Precision agriculture is dependent on precise crop identification to maximize resource utilization and enhance yield forecasting. This paper investigates the use of Vision Transformers (ViTs) for crop classification from high-resolution satellite images. In contrast to traditional deep learning models, ViTs use self-attention mechanisms to capture intricate spatial relationships and improve feature representation. The envisioned framework combines preprocessed multispectral satellite imagery with a Vision Transformer model that is optimized to classify heterogeneous crop types more accurately. Experimental outcomes confirm that ViTs are superior to conventional Convolutional Neural Networks (CNNs) in processing big agricultural datasets, yielding better classification accuracy. The proposed model was tested on a multispectral satellite image from Sentinel-2 and Landsat-8. The results shows that ViTs efficiently captured long-range dependencies and intricate spatial patterns and attained a high classification accuracy of 94.6% and a Cohen’s kappa coefficient of 0.91. The incorporation of multispectral characteristics like NDVI and EVI also improved model performance, allowing for improved discrimination between crops with comparable spectral signatures. The results point out the applicability of Vision Transformers in remote sensing for sustainable and data-centric precision agriculture. Even with the improvements made in this study, issues like high computational expense, data annotation needs, and environmental fluctuations are still major hurdles to widespread deployment.