Accurate trajectory tracking is fundamental to the autonomous operation of pineapple field management vehicles. To improve navigation accuracy, a control method was proposed. Firstly, a vehicle platform was developed and its kinematic model was established. Secondly, a model predictive control (MPC) algorithm was constructed based on this kinematic model. Then, to achieve high-precision trajectory tracking, a method was proposed that integrated MPC and Adaptive-PID. Both simulation and field experiments were conducted to verify the effectiveness of this method, and a systematic comparison was made among the Adaptive-PID integrated MPC method, the MPC method and the Adaptive-PID method. In the simulation results, the maximum lateral errors of the MPC method and the Adaptive-PID method were 1.38~2.05 times and 4.22~17.09 times higher, respectively, than those of the AdaptivePID integrated MPC method. In the field test results, the maximum lateral errors of the MPC method and the Adaptive-PID method were 1.86~2.30 times and 3.76~6.69 times higher, respectively, than that of the Adaptive-PID integrated MPC method. Both simulation and field experiments demonstrated that, compared with the MPC method and the Adaptive-PID method, the Adaptive-PID integrated MPC method achieved higher trajectory tracking accuracy. The research result can provide an effective technical solution and solid theoretical support for trajectory tracking control of unmanned pineapple field management vehicles, and demonstrated promising application prospects for improving the accuracy and efficiency of agricultural automation equipment.