Food Security

Food security research focuses on predicting and mitigating food shortages and malnutrition, aiming to improve resource allocation and humanitarian response. Current efforts utilize machine learning, particularly deep learning models like Random Forests, Bi-LSTMs, and Reservoir Computing, along with traditional statistical methods like Vector Autoregression, to forecast food prices, consumption, and crop yields, often incorporating diverse data sources such as weather patterns, economic indicators, and news articles. These advancements enhance early warning systems for famine and inform efficient food distribution strategies, ultimately impacting both humanitarian aid and agricultural practices. The integration of AI foundation models shows promise for further improving the accuracy and scope of these predictions.