Work

A Study on Tree Species Recognition in UAV Remote Sensing Imagery Based on an Improved YOLOv11 Model

Year: 2025

Type: article

Abstract: Unmanned aerial vehicle (UAV) remote sensing has become an important tool for high-resolution tree species identification in orchards and forests. However, irregular spatial distribution, overlapping canopies, and small crown sizes still limit detection accuracy. To overcome these challenges, we propose YOLOv11-OAM, an enhanced one-stage object detection model based on YOLOv11. The model incorporates three key modules: omni-dimensional dynamic convolution (ODConv), adaptive spatial feature fusion (ASFF), and a multi-point distance IoU (MPDIoU) loss. A class-balanced augmentation strategy is also applied to mitigate category imbalance. We evaluated YOLOv11-OAM on UAV imagery of six fruit tree species—walnut, prune, apricot, pomegranate, saxaul, and cherry. The model achieved a mean Average Precision ([email protected]) of 93.1%, an 11.4% improvement over the YOLOv11 baseline. These results demonstrate that YOLOv11-OAM can accurately detect small and overlapping tree crowns in complex orchard environments, offering a reliable solution for precision agriculture and smart forestry applications.

Source: Applied Sciences

DOI: 10.3390/app15168779

Authors: Qian Wang, Zhi Pu, Lei Luo, Lei Wang, Jian Gao

Institutions: Xinjiang Agricultural University, Xinjiang Academy of Forestry

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Zero hunger, Life on land

Open Access status: gold

Estimated APC paid: $2,896