Revolutionizing Global Food Security: Empowering Resilience through
  Integrated AI Foundation Models and Data-Driven Solutions
Mohamed R. Shoaib Heba M. Emara Jun Zhao
Food security, a global concern, necessitates precise and diverse data-driven
solutions to address its multifaceted challenges. This paper explores the
integration of AI foundation models across various food security applications,
leveraging distinct data types, to overcome the limitations of current deep and
machine learning methods. Specifically, we investigate their utilization in
crop type mapping, cropland mapping, field delineation and crop yield
prediction. By capitalizing on multispectral imagery, meteorological data, soil
properties, historical records, and high-resolution satellite imagery, AI
foundation models offer a versatile approach. The study demonstrates that AI
foundation models enhance food security initiatives by providing accurate
predictions, improving resource allocation, and supporting informed
decision-making. These models serve as a transformative force in addressing
global food security limitations, marking a significant leap toward a
sustainable and secure food future.