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

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 > 30%, though sensor errors can cause positioning deviations of 0.3-0.8 m. Despite considerable promise, this integrated technology faces challenges in large-scale applications, including perception degradation under environmental disturbances, limitations in algorithm generalization, poor pesticide formulation adaptability in mixing, and system coordination issues. To overcome these barriers, this review proposes interdisciplinary solutions: (i) Deploying lightweight edge devices and pruned neural networks to address decision-making delays and enhance real-time responsiveness; (ii) Optimizing mixer structures (e.g., helical baffle angles) based on computational fluid dynamics (CFD) simulations to reduce dead zones and improve mixing homogeneity for SCs; (iii) Integrating multi-sensor technology for drift compensation to enhance UAV spraying stability. By integrating and optimizing these key technologies, the closed-loop framework holds significant potential to markedly improve pesticide utilization efficiency, minimize environmental impact, and offer a practical framework for achieving on-demand application, thereby advancing sustainable smart agriculture.