## 6 Conclusions  
This study addresses the challenge of sugarcane tail tip detection in complex field environments by proposing a lightweight object detection algorithm, Slim-YOLO, based on an improved YOLO1n. The experimental results show that the improved model achieves a mean average precision (mAP50) of 92.2% and a mAP50:95 of 48.2% on the sugarcane tail tip dataset, representing an increase of 8.2% and 6.1%, respectively, compared to the original YOLO1n. At the same time, the number of parameters is reduced by 48.4%. While maintaining high detection accuracy, this model significantly reduces computational complexity and parameter count, demonstrating superior real-time performance and potential for mobile deployment. It provides both theoretical and technical references for the automation control of sugarcane harvester cutting devices.  
Although Slim-YOLO performs well in complex sugarcane tail tip detection tasks, there is still room for further optimization, such as improving detection stability under extreme lighting conditions or in higher-density scenarios. In the future, we plan to integrate multimodal information (e.g., LiDAR point cloud data) to further enhance detection robustness, while also exploring more efficient model compression and acceleration techniques to reduce computational costs and advance the development of intelligent detection technologies in smart agriculture.  
C.W.: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing--original draft, Writing--review and editing. Y.C.: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing--original draft, Writing--review and editing. S.L.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization. L.L.: Conceptualization, Data curation, Investigation, Methodology, Validation. Q.L.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology. K.L.: Conceptualization, Data curation, Investigation, Validation. Y.H.: Conceptualization, Data curation, Funding acquisition, Investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Science and Technology Major Project (Guike AA22117006), the Open Project of Key Laboratory of Artificial Intelligence and Information Processing in Guangxi Colleges and Universities (2024 GXZDSY017), the Key Project of Teaching Reform of the Guangxi Zhuang Autonomous Region in 2022 (2022)GZ121), the Project of Reform of Degree and Postgraduate Education in Guangxi in 2023 (JGY2023115), and 2020 Guangxi Degree and Postgraduate Education Reform Project (JGY2020066).  
Not applicable.  
The raw/processed data required to reproduce the above findings cannot be shared at this time, as the data also form part of an ongoing study.  
The authors would like to express their sincere thanks to Nanning Taivin Technology Co., Ltd. for their great support during the experimental period. We also sincerely appreciate the valuable contributions of [PERSON], [PERSON], and [PERSON] to this work. Their assistance in data collection, technical support, and discussions has been greatly beneficial.  
The authors declare no conflicts of interest.