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Volume 15
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10.3390/plants15030376
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Article

Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies

by
Junxiang Tan
1,
Hao Chen
1,2,*,
Kaihui Zhang
1,
Hao Yang
1,
Xiongjie Wang
1,
Ronghao Yang
1,
Guyue Hu
1,
Shaoda Li
1,
Jianfei Liu
3 and
Xiangjun Wang
2,*
1
College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
2
Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
3
Chengdu Alundar Technology Co., Ltd., Chengdu 610050, China
*
Authors to whom correspondence should be addressed.
Plants 2026, 15(3), 376; https://doi.org/10.3390/plants15030376
Submission received: 11 November 2025 / Revised: 20 January 2026 / Accepted: 21 January 2026 / Published: 26 January 2026
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research—2nd Edition)
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Abstract

Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping.
Keywords: rubber trees; point cloud; fusion; unmanned aerial vehicle laser scanning; handheld laser scanning rubber trees; point cloud; fusion; unmanned aerial vehicle laser scanning; handheld laser scanning

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MDPI and ACS Style

Tan, J.; Chen, H.; Zhang, K.; Yang, H.; Wang, X.; Yang, R.; Hu, G.; Li, S.; Liu, J.; Wang, X. Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies. Plants 2026, 15, 376. https://doi.org/10.3390/plants15030376

AMA Style

Tan J, Chen H, Zhang K, Yang H, Wang X, Yang R, Hu G, Li S, Liu J, Wang X. Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies. Plants. 2026; 15(3):376. https://doi.org/10.3390/plants15030376

Chicago/Turabian Style

Tan, Junxiang, Hao Chen, Kaihui Zhang, Hao Yang, Xiongjie Wang, Ronghao Yang, Guyue Hu, Shaoda Li, Jianfei Liu, and Xiangjun Wang. 2026. "Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies" Plants 15, no. 3: 376. https://doi.org/10.3390/plants15030376

APA Style

Tan, J., Chen, H., Zhang, K., Yang, H., Wang, X., Yang, R., Hu, G., Li, S., Liu, J., & Wang, X. (2026). Co-Registration of UAV and Handheld LiDAR Data for Fine Phenotyping of Rubber Plantations with Complex Canopies. Plants, 15(3), 376. https://doi.org/10.3390/plants15030376

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