## 2 Materials and Methods
### Object Detection Model
#### 2.2.2 Proposed Network Architecture for SugarRow-YOLO  
In this study, the YOLOv11n model was selected as the improved baseline. To further enhance the feature extraction capability, the C3k2_WTConv module (see Figure 4) was proposed to replace the original C3k2 module in YOLOv11n. Specifically, WTConv [22] was introduced to replace the deep convolutional layer within the Bottleneck module of C3k2. WTConv can directly substitute depthwise separable convolutions in neural network architectures without requiring additional modifications. The core concept of WTConv is implemented through the following three key steps: 1. Wavelet Transform:  
Figure 3: The original network structure of YOLOv11. Multi-level decomposition of the input image using wavelet transform separates it into low- and high-frequency components. Through recursive application of the transform, the image is further decomposed into subbands at multiple scales. The low-frequency subbands primarily capture the global structure and overall content of the image, whereas the high-frequency subbands preserve localized edge and fine-detail information. 2. Multi-frequency Convolution: Distinct convolution operations are applied to different frequency subbands. As the wavelet transform reduces the spatial resolution of each subband, smaller convolutional kernels can achieve relatively larger receptive fields. This design effectively expands the receptive field while limiting the increase in model parameters. 3. Wavelet Decomposition: The convolution results from different frequency subbands are fused to reconstruct the image through filtering, upsampling, and weighted summation. This process effectively integrates multi-scale features, enabling the model to capture richer and more detailed information. By replacing the conventional convolutional layer in the C3k2 module with the WTConv module, the model effectively reduces parameter redundancy while expanding the receptive field, thereby mitigating the issue of over-parameterization. Secondly, given the demonstrated effectiveness of attention mechanisms in agricultural target detection [23], this study further strengthens the model's ability to capture spatial relationship features within images by introducing the C2 PSA_iRMB module. This is achieved by replacing the original multi-head attention mechanism in C2 PSA with the iRMB [24], as illustrated in Figure 5. The Inverted Residual Block (IRB) effectively reduces computational complexity by first expanding the number of channels, then applying DWConv, and finally reducing the dimensionality. Furthermore, the iRMB extends the IRB framework to attention-based models by combining the static short-range modeling advantages of convolutional neural networks (CNNs) with the dynamic long-range feature interaction capabilities of Transformers, thereby enabling more robust and expressive feature representations. The core components of the iRMB module include DWConv and a self-attention mechanism. Specifically, DWConv is employed to capture local spatial features, while the self-attention mechanism models global dependencies among features. This combination enables the model to more accurately perceive the spatial arrangement and geometric relationships of different crop regions within an image. Figure 4: Architecture of the C3k2_WTConv network. Finally, to address the issue of potential false positives and missed detections in high-density crop scenarios, this study adopts the UIOU loss function [25] as a replacement for the original CIOU loss used in YOLOv11n. The CIOU loss primarily focuses on the geometric discrepancies between predicted and ground truth bounding boxes. However, its representational capacity is limited when significant size or positional deviations exist between the two. In contrast, the UIOU loss emphasizes the weighting of bounding boxes with varying quality. By incorporating dynamic weight allocation and a dual attention mechanism, UIOU is better suited to handle dense object detection scenarios with improved adaptability and robustness.