Abstract: Focusing on plant protection quadrotor unmanned aerial vehicles (UAVs) operating in complex agricultural environments, this paper addresses the critical challenge of achieving precise trajectory tracking within a user-specifiable, predefined convergence time. To meet the stringent requirements of both convergence time and tracking accuracy in modern agricultural applications, a novel adaptive predefined-time trajectory tracking control strategy is proposed. Initially, a comprehensive and realistic system model was established, recognizing that plant protection UAVs are typically underactuated systems characterized by nonlinearity and strong coupling. A unified six-degree-of-freedom dynamic model was developed to comprehensively incorporate various external disturbances inherent to complex operating conditions, such as wind gusts, unmodeled internal system dynamics, and noise. This unified expression for six-degree-of-freedom dynamics overcomes the tedious and repetitive design issues often encountered in conventional control methodologies. The control objective was rigorously defined as designing a predefined-time adaptive trajectory tracking control scheme that ensures all signals in the closed-loop system remain uniformly ultimately bounded and, most importantly, guarantees that the trajectory tracking errors converge to an arbitrarily small neighborhood around the origin within the predefined time. This convergence time is explicitly determined by a user-defined parameter and is independent of the system’s initial conditions, a crucial advantage for practical deployment scenarios. The core innovation of this work lies in the synergistic integration of advanced control techniques within a predefined-time stability framework. The proposed strategy achieved robust performance and enhanced flexibility through several sophisticated components by incorporating freely adjustable control parameters, which allow designers to directly adjust the system’s transient performance, including the flexible adjustment of the convergence time and tracking accuracy. This represents a significant leap beyond existing predefined-time controllers. To ensure robustness and practicality in the presence of external wind disturbances and unknown nonlinear functions, the scheme integrates a nonlinear disturbance observer designed to provide real-time estimation and compensation for external disturbances. Additionally, radial basis function (RBF) neural networks are employed for the online approximation and compensation of uncertain nonlinear system functions and unmodeled dynamics, effectively handling the complexity of real-world systems. Adaptive control laws are rigorously designed to update the unknown parameters, including the neural network weights. Furthermore, a command-filtered backstepping technique is adopted to construct the control algorithm. This method effectively resolves the computational burden of the complexity explosion problem associated with conventional backstepping methods, which typically require repeated analytical differentiation of virtual controllers. The stability of the entire closed-loop system was rigorously verified using Lyapunov’s theory for predefined-time stability. The theoretical analysis determined that all signals within the closed-loop system are uniformly ultimately bounded, and the trajectory tracking errors are guaranteed to converge to a small neighborhood around the origin within the predefined time. The efficacy and superiority of the proposed control scheme were validated through extensive numerical simulations performed in MATLAB. Simulations with varying predefined times successfully verified the desired predefined-time convergence property and showed that a faster convergence time requires a larger control input. Comparative studies against adaptive backstepping and sliding mode control methods demonstrated that the proposed strategy exhibits superior performance in terms of faster convergence speed, higher steady-state tracking accuracy, and smoother control signal generation, thereby confirming its practical value for high-precision agricultural plant protection tasks.