A Study on Tree Species Recognition in UAV Remote Sensing Imagery Based on an Improved YOLOv11 Model  
[PERSON]  
1 Xinjang Uygur Autonomous Region Academy of Forestry Sciences, Urumqi 830063, China; [EMAIL_ADDRESS] (Q.  
management [10], and aboveground biomass estimation [11]. In orchard scenarios, UAV imagery creates new opportunities for species-level recognition [12]. Nonetheless, several challenges remain. The spatial heterogeneity and spectral similarity of tree canopies complicate classification. Dense planting leads to crown overlap and occlusion, which blur boundaries and reduce accuracy. In addition, variations in illumination and background clutter further weaken model robustness [13; 14]. These issues underscore the necessity of developing specialized models capable of handling small objects, high inter-class similarity, and complex spatial structures. To overcome these challenges, researchers have applied automatic image analysis in agricultural remote sensing by integrating multi-source data, such as hyperspectral and LiDAR, into machine learning approaches to improve classification accuracy [15; 16]. For example, [PERSON] et al. [17] achieved a classification accuracy exceeding 78% using a data fusion strategy, demonstrating the feasibility of this method in orchard environments. Nonetheless, under dense canopies, pixel-level segmentation often struggles to detect small objects and delineate boundaries precisely, leading to omissions or inaccurate segmentation [18]. In recent years, research attention has increasingly shifted toward deep learning-based instance-level object detection methods, which are well-suited to complex spatial layouts and fine-grained classification tasks. Convolutional neural networks (CNNs), owing to their strong spatial and semantic representation capacity, have been widely applied in remote sensing for image classification and detection. Current detection frameworks are typically divided into two-stage models (e.g., Faster R-CNN) and one-stage models (e.g., SSD, YOLO) [19; 20; 21; 22]. Among them, the YOLO (You Only Look Once) family has emerged as a widely adopted solution for real-time detection because it achieves a favorable balance between accuracy and computational efficiency. Recently developed YOLOv8, YOLOv9 and YOLOv10 have demonstrated competitive performance on general object detection benchmarks [23; 24]. YOLOv8 introduced an anchor-free design and lightweight architecture, which improved training efficiency and inference speed; however, it suffered from reduced accuracy under densely overlapping tree crowns. YOLOv9 incorporated decoupled detection heads and advanced normalization techniques, yielding higher benchmark precision but showing unstable convergence and limited robustness in complex agricultural environments. YOLOv10 achieved a better balance between multi-task performance and efficiency, yet it still faced challenges in small-object detection and fine-grained category differentiation. Overall, these models were primarily designed for natural image datasets, and their transferability to agricultural remote sensing tasks remains insufficient. To address these limitations, Ultralytics released YOLOv11 in 2024, introducing major architectural innovations such as the C3k2 module, Spatial Pyramid Pooling-Fast (SPPF), and the channel-spatial attention mechanism (C2 PSA), which substantially enhanced feature extraction and detection efficiency [25; 26]. Compared with its predecessors, YOLOv11 exhibited greater training stability and more precise boundary localization, positioning it as a suitable baseline for UAV-based orchard detection. Building on this foundation, several variants further confirmed its effectiveness in UAV scenarios. For instance, [PERSON] et al. proposed MASFYOLO, which improved [EMAIL_ADDRESS] by 4.6% over YOLOv11s on VisDrone2019 at a lower computational cost [27]; [PERSON] and [PERSON] developed LIGHTYOLOv11, a lightweight model optimized for UAV imagery [28]. [PERSON] et al.