## 3 Experiments and Results
### Ablation Study  
We performed a series of ablation experiments to evaluate the individual contributions of ODConv, ASFF, and MPDIoU to overall detection performance. Starting from the YOLOv11 baseline, each component was incrementally added and assessed on a six-class fruit tree dataset using standard metrics, including precision, recall, F1-score, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]. The results are summarized in Table 6 and visualized in Figure 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **Recall/\%** & **F1 Score/\%** & **[EMAIL_ADDRESS]/\%** & **Precision/\%** \\ \hline YOLOv11 & 90.9 & 85.67 & 85 & 70.61 & 84.27 \\ YOLOv11-ODConv & 92.26 & 86.03 & 86.3 & 71.12 & 86.63 \\ YOLOv11-ASFF & 91.9 & 85.05 & 85.5 & 71.11 & 86.05 \\ YOLOv11-MPIoU & 91.4 & 85.15 & 85.1 & 70.81 & 85.04 \\ YOLOv11-ODConv-ASFF & 92.75 & 86.52 & 86.5 & 72.28 & 86.41 \\ YOLOv11-ODConv-MPIoU & 92.14 & 85.96 & 85.8 & 71.37 & 85.74 \\ YOLOv11-OAM & 93.15 & 87.87 & 88.5 & 74.08 & 87.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison of Different Models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **[EMAIL_ADDRESS]\%** & **Recall/\%** & **[EMAIL_ADDRESS]/\%** & **Precision/\%** \\ \hline Pomegranate Tree & 93.90 & 90.44 & 78.90 & 87.80 \\ Prune Tree & 93.95 & 86.93 & 76.71 & 89.89 \\ Walnut Tree & 92.84 & 91.22 & 72.79 & 87.63 \\ Saxaul Tree & 91.31 & 84.55 & 66.48 & 86.43 \\ Cherry Tree & 92.58 & 87.62 & 74.51 & 85.91 \\ Apricot Tree & 94.37 & 90.70 & 74.52 & 87.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection performance metrics for different tree species. Figure 9: Bar chart of performance for seven object detection models. The YOLOv11-ODConv variant improved [EMAIL_ADDRESS] by 1.36% and [EMAIL_ADDRESS] by 0.51%, indicating that omni-dimensional dynamic convolution is effective for capturing fine-grained visual features. Incorporating AFF further enhanced spatial feature alignment, yielding gains of 1.01% and 0.5% in [EMAIL_ADDRESS] and [EMAIL_ADDRESS], respectively. MPDIoU alone produced a modest improvement of +0.5% in [EMAIL_ADDRESS] but substantially strengthened localization accuracy and training stability, especially for small or visually ambiguous targets. When integrated, the modules produced synergistic gains. The complete YOLOv11-OAM model achieved the highest performance, with [EMAIL_ADDRESS] = 93.15%, [EMAIL_ADDRESS] = 74.08%, and F1-score = 88.5%. These results demonstrate the complementary advantages of architectural enhancements and advanced loss functions, which together substantially improve overall detection performance. Figure 10 presents qualitative results on UAV imagery. The YOLOv11 baseline frequently produced false positives and misclassified morphologically similar species, such as incorrectly labeling non-walnut trees as walnut. ODConv alleviated these issues by enhancing feature discrimination, but redundant detections and weak boundary localization persisted. Adding Detect_ASFF reduced duplicate detections and improved boundary alignment, though minority classes still showed low-confidence predictions. MPDIoU further enhanced spatial localization and score calibration, leading to more reliable detection of small and occluded targets. Overall, the integrated YOLOv11-OAM model achieved the most balanced performance, accurately distinguishing fine-grained categories while maintaining stable confidence across all species. Figure 10: Cont. Figure 10: Detection results of seven models on sample UAV images. (Note: Each detected object is marked with a rectangular bounding box, with the predicted class label and confidence score displayed above (e.g., walnut_trees: 0.74). Yellow circles indicate missed detections (false negatives), red circles represent misclassifications (false positives), and green circles denote duplicate detections (multiple boxes on the same object).