Research on Field Weed Detection Methods for Sweet Corn Seedlings and Laser Weed Control Experiments

Sweet corn has high economic value and is consumed directly, requiring strict environmental and management conditions. However, weed infestation during growth seriously affects yield and quality, while conventional chemical weed control may compromise product safety. Laser weeding offers an effective alternative, with precise weed detection and localization as its core requirement. This study proposes YOLO-GFD, a lightweight weed detection algorithm for sweet corn fields. Compared with the original model, YOLO-GFD increased mAP@0.5 by 10.61 percentage points, reduced floating-point operations by 0.6 percentage points, and achieved an average precision of 95.77%. Field trials further showed a real-time weed detection rate of 93.1% and a corn seedling misdetection rate of 1.1%, indicating strong practical applicability. In addition, weed control experiments using 110 W near-infrared and blue lasers under different power levels and irradiation durations identified suitable laser parameters for field laser weeding. Overall, YOLO-GFD meets the real-time accuracy requirements of autonomous laser weeding and provides a reliable basis for visual recognition and laser parameter optimization in sweet corn production.