# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 3 Experiments
### Results and Analysis
#### 3.2.4 Ablation Study  
To analyze the actual contribution of each newly designed module type to the performance improvement of the proposed BCS_YOLO model, we performed detailed ablation study experiments on the first (public) dataset, using YOLOv11n as a baseline. Performance evaluation was conducted by separately removing or combining the module types to evaluate their contribution to the performance of the resultant model configuration. The results obtained are shown in Figure 10 and Table 6.  
Specifically, Figure 10 shows the _precision-recall_ curves of different configurations of the proposed model. It can be seen that as the newly designed module types are gradually introduced, the model's overall _precision-recall_ curve significantly moves upward. This indicates that each module type can bring performance gain at different _recall_ levels. The _precision-recall_ curve of the final model configuration (BCS_YOLO) is the best one, validating the effectiveness and complementarity of the three-module joint design. To comprehensively evaluate the effectiveness of different model configurations and thoroughly analyze the specific contributions of each module type to the model performance, we conducted extensive ablation studies across multiple evaluation metrics. Table 6 provides quantitative analysis of the comparison of various model configurations in terms of detection performance and computational overhead. From the obtained results it is clear that adding any single module type alone to the baseline brings corresponding performance improvement, with the most significant improvement brought by HLFFE with respect to _precision_, _F1 score_, and _mAP@50_, and by SPCGA in terms of _recall_. This indicates that under complex leaf-background conditions, fine-grained feature extraction capability is particularly critical for disease detection. Different pairwise combinations of module types further bring synergistic model improvements, with the biggest one brought by the 'SPCGA + LAE' combination according to _recall_ and _mAP@50_, and the 'SPCGA + HLFFE' combination based on _precision_ and _F1 score_. Finally, after combining all three newly  
Figure 9: Sample north leaf spot detection results of the proposed BCS_YOLO model, obtained on the first (public) dataset. designed module types, the resultant model (BCS_YOLO) achieved the highest _precision_, _F1 score_, and _mAP@50_ values, and the second-best _recall_ value. The achieved detection performance improvements are obvious, validating the effectiveness and efficiency of the final model's design architecture. The obtained experimental results of the performed ablation study fully demonstrate that each newly designed module type has an independent and complementary contribution to overall model performance improvement. The organic integration of these three newly designed module types allows optimization of key aspects, such as multi-scale feature extraction, context modeling, and attention allocation, while also striking a very good balance between detection performance and efficiency, without significantly increasing the computational burden.