A Two-Stage Weed Stem Localization Method Based on Crop Region Exclusion in Maize Seedling Fields

Accurate weed stem localization is essential for site-specific weed control, including precision spraying, laser weeding, and other targeted weed-control operations. To address species diversity, morphology, and costly multiclass annotation in maize seedling fields, this study proposes a two-stage method based on crop-region exclusion. First, MSDNet, a lightweight YOLOv8n-based maize detector integrating ShuffleNetV2, enhanced feature fusion, coordinate attention, and Wise-IoU loss, detects maize seedlings; pixels within the detected boxes are set to zero. Second, hue–saturation–value thresholding, morphological processing, and area filtering extract vegetation and suppress soil noise. Principal component analysis determines each weed contour’s principal axis, and the image-moment centroid is projected onto this axis to estimate the stem center. MSDNet achieved a mean average precision of 93.4% at an intersection-over-union threshold of 0.5, 8.7 percentage points above the baseline, while reducing parameters by 28.66%. Vegetation segmentation achieved a mean pixel accuracy of 97.6% and a mean intersection over union of 93.8%. Within a 15-pixel tolerance (9.50 mm), stem detection rate and localization precision reached 90.1% and 92.5%, respectively, with a mean localization error of 10.65 pixels (6.74 mm). The proposed method provides visual perception and target-localization support for site-specific weed control while reducing reliance on fine-grained multiclass annotation and species-specific models.