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Aiming at the problems of slow steering response, crop row detection methods, and poor stability of tracking controllers in existing sprayers, a four-wheel-drive, differential-steering spraying robot was designed based on a light detection and ranging （LiDAR） navigation system. The whole structure of the robot was firstly designed, and the key components were designed according to the working principle. Then a crop row detection method based on 3D LiDAR was proposed. This method involved obtaining the crop point cloud in front of the robot through point cloud preprocessing and ground point cloud filtering. Subsequently, different crop rows were identified by analyzing the distribution of point cloud data across the transverse coordinate axes. The centerlines of the crop rows were then determined by fitting segmented geometric centers. Meanwhile, a dual-input single-output fuzzy controller was designed to use the yaw angle and lateral deviation obtained from the centerlines of the crop rows as inputs. The controller performed fuzzy inference by using 49 fuzzy rules and the Mamdani method. The outputs were then defuzzified into the differential wheel speeds for the wheels on both sides of the wire-controlled chassis by using the center-of-gravity method. Finally, the robot driving performance test and navigation performance test were conducted in the seeding cornfield. The results showed that the robot can successfully climb slopes over 20°, and the average deviation of the geometric center was 7.66 cm when performing a turn at differential speed. This indicated that the robot possessed adequate driving force and excellent steering flexibility. When LiDAR detected corn crop rows at the three-leaf stage and the small trumpet stage, the average error angles were 0.93° and 0.85°, respectively, with an average running time of 0.031 s. Utilizing this localization information, the robot achieved an average tracking error of 0.061 m with a standard deviation of 0.038 m when navigating the crop rows through the fuzzy control algorithm. This level of accuracy can meet the requirements for automatic navigation in corn fields during the seedling stage.