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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.

To address poor accuracy in identifying open-field vegetables when light is blocked, this investigation used cabbage as the subject. It established an online target identification model based on deep learning. Using YOLOv8n as the backbone network, an approach was formulated to reduce the impact of light occlusion on the precision of online cabbage identification. A sequence of cabbage image filters was engineered to remove the consequences of light occlusion. A unit for adaptive parameter acquisition tailored to the cabbage image filters was built. The image filter sequence and the adaptive acquisition unit were woven into the YOLOv8n object detection architecture. This fusion enabled precise, real-time identification of cabbage in light-occluded environments. Experimental outcomes indicated identification precisions of 97.5% with the normal lighting dataset, 93.1% with the light occlusion dataset, and 95.0% with the mixed dataset. When the degree of light occlusion exceeded 0.4, the identification precision increased by 9.9% and 13.7% compared with the YOLOv5n and YOLOv8n architectures. The model achieved identification precisions of 99.3% with the Chinese cabbage dataset and 98.3% with the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge device, where it processed images at 26.32 frames per second. Field assessments showed identification precisions of 96.0% in ordinary light and 91.2% when light was blocked. The presented online cabbage identification model facilitates instant recognition and spatial location of cabbage in complex open-field conditions, providing technical support for targeted spraying.