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Orchard variable-rate spraying method integrating GNSS and wind-excited audio-conducted leaf area density
Hangxing Zhao1,2,3, Shenghui Yang1,2,3, Wenwei Li2,3
1Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, China.
Frontiers in Plant Science
|August 4, 2025
Summary
This study introduces a new variable-rate spraying system for orchards that uses audio sensing and GNSS to accurately measure leaf density. This precision spraying technology significantly reduces pesticide waste and improves droplet distribution on fruit trees.
Area of Science:
- Agricultural Engineering
- Precision Agriculture
- Sensor Technology
Background:
- Conventional sprayers cause pesticide waste and uneven application.
- Precision spraying needs accurate canopy data, but leaf shading hinders leaf area density measurement.
- Existing variable-rate systems based on leaf area density are limited.
Purpose of the Study:
- To develop a variable-rate spraying method using GNSS and audio sensing for precise pesticide application.
- To overcome challenges in automated leaf area density measurement caused by leaf shading.
- To create a system for intelligent and sustainable orchard plant protection.
Main Methods:
- Integrated GNSS positioning with wind-excited audio-conducted estimation of canopy leaf area density.
- Developed a self-propelled spraying platform for real-time data acquisition.
- Created prescription maps and a variable-rate spray control model based on leaf area density.
Main Results:
- Achieved 5.52% mean relative error in spray flow rate regulation.
- Reduced longitudinal droplet deposition variation by up to 55.75% and transverse variation by 62.32%.
- Decreased spray solution runoff by 62.29% and ensured deposition density exceeded 25 droplets/cm².
Conclusions:
- The integrated GNSS and audio sensing system enables precise, efficient pesticide application in orchards.
- The system improves droplet deposition uniformity and reduces environmental losses.
- This technology offers a viable solution for intelligent and sustainable orchard management.
