Abstract: In recent years, deep learning has made great progress in many fields, such as image processing and data analysis. The current fine extraction of crop areas relies mainly on computer-assisted manual visual interpretation. Since there are many disadvantages in crop planting area estimation using method of traditional remote sensing, which are poor timeliness, relying on manual operation experience and high human resource consumptions, a remote sensing crop planting area classification method based on deep learning was proposed using Sentinel-2 satellite remote sensing image. In order to obtain the peanut planting area, the Sentinel-2 remote sensing image data was firstly preprocessed, and then the peanut planting part was manually labeled to get the training and test data set. The training data set was then input into the image segmentation network for training. After training, the test data set was input into the network to obtain the test result: the detection accuracy rate was 89.20%, and the detection recall rate was 79.22%.