Abstract: Abstract: To precisely control the tillage depth and ensure uniform tillage depth, in this study, measured values of the tillage depth are corrected based on the tillage depth measurement methods and the causes of the measurement errors. In indirect measurement of the tillage depth, the tillage depth is calculated by monitoring the angle of the lifting arm based on the geometric relationship between the position of the suspension mechanism and the angle of the lifting arm. The tillage depth can also be measured directly by monitoring the height change of the plow frame relative to the land that has not been tilled. Due to the excellent imitation real-time response of the land wheels and the high measurement accuracy of the pull rope sensor that is less susceptible to the impact of the operating environment, with the system made up by the land wheels and the pull rope sensor as the measuring basis, the angular transducer and ultrasonic transducer as the cases, the principles of the tillage depth measurement, both direct and indirect, are analyzed. Besides, the prototype is rebuilt and the tillage and imitation tests are carried out in the Mahindra Tractor Experimental Field (Nanchang, China). The causes of errors in the indirect and direct measurement of tillage depth are explored based on the collected data. As shown by the experiment results, due to the changed measuring basis that might be caused by the wheel sinkage and the tilted tractor body, the indirectly measured tillage depth is always smaller than the target tillage depth, and the deviation becomes larger as the target tillage depth increases; there is a great fluctuation in the values obtained using the direct measurement method, for the sensor cannot conduct accurate sampling because of the factors like soil unevenness, plant residuals, and unit vibration. Based on the causes of the measurement errors, both the direct and indirect measurement results are corrected by the fitting and the Kalman prediction, respectively. As suggested in the modification results, there is an approximate linear correlation between the tillage depth deviation from the indirect measurements and the actual tillage depth (R2=0.986 1), which can effectively reduce the deviation based on the fitting formula (before compensation: 3.20, 4.48, 5.61, 6.90 cm; after compensation: 0.14, 0.19, 0.16, 0.17 cm) and ensure that the standard deviation after compensation remains approximately unchanged (before compensation: 0.042, 0.08, 0.032, 0.07 cm; after compensation: 0.047, 0.06, 0.03, 0.082 cm); Kalman prediction can accurately predict the arrival time of the echo and reduce the noises in the measurements through the state update, which reduces the standard deviation of tillage depth (before: 1.60, 1.83, 1.33, 1.83 cm; later: 0.032, 0.010, 0.042, 0.092 cm) and ensures that the average tillage depth (before: 15.06, 20.05, 25.42, 29.33 cm; later: 15.02, 20.08, 25.07, 30.137 cm) and deviation (before: 0.064, 0.05, 0.42, 0.65 cm; later: 0.02, 0.084, 0.07, 0.17 cm) are approximately the same. Through fitting, the variation patterns of the limited observation data and the tillage depth deviations can be explored, and Kalman prediction can minimize the impact of noises on the identification of the system state. As fitting and Kalman prediction play an effective role in analyzing the influence of different parameters on test results and noise reduction, they are applicable to the correction of tillage measurement errors. The method to correct errors in indirect and direct measurements of the tillage depth proposed in this study provides a new solution for precisely controlling the electronic suspension and ensuring the seeding depth.