Field Study of UAV Variable-Rate Spraying Method for Orchards Based on Canopy Volume

Pengchao Chen, Haoran Ma, Zongyin Cui, Zhihong Li, Jiapei Wu, Jianhong Liao, Hanbing Liu, Ying Wang and Yubin Lan ()
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Pengchao Chen: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Haoran Ma: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Zongyin Cui: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Zhihong Li: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Jiapei Wu: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Jianhong Liao: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Hanbing Liu: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Ying Wang: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Yubin Lan: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 13, 1-18

Abstract: The use of unmanned aerial vehicle (UAV) pesticide spraying technology in precision agriculture is becoming increasingly important. However, traditional spraying methods struggle to address the precision application need caused by the canopy differences of fruit trees in orchards. This study proposes a UAV orchard variable-rate spraying method based on canopy volume. A DJI M300 drone equipped with LiDAR was used to capture high-precision 3D point cloud data of tree canopies. An improved progressive TIN densification (IPTD) filtering algorithm and a region-growing algorithm were applied to segment the point cloud of fruit trees, construct a canopy volume-based classification model, and generate a differentiated prescription map for spraying. A distributed multi-point spraying strategy was employed to optimize droplet deposition performance. Field experiments were conducted in a citrus ( Citrus reticulata Blanco) orchard (73 trees) and a litchi ( Litchi chinensis Sonn.) orchard (82 trees). Data analysis showed that variable-rate treatment in the litchi area achieved a maximum canopy coverage of 14.47% for large canopies, reducing ground deposition by 90.4% compared to the continuous spraying treatment; variable-rate treatment in the citrus area reached a maximum coverage of 9.68%, with ground deposition reduced by approximately 64.1% compared to the continuous spraying treatment. By matching spray volume to canopy demand, variable-rate spraying significantly improved droplet deposition targeting, validating the feasibility of the proposed method in reducing pesticide waste and environmental pollution and providing a scalable technical path for precision plant protection in orchards.

Keywords: precision agriculture; variable-rate spraying; LiDAR; UAV (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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