## 3 Materials and Methods
### Model Improvement
#### 3.4.1 Replacing the YOLO11 Backbone
The reduction in computational complexity improves efficiency during inference and is particularly beneficial for mobile devices by eliminating the computational and memory costs associated with skipping connections. To more effectively represent the contribution of the backbone RepViT structure to the network, this paper uses Grad-CAM to visualize and compare the photos. Grad-CAM can visualize the identification of targets by means of heat maps. The thermal effect of the YOLO11-RepViT network after replacing the backbone with the original YOLO11 network for target identification is shown in Figure 6 after testing the network through Grad-CAM. It can be observed that the structure after replacing the backbone RepViT has a darker color for the overall region of the cane tailing compared to the original YOLO11 structure, indicating a higher thermal value, while the red area is more concentrated in the cane tailing region, suggesting that the improved model is better able to focus on the characteristics of the overall region of the cane tailing. Moreover, the heat map generated by the improved model can distinguish the contour of the overall area of the sugarcane tail tip more clearly than the original model heat map, and the identification position is more accurate and reliable. 4.2 Proposing an Efficient Feature Fusion Neck Network ELANSlimNeck to Solve the Mutual Occlusion Problem  
For the later deployment of the model, we consider that field applications usually need to have low computational power, and the presence of multi-scale targets further exacerbates the difficulty of detection, considering that the targets in the overall area of the sugarcane tailing may be partially occluded by the target itself and that the dimensions of the overall area of the sugarcane tailing may change due to variations in the shooting angles and distances. This places higher demands on the feature extraction capabilities of the model. To this end, we propose the ELANSlimNeck neck network, the structure of which is shown in Figure 7, using the feature extraction-fusion module RepNCSPELAN4 from YOLOv9 [14] to replace the VoVGSCSP module in the original Slim-Neck [15]. The part labeled in red in Figure 7 is the replacement part. Compared to the VoVGSCSP module, RepNCSPELAN4 has significant advantages in terms of computational efficiency, multi-scale feature processing capability, lightweight design, adaptability, and performance-efficiency balance. The RepNCSPELAN4 module enhances the expressive power of the model in the training phase through structural reparameterization techniques and significantly reduces the computational complexity in the inference phase by simplifying the structure to meet the demand for high efficiency in field equipment. The Spatial Pyramid Enhancement (SPE) mechanism introduced in the module effectively captures multi-scale target features and further enhances the model's ability to perceive the overall area of sugarcane tails at different scales. The efficient feature fusion mechanism ensures the accurate recognition of targets in the overall area of sugarcane tail tips under complex backgrounds, while the  
Figure 5: RepViT network structure diagram. Figure 6: YOLO11-RepViT vs. original YOLO11 heatmap: (**a**) heatmap of the original YOLO11 model; (**b**) heatmap after replacing the backbone with RepViT. design of normalization and convolution (NC) stabilizes the performance of the model under complex lighting conditions. These features make RepNCSPELAN4 more advantageous than VoV-GSCSP in resource-constrained scenarios (e.g., real-time target detection in complex environments in the field). The structure of the RepNCSPELAN4 module is shown in Figure 8.  
At the same time, with the GSConv and channel compression technology, the aim is to reduce redundant information interference, improve the detection performance of different scales of targets, ensure the detection accuracy while significantly improving the detection speed and achieve a balance between performance and efficiency.