## 1 Introduction  
Yunnan Province, located across the Tropic of Cancer, is the birthplace of Pu'er tea and boasts a vast area of favorable climate [1, 2]. With abundant rainfall and excellent ecological conditions, it has become an ideal place for tea cultivation [3, 4]. However, the favorable ecological environment has also fostered a diverse range of pests, leading to aggravated pest infestation in tea gardens [5, 6]. The status of pests and diseases in organic tea gardens directly affects the yield and quality of tea leaves [7, 8, 9]. With the development of applied science in recent years, the adoption of advanced agricultural science and technology, including high-efficiency, targeted smart pest management techniques, has become an indispensable component in addressing insect pest challenges within Yunnan's tea plantations [10, 11]. The challenges faced in the realm of intelligent and precise pest control encompass the accurate identification of pests and the difficulties in deployment due to the high complexity of models [12; 13; 14]. Traditional recognition algorithms typically necessitate manual feature extraction and the use of basic classifiers, which, when tasked with identifying complex pest and disease samples in the variable agricultural environment, limit the accuracy and generalizability of the model. [15; 16]. Moreover, the complexity of these models imposes constraints on their practical application due to limitations in computational power and hardware conditions [17; 18]. The limitations in feature engineering and insufficient reliance on extensive datasets further impede the ability to rapidly and accurately identify pests and diseases [19; 20]. In recent years, with the development of technologies such as deep learning, the Internet of Things, and big data, the field of intelligent pest control in agriculture has progressed rapidly [21; 22; 23]. Currently, identification algorithms for tea garden pests have largely transitioned to employing deep learning models, with YOLO (You Only Look Once) emerging as a predominant methodology. This deep learning-based algorithm, rooted in advanced convolutional neural network architectures, excels in analyzing pest image characteristics and accurately differentiating between various pest species [24; 25; 26; 27]. [PERSON] et al. proposed MD-YOLO, Multi-scale Dense YOLO, for small target pest detection, achieving an mAP value of 86.2%; this model enhances the accurate identification of pests across various scales, thereby improving both the efficiency and accuracy of detection tasks [28]. [PERSON] et al. developed ESA-Net, an efficient scale-aware network for small-crop pest detection, which achieved an mAP50 value of 68.8% on the LMPD2020 dataset and 75.3% on the APHIDc dataset, underscoring the efficacy of this YOLO variant in addressing variations in pest sizes and reinforcing detection capabilities [29]. [PERSON] et al. introduced Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection. A recall rate of 83.5% highlights the notable contribution of this YOLO derivative in broadening the scope of detection and mitigating missed detections [30]. [PERSON] et al. proposed TeaViTNet: Tea Disease and Pest Detection Model Based on Fused multi-scale Attention. Achieving an average accuracy of 89.1%, this innovative application of the YOLO framework not only augments detection precision but also bolsters the model's ability to capture pest features within complex scenarios through its multi-scale attention mechanism [31]. While acknowledging the advancements made, the existing pest identification models grapple with limitations pertaining to regional adaptability, the inadequate capture of features, and elevated costs. Specifically, these models encounter difficulties when deployed across different regions and struggle to precisely extract vital information due to ecological and environmental disparities, thereby undermining their recognition accuracy.