High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system’s accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error (MAE) of 0.083 cm, root mean square error (RMSE) of 0.103 cm, and correlation coefficient (R) of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average MAE and RMSE are reduced by 0.071 cm and 0.075 cm, respectively, while the average R is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.

Because the initial speed of the seeds leaving the seed disk is too high, they collide and bounce off the inner wall of the seed guide tube, resulting in poor sowing quality when corn is sown at high speeds above 12 km/h. This study clarifies the primary factors affecting the stability of seed receiving and the accuracy of the seed entering the seed cavity, establishes the dynamic model of seed clamping, transportation, and releasing, and investigates the belt-type high-speed corn seed guiding device with the seed receiving system as the research object. It also proposes an improved method of adding herringbone lines on the finger surface to address this issue. Using EDEM software, a virtual experiment of seed-receiving performance was conducted, and the change trend of stress on seeds with and without a herringbone pattern and different wheel center distance as well as the change trend of the speed of seeds with various feeder wheel speeds and finger length, were both examined. The outcomes of the simulation demonstrate that the herringbone-lined feeder wheel could increase the stress on seeds. The average value of the stress on the seeds is the highest at the wheels’ center distance of 37 mm. The stability and speed fluctuation of seeds introduced into the seed cavity were better when the feeder wheel speed was 560 r/min. The speed of fluctuation and stability of the seeds introduced into the seed cavity were better when the finger length was 12 mm. The high-speed camera test on the test bench was used to verify the seed guiding process in accordance with the simulation results, and the outcomes were largely consistent. The study’s findings can serve as a theoretical foundation for a belt-type high-speed corn seed guiding device optimization test.