Crop precision remote sensing mapping holds significant importance for agricultural resource surveys and management. Deep learning provides technical support for achieving accurate and efficient crop mapping.To alleviate the dependency of deep learning on labeled samples, an improved semisupervised remote sensing crop mapping method was proposed based on AdvSemiSeg. The proposed method introduced STMF-DeepLabv3+ as the generator in the adversarial learning framework, enhancing the feature encoding and semantic expression capabilities of the generator through Swin Transformer (ST) and multi-scale fusion (MF) modules, thus improving the segmentation performance of remote sensing crop images. Additionally, the efficient channel attention (ECA) module was introduced after each convolutional layer of the discriminator to adaptively learn the representation information of different channel feature maps, enhancing the discriminator’s perception of different channel features. During the training process, the discriminator provided high-quality pseudo-labels and adversarial losses to the generator, effectively improving the generalization ability of the generator. Compared with several advanced semi-supervised semantic segmentation methods, the proposed method achieved optimal performance in extracting planting information from remote sensing images in the Hetao Irrigation District of Inner Mongolia.