A new version of this entry is available:
Loading...
Article
2023
Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV
Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV
Abstract (English)
Precision application of pesticides based on tree canopy characteristics such as tree height is more environmentally friendly and healthier for humans. Offline prescription maps can be used to achieve precise pesticide application at low cost. To obtain a complete point cloud with detailed tree canopy information in orchards, a LiDAR-RTK fusion information acquisition system was developed on an all-terrain vehicle (ATV) with an autonomous driving system. The point cloud was transformed into a geographic coordinate system for registration, and the Random sample consensus (RANSAC) was used to segment it into ground and canopy. A 3D voxel prescription map with a unit size of 0.25 m was constructed from the tree canopy point cloud. The height of 20 trees was geometrically measured to evaluate the accuracy of the voxel prescription map. The results showed that the RMSE between tree height calculated from the LiDAR obtained point cloud and the actual measured tree height was 0.42 m, the relative RMSE (rRMSE) was 10.86%, and the mean of absolute percentage error (MAPE) was 8.16%. The developed LiDAR-RTK fusion acquisition system can generate 3D prescription maps that meet the requirements of precision pesticide application. The information acquisition system of developed LiDAR-RTK fusion could construct 3D prescription maps autonomously that match the application requirements in digital orchard management.
File is subject to an embargo until
This is a correction to:
A correction to this entry is available:
This is a new version of:
Notes
Publication license
Publication series
Published in
Drones, 7 (2023), 4, 242.
https://doi.org/10.3390/drones7040242.
ISSN: 2504-446X
Faculty
Institute
Examination date
Supervisor
Edition / version
Citation
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
330 Economics
Collections
Original object
Standardized keywords (GND)
BibTeX
@article{Han2023,
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16379},
doi = {10.3390/drones7040242},
author = {Han, Leng and Wang, Shubo and Wang, Zhichong et al.},
title = {Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV},
journal = {Drones},
year = {2023},
volume = {7},
number = {4},
}