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Accurate extraction of ridges is an important prerequisite for digital agricultural management. However, due to the interference of factors such as occlusion and alopecia areata, it brings challenges for the semantic segmentation method to extract the ridge area. A U-Net segmentation network model was proposed based on a multi-information attention mechanism and an edge-aware module. Firstly, multi-information attention was introduced into the down-sampling of the U-shaped network to enhance the context information between adjacent layers and improve the representation ability of the semantic features of the ridge area. Secondly, the edge-aware segmentation module was applied to each layer of the U-Net decoding part, and the ridge edge information was extracted in different semantic feature layers to improve the semantic segmentation accuracy of the ridge region. Finally, the joint edge-aware loss and semantic segmentation loss were used to construct a joint loss function for overall network optimization. The training and model validation were carried out with the UAV wheat field data set collected by the wheat experimental base in Suixi County, Huaibei City, Anhui Province. The experimental results showed that the pixel accuracy of semantic segmentation of crop plants in different datasets was as high as 95.57%, and the average intersection ratio was 77.48%.