# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 1 Introduction  
Corn is one of the most widely distributed and highest-yielding food crops in the world. It is extensively used for food, animal feeding, and energy production. The quality and yield of corn production are directly linked to food security and agricultural economic development [1, 2, 3]. However, climate change is intensifying, and the scale of planting is expanding. Consequently, the frequency and severity of corn leaf diseases and pests havesignificantly increased. This leads to obstructed photosynthesis in plants and premature plant aging, resulting in substantial declines in yield and quality. Such developments pose threats to regional and even global food supplies [4; 5; 6]. To mitigate the economic losses and food security risks associated with corn leaf diseases and pests, accurate detection and identification of these at early stages are essential [7; 8; 9]. Traditional corn leaf disease and pest detection methods mainly rely on trained personnel for field visual inspection and manual image analysis. These methods are often inefficient and limited in accuracy, requiring extensive specialist knowledge and professional judgment. This can lead to delays in prevention and treatment, and in addition causes pesticide abuse [10; 11; 12]. Therefore, under the background of global food security and rapid development of smart agriculture, developing efficient and precise intelligent detection technology for diseases and pests is crucial. Such technology can help reduce crop losses, lower economic costs and environmental impacts, ensure food security, and promote smart agriculture development [13; 14]. In recent years, computer vision and artificial intelligence have developed rapidly with deep learning technologies being extensively studied. This advancements provide strong technical support for building automated and intelligent crop health monitoring systems [15; 16]. Convolutional Neural Networks (CNNs) and their advanced derivative architectures have shown excellent performance in image recognition and analysis tasks [17]. These networks can automatically learn and extract multi-level features related to diseases and pests from images, achieving precise localization and classification of lesions or pests [18; 19]. Research on disease and pest detection based on deep learning has made progress on various crops. Current object detection models could be divided into two-stage and single-stage groups. The leading model in the two-stage detection group is the Faster Region-based CNN (Faster R-CNN) [20], while the prominent models in the single-stage group include the You Only Look Once (YOLO) versions [21; 22]. [PERSON] et al. [23] proposed the Pest R-CNN model for fall armyworm detection aiming to achieve early monitoring of infestation through the integration of channel and spatial attentions, group and deformable convolutions, and multi-scale aggregation strategies. Based on the traditional Faster R-CNN, this model improves the detection accuracy by 12% compared to Faster R-CNN. The good performance of Pest R-CNN provides a convenient and efficient solution for pest monitoring in large-scale corn fields. [PERSON] et al. [24] proposed an MFaster R-CNN model for maize leaf disease detection, based on machine vision, which enhances Faster R-CNN by incorporating batch normalization layers in the convolutional layers to accelerate network convergence and improve model generalization. Furthermore, these authors proposed a center cost function to create a hybrid loss function, which enhances detection accuracy for similar lesions. Experiments showed that MFaster R-CNN performs best when Visual Geometry Group 16 (VGG16) convolutional layer structure serves as the feature extraction network. MFaster R-CNN outperforms Faster R-CNN and SSD models in both detection speed and accuracy. This research provides an efficient solution for intelligent diagnosis of corn diseases and has important practical application value. [PERSON] et al. [25] proposed a fall armyworm (FAW) detection model based on Mask R-CNN.