Abstract: This paper presents a comprehensive study on adaptive predefined-time trajectory tracking control for plant protection quadrotor unmanned aerial vehicles (UAVs), specifically designed to address the challenges posed by complex operational environments and the critical need for precise convergence time and accuracy in modern agricultural applications. The research is motivated by the imperative to enhance the autonomy and reliability of UAVs in tasks such as crop spraying, where external disturbances, and inherent system nonlinearities can significantly compromise performance. To establish a robust control framework, a unified six-degree-of-freedom dynamic model of the quadrotor UAV is developed, explicitly incorporating aerodynamic effects and external disturbances. This model serves as the foundation for the controller design, enabling a systematic approach to handling the system's multivariable and underactuated nature. Central to the proposed strategy is the integration of adaptive control techniques with neural network-based approximation. Specifically, Radial Basis Function neural networks are employed to accurately estimate and compensate for the system's uncertain nonlinear functions and unmodeled dynamics online. Adaptive laws are meticulously designed to update the unknown parameters, including the optimal weights of the neural networks and compensation signals, thereby ensuring robustness against variations and disturbances. Compared to existing fixed-time or limited-time controllers, predefined-time controllers offer the flexible feature of adjustable convergence time. The control algorithm is constructed using a command-filtered backstepping technique, which effectively resolves the issue of "complexity explosion" commonly associated with conventional backstepping methods by obviating the need for analytic differentiation of virtual controls. A significant innovation of this work is the incorporation of free control parameters into the predefined-time stability framework. These parameters provide designers with substantial flexibility to adjust the system's transient performance and convergence behavior according to specific operational requirements, offering a greater optimization potential compared to existing predefined-time controllers. The stability of the entire closed-loop system is rigorously proven using Lyapunov theory for predefined-time stability. The analysis conclusively demonstrates that all signals within the system remain uniformly ultimately bounded. Most importantly, the trajectory tracking errors are guaranteed to converge to an arbitrarily small neighborhood of the origin within a 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. The efficacy and superiority of the proposed control scheme are validated through extensive numerical simulations. These simulations evaluate the controller's performance under various predefined time settings and in the presence of continuous external disturbances. The results consistently show rapid convergence, high tracking accuracy, and strong disturbance rejection capabilities. A comparative analysis with other advanced control methods, further highlights the enhanced performance and flexibility of the proposed approach, particularly in achieving a user-specified convergence time without compromising on precision or robustness. In summary, this research contributes a novel, adaptive, and computationally efficient predefined-time control solution that guarantees high-performance trajectory tracking for plant protection UAVs. The theoretical framework and experimental validation provide a solid foundation for the application of such intelligent control systems in precision agriculture, promising improved efficiency and reliability in autonomous farming operations.