Field Schedule of UAV-Assisted Pollination for Hybrid Rice Based on CFD–DPM Coupled Simulation

Le Long, Peng Fang, Jinlong Lin, Muhua Liu, Xiongfei Chen (), Liping Xiao, Yonghui Li and Yihan Zhou
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Le Long: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Peng Fang: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Jinlong Lin: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Muhua Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Xiongfei Chen: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Liping Xiao: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Yonghui Li: Jiangxi Super-Rice Research and Development Center, Jiangxi Provincial Key Laboratory of Rice Germplasm Innovation and Breeding, Jiangxi Academy of Agricultural Sciences, National Engineering Research Center for Rice, Nanchang 330200, China
Yihan Zhou: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China

Agriculture, 2025, vol. 15, issue 17, 1-19

Abstract: UAV pollination holds significant promise for enhancing hybrid rice seed production, yet the mechanisms of pollen diffusion under UAV downwash and the lack of theoretical guidance for operational parameter optimization remain critical challenges. To address this, this study employed a coupled Computational Fluid Dynamics–Discrete Phase Model (CFD–DPM) numerical simulation to systematically investigate the interaction between the UAV-induced wind field and pollen particles. A validated CFD model was first developed to characterize the UAV wind-field distribution, demonstrating good agreement with field measurements. Building upon this, a coupled wind field–pollen CFD–DPM model was established, enabling a detailed visualization and analysis of airflow patterns and pollen transport dynamics under varying flight parameters (speed and height). Using the pollen disturbance area and effective settling range as key evaluation metrics, the optimal pollination parameters were identified as a flight speed of 3 m/s and a height of 4 m. Field validation trials confirmed that UAV-assisted pollination using these optimized parameters significantly increased the seed yield by 21.4% compared to traditional manual methods, aligning closely with simulation predictions. This study establishes a robust three-tier validation framework (“numerical simulation—wind-field verification—field validation”) that provides both theoretical insights and practical guidance for optimizing UAV pollination operations. The framework demonstrates strong generalizability for improving the efficiency and mechanization level of hybrid rice seed production.

Keywords: wind field–pollen coupling; numerical simulation; UAV-assisted pollination; pollen distribution; hybrid rice (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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