# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 5 Conclusions  
This study improves upon the YOLOv8n object detection model, focusing on aspects such as replacing the backbone network, integrating deep convolutional architectures and attention mechanisms, and optimizing the loss function. Although the model's parameter count has increased, the improved model shows significant gains in detection accuracy for stacked sugarcane targets, particularly in efficiently recognizing unordered stacked sugarcane. Additionally, the model maintains good computational efficiency, making it suitable for deployment on mobile devices and in resource-constrained environments.  
Through ablation experiments and comparisons with other object detection algorithms, we draw the following conclusions:  
1. Architectural Optimization: Replacing the original backbone network of the YOLOv8n model with VanillaNet significantly enhances feature extraction ability and computational efficiency. The introduction of the InceptionNeXt deep convolutional architecture further improves the model's ability to recognize sugarcane targets of different shapes and sizes. At the same time, the integrated Efficient Channel Attention (ECA) mechanism enhances the model's focus on key sugarcane features, while the optimized MPDIOU loss function improves accuracy in overlapping target scenarios, significantly reducing misdetection and missed detection.
2. Experimental Results: The improved VIEM-YOLOv8n model achieved an average precision ([EMAIL_ADDRESS]) of 96.0% and a comprehensive average precision ([EMAIL_ADDRESS]:0.95) of 71.5% on the sugarcane dataset, significantly outperforming the original YOLOv8n model and other existing mainstream object detection algorithms. Furthermore, experiments on two public datasets further validated the generalizability and robustness of the VIEM-YOLOv8n model across different application scenarios.  
Despite the promising experimental results, we recognize that the applicability and generalizability of this method in real-world applications still need further verification. Future work will focus on testing the model in more complex real-world scenarios, including varying lighting conditions, different sugarcane species, and stacking arrangements. Further optimization of the model's computational efficiency and improving its deployment capability on edge computing devices are also important directions for future research. In conclusion, this study provides an efficient solution for stacked sugarcane target detection, offering technological support for automated sugarcane harvesting and mechanized planting, with significant application prospects.  
Conceptualization, J.L. and B.Z.; methodology, B.Z.; software, B.Z.; validation, B.Z.; formal analysis, H.X. and L.Z.; investigation, X.Z.; resources, J.L.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing. J.L. and B.Z.; visualization, B.Z.; supervision, B.Z.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant 52265028) and the Natural Science Foundation of Guangxi (Grant 2021 JJA160046).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.