# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 4 Discussion  
Corn leaf disease and pest detection in farmland environments face many challenges, such as complex background interference, significant target scale differences, and variable lighting conditions. The fundamental cause of these problems lies in the limitations of traditional CNNs in feature representation capability, multi-scale information fusion, and environmental adaptability. The current paper has deeply analyzed these technical bottlenecks and proposed a three-module collaborative model architecture, based on YOLOv11n, offering a new technical approach to address the aforementioned challenges from both theoretical and practical perspectives. The significant improvement in model detection performance not only validates the effectiveness of the technical solution but also reveals the potential advantages and application value of multi-module fusion in complex visual tasks. Beyond performance improvements, the practical value of corn leaf disease and pest detection systems ultimately depends on their ability to support agricultural decision-making processes. Current precision agriculture workflows rely heavily on integrated decision support systems that combine multiple data sources to generate actionable management recommendations. The proposed BCS_YOLO model could potentially serve as a critical component within such frameworks by providing accurate disease detection inputs. Integration with agricultural decision support systems would involve several key considerations. First, the model's detection outputs would need to be contextualized within broader farm management workflows, where disease identification triggers specific decision pathways regarding treatment timing, pesticide selection, and application rates. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **Number of** & **FLOPS** \\  & & & & & **Parameters (M)** & **(G)** \\ \hline Faster R-CNN & 0.725 & 0.641 & 0.680 & 0.684 & 41.4 & **134.0** \\ RT-DERT & 0.729 & 0.633 & 0.678 & 0.647 & 32.0 & 103.5 \\ YOLOv5n & 0.721 & 0.599 & 0.654 & 0.657 & **2.5** & 7.1 \\ YOLOv8n & 0.723 & 0.651 & 0.685 & 0.680 & 3.0 & 8.1 \\ YOLOv10n & 0.731 & 0.621 & 0.672 & 0.661 & 2.6 & 8.2 \\ YOLOv11n & 0.735 & 0.616 & 0.670 & 0.677 & **2.5** & 6.3 \\ BCS\_YOLO & **0.750** & **0.668** & **0.707** & **0.710** & 6.7 & 10.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Generalization ability comparison of the proposed BCS_YOLO model with mainstream object detection models obtained on the second (private) dataset (the best values are shown in **bold**). Second, the integration would require the development of threshold-based alert systems that translate detection confidence scores into risk assessments meaningful to agricultural practitioners. Third, the system would need to interface with existing farm management databases to correlate current detection results with historical disease patterns, treatment efficacy records, and economic cost-benefit analyses. Although the proposed BCS_YOLO model has achieved breakthrough progress in improving the detection of corn leaf diseases and pests, there are still some limitations that need to be addressed in future research. The model's parameter counts (6.7 M) and computational complexity (10.1 G FLOPS) are higher than those of some lightweight YOLO variants, which may limit its deployment in resource-constrained environments. The balance between real-time requirements in actual field applications and model complexity is a key issue that needs continued optimization. Beyond computational constraints, several practical deployment challenges warrant consideration. Economic scalability across different farm scales and management systems remains a critical factor, particularly for small-scale farming operations where cost effectiveness directly impacts adoption feasibility.