However, their experiments only compared three classic models without considering other potentially better models. [PERSON] et al. [11] proposed the MobileNet-CA-YOLO model by improving YOLOv7 [12], optimizing it for rice pest and disease identification, and achieving an [EMAIL_ADDRESS] of 93.7% and lightweight model. However, compared to the non-lightweight model YOLOv7, its accuracy is lower. Additionally, [PERSON] et al. [13] proposed the YOLOv8-FMDI model based on YOLOv8n [14] for promptly detecting tomato pests and diseases. While maintaining a lightweight design, this model improved the [EMAIL_ADDRESS] by 1.2%. However, they did not mention whether the dataset included tomato leaf images under different environmental and cultivation conditions, which may affect the model's generalization ability. Although these studies provide reliable references for object detection methods in eggplant disease detection, existing object detection models still face numerous challenges in detecting eggplant diseases in complex farmland environments. These challenges are mainly manifested in the following aspects: firstly, changes in lighting conditions (such as sunny and cloudy days) have a significant impact on image quality; secondly, there are inevitably some interfering factors in actual farmland environments, such as weeds and other plant leaves obstructing disease targets. Furthermore, diseases themselves exhibit diversity and complexity, such as varying scales and irregular shapes. These factors lead to difficulties in eggplant disease detection: the various scales and different shapes of disease targets make it difficult for existing models to fully learn complex disease features; in real-world scenarios, there are numerous small target instances, and these small targets exhibit inconspicuous features in images, easily leading to missed detections and false detections. Factors such as target occlusion, overlap, and illumination changes may result in unstable model detection, placing high demands on the robustness of the detection algorithm. In addition, deep learning models typically contain numerous parameters and involve complex computations, which are unfavorable for practical deployment. To address the above challenges, after conducting thorough research and comparative analysis of various object detection algorithms, this study selected YOLOv8n as the baseline model and proposed a more efficient eggplant disease detection algorithm, YOLOv8-E. This algorithm optimizes YOLOv8n to improve detection accuracy while considering its practicality for mobile devices. The core contributions of this paper include the following: (1) To address the issues of varying sizes and complex shapes of eggplant diseases, we designed the C2f_SAConv module by combining switchable atrous convolution (SAConv) and the C2f module, and replaced some C2f modules in the YOLOv8n network with it to enhance the model's ability to extract disease features. (2) We reconstructed the Neck network of YOLOv8n using the SlimNeck module to reduce parameters and computational complexity. (3) In order to solve the problem of missed detection of small targets, we integrated the large separable kernel attention (LSKA) module into the SlimNeck structure to enhance the focus on fine-grained information. (4) We introduced the Inner-MPDIoU loss by combining intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), which accelerates the convergence speed of the loss function and accurately detects occluded and overlapping targets. Overall, these contributions aim to advance the development of intelligent and autonomous detection of eggplant diseases, providing new strategies for improving the accuracy and efficiency of agricultural disease management.