A high-speed no-tillage seeder seeding depth monitoring system based on the improved wild horse optimizer-extended Kalman filter (IWHO-EKF) was proposed. The system addressed the mechanical vibration issues caused by uneven terrain during operation, which led to a decrease in accuracy of the seeding depth monitoring. Additionally, it improved the poor reliability of a single monitoring sensor. Firstly, a mathematical model for monitoring seeding depth was established by using laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit. Secondly, a Kalman filtering algorithm was implemented to filter the measurements from the three individual sensors separately. Lastly, the IWHO proposed the use of the Levy flight and Gaussian mutation algorithms to optimize the key parameters of the EKF for data fusion. Qsigma, Rsigma1, Rsigma2, and Rsigma3 were the parameters that were optimized for the fusion of filtered measurements from the three sensors. Technical term abbreviations such as EKF were explained when first used. The aim was to reduce interference from mechanical vibration, decrease sensor measurement errors and ensure accurate and reliable real-time seeding depth monitoring during high-speed operation of the no-till seeder. To ascertain the effectiveness of the proposed method, simulation experiments and field validation experiments were conducted, comparing the IWHO-EKF with original sensor measurements, filtered seeding depth values and the WHO-EKF. The results from simulation experiments demonstrated that the IWHO-EKF algorithm had a mean absolute error (MAE) and root mean squared error (RMSE) of 0.073cm and 0.090cm, respectively, with a high correlation coefficient (R) of 0.983. This suggested a high level of accuracy and significant improvements in precision compared with measurements from the original sensor and filtered values, as well as the WHO-EKF. Technical term abbreviations were explained when it was firstly used. Field validation tests confirmed that the IWHO-EKF for seeding depth monitoring system in high-speed no-till seeders reduced the average MAE and RMSE by 0.063cm and 0.067cm, respectively, when compared with data from the three sensors. Additionally, the average R was increased by 0.027. This system offerred improved, accurate, and dependable monitoring values for seeding depth. The research result can provide lessons and references for high precision seeding depth monitoring during high-speed seeding.