Abstract:Fruit-tree canopy structure is a key determinant of fruit yield and quality, and its threedimensional (3D) reconstruction can provide essential data support for precision orchard management and the development of smart orchards. Traditional manual measurements are inefficient and error-prone, and are increasingly inadequate for moder orchard management, whereas 3D monitoring systems based on LiDAR, unmanned aerial vehicles (UAVs) and multi-sensor fusion have been widely used for the intelligent detection of canopy volume, leaf-wall area and other structural parameters owing to their advantages in accuracy, automation and repeatability. To this end, this paper focuses on the theme of 3D reconstruction for detecting the structural parameters of fruit-tree canopies. By folloving the "equipmentmethods-workflow" framework, it systematically reviews recent research progress and discusses current challenges and future development trends. Firstly, the functional characteristics and key performance metrics of aerial, ground-based and fixed/ mobile scanning devices and platforms for 3D data acquisition were categorized and described. Secondly, the image-based, point-cloud-based and deep-learningassisted reconstruction methods were comparatively analyzed in terms of their specific advantages, limitations and suitable application scenarios. Furthermore, for the standardized workflow of fruit-tree canopy point-cloud processing (" registration-ground separation-single-tree segmentation-task-oriented reconstruction"), the processing steps and error sources involved in deriving typical canopy structural parameters were clarified, and coverage and other quality-control indicators were used to jointly assess parameter-detection accuracy and the methodological generalizability across orchards and platforms. Finally, key research challenges such as occlusion in complex canopies and muli-platform coordination were summarized, and future development directions for fruit-tree canopy reconstruction and monitoring that were jointly driven by unified standards and task-specific requirements were proposed. This review provided a systematic technical framework for 3D information acquisition and standardized detection of canopy structural parameters in smart orchards, and offered effective support for fine-scale operations such as variable-rate spraying and pruning optimization.
