# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 5 Conclusions  
This paper has addressed problems in corn leaf disease and pest detection related to background interference and variability in disease and pest morphology. Based on YOLOv11n, it has proposed a novel biotic condition screening (BCS) single-stage object detection model, called BCS_YOLO. First, a newly designed Self-Perception Coordinated Global Attention (SPCGA) module was incorporated into YOLOv11n in order to fuse channel attention, global spatial attention, and self-attention mechanisms. This module enables collaborative modeling of multi-dimensional features and improves recognition ability for early appearing lesions and tiny insects. Second, to address the complex frequency information problem in field images, a novel High/Low-Frequency Feature Enhancement (HLFFE) module was designed. The high-frequency branch bolsters detail feature capturing, while the low-frequency branch enhances contour representation. This leads to fine modeling of features in different frequency domains. Finally, a newly designed Local Attention Enhancement (LAE) module was introduced. This module combines soft pooling mechanisms with channel gating strategies, which effectively suppresses redundant background interference and highlights key disease and pest information. Conducted experiments demonstrate that the proposed BCS_YOLO model outperforms the mainstream object detection models in corn leaf disease and pest detection tasks, demonstrating a strong balance between detection performance and computational complexity, with excellent application potential.  
Conceptualization, S.H. and Z.J.; investigation, E.G. and Z.J.; data curation, S.H. and Z.J.; validation, E.G. and I.G.; methodology, S.H. and E.G.; writing--original draft preparation, S.H. and E.G.; writing--review and editing, Z.J. and I.G.; project administration, Z.J. and I.G.; resources, Z.J. and I.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication has emanated from research conducted with the financial support of the National Key Research and Development Program of China under the Grant No. 2017 YFE0135700 and realized in part with the help of the infrastructure purchased under the Bulgarian National Roadmap for Scientific Infrastructure, financially coordinated by the Ministry of Education and Science of the Republic of Bulgaria under Grant D01-98/26.06.2025.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available in the public datasets CD&S ([[https://osf.io/s/gru5/](https://osf.io/s/gru5/)]([https://osf.io/s/gru5/](https://osf.io/s/gru5/))) and IP102 ([[https://github.com/xpwu95/IP102](https://github.com/xpwu95/IP102)]([https://github.com/xpwu95/IP102](https://github.com/xpwu95/IP102))), last accessed on 16 June 2025. The private dataset used for generalization ability experiments is available at [[https://doi.org/10.5281/zenodo.15857001](https://doi.org/10.5281/zenodo.15857001)]([https://doi.org/10.5281/zenodo.15857001](https://doi.org/10.5281/zenodo.15857001)), last accessed on 16 June 2025.  
**Conflicts of Interest:** The authors declare no conflicts of interest.