## 2 Materials and Methods
### YOLOv10 Network Improvement  
As a novel real-time object detection network, YOLOv10 greatly reduces computational redundancy and parameter count through the use of a lightweight classification head and spatial-channel decoupled downsampling. To achieve efficient feature extraction, multi-scale feature fusion, and fully retained fine-grained information, rank-guided block design is further introduced. In addition, by incorporating large-kernel convolution and selected self-attention modules, the network effectively expands its receptive field and enhances its global information modeling capabilities, thereby significantly improving detection accuracy and robustness [17]. However, the YOLOv10 network exhibits limited ability to extract fine-grained features from low-resolution images and complex backgrounds. In tea cultivation disease images acquired through unmanned aerial vehicle remote sensing, where the disease distribution density is low, the disease objects are small, and issues such as overlap and occlusion are prevalent, its detection accuracy and robustness are noticeably insufficient.  
Therefore, based on the YOLOv10 network, this study uses Shape-IoU to optimize the Bounding Box Regression Loss of the YOLOv10 network for the detection error caused by morphological diversity in the case of overlap and occlusion. Aiming to solve the problems of weak response to low-frequency information in traditional convolution and the high computational complexity of large-kernel convolution, the input image is decomposed into low-frequency and high-frequency components by Wavelet Transform Conv improvement, and then multi-level decomposition and small-kernel deep convolution are used to achieve efficient extraction of frequency components. Aiming to solve the problem that the fixed receptive field cannot take into account both local details and global background information, Histogram Transformer is introduced to improve the Neck layer of the YOLOv10  
Figure 3: Dataset label visualization.  
network. The optimized YOLOv10 network structure is shown in Figure 4, and the detailed parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **From** & **Params** & **Module** & **Arguments** \\ \hline
0 & \(-1\) & 464 & Conv & [3, 16, 3, 2] \\
1 & \(-1\) & 4672 & Conv & [16, 32, 3, 2] \\
2 & \(-1\) & 5216 & WTConv & [32, 32] \\
3 & \(-1\) & 18,560 & Conv & [32, 64, 3, 2] \\
4 & \(-1\) & 20,864 & WTConv & [64, 64] \\
5 & \(-1\) & 73,984 & Conv & [64, 128, 3, 2] \\
6 & \(-1\) & 41,728 & WTConv & [128, 128] \\
7 & \(-1\) & 295,424 & Conv & [128, 256, 3, 2] \\
8 & \(-1\) & 41,728 & WTConv & [256, 256] \\
9 & \(-1\) & 164,608 & SPPF & [256, 256, 5] \\
10 & \(-1\) & 249,728 & PSA & [256, 256] \\
11 & \(-1\) & 0 & Upsample & [None, 2, ’nearest’] \\
12 & [\(-1\),6] & 0 & Concat & [1] \\
13 & \(-1\) & 148,224 & C2f & [None, 2, ’nearest’] \\
14 & \(-1\) & 0 & Upsample & [1] \\
15 & [\(-1\),4] & 0 & Concat & [192, 64, 1] \\
16 & \(-1\) & 37,248 & C2f & [64] \\
17 & \(-1\) & 59,540 & Transformer & [64, 64, 3, 2] \\
18 & \(-1\) & 36,992 & Conv & [1] \\
19 & [\(-1\),13] & 0 & Concat & [192, 128, 1] \\
20 & \(-1\) & 123,648 & C2f & [128, 128, 3, 2] \\
21 & \(-1\) & 147,712 & Conv & [1] \\
22 & [\(-1\),10] & 0 & Concat & [384, 256, 1] \\
23 & \(-1\) & 493,056 & C2f & [1] \\
24 & [17; 20; 23] & 862,888 & v10 Detect & [4, [64, 128, 256]] \\ \hline \end{tabular} Note: Params represents the number of parameters used in the network module. The Module column represents the module of the layer. Arguments is the specific parameter configuration of the module, which describes the detailed information of the input and output dimensions of the module.  
\end{table}
Table 1: Detailed parameters of the improved YOLOv10 network.  
Figure 4: Structure of the improved YOLOv10 network.