## 5 Discussion  
In this study, a YOLO1n improved sugarcane tail tip recognition algorithm entitled Slim-YOLO is proposed and optimized in terms of the backbone network, neck structure, and loss function for the challenge of target detection in complex environments in the field, respectively. The experimental results show that Slim-YOLO achieves a good balance of detection accuracy, inference speed, and computational efficiency and especially outperforms traditional methods when dealing with the challenges of complex backgrounds, multi-scale targets, and illumination variations. Compared to the original YOLO1n, we adopt RepViT as the backbone network, which, by virtue of its structure-heavy parame \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & **P** & **R** & **mAP50** & **mAP50-95** \\ \hline
1 & Slim-YOLO (CIoU) & 0.844 & 0.840 & 0.910 & 0.470 \\
**2** & **Slim-YOLO (UIoU)** & **0.853** & **0.856** & **0.922** & **0.482** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments on the loss function. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Model** & **RepViT** & **RepNCSPELAN4** & **GSConv** & **P** & **R** & **mAP50** & **mAP50-95** & **GFLOPs** & **Params** \\ \hline
1 & Base & & & & 0.808 & 0.835 & 0.840 & 0.421 & 6.3 & 2,582,347 \\
2 & A & \(\surd\) & & & 0.841 & 0.854 & 0.906 & 0.460 & 4.1 & 1,646,379 \\
3 & B & & \(\surd\) & & 0.838 & 0.882 & 0.919 & 0.470 & 6.3 & 2,209,459 \\
4 & C & & & \(\surd\) & 0.812 & 0.895 & 0.910 & 0.474 & 6.2 & 2,492,779 \\
5 & A + B & \(\surd\) & \(\surd\) & & 0.842 & 0.858 & 0.915 & 0.459 & 3.6 & 1,421,403 \\
6 & A + C & & & \(\surd\) & 0.847 & 0.855 & 0.913 & 0.460 & 5.3 & 2,022,519 \\
7 & B + C & & \(\surd\) & \(\surd\) & 0.838 & 0.878 & 0.921 & 0.471 & 6.1 & 2,119,891 \\
**8** & **Slim-YOLO** & \(\surd\) & \(\surd\) & **0.853** & **0.856** & **0.922** & **0.482** & **3.5** & **1,331,835** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments with different modules. terized design, significantly reduces the computational overhead in the inference phase while improving the expressive and feature extraction capabilities of the model, enabling it to maintain a high detection performance under lightweight conditions. In addition, our proposed ELANSIimNeck neck structure further enhances the feature extraction and fusion capabilities by replacing the VoV-GSCSP in the original Slim-Neck structure with RepNCSPELAN4. RepNCSPELAN4's improvements in feature pyramid fusion and channel attention mechanisms make the model more stable in multi-scale target detection and improve the detection accuracy in small targets and complex backgrounds. Meanwhile, in terms of the loss function, we introduce Unified-IoU (UIoU), which enables the model to focus on optimizing low-quality prediction frames in the early stage of training to speed up convergence while shifting to high-quality prediction frames in the late stage of training to improve the final detection accuracy. Compared to the original CIOU loss, the regression accuracy of UIoU is superior at high IoU thresholds, which further enhances the bounding box stability and detection robustness of Slim-YOLO. Despite improvements in several areas, Slim-YOLO still has some limitations. For example, although RepViT as a backbone network reduces the computational effort, there is still room for further optimization of the feature extraction capability under extreme lighting conditions. In addition, although RepNCSPELAN4 performs well in small target detection, false detections may still occur in heavily occluded scenes.