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A precision spray perception system using LiDAR was designed, algorithmically developed, and experimentally verified to address the problem of low navigation line extraction accuracy in self-propelled sprayers, which causes wheel encroachment on ridges and pesticide overuse. For this perception system, a data processing framework was built encompassing preprocessing, adaptive segmentation separating crops from ditches, and the derivation of navigation lines and crop positioning, all originating from raw LiDAR point cloud species. To validate the system’s stability, field data on cabbages at different growth stages were collected and analyzed. A controllable, constant-speed experimental configuration was set up to benchmark LiDAR against a depth camera within the same field conditions. The trial outcomes reveal that at sprayer speeds of 0.5 and 1 ms⁻¹, the peak lateral deviation reaches 0.112 m within cabbage ridge settings containing inter-row weeds. In comparison, the mean absolute lateral error stands at 0.059 m. Each frame is processed in no more than 43 ms. Compared with the machine vision approach, the proposed technique reduces the mean processing time by 122 ms. In terms of identification accuracy, computational speed, and overall robustness for crop detection and navigation line derivation, the system introduced here outperforms its machine vision counterpart.