## 4 Conclusions  
In the traditional YOLOv10, only the geometric relationship is considered. In this study, by optimizing the Bounding Box Regression Loss and incorporating disease morphology and aspect ratio factors, the accuracy and robustness of disease target localization have been improved. The Wavelet Transform Conv optimization employs the Haar Wavelet Transform to decompose low- and high-frequency information, and combines multi-level decomposition with small-kernel depthwise convolution to strengthen the extraction of low-frequency features, thereby improving detection accuracy under complex backgrounds. The Histogram Transformer optimization utilizes histogram grouping combined with dynamic self-attention and a Dual-scale Gated Feed-Forward mechanism to simultaneously capture local details and global background information, effectively reducing the rates of missed detections and false detections. Experimental results indicate the following:  
1. Regarding the model loss function, under the One-to-Many Head, the improved YOLOv10 exhibits Box Loss, Cls Loss, and DFL Loss values of 1.16, 0.66, and 1.24, respectively, which represent reductions of 15.94%, 13.16%, and 8.82% compared to  
Figure 10: External validation comparison.  
the original network values of 1.38, 0.76, and 1.36. Under the One-to-One Head, the Box Loss, Cls Loss, and DFL Loss are 1.23, 0.65, and 1.23, respectively, corresponding to reductions of 14.58%, 17.72%, and 8.89% relative to the original YOLOv10 network values of 1.44, 0.79, and 1.35.
2. In terms of detection performance, compared with YOLOv10, YOLOv12, CornerNet, and SSD, the improved YOLOv10 achieves increases in precision of 3.40%, 9.22%, 16.25%, and 13.51%, respectively; recall increases of 10.05%, 12.05%, 18.59%, and 17.53%; F1 score increases of 6.75%, 10.62%, 17.40%, and 15.51%; and mAP increases of 11.95%, 12.59%, 20.31%, and 18.04%, respectively. In the confusion matrix, compared with the original YOLOv10 network, the improved YOLOv10 shows an increase in detection accuracy of anthracnose by 6%, bacterial coil disease by 20%, and leaf blight by 2%. The improved YOLOv10 network not only exhibits excellent adaptability in addressing issues such as blurry images, complex backgrounds, strong illumination, and occlusion in disease detection, but also achieves high levels of precision and recall, thereby laying a solid technological foundation for precision agriculture and intelligent decision-making.  
Conceptualization, writing--original draft preparation, methodology, X.G. and C.Y.; software, investigation, Z.W.; formal analysis, J.Z.; conceptualization, writing--review and editing, funding acquisition, S.Z. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Study on the screening mechanism of phenotypic plasticity characteristics of large-leaf tea plants in Yunnan driven by AI based on data fusion (202301 AS070083); Yunnan Menghai County Smart Tea Industry Science and Technology Mission (202304B1090013); and National Natural Science Foundation (32460782).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.