Abstract: Currently, during the process of high-speed seeding, the corn seeds will bounce when falling and upon landing, which affects the placement of the seeds. Belt-type seed delivery mechanisms applied in high-speed seeding operations can effectively restrain seeds during their descent, reducing vibration and bounce during the seeding process to enhance seeding quality. However, due to the physical limitations of the seed guide belt, traditional seeding quality monitoring systems are difficult to implement. Furthermore, belt-type seed delivery mechanisms require the seed guiding motor and seeding motor to operate in synchronized motion. The current dual-motor speed matching control has low precision, resulting in poor seeding performance and a high coefficient of variation in plant spacing. Therefore, this study designed a monitoring and control system for belt-type seed delivery mechanisms based on an improved PSO-Fuzzy-PID (particle swarm optimization-fuzzy-proportion integral derivative) algorithm. By employing photoelectric monitoring, a dual-channel monitoring algorithm based on the coupling relationship between the seed guide belt and corn seeds is proposed. This algorithm precisely monitors seeding quality by detecting the high-low level changes generated when the teeth of the seed guide belt pass through the monitoring sensor. By analysis of the seeding process, the speed matching relationship between the seed guiding motor and the seeding motor was determined. To prevent the algorithm from getting stuck in a local optimal state, an improved algorithm combining the PSO and the GA was adopted. A multi-objective comprehensive fitness function for tracking performance and control error was constructed to optimize the proportional and quantization factors in the Fuzzy-PID algorithm. Eventually, the optimal parameter solution was obtained. This method achieves speed matching control between the guiding motor and the seeding motor. The control performance of three control strategies, namely PID, Fuzzy-PID, and PSO-Fuzzy-PID, was compared and tested through simulation. Results indicate that the improved particle swarm optimization algorithm achieves optimal control performance, with no overshoot and both settling time and steady-state error being smaller than those of the other two algorithms. A test platform was constructed to evaluate monitoring performance, motor speed control accuracy, and seeding performance. Bench tests on monitoring performance revealed that at simulated operating speeds of 12～16 km/h, the system maintained seeding rate monitoring accuracy at no less than 96%. The average monitoring error of the plant spacing qualified rate is no more than 2.66%, the average monitoring error of the missed seeding rate is no more than 0.75%, and the average monitoring error of the multiple seeding rate is no more than 2.17%. The actual machine test on the accuracy of motor speed control shows that: when the simulated operating speed is 12～16 km/h, compared with the set theoretical speed, the standard deviations of the speed of the seeding motor and the guiding motor are both less than 10 r/min. After speed ratio conversion, the absolute error between the speed of the guiding motor and the speed of the seeding motor reaches its maximum at the operating speed of 16 km/h, which is 19.27 r/min; the root mean square error of the speed of both motors are less than 11 r/min. Seeding performance bench tests indicate: Based on the improved PSO-Fuzzy-PID control strategy, the plant spacing qualified rate is no less than 97.56%, the missed seeding rate below 2.22%, the multiple seeding rate below 0.45%, and a coefficient of variation not exceeding 14.91%. Field experiment indicate that when operating speeds are less than or equal to 16 km/h, the plant spacing qualified rate remains no less than 91.87%, the missed seeding rate below 5.60%, the multiple seeding rate below 3.33%, and the variability coefficient below 19.8%. These results meet the requirements for high-speed seeding operations. These findings provide valuable insights for developing quality monitoring and electric drive control systems for high-speed precision seeding with belt-type seed delivery mechanisms.