# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 1 Introduction
These authors train their model by preprocessing, enhancing, and annotating the FAW insect dataset, allowing the model to reach a mean average precision (\(mAP\)) of 94.21%, which is better than other object detection models. The proposed model can detect FAW in early growth stages, thus significantly reducing the farmers' workload and time. The two-stage object detection models achieve high accuracy through region proposal and feature classification. However, they exhibit high computational complexity and difficulties in meeting the needs of real-time farmland monitoring. To balance detection efficiency and accuracy, researchers have started focusing on one-stage end-to-end object detection models, which offer higher detection speed and lower resource consumption compared to the two-stage models. Thus, the one-stage object detection models, especially YOLO, are gradually becoming a research focus in smart agriculture. The most recent YOLO version is YOLOv11, released in late 2024 by Ultralytics, which was built upon YOLOv8/v10 by introducing key architectural innovations, such as the kernel-2 CSP (C3k2) block for efficient feature extraction and the Cross-Stage Partial + Spatial Attention (C2 PSA) block for improved spatial feature focus. There are five versions of YOLOv11--YOLOv11n (nano), YOLOv11s (small), YOLOv11m (medium), YOLOv111 (large), and YOLOv11s (extra-large). Among these, YOLOv11n was used in the current study as a baseline for creating the proposed BCS_YOLO model. Recently, [PERSON] et al. [26] proposed the DCH-YOLO model for corn pest detection in complex environments. This model is based on the YOLOv8 object detection model by incorporating a deformable convolutional network (DCN) module, a Convolutional Block Attention Module (CBAM) module, and a Harved Intersection over Union (HIoU) module. Experimental results showed that, compared with other detection models, DCH-YOLO reached higher detection performance in corn pest detection, with mAP of 77.2%. [PERSON] et al. [27] combined the Adan optimizer with YOLOv7 and proposed a new corn pest recognition model, which addresses the issues of high computational costs and low recognition accuracy found in traditional models. The Adan optimizer allows the model to perceive surrounding gradient information and avoid local minimum problems, thus improving the model's robustness and accuracy while reducing computational costs. [PERSON] et al. [28] proposed a maize leaf disease detection model, called YOLO MSM, based on YOLOv8, which integrates a multi-scale variable kernel convolution (MKConv) module, a C2f-SK attention mechanism module, and a Minimum Point Distance Intersection over Union (MPDIoU) loss function. These enhancements improve the model's feature extraction performance and enhance its detection accuracy with respect to corn leaf disease areas. Experiments show that compared with the baseline, YOLO MSM improves _precision_ and _recall_ by 0.66% and 1.61%, respectively. In addition, YOLO MSM has only 5.4 M parameters, providing significant lightweight advantages, which makes it suitable for deployment in smart agriculture scenarios with limited computing resources. [PERSON] et al. [29] introduced YOLOv11-RCDWD, an enhanced and lightweight object detection model, specifically designed for identifying maize leaf diseases under complex field conditions. This model improves upon the standard YOLOv11 by employing the RePLKNet backbone for better feature extraction, the CBAM attention mechanism for multi-scale feature refinement, and the Dynamic Head module for unified scale and spatial awareness. [PERSON] et al. [30] developed SMC-YOLO, a high-precision maize insect pest detection model, based on YOLOv8, incorporating three novel modules: a Spatial Pyramid Convolutional Pooling Module (SPCPM) to enhance backbone feature extraction, a Multi-Dimensional Feature Enhancement Module (MDFEM) to enrich neck-level features, and a Cross-Scale Feature-Level Non-Local Module (CSFLNLM) to improve the detection head's global perception.