Design and Experimental Evaluation of a Machine Vision-Based Delay Compensation Algorithm for Corn Row-Following Spraying

To address the reduction in nozzle row-following accuracy caused by sensing–execution latency during corn row-following operations, a delay compensation method based on machine vision and dynamic region of interest (ROI) adjustment was proposed. The method integrates real-time forward-velocity information from a global navigation satellite system/inertial measurement unit (GNSS/IMU), decomposes the delays associated with image processing, command transmission, and actuator motion, and calculates a visual look-ahead distance from the total response delay and robot forward velocity. Inverse-perspective mapping was used to establish the relationship between pixel and world coordinates, and the ROI position was dynamically shifted to synchronize the sensing–execution process. Indoor bench tests showed that, under variable conveyor-belt speeds ranging from 0 to 0.25 m/s, the algorithm achieved a row-following accuracy of 93.75% and a lateral mean absolute error of 0.019 m; compared with the average result of the three fixed-ROI tests, the lateral mean absolute error was reduced by 24.8%. Whole-machine tests showed that, under random platform forward speeds of 0–1.00 m/s, the row-following accuracy remained above 85.71%, with a lateral mean absolute error of 0.034 m. The results indicate that the proposed method effectively compensates for system delay under different speed conditions and reduces lateral tracking errors caused by longitudinal spatiotemporal mismatch, providing technical support for the development of precision corn row-following spraying equipment.