Semantic Segmentation of Sparse 3D Point Cloud Based on Geometrical Features for Trellis-Structured Apple Orchard

Orchard operations such as mechanical pruning and spraying are heavily affected by tree architectures. Quantified inputs (e.g., cutting locations for mechanical pruning, and canopy distribution and density for variable-rate precision spraying) are necessary information for achieving precise control of these orchard operations. Even in planar orchard systems, trees grow differently. Therefore, it is essential to measure the canopy at the individual tree level. A three-dimensional (3D) light detection and ranging (Lidar) sensor imaging system was developed to estimate the main canopy specifications. The Lidar sensor was installed on a utility vehicle and driven alongside tree rows in an apple orchard. A total of 1,138 frames of point cloud data were acquired from 69 apple trees in a tall spindle architecture. An algorithm was developed in the MATLAB environment to segment trellis wires, support poles, and tree trunks in these point cloud images. The results indicated that the proposed algorithm achieved overall accuracy values of 88.6%, 82.1%, and 94.7%, respectively, in identifying the corresponding three objects. Furthermore, canopy density and depth maps were created with the distribution of points in the point cloud images. The outcomes from this study provide baseline information for precision orchard operations such as mechanical pruning and precision spraying.

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Work Title Semantic Segmentation of Sparse 3D Point Cloud Based on Geometrical Features for Trellis-Structured Apple Orchard
Access
Open Access
Creators
  1. Lihua Zeng
  2. Juan Feng
  3. Long He
Keyword
  1. Apple trees
  2. Canopy density
  3. Point cloud
  4. Sematic segmentation
  5. Three-dimensional Lidar
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Acknowledgments
  1. This research was supported in part by the United States Department of Agriculture’s (USDA) National Institute of Food and Agriculture Federal Appropriations (Project PEN04653; Accession No. 1016510)
  2. The State Horticultural Association of Pennsylvania (SHAP)
  3. The Natural Science Foundation of Hebei Agricultural University (Grant No. ZD201701), and a study-abroad program for young teachers sponsored by Hebei Agricultural University.
Publication Date August 2020
Deposited February 26, 2021

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  • Updated Acknowledgments Show Changes
    Acknowledgments
    • This research was supported in part by the United States Department of Agriculture’s (USDA) National Institute of Food and Agriculture Federal Appropriations (Project PEN04653; Accession No. 1016510), The State Horticultural Association of Pennsylvania (SHAP), The Natural Science Foundation of Hebei Agricultural University (Grant No. ZD201701), and a study-abroad program for young teachers sponsored by Hebei Agricultural University.
  • Added Creator Lihua Zeng
  • Added Creator Juan Feng
  • Added Creator Long He
  • Added Semantic segmentation of sparse 3D point cloud_accepted.pdf
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    License
    • https://creativecommons.org/licenses/by-nc/4.0/
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    License
    • https://creativecommons.org/licenses/by-nc/4.0/
    • https://creativecommons.org/licenses/by-nc-nd/4.0/
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