## 5 Conclusions  
This study aims to address the challenges of intelligent pest management at tea plantations in Yunnan Province, China. It focuses on resolving issues such as insufficient detection accuracy, high model complexity, and poor regional adaptability of pest models in tea gardens. Accordingly, an efficient and accurate pest monitoring method is introduced, which is based on the original YOLOv8n model, having been improved using three additional modules to meet the needs of intelligent intervention for pest control in tea plantations by implementing the WIoU-v3 loss function, SCConv for feature redundancy reduction, and the EMA attention mechanism. Experimental results indicate that the model's accuracy, recall, and mAP reached 95.08%, 94.19%, and 97.95%, respectively. Compared to the original model, the improved version achieved these gains in primary precision metrics while also reducing the model's parameter count and size, demonstrating its streamlined architecture. Comparisons with the original YOLOv8n and other classic models show that the proposed model achieves marked enhancements over the original YOLOv8 architecture, evidenced by a 3.12% rise in precision, a 5.65% increment in recall, a 2.18% boost in the mean average precision (mAP), and a 4.43% augmentation in the F1 score. Furthermore, in comprehensive evaluations against Faster R-CNN, SSD, and YOLOv8n, our model exhibits substantial mAP improvements of 14.34%, 8.85%, and 2.18%, respectively. To conclude, YOLOv8n-WSE-pest demonstrates significant advantages over other classic object detection models in terms of both its lightweight design and high accuracy, making it critically valuable for future tea garden and agricultural pest management. Its lightweight yet precise design meets the demands of real-time precision, making it highly suitable for deployment on small-scale devices. In our subsequent sustainable work, we will combine more methods and IoT technologies to directly deploy micro cameras in the tea garden to capture more dynamic images of insects. Subsequent research endeavors will concentrate on acquiring broader datasets and integrating atmospheric environmental parameters such as humidity, light intensity, and wind speed, aiming to achieve multimodal fusion of image and environmental information. This integration aims to enhance the complexity of intelligent pest monitoring and management practices, thus advancing the field of applied science and smart agricultural technologies. Conceptualization and writing--original draft preparation, H.L. ; methodology, W.Y. ; software implementation and support, Y.X. ; data validation and quality assurance, Z.W. ; data curation and management, J.H. ; writing--review, editing, and refinement, Q.W. and S.Z. ; formal analysis and interpretation of results, L.L. and F.Y. ; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This study received funding from various sources, such as the Innovative Team for AI and Big Data Applications in Yunnan's Tea Industry (202405 AS530025), the grants for the Development and Demonstration of Intelligent Agriculture Data Sensing Technology and Equipment in Plateau Mountainous Areas (202302 AE09002001), the Study of Yunnan Big Leaf Tea Tree Phenotypic Plasticity Characteristics Selection Mechanism Based on AI-driven Data Fusion (202301 AS070083), the Smart Tea Industry Technology Task of Menghai County, Yunnan Province (202304 BI090013), and the Yunnan Province Lancang County Xuelinsi Wa Ethnic Township, Nuofu Township Science and Technology Special Dispatch Team (202204 BI090079). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors express profound gratitude to the editor and reviewers for their insightful feedback and recommendations, which significantly enhanced the quality of this paper.