<p>The present study proposes the deep learning-enabled U-Net model for crop classification required in crop area calculation, crop status monitoring, and food production. The traditional approaches are time-consuming, costly, and insufficient. Therefore, the Sentinel-2 images were initially pre-processed in this study, and spectral features were extracted from time-series data using the Normalized Difference Vegetation Index (NDVI) method. Then, the composite of Sentinel-2 bands and NDVI output was generated for data stacking. Lastly, classification algorithms such as random forest, artificial neural network (ANN), and U-Net models were implemented on staked data based on training samples. In this case, we used cloud-based open-source Google Earth Engine (GEE) and Google Colab programming for crop classification using Sentinel-2 L2A (S2) images. The results depict that the U-Net model is superior in terms of overall accuracy (94.8%) than the random forest (91.2%) and ANN (93%), with a kappa statistic of 89%, 85.7%, and 87.9%, respectively, for crop classification. The present study is suitable for farmland and crop status monitoring for future food security and decision-making.</p>