Weed Identification in Maize Fields Based on Improved Swin-Unet

نویسندگان

چکیده

The maize field environment is complex. Weeds and have similar colors may overlap, lighting weather conditions vary. Thus, many methods for the automated differentiation of weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet proposed. first performs semantic seedlings uses resulting mask to identify weeds. U-Net acts as framework, Swin transformer module introduced improve performance. DropBlock regularization, which randomly hides some blocks crop feature maps, applied enhance generalization ability model. Finally, areas are identified segmented with aid an morphological processing algorithm. DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, proposed models trained dataset seedling images. outperforms others, achieving mean intersection over union 92.75%, pixel accuracy 95.57%, inference speed 15.1 FPS. Our could accurate, real-time crops reference development intelligent agricultural equipment.

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ژورنال

عنوان ژورنال: Agronomy

سال: 2023

ISSN: ['2156-3276', '0065-4663']

DOI: https://doi.org/10.3390/agronomy13071846