From Pixels to Plow: Enhancing Agricultural Yield with Satellite Data

This research paper focuses on advancing agricultural predictions in the Indian Subcontinent's specific region, with a primary emphasis on Karnataka. We employ twelve years of historical data, computing monthly averages to address decadal seasonality. Our methodology harnesses Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and long short-term memory networks (LSTMs) for predictions. MODIS imagery aids in extracting critical information, while LSTM models capture temporal patterns in the data. By combining these tools, we aim to provide robust predictions using the normalized difference vegetation index (NDVI), a widely accepted vegetation metric. Our innovative approach aims to enhance agricultural yield forecasts and agricultural management strategies in the region.