A sustainable crop protection through integrated technologies: UAV-based detection, real-time pesticide mixing, and adaptive spraying

作者：Wen J. Li, Yahui Luo, Ping Jiang, Xiang Dong, Kaiwen Tang, Zhanchuo Liang, Yixin Shi · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-19473-x · 被引用次数：22 · 研究领域：Plant Surface Properties and Treatments、Insect behavior and control techniques、Forest Insect Ecology and Management

Chemical control using pesticides remains an essential component of crop pest and disease management, while precision pesticide application is a core element for achieving sustainable agriculture. Precision spraying technology-integrating UAV-based detection, real-time pesticide mixing, and adaptive variable-rate spraying-provides a critical pathway for sustainable crop protection by establishing a "perception-decision-execution" closed-loop framework.While previous reviews have predominantly focused on compartmentalized analyses of individual technologies (e.g., sensing or actuation), this study establishes a unified Perception-Decision-Execution (PDE) framework to, for the first time, quantitatively assess the synergistic interactions and systemic Bottlenecks across all three layers.This paper systematically reviews 168 core publications from 2013 to 2024, proposing for the first time and quantitatively assessing the synergistic effects of technologies within this closed-loop framework. The findings reveal that: (1) UAV-deep learning systems achieve pest identification accuracy rates of 89-94%, but this significantly declines to 60-70% under strong light or occlusion conditions; (2) Real-time mixing systems attain a mixing homogeneity coefficient (γ) > 85% for liquid pesticides, while for suspension concentrates (SCs), γ decreases to 70-75% due to particle sedimentation effects; (3) PWM-based variable-rate spraying reduces pesticide usage by 30-50% and off-target drift by >...