Architected an advanced Vision Transformer (ViT) framework for precision agriculture, processing multi-temporal Sentinel-2 satellite imagery.&nbsp; Project Highlights:• Implemented Self-Attention mechanisms to capture long-range temporal dependencies between weather patterns and crop phenology• Applied Atmospheric Correction techniques and extracted vegetation indices (NDVI, EVI) for enhanced accuracy• Achieved 92% F1-score, outperforming traditional CNN models in complex regions with overlapping spectral signatures• Processed multi-spectral satellite data with custom preprocessing pipelines Technical Stack: Python, PyTorch, Vision Transformers (ViT), NumPy, Pandas, Scikit-learnData Source: Sentinel-2 Multispectral Satellite Imagery

AgroClimateFormer: Precision Agriculture Using Vision Transformers

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