## 1 Introduction  
In recent years, the worldwide spread of spider mites, _Brevipalpus_ spp. [1, 2, 3], have become significant pests on wine grapes across the northwest of China, which can cause economic losses of more than 30% when the damage is serious [4, 5]. _Brevipalpus_ spp. have a tiny body size ranging from 200 to 400 microns [6], so they are difficult to directly monitor with gross observation. Therefore, it was urgent to develop a computer vision model that could quickly and accurately monitor _Brevipalpus_ spp. and provide an accurate time for pest management. Currently, deep learning-based research on the target detection of plant pests and diseases continues to evolve, and the scope of the study includes not only insect pest and disease classification [7, 8, 9] and detection [10, 11, 12, 13] but also more complex tasks such as recognizing the severity of pests and diseases [14, 15, 16]. Among these, the YOLO series [17, 18, 19, 20] has been widely used in the detection of grapevine pests and diseases, but the current research is mainly focused on category detection. Less complex grading recognition research hasbeen conducted on the recognition of species, and, for _Brevipalpus_ spp., accurate grading recognition has not yet been performed in the literature. Algorithms for pest and disease detection that utilize deep learning techniques have advanced considerably and can be classified into two categories according to the detection phase. The first category includes two-stage detection algorithms, such as the Region-Convolutional Neural Network (R-CNN) [21] and the Faster Region-Convolutional Neural Network (Faster R-CNN) [22]. However, it is challenging to meet the requirements for real-time monitoring due to the two-stage detection mechanism's high computing complexity and low real-time performance. The other category is the single-stage target detection algorithms, and the representative algorithms include the SSD [23] and YOLO series. SSD is widely used in real-time pest monitoring because of its fast detection speed, but it is less accurate when dealing with small targets and complex backgrounds. In contrast, the YOLO series achieves a balance between speed and accuracy through the single-stage detection mechanism. Among them, YOLOv8n is particularly well suited for deployment on devices with limited resources because it has a tiny model size, delivers an effective detection speed, and uses an anchor-free architecture. YOLOv8, as an anchor-free (AF) detection method, not only ensures a lightweight model but also demonstrates superior feature representation capabilities by acquiring richer gradient flow information. Among this series, YOLOv8n distinguishes itself with its tiny model size and efficient detection speed. However, while YOLOv8n excels at general object detection tasks, its smaller model size can limit its ability to capture fine-grained features, which are crucial for specialized tasks such as _Brevipalpus_ spp. grape damage severity classification. Therefore, we propose an improved YOLOv8n model for _Brevipalpus_ spp. damage symptoms classification, termed SEM-YOLOv8n. The proposed SEM-YOLOv8n model, as shown in Figure 1, achieves an exceptional balance between detection accuracy and model size, outperforming other YOLO series models. The contributions of our work are as follows:  
1. We have established a five-level grading system for the grade damage symptoms of _Brevipalpus_ spp. and constructed a self-built dataset for this purpose. 2. Due to the limited feature information between the different grades of grape damage in a complex background, we have added the EMA attention mechanism after the backbone network, which helps to improve the model's ability to extract the subtle differences between grades. 3. We improved the traditional loss function from CIOU to MPDIoU, which effectively solves the detection frame distortion caused by fruit occlusion and reduces the problem of target misdetection. 4.