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Farmland is a significant resource for human survival and development. Rapid acquisition of farmland information is the basis for dynamic crop detection and sustainable land development. The continuous development of high-resolution remote sensing imagery makes it possible to make a wide range of refined earth observation. With better image interpretation ability, image segmentation method based on deep learning can bring specific results from high-resolution imagery and is widely used in remote sensing. However, existing image segmentation methods based on semantic segmentation have difficulties to extracting refined farmland parcels. Deep neural network is used to detect farmland edge. We use high-resolution network to achieve feature extraction that retains high-resolution features, strengthens the feature representation of network context information based on object-contextual representations module, and carries out more complete interpretation of farmland and its boundary. Finally, we design a farmland edge postprocessing method to connect the disconnected boundary based on the direction information generated by the connectivity attention module, and finally obtained the farmland boundary which is complete enough to be closed for generating farmland parcels. To verify our method, we used Google Earth image to label farmland boundaries and conduct experimental verification. The results show that our proposed model has a higher precision for farmland edge detection, and the postprocessing method of boundary connection can effectively close the boundary lines and achieve more detailed and complete farmland parcels.