## 3 Experiments and Results
### Experimental Results  
To comprehensively evaluate YOLOv11-OAM in UAV-based tree species recognition, we examined its classification accuracy, data distribution, robustness, and training convergence. These results are illustrated in Figure 8.  
As shown in Figure 8a, most tree species were classified accurately, with Apricot, Pomegranate, and Prune achieving the highest precision. In contrast, Sand Jujube showed a higher false-negative rate due to blurred boundaries and crown overlap. Figure 8b,c illustrate class frequency, spatial distribution, and bounding box aspect ratios, demonstrating that the dataset is well-structured and suitable for multi-scale learning.  
Figure 8d-f show model performance under varying confidence thresholds. The model achieved its best F1-score (0.86) at a confidence threshold of 0.434. All curves remained smooth and stable, indicating strong discriminative ability and robustness. However, the PR curve for Sand Jujube dropped sharply, with about 25% of instances misclassified as background, resulting in an AP of only 91.0%.  
Figure 8: _Cont._  
As shown in Figure 8g, both training and validation losses decreased rapidly during the first 50 epochs and then stabilized, while evaluation metrics improved steadily. The close alignment of training and validation curves indicates good convergence and generalization, with no evidence of overfitting.  
To further evaluate model stability, we conducted three independent training runs with different random seeds. The results were consistent ([EMAIL_ADDRESS] = 93.15% \(\pm\) 0.24; [EMAIL_ADDRESS] = 74.08% \(\pm\) 0.31), indicating stable performance with low variance. In addition, bootstrap resampling with 1000 iterations was used to estimate 95% confidence intervals for the improvements over the YOLOv11 baseline. The analysis confirmed that the performance gains of YOLOv11-OAM were statistically significant (\(p<0.05\)), confirming the robustness and reliability of the proposed method.  
Table 5 reports the class-wise performance metrics, including precision, recall, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95. The results demonstrate strong performance for dominant species with abundant training samples. However, challenges persist for categories with ambiguous boundaries or high inter-class visual similarity, where performance remains comparatively lower.  
These results confirm the effectiveness of integrating the three key components--ASFF, ODConv, and MPDIoU--into the detection architecture. ASSF enables adaptive feature aggregation across multiple spatial scales, improving resolution-sensitive representation. ODConv enhances the flexibility of convolutional operations by generating dynamic responses along spatial, channel, and kernel dimensions. MPDIoU introduces geometry-aware localization by penalizing spatial misalignment at multiple points between predicted and ground-truth boxes. Collectively, these modules substantially improve the model's ability to detect small, densely distributed, and visually ambiguous tree species. Their contributions are further examined through ablation studies in Section 3.4.  
Figure 8: Performance evaluation results of the YOLOv11-OAM model for tree species recognition. (**a**) Normalized confusion matrix; (**b**) Annotation distribution statistics, including category frequency, spatial distribution, and bounding box dimensions; (**c**) Recall–confidence curve; (**d**) F1–confidence curve; (**e**) Precision–recall curves for each class and overall performance ([EMAIL_ADDRESS] = 0.927); (**f**) Precision–confidence curves (all classes reach precision = 1.00 at confidence = 0.958); (**g**) Training and validation curves of losses (box, cls, dfl) and evaluation metrics (precision, recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS]).