Abstract: Path tracking performance and tracking accuracy are often required during the autonomous navigation of the soil sampling platforms. In this study, a path tracking was proposed for the soil sampling platforms using MPC-Stanley. Firstly, a hardware architecture of the navigation system was designed for the soil sampling platform. A control scheme was also formulated for the navigation system. Secondly, a kinematic model was constructed using a bicycle model. Subsequently, the model predictive control (MPC) was selected as the path tracking controller, thus deriving the control processes for both the MPC and Stanley controllers. The Stanley controller was employed to optimize the steering angle of the MPC controller. The high computational complexity of the MPC controller was solved within the specified time constraints. The steering angle was calculated by the Stanley controller, and then served as the input into the MPC controller. The control quantities were modified using an exponential form. The model simplification was fully met the real-time control requirements of the navigation system. Finally, an inertial measurement unit (IMU) and satellite positioning module were employed to obtain the real-time pose information of the platform. The field path tracking experiments were conducted using a soil sampling platform. According to the straight path Tr₁ and curved path Tr₂ as the reference paths, tracking performance tests were conducted at a platform speed of 0.8 m/s for the MPC-Stanley controller, pure pursuit (PP) controller, and proportional integral derivative (PID) controller on both straight and curved trajectories. The results demonstrated that the MPC-Stanley controller was achieved in the best performance of the path tracking. The state variables of the system were predicted using the motion model. The MPC-Stanley controller was effectively resolved the overshoot and oscillation in the PID controller, indicating the superior robustness, compared with the PP controller. Subsequently, the tracking error tests were conducted for the MPC-Stanley controller, PP controller, and PID controller on the straight trajectory Tr₁ and curved trajectory Tr₂ at four speeds: 0.8, 1.6, 2.4, and 3.2 m/s. Test results indicated that the MPC-Stanley controller was achieved in the average absolute deviation, the maximum absolute deviation, and standard deviation of 3.1, 4.7 and 1.2 cm, respectively, during Tr₁ straight-line path tracking at all four speeds, which were reduced by 43.6%, 43.4%, and 14.3%, respectively compared with the PP controller, and 20.5%, 23.0%, and 7.7% respectively, compared with the PID controller. In the MPC-Stanley controller, the dynamic model was fully leveraged to avoid the large system errors that caused by the absence of the angular control in the PP controller. The MPC-Stanley controller demonstrated the more stable performance during path Tr₁ tracking, compared with the PID controller. The MPC-Stanley controller was achieved in the average absolute deviation, maximum absolute deviation, and standard deviation of 3.9, 6.6, and 1.5 cm, respectively, during curved path Tr₂ tracking, which were reduced by 80.2%, 79.8%, and 85.7% over the PP controller, and 93.0%, 89.8%, and 90.5% over the PID controller. The MPC-Stanley controller can be expected to enable the adaptive parameter tuning using system inputs and outputs, delivering the superior control performance. The path-tracking was effectively enhanced the tracking accuracy of the soil sampling platform. Furthermore, the framework can be extended universally applicable to various agricultural platforms with the structures similar to the soil sampling platform. The finding can also provide a technical reference for the high-precision navigation in the field.