Aiming to address the challenges of prolonged inability to maintain constant temperature control and excessive reliance on manual assistance in the curing phase of white pepper primary processing production lines, a proportion integration differentiation (PID)-based control system was developed to control the curing temperature of the white pepper during processing. It is a high demand to maintain the constant curing temperature. Specifically, too high curing temperature can lead to the internal physicochemical properties of the destruction, whereas, too low curing temperature can lead to curing not complete, which makes the peeling rate decreased. The control system with an ST Microelectronics 32-bit Microcontroller (STM32) and a touchscreen was utilized to control the start/stop of the steam generator and the opening of the electric regulating valve. A temperature sensor was installed at the outlet of the curing machine, and a PT100 temperature sensor was employed to collect the curing temperature in real-time. Subsequently, the collected data was fed back to the STM32 microcontroller. The PID closed-loop control algorithm was applied to calculate the actuator, adjusting parameters appropriately to ensure stable control of the curing temperature by modulating the steam flow. A systematic analysis of the convective heat exchange process between white pepper and steam at temperature was conducted. A theoretical model of heat transfer was established by using the step response curve method, and the data curve was processed (R2=0.969) to derive the control model for the temperature inside the curing machine over time. Simulation analysis was performed by using the Simulink platform to determine the optimal parameters for PID control. Response curves from four PID parameter tuning methods, including the Ziegler-Nichols method, the decay curve method, the critical proportional method, and the sparrow search algorithm-based particle swarm optimization method (SSAPSO), were compared. Ultimately, it was found that the SSAPSO-based method yielded the best control effect in terms of dynamic performance indicators with PID parameters (proportional coefficient Kp=0.8759, integral coefficient Ki=0.02, and differential coefficient Kd=4.3255). The response time of the PID controller obtained by the SSAPSO-based method was approximately 40s with an overshoot of about 2.5%. Systematic experimental studies demonstrated that throughout the entire 8 minutes curing process, the current curing temperature was sampled every minute. Due to direct convective heat exchange between the curing machine and the air, the temperature remained stable within the range of (99±1.5)℃. The average relative error of the curing temperature was less than 1.2%, and the coefficient of variation was less than 1.3%, thereby achieving automated, precise, and efficient temperature control during the curing process.