Spatiotemporal synchronization-based seed position estimation for multi-row precision planting

A seed position estimation system was developed to address the inconsistent clock bases among multiple controllers and the difficulty of accurately matching seed-drop events with GNSS positioning epochs during multi-row precision planting. The system comprised a GNSS module, a central controller, row controllers, seed-drop detection sensors, and an interactive terminal. A spatiotemporal synchronization-based seed position estimation method was then proposed. In this method, pulse-per-second (PPS) signals establish the correspondence between the central-controller time and UTC epochs. Periodic CAN synchronization is used to estimate the clock offset and frequency drift of each row controller, and single-seed ground coordinates are obtained by combining cubic Hermite trajectory interpolation, seed-drop delay compensation, antenna lever-arm compensation, and row-offset transformation. Bench tests showed that the four-row combined RMSE was 4.731–5.464 μs under synchronization periods of 50, 100, and 200 ms, and the mean equivalent seed travel time from the detection section to the theoretical landing point was 111.12 ms. Trajectory-continuity simulations showed that cubic Hermite interpolation substantially reduced inter-epoch position-estimation error under variable-speed and low-curvature turning conditions, particularly at lower GNSS sampling frequencies. Field tests were conducted at operating speeds of 5, 7, and 9 km h −1 with 810 seed samples. After excavation, the actual ground coordinate of each seed was measured using RTK positioning and matched with the corresponding estimated position. The static RTK planar RMSE was 0.905 cm, while the seed two-dimensional position error had a mean of 2.931 cm and an RMSE of 3.188 cm. Residual errors were mainly associated with seed travel time variation, GNSS uncertainty, implement geometry, and random seed motion after detection. These results indicate that the proposed method can achieve centimeter-level seed position estimation and can provide a data basis for seeding-quality evaluation and plant-level operation traceability.