Method of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGV

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dc.contributor.authorHan, Leng
dc.contributor.authorWang, Shubo
dc.contributor.authorWang, Zhichong
dc.contributor.authorJin, Liujian
dc.contributor.authorHe, Xiongkui
dc.date.accessioned2024-09-03T08:32:14Z
dc.date.available2024-09-03T08:32:14Z
dc.date.issued2023de
dc.description.abstractPrecision 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.en
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16379
dc.identifier.urihttps://doi.org/10.3390/drones7040242
dc.language.isoengde
dc.rights.licensecc_byde
dc.source2504-446Xde
dc.sourceDrones; Vol. 7, No. 4 (2023) 242de
dc.subjectPrecision agriculture
dc.subjectPrescription map
dc.subjectDigital orchard management
dc.subjectAutonomous platform
dc.subjectUnmanned ground vehicle
dc.subject.ddc330
dc.titleMethod of 3D voxel prescription map construction in digital orchard management based on LiDAR-RTK boarded on a UGVen
dc.type.diniArticle
dcterms.bibliographicCitationDrones, 7 (2023), 4, 242. https://doi.org/10.3390/drones7040242. ISSN: 2504-446X
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.journaltitleDrones
dcterms.bibliographicCitation.volume7
local.export.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}, }
local.export.bibtexAuthorHan, Leng and Wang, Shubo and Wang, Zhichong et al.
local.export.bibtexKeyHan2023
local.export.bibtexType@article

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