Simulation spatial model for analyzing cooling effects of downwash turbulence on maize canopies to enhance pollination efficiency using thermal imaging data collected from unmanned aerial vehicles

Abstract

Importance of the work: Maize pollination is sensitive to canopy microclimate, including temperature, humidity, and airflow. UAV-assisted pollination may improve pollen dispersal; however, the effects of UAV-induced downwash turbulence on canopy cooling and pollen movement remain unclear.

Objectives: To develop a simulation model to analyze the cooling effects of UAV-induced downwash turbulence on maize canopies, using thermal imaging to optimize pollination efficiency.

Materials and Methods: Advanced techniques (the lattice Boltzmann method (LBM) and finite-difference time-domain (FDTD) simulations) were used to evaluate the impact of UAV airflow on canopy temperature and pollen dispersion during sensitive pollination stages. Data from UAVs equipped with thermal cameras (DJI Matrice 300 RTK and Zenmuse H20T) were combined with global navigation satellite systems to capture spatial variations in land surface temperature and airflow patterns.

Results: UAV-induced turbulence reduced canopy temperature and humidity, enhancing pollination success, while cooling the surrounding soil and vegetation.

Main finding: These findings highlight the importance of UAV airflow management in precision agriculture, demonstrating its potential to improve crop health and pollination outcomes through advanced environmental monitoring and control.