## 4 Conclusions  
This study presents a dedicated approach to addressing the challenges of tree species detection in high-resolution UAV-based remote sensing imagery. We propose YOLOv11-OAM, an enhanced object detection model that incorporates multiple architectural improvements. A specialized fruit tree dataset was constructed, and advanced augmentation techniques--including noise injection, random cropping, affine transformations, and flipping--were employed to enrich data diversity, reduce overfitting, and improve generalization. Building upon the YOLOv11 framework, the proposed model introduces three key innovations. First, the ODConv module enhances spatial adaptability through omni-dimensional convolutional kernel modulation. Second, the Adaptive Spatial Feature Fusion (ASFF) mechanism enables effective multi-scale feature integration, thereby improving recognition accuracy across objects of varying sizes. Third, the MPDIoU loss function provides geometry-aware localization refinement, delivering more precise bounding box regression, particularly for small or visually ambiguous targets. In summary, YOLOv11-OAM demonstrates high detection accuracy, stable training, and strong robustness in both small-object and multi-class detection tasks. The model offers a practical and efficient solution for UAV-assisted orchard monitoring and establishes a technical foundation for scalable applications in precision agriculture and intelligent forestry management. Future WorkTo further enhance the robustness, generalization, and practical deployment of the YOLOv11-OAM model, future research may pursue several key directions. First, ensemble learning strategies--particularly those guided by expert cognition--can improve stability under domain shifts and increase external validation accuracy. For instance, the work by [PERSON] et al. [47] demonstrates how cognition-driven ensemble deep learning can effectively improve generalizability in high-variance, small-sample environments. This approach provides valuable insights for developing more resilient tree species detection models tailored to real-world forestry applications. Second, the incorporation of attention mechanisms or architectural refinements informed by expert knowledge may improve the model's ability to address class imbalance and fine-grained visual variation, especially for underrepresented or morphologically similar species. Third, expanding the data modalities beyond RGB imagery is a promising direction. Integrating hyperspectral and LiDAR data can provide richer spatial-spectral features and structural information, improving both classification accuracy and boundary localization in dense orchard or forest canopies. Recent multimodal studies have confirmed these advantages; for example, [PERSON] et al. [17] demonstrated that UAV-based hyperspectral and LiDAR fusion significantly improved tree species classification accuracy in forest plots, and [PERSON] et al. [15] further highlighted the benefits of combining structural and spectral cues for fine-grained vegetation mapping. These findings substantiate our proposed future extension toward multimodal fusion. Finally, optimizing the YOLOv11-OAM model for UAV and edge deployment is essential. Recent works such as [PERSON] and [PERSON] on quantized YOLOv4-Tiny deployment on Raspberry Pi 5 [48], [PERSON] et al. [49] with LW-YOLOv8, and [PERSON] et al. [29] with PS-YOLO demonstrate how lightweight YOLO variants can achieve efficient inference under resource constraints. Additionally, [PERSON] et al. [50] with Squeezed Edge YOLO showcases extreme compression and deployment on Jetson Nano. A broader survey by [PERSON] et al. [51] details the algorithm-hardware co-design strategies for safe and efficient model compression. Building on these advances, future work will explore pruning, quantization, and knowledge distillation for real-time YOLOv11-OAM deployment on embedded GPUs such as NVIDIA Jetson Orin or Nano.