Version 1
: Received: 17 May 2024 / Approved: 17 May 2024 / Online: 17 May 2024 (05:46:48 CEST)
How to cite:
Li, W.; Tang, B.; Hou, Z.; Wang, H.; Bing, Z.; Yang, Q.; Zheng, Y. Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds. Preprints2024, 2024051153. https://doi.org/10.20944/preprints202405.1153.v1
Li, W.; Tang, B.; Hou, Z.; Wang, H.; Bing, Z.; Yang, Q.; Zheng, Y. Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds. Preprints 2024, 2024051153. https://doi.org/10.20944/preprints202405.1153.v1
Li, W.; Tang, B.; Hou, Z.; Wang, H.; Bing, Z.; Yang, Q.; Zheng, Y. Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds. Preprints2024, 2024051153. https://doi.org/10.20944/preprints202405.1153.v1
APA Style
Li, W., Tang, B., Hou, Z., Wang, H., Bing, Z., Yang, Q., & Zheng, Y. (2024). Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds. Preprints. https://doi.org/10.20944/preprints202405.1153.v1
Chicago/Turabian Style
Li, W., Qiong Yang and Yongqiang Zheng. 2024 "Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds" Preprints. https://doi.org/10.20944/preprints202405.1153.v1
Abstract
Crop phenotyping data collection is the basis for precision agriculture and smart decisionmaking applications. In this study, a dynamic slicing and reconstruction canopy volume (DR) algorithm is proposed to accurately estimate citrus canopy volume. The algorithm dynamically slices nearby slices based on their proportional area change and density difference, subsequently conducting AS reconstruction and volume calculation for each slice using an iterative mean point spacing as the α-value. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal. In conclusion, we recommend selecting appropriate operating environments for each algorithm based on their principles, geometric properties, volumetric values, running time, and linear relationships with one another to guide orchard mechanization and intelligent operation.
Keywords
Citrus tree; LiDAR; Canopy volume; Point cloud reconstruction; Dynamic slicing
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
