## 5 Conclusions  
Eggplant diseases can severely affect eggplant yields and farmers' profits. Employing deep learning-based object detection algorithms can aid in the precise control of eggplant diseases. However, due to issues such as varying disease sizes, occlusion, and small targets in natural environments, the detection performance of existing deep-learning methods is often unsatisfactory. In response to these issues, this study proposed an object detection algorithm, YOLOv8-E, for eggplant diseases in field environments.  
First, SAConv was utilized to improve C2f, replacing some C2f modules in the YOLOv8n's Backbone network with C2f_SAConv to enhance feature extraction capabilities. Then, SlimNeck modules were employed to reconstruct the Neck network of YOLOv8n, thereby making the model lightweight. The LSKA modules were introduced into SlimNeck to facilitate the model's attention to fine-grained information through cascaded depthwise convolution and depthwise dilated convolution, reducing missed detection of small targets. Finally, the Inner-MPDIoU loss function was designed to further improve the detection accuracy for occluded and overlapped disease targets. The experimental results demonstrated that YOLOv8-E can effectively extract features of eggplant diseases, achieving an [EMAIL_ADDRESS] of 79.4% and an F1 score of 75.7% on eggplant disease datasets, which are 5.5% and 4.5% higher than the YOLOv8n model, respectively. GFLOPs and parametric count are 1.1 lower and 0.4 MB smaller than YOLOv8n, respectively, indicating that it achieved higher computational efficiency while maintaining high accuracy. Compared with  
Figure 15: Comparison of visual results of YOLOv8n and YOLOv8-E. (**a**) Visual result of YOLOv8n. (**b**) Visual result of YOLOv8-E.  
other algorithms, this model exhibited superior comprehensive detection performance, proving the effectiveness of the algorithm and its applicability in complex environments for eggplant disease detection. Considering the reduced parameters, this algorithm aligns with the requirements for deployment on mobile devices.  
Currently, the dataset used covers only three categories of diseases and does not fully reflect all types of diseases that may be encountered in practical environments. In future research, we intend to expand the dataset to encompass a broader range of disease types and continually optimize the algorithm to further enhance the model's generalization capability and practical applicability.  
Conceptualization, Y.H.; Methodology, Y.H.; Software, Y.H.; Validation, Y.H. and J.W.; Investigation, H.Z.; Resources, H.Z.; Data curation, J.W.; Writing--original draft preparation, Y.H.; Writing--review and editing, H.Z.; Visualization, Y.H. and J.W.; Supervision, H.Z.; Project administration, H.Z.; Funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This study is funded by the National Natural Science Foundation of China [Grant No. 61863009].  
Not applicable.  
All data are contained within the article. To request the data and code, please send an email to the first or corresponding author.  
The authors declare no conflicts of interest.