aFaculty of Environment and Resource Studies, Mahidol University, Nakhon Pathom 73170, Thailand
bCentral Laboratory and Greenhouse Complex, Research and Academic Service Center Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand
cDepartment of Agronomy, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand
*Corresponding author, e-mail:
rdiprv@ku.ac.th
Abstract
Importance of the work: Unmanned Aerial Vehicles (UAVs) have revolutionized agricultural practices, particularly in the realm of maize pollination Objectives: This study develops a simulation model to analyse the cooling effects of UAV-induced downwash turbulence on maize canopies, using thermal imaging to optimize pollination efficiency. Materials and Methods: Advanced techniques, including Lattice Boltzmann Method (LBM) and finite-difference time-domain (FDTD) simulations, evaluate the impact of UAV airflow on canopy temperature and pollen dispersion during sensitive pollination stages. Data from UAVs equipped with thermal cameras, such as the DJI Matrices 300 RTK and Zenmuse H20T, were combined with GNSS systems to capture spatial variations in Land Surface Temperature (LST) and airflow patterns. Results: The results reveal that UAV-induced turbulence reduces canopy temperature and humidity, enhancing pollination success while cooling the surrounding soil and vegetation. Main finding: These findings highlight the importance of UAVs airflow management in precision agriculture, demonstrating its potential to improve crop health and pollination outcomes through advanced environmental monitoring and control.
Keywords
Downwash turbulence on maize canopy, Land surface temperature (LST), Pollination of maize, Thermal imaging, Unmanned Aerial Vehicles (UAVs)