## 5 Conclusions  
This study investigated the precise hierarchical identification of grapevine damage symptoms caused by _Brevipalpus_ spp. to meet the demand for accurate pest monitoring and management, and in response to the need for pest control. For the first time, we constructed a five-level dataset of _Brevipalpus_ spp. damage symptoms in grapes. We improved the YOLOv8n algorithm by introducing the EMA attention mechanism after the SPPF layer of the backbone, adopting the MPDIoU loss function, and utilizing the Slim-Neck structure in the neck part. The hierarchical identification model for _Brevipalpus_ spp. damage (SEM-YOLOv8n) achieved a mean average precision (mAP) of 97.6% after training.  
Figure 9: Detection effect before and after improvement and its visualization. (**a**) False detection: YOLOv8n incorrectly classifies grape damage at Grade IV as Grade III. (**b**) Occlusion: SEM-YOLOv8n successfully detects a target with partial occlusion in the lower-left corner, while YOLOv8n fails to recognize it. (**c**) Missed detection: YOLOv8n fails to detect the target, while SEM-YOLOv8n accurately locates it.  
The extensive experimental and comparative results show that the model proposed in this paper is superior and practical, and has important reference value for the management for _Brevipalpus_ spp. The application of this model is expected to help improve the yield and quality of wine grapes, which is of great significance to the wine industry.  
Although the proposed model shows good performance, there are still some key issues that deserve further exploration. First, the sensitivity of the environment. In real-world applications, factors such as illumination changes and background interference may affect the stability of the model. Therefore, future research will focus on employing adversarial training to enhance the robustness of the model to these environmental changes. Second, computational efficiency and memory consumption are key considerations when deploying models to mobile devices. Despite reducing the parameters of the model through the lightweight structure of the Slim-Neck, it may still face computational and memory bottlenecks when processing high-resolution images. In future work, we will further optimize the computational efficiency and memory consumption of the model by using techniques such as quantization and pruning to compress the model and improve the inference speed to adapt to the resource constraints of edge devices and mobile devices. In addition, we will also explore the collaboration of UAV swarms for real-time pest monitoring and management by synchronizing the capture of image data and real-time analysis by UAV clusters, so as to improve the monitoring efficiency and provide accurate pest warning and control solutions for large-scale agricultural production.  
Methodology, C.W. and W.T.; Supervision, C.W.; Dataset Acquisition, W.T., G.M. and L.Z.; Writing--original draft, W.T.; Writing--review and editing, C.W. and L.Z.; Resources, G.M.; Conceptualization; L.Z.; Funding acquisition, L.Z.; Project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chinese Agriculture Research System (CARS-29-bc-4) and the National Natural Science Foundation of China (32471597).  
Not applicable.  
The datasets for this article are available from W.T. on request.  
The authors declare no conflicts of interest.