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A maize sowing position prediction system based on spatial temporal coupling was designed for maize sowing process. The system integrated an opposed infrared photoelectric sensor, a GNSS-RTK high-precision positioning module and a data transmission unit to predict the spatial position of seed landing by real-time monitoring of seed falling signals and combining the sowing machine heading, speed and spatial-temporal lag compensation model, and data would eventually be uploaded to the cloud. The system adopted STM32F103 microcontroller as the central controller, and a segmented spatial position conversion model was constructed to solve the offset problem between the main antenna of the planter and the infrared sensors; the spatial temporal hysteresis compensation model was introduced, and the seed falling delay, the localization information transmission delay, and the program execution delay were measured to be 107.7ms, 50ms, and 39.5ms, respectively, and finally the position deviation of the planter’s forward direction was corrected. The final shape of the coupled prediction model was clarified by formulating the directional response rules for the positive and negative values of latitude and longitude deviations in different intervals. The results of the field test showed that the average deviation of the seed landing position predicted by the system from the actual position was 36.86mm, with a standard deviation of 3.57mm and a coefficient of variation of 9.69%, which verified the effectiveness of the model. The system was capable of real-time recording and cloud storage of seeding position data, providing a reference for the subsequent precise and collaborative management of mid-tillage and fertilization.