# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 2 Materials and Methods
### Proposed Model: BCS_YOLO  
Based on YOLOv11n, we designed a novel object detection model for corn leaf disease and pest detection with high performance in complex field scenarios, including similar disease symptoms, varying lighting conditions, overlapping leaves, etc. YOLOv11n is one of the lightweight detectors in the YOLO series. With its good balance of detection speed and performance, it provides a solid foundation for rapid model deployment and real-time monitoring. To further improve its performance in small-target detection, complex background perception, and multi-scale feature modeling, we propose here the BCS_YOLO model as an improved version of YOLOv11n. Its overall architecture is shown in Figure 2.  
BCS_YOLO retains the overall three-stage architecture of YOLOv11n, consisting of a backbone for feature extraction, a neck for feature fusion, and a head for detection output, while optimizing detection performance for corn leaf diseases and pests by incorporating three newly designed feature enhancement module types: SPCGA, HLFFE, and LAE. Combining spatial, channel, and self-attention mechanisms, the SPCGA modules are incorporated into the backbone. The combination of different attention mechanisms enhances the model's response ability to fine-grained lesion spots and tiny insect body features in different dimensions, while reducing information attenuation during feature transmission. As a result, the model's ability to identify early signs of disease and pest targets is improved. The HLFFE module is introduced at the end of the backbone. This module uses a dual-path processing structure. It divides input features into high-frequency and low-frequency branches. The high-frequency branch uses large receptive field depth convolution combined with inverted bottleneck structure to strengthen the detail information capturing. The low-frequency branch combines multi-scale edge enhancers to improve the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Number of Images** & **Number of Images** & **Number of Images** & **Total Number** \\  & **in Training Subset** & **in Validation Subset** & **in Test Subset** & **of Images** \\ \hline Fall armyworm larva & 590 & 65 & 74 & 729 \\ Yellow stem borer larva & 370 & 43 & 52 & 465 \\ Yellow stem borer & 518 & 76 & 65 & 659 \\ \hline Maize streak disease & 331 & 44 & 36 & 411 \\ Leaf rust & 356 & 54 & 56 & 466 \\ Gray leaf spot & 413 & 53 & 46 & 512 \\ \hline
**Total Number of Images** & **2578** & **335** & **329** & **3242** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of images in each category and each subset of the private dataset, used for evaluating the generalization ability of models. model's contour boundary expression ability. Through frequency domain separation and structural decoupling, the HLFFE module can significantly improve the model's perception ability for target boundaries in complex backgrounds while retaining local information. Additionally, to further improve the model's target discrimination ability in the feature fusion stage, LAE modules are embedded in the neck. Each of these modules establishes a local attention mechanism that guides the network to assign greater weights to important feature channels related to lesion spots or insect bodies, while minimizing the impact of redundant background channels. To demonstrate the innovations of BCS_YOLO more clearly, Table 3 summarizes the main differences between the baseline (YOLOv11n) and the proposed BCS_YOLO model, which are due to the incorporation of the three newly designed module types.