## 2 Materials and Methods
### Dataset Construction and Augmentation  
Approximately 50,000 UAV images were initially collected using the DJI Mavic 3E platform (DJI, Shenzhen, China)across multiple orchard environments. High-resolution orthophotos were generated with Pix4 Dmapper (version 4.5.6, Pix4D SA, Prilly, Switzerland) and segmented into 1024 \(\times\) 1024 pixel patches using a custom Python (Python 3.10.0) script. Each patch was manually inspected, and those containing non-vegetated elements such as field boundaries, cotton plots, or roads were discarded. A total of 4096 high-quality image tiles were retained for annotation. Figure 1: Study area and UAV imagery in Hotan, Xinjiang. The upper-left and lower-left panels show the geographic locations of Xinjiang and Hotan within China. The central panel marks the sampling sites of six tree species: apricot, walnut, saxaul, cherry, pomergnate, and prune. Panels (**a–f**) show UAV image samples corresponding to each species. Manual annotation followed the Pascal VOC standard [40] and was performed with the open-source tool LabelImg (version 1.8.6) [41]. Bounding boxes were drawn around individual tree crowns and labeled with the corresponding species. Annotation files were saved in XML format [42], containing class labels, bounding box coordinates, and image metadata, and served as the ground truth for model training and evaluation. To address class imbalance and expand the dataset, data augmentation was applied to the 4096 annotated tiles. For underrepresented classes, techniques such as flipping, random rotation, brightness adjustment [43; 44], and scaling were applied to increase sample diversity. Specifically, random rotations of \(\pm 15^{\circ}\), \(\pm 30^{\circ}\), and \(\pm 45^{\circ}\) were used, brightness levels were varied within \(\pm 20\%\), and scaling factors between 0.9 and 1.1 were applied to preserve object proportions while minimizing distortion. Horizontal and vertical flipping were each performed with a 50% probability. The effectiveness of such class-specific augmentation is consistent with prior findings that class-balanced strategies can alleviate dataset imbalance and improve detection performance. For example, [PERSON] et al. [44] reviewed augmentation techniques for imbalanced datasets and highlighted the role of targeted augmentation in improving minority-class representation, while [PERSON] et al. [43] demonstrated that class-wise augmentation significantly boosted detection accuracy in UAV fruit detection tasks. As a result of this targeted augmentation strategy, the dataset expanded to 12,747 image tiles, substantially enhancing diversity and generalization, particularly for classes with limited original samples. The dataset was then randomly divided into training, validation, and test sets in an 8:1:1 ratio. Figure 2 illustrates the complete data preparation pipeline, while Table 2 reports dataset distribution statistics and Table 3 details class-wise species composition. To evaluate the effectiveness of the augmentation strategy, a baseline YOLOv11 model was trained on both the original and augmented datasets. As shown in Figure 3, the model trained with augmented data achieved faster convergence and consistently outperformed the baseline in terms of loss, precision, recall, and mean Average Precision (mAP). Figure 2: Workflow from UAV image acquisition to model training. Table 4 summarizes the augmentation ratio applied to each species. Four underrepresented classes--cherry, apricot, walnut, and saxaul--were increased by 400%, whereas prune and pomegranate were not augmented due to sufficient representation. This targeted augmentation approach helped balance the dataset and mitigate overfitting to dominant classes. Although augmentation improved class balance, no additional methods--such as class-weighted loss functions, focal loss, or oversampling--were considered in this study. These techniques will be investigated in future work to further enhance model robustness under class-imbalanced conditions.