AI-Powered crop yield forecasting using multispectral satellite data and LSTM-based sequence models

Accurate crop yield forecasting is critical for sustainable agriculture and food security. To forecast agricultural yields from time series of multispectral satellite data, including NDVI, EVI, and reflectance from the red, near-infrared & shortwave infrared (NIR) bands, this research builds a deep learning framework that makes use of long short-term memory (LSTM) models. The model integrates these spectral inputs to capture crop physiological and environmental dynamics over time. Multi-year satellite and ground-truth yield data assessed performance evaluation indicates greater accuracy than typical machine learning techniques, with a R 2 value greater than 0.85. Ablation studies measure the contribution of individual spectral features, emphasizing the necessity to combine multispectral information. The study demonstrates how temporal multispectral data augmented into a LSTM network can be used to enhance crop yield predictions to support decision-making processes in precision agriculture and resource allocation across various geographical areas.