TY - JOUR
T1 - Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications
AU - Sultan Mahmud, Md
AU - Zahid, Azlan
AU - He, Long
AU - Choi, Daeun
AU - Krawczyk, Grzegorz
AU - Zhu, Heping
AU - Heinemann, Paul
N1 - Funding Information:
This study was supported in part by United States Department of Agriculture ( USDA )’s National Institute of Food and Agriculture ( NIFA ) Federal Appropriations under Project PEN04653 and Accession No. 1016510 , a USDA NIFA Crop Protection and Pest Management Program (CPPM) competitive grant (Award No. 2019-70006-30440), and a Northeast Sustainable Agriculture Research and Education (SARE) Graduate Student Grant GNE20-234-34268. The authors would like to give special thanks to Xiaohu Jiang (visiting scholar) and Mingsen Huang (visiting scholar) for their assistance during the field experiments.
Publisher Copyright:
© 2021
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101132432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101132432&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2021.106053
DO - 10.1016/j.compag.2021.106053
M3 - Article
AN - SCOPUS:85101132432
SN - 0168-1699
VL - 182
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 106053
ER -