Soybean Field Weed Segmentation and Prescription Map Generation Based on SCG-UNet Fusion of UAV RGB and Multispectral Images

Weed segmentation in soybean fields is essential to improving herbicide use efficiency and supporting precision variable-rate spraying. This study developed an SiLU–CPCA–Gate U-Net (SCG-UNet) using fused UAV RGB and multispectral imagery to improve the delineation of small and partially occluded weeds under complex canopy conditions. SCG-UNet integrates channel–spatial feature enhancement, attention-guided skip-feature fusion, and smooth nonlinear activation within a U-Net framework. A total of 400 spatially aligned RGB–multispectral image groups collected from a soybean field in Henan Province, China, were manually annotated for model development and evaluation. Paired bootstrap comparisons showed that RGB+NIR achieved the highest numerical performance among the tested inputs and significantly outperformed RGB, RGB+R, and RGB+G in mIoU after Holm correction, while remaining statistically comparable to RGB+REdge and RGB+NIR+REdge. With RGB+NIR input, SCG-UNet achieved an mPA of 92.35%, an mIoU of 83.43%, a Dice coefficient of 79.50%, and an F1-score of 80.77%, exceeding the baseline U-Net by 0.71, 1.50, 2.19, and 2.09 percentage points, respectively. Five-fold spatial block cross-validation yielded an mIoU of 82.92 ± 0.29% and an F1-score of 80.06 ± 0.40%, indicating stable performance across different regions of the same field. SCG-UNet also achieved the highest numerical mIoU among the evaluated convolutional, high-resolution, and Transformer-based models, exceeding TransUNet and LeViT-UNet by 0.90 and 0.71 percentage points, respectively, while requiring fewer parameters and lower reported memory consumption. The segmentation results were further converted into a conceptual variable-rate spraying prescription map with five spray volume levels ranging from 220 to 300 L/ha. These results demonstrate the potential of RGB–multispectral fusion for soybean weed mapping, although field validation of prescription execution, weed control efficacy, and economic benefits remains necessary.