# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 3 Experiments
### Results and Analysis
#### 3.2.3 Detection Performance Analysis  
The corn leaf disease and pest detection performance of the proposed BCS_YOLO model was comprehensively evaluated on the first (public) dataset, whereby we analyzed both the quantitative metrics and qualitative detection results across eight categories of corn leaf diseases and pests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & \
\begin{tabular}{c} **Number of** \\ **Parameters (M)** \\ \end{tabular} &
\begin{tabular}{c} **FLOPS** \\ **(G)** \\ \end{tabular} \\ \hline Faster R-CNN & 0.723 & 0.726 & 0.724 & 0.791 & 41.4 & **168.0** \\ RT-DERT & 0.772 & 0.673 & 0.719 & 0.733 & 32.0 & 103.5 \\ YOLOv5n & 0.731 & 0.655 & 0.691 & 0.748 & **2.5** & 7.1 \\ YOLOv8n & 0.735 & 0.684 & 0.709 & 0.756 & 3.0 & 8.1 \\ YOLOv10n & 0.765 & 0.676 & 0.718 & 0.739 & 2.6 & 8.2 \\ YOLOv11n & 0.754 & 0.704 & 0.728 & 0.774 & **2.5** & 6.3 \\ BCS_YOLO (_proposed_) & **0.784** & **0.737** & **0.760** & **0.820** & 6.7 & 10.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Corn leaf disease and pest detection performance comparison of the proposed BCS_YOLO model with mainstream object detection models, obtained on the first (public) dataset (the best values are shown in **bold**).  
Figure 7: Sample corn leaf disease and pest detection results of the proposed BCS_YOLO model, compared to the baseline (YOLOv11n), obtained on the first (public) dataset: (**a**) Gray leaf spot; (**b**) Northern leaf spot; (**c**) Aphids.  
Table 5 presents the detailed quantitative results, including _precision_, _recall_, _F1 score_, and _mAP@50_ for each category. The experimental results reveal that pest detection of BCS_YOLO is generally better than its corn leaf disease detection. Among the pest categories, the peach borer was detected with the highest _precision_ of 0.935, indicating exceptional BCS_YOLO accuracy in positive predictions. The best _F1 score_ (0.855) was achieved in detecting the red spider, demonstrating balanced BCS_YOLO performance between _precision_ and _recall_. The armyworm exhibit strong _recall_ performance at 0.861, suggesting effective BCS_YOLO detection of most instances. The corn leaf disease categories present greater detection challenges, with gray leaf spot and northern leaf spot showing relatively lower _mAP@50_ values of 0.667 and 0.649, respectively, indicating reduced BCS_YOLO accuracy in object localization and classification compared to pest categories. The observed BCS_YOLO performance variations across different categories reflect the inherent complexity of multi-class detection in agricultural scenarios.  
To further evaluate the proposed model's practical applicability, representative visual detection examples are presented in Figures 8 and 9 to illustrate both its capabilities and limitations. Specifically, Figure 8 shows sample successful BCS_YOLO detection results for corn borer and gray leaf spot, demonstrating the effectiveness of the proposed model in accurately identifying and locating targets of different pest categories.  
Figure 9 presents sample detection results of BCS_YOLO for north leaf spot, showing that the proposed model exhibits missed detection issues in north leaf spot detection. From the perspective of small-object detection, the missed detection phenomenon is primarily attributed to the limited pixel coverage of early-stage disease lesions in images, resulting in insufficient feature representation that constrains effective detection. Additionally, the lack of contextual information surrounding small-scale targets further weakens the model's feature learning and discrimination performance. These analytical findings contribute to a deeper understanding of the proposed model's performance bottlenecks in complex agricultural scenarios and provide theoretical foundations for subsequent model optimization.