Abstract: Accurate trajectory tracking is often required for autonomous wheeled robots in orchard and forestry environments. However, the control performance is also constrained by the external disturbances and parameter uncertainties caused by uneven terrain, soft soil, and frequent steering maneuvers, particularly when the wheel slip and steering-induced slip occur in practical agricultural scenarios. This study aims to propose a prediction-accuracy-enhanced explicit nonlinear model predictive control (ENMPC) algorithm integrated with a nonlinear disturbance observer (NDOB) for autonomous orchard wheeled robots. An extended kinematic model was firstly established to incorporate the typical disturbances in the field environments (including the wheel slip and steering slip) into an ideal kinematic framework. The trajectory tracking error dynamics were approximated over a finite receding horizon using a Taylor series expansion. An explicit analytical control law of the ENMPC was derived offline under the assumption that external disturbances were measurable. The requirement was fully met for the nonlinear optimization rather than the online one. As such, the controller significantly reduced the computational complexity for the real-time performance, which was critical for the embedded agricultural robotic systems with the limited computing resources. A nonlinear disturbance observer was designed to compensate for the external disturbances in real time in order to enhance the robustness against time-varying disturbances and modeling uncertainties. Furthermore, the disturbance estimates were treated as the additional corrective inputs using conventional feedforward compensation. By contrast, the approach directly integrates the disturbance estimation into the output prediction of the ENMPC. Therefore, the predicted system outputs were explicitly accounted for the disturbances in the prediction horizon, thereby achieving the zero steady-state offset in trajectory tracking. The closed-loop stability of the NDOB-ENMPC controller was analyzed under bounded disturbances. Simulation studies were conducted to evaluate the performance under multiple disturbance scenarios, including the constant disturbances, time-varying disturbances, and combined slip. The results demonstrate that the better performance was achieved under complex and uncertain working conditions. The NDOB-ENMPC algorithm effectively suppressed the disturbance influences. The superior accuracy and robustness were achieved in the trajectory tracking, compared with the conventional nonlinear model predictive control. Field experiments were further performed using an autonomous orchard wheeled robot in a forest grassland environment. Experimental results show that the NDOB-ENMPC reduces the maximum absolute lateral and longitudinal tracking errors by 39.42% and 49.01%, respectively, compared with a standard NMPC algorithm. While the mean absolute errors are also reduced by 29.45% and 44.01%, respectively. In addition, the average computation time is reduced by 97.47%. The maximum absolute lateral and longitudinal errors are reduced by 17.86% and 37.64%, respectively, and the mean absolute errors by 16.41% and 20.59%, respectively, compared with a Feedforward Compensated NMPC algorithm. While a 97.57% reduction is obtained in the average computation time. The NDOB-ENMPC algorithm fully meets the requirements of the real-time implementation, high tracking accuracy, and robustness for the autonomous navigation of the wheeled robots in complex orchard environments. The finding can provide the computationally efficient solution to deploy the optimal control strategies on the low-cost agricultural robotic platforms. There is also strong potential for practical applications in intelligent orchard and forestry environments.