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Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications

Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications

Authors :
Daeun Choi
Grzegorz Krawczyk
Long He
Azlan Zahid
Paul Heinz Heinemann
Sultan Mahmud
Heping Zhu
Source :
Computers and Electronics in Agriculture. 182:106053
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

An unmanned ground-based canopy density measurement system to support precision spraying in apple orchards was developed to precisely apply pesticides to orchard canopies. The automated measurement system was comprised of a light detection and ranging (LiDAR) sensor, an interface box for data transmission, and a laptop computer. A data processing and analysis algorithm was developed to measure point cloud indices from the LiDAR sensor to describe the distribution of tree canopy density within four sections according to the position of the trellis wires. Experiments were conducted in two orchard sites, one with GoldRush (larger trees) and the other one with Fuji (smaller trees) apple trees. Tree leaves were counted manually from each section separated by trellis wires. Field evaluation results showed a strong correlation of 0.95 (R2 = 89.30%) between point cloud data and number of leaves for the Fuji block and a correlation of 0.82 (R2 = 67.16%) was obtained for the GoldRush block. A strong correlation of 0.98 (R2 = 95.90%) was achieved in the relationship between canopy volume and number of leaves. Finally, a canopy density map was generated to provide a graphical view of the tree canopy density in different sections. Since accurate canopy density information was computed, it is anticipated that the developed prototype system can guide the sprayer unit for reducing excessive pesticide use in orchards.

Details

ISSN :
01681699
Volume :
182
Database :
OpenAIRE
Journal :
Computers and Electronics in Agriculture
Accession number :
edsair.doi...........6c0fff79d2880fba7f4d16cfab3f1f96
Full Text :
https://doi.org/10.1016/j.compag.2021.106053