# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 1 Introduction
Evaluation results demonstrated that SMC-YOLO achieved superior performance across multiple metrics compared to YOLOv8 and other leading models. [PERSON] et al. [31] proposed an improved corn leaf multi-disease detection model, named GAF-Net, which is based on YOLOv8. GAF-Net utilizes a Feature-Adaptive Spatial Feature Fusion (FASFF) strategy to efficiently fuse multi-scale lesion features, thereby improving its ability to recognize complex disease patterns and small targets. In addition, it utilizes a Grouped Spatial Convolution (GSConv) module for optimizing the feature extraction and fusion, thus effectively enhancing its detection accuracy and reducing redundant information interference, and demonstrating robustness in detecting various corn leaf diseases in complex agricultural environments. Although existing deep learning-based object detection models (such as Faster R-CNN, YOLO series, etc.) have achieved certain progress in corn disease and pest detection, the following shortcomings still exist. First, complex farmland backgrounds, lighting changes, and leaf overlapping make the boundaries between diseases and backgrounds blurred. Traditional CNNs show insufficient performance in robustness and background suppression. These networks easily produce false detections or missed detections. Second, lesions and pests present diverse morphological features at different growth stages. These range from early small dot-like lesions to later large necrotic areas, and from tiny nymphs to larger adult insects. This puts higher requirements on the detection model's expressive ability and perception range. Based on the aforementioned challenges, this paper aims to construct a deep learning network capable of dynamically focusing on key regions and enhancing global contextual awareness for high-precision detection of corn leaf diseases and pests. As a result, a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, is proposed here based on YOLOv11n. By integration of newly designed attention mechanisms, multi-scale feature extraction, and background suppression modules, the proposed BCS_YOLO model comprehensively improves the detection capability of the baseline with respect to corn leaf disease and pest targets. The main contributions of this paper could be summarized as follows:  
1. A novel Self-Perception Coordinated Global Attention (SPCGA) module is proposed, which fuses global spatial attention, channel attention, and self-attention mechanisms. It enhances the network's perceptual ability to identify target areas and key features from multiple dimensions. By focusing on salient regions, strengthening important feature channels, and modeling global context dependencies, this module effectively improves the network's ability to capture fine-grained features of corn leaf diseases and pests. 2. A newly designed High/Low-Frequency Feature Enhancement (HLFFE) module is proposed, which adopts a dual-branch frequency processing architecture. The high-frequency branch enhances detail capturing through large kernel depth convolution and an inverted bottleneck structure. The low-frequency branch integrates multi-scale edge enhancers to strengthen contour features. This design improves the proposed model's perceptual and expressive abilities for lesion edge features in complex backgrounds. 3. A novel Local Attention Enhancement (LAE) module is proposed, which aims to enhance the importance of local features related to pests and diseases. It uses global context information extracted by depth convolutional layers. Through adaptive weight allocation, the module improves the proposed model's ability to discriminate between disease targets and interfering backgrounds.