.5 times, and the image was randomly scaled on the x-axis and y-axis, as shown in Figure 2[15].  
After data augmentation, a total of 7128 images were selected to construct the dataset, with the labeling details shown in Figure 3. Figure 3A illustrates the distribution of the number of labels for each disease in the dataset; Figure 3B shows the width and height  
Figure 1: Data collection.  
Figure 2: Data enhancement.  
of the label box after unifying the x and y coordinates of all labels. Figure 3C displays the distribution of the label x and y coordinates within the images. Figure 3D reflects the distribution of the aspect ratios of the labels in the dataset. Figure 3E shows the detailed information of the label distribution in the dataset.  
To further enhance data utilization efficiency and reduce bias caused by erroneous data, this study employed a five-fold cross-validation strategy during dataset partitioning. The entire dataset was evenly divided into five parts, with four parts used as the training set and one part as the test set in each iteration of model training. After five rounds of training, the model with the highest accuracy was selected as the final model for that round [16].