Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with highly cost-effective and flexible usage scenarios. Although many papers have reviewed the application of UAVs in agriculture, the review of the application for tree detection is still insufficient. This paper focuses on tree detection methods applied to UAV data collected by UAVs. There are two kinds of data, the point cloud and the images, which are acquired by the Light Detection and Ranging (LiDAR) sensor and camera, respectively. Among the detection methods using point-cloud data, this paper mainly classifies these methods according to LiDAR and Digital Aerial Photography (DAP). For the detection methods using images directly, this paper reviews these methods by whether or not to use the Deep Learning (DL) method. Our review concludes and analyses the comparison and combination between the application of LiDAR-based and DAP-based point cloud data. The performance, relative merits, and application fields of the methods are also introduced. Meanwhile, this review counts the number of tree detection studies using different methods in recent years. From our statics, the detection task using DL methods on the image has become a mainstream trend as the number of DL-based detection researches increases to 45% of the total number of tree detection studies up to 2022. As a result, this review could help and guide researchers who want to carry out tree detection on specific forests and for farmers to use UAVs in managing agriculture production.

翻译：无人机作为前沿技术，具有高性价比和灵活多样的应用场景。尽管已有众多论文综述了无人机在农业领域的应用，但针对树木检测的综述仍显不足。本文聚焦于基于无人机采集数据的树木检测方法，这些数据包括由激光雷达传感器获取的点云和由相机获取的影像。针对点云数据的检测方法，本文主要依据激光雷达与数字航空摄影进行分类；针对直接使用影像的检测方法，则依据是否采用深度学习技术进行梳理。本文综述并分析了基于激光雷达与数字航空摄影点云数据的应用比较与融合，同时介绍了各类方法的性能、优缺点及应用领域。此外，本文统计了近年来采用不同方法的树木检测研究数量。统计显示，截至2022年，基于深度学习的影像检测方法已成为主流趋势，其研究数量占树木检测研究总数的45%。因此，本综述可为研究者针对特定林区开展树木检测提供指导，并助力农户利用无人机进行农业生产管理。