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Food security, a global concern, necessitates precise and diverse data-drivensolutions to address its multifaceted challenges. This paper explores theintegration of AI foundation models across various food security applications,leveraging distinct data types, to overcome the limitations of current deep andmachine learning methods. Specifically, we investigate their utilization incrop type mapping, cropland mapping, field delineation and crop yieldprediction. By capitalizing on multispectral imagery, meteorological data, soilproperties, historical records, and high-resolution satellite imagery, AIfoundation models offer a versatile approach. The study demonstrates that AIfoundation models enhance food security initiatives by providing accuratepredictions, improving resource allocation, and supporting informeddecision-making. These models serve as a transformative force in addressingglobal food security limitations, marking a significant leap toward asustainable and secure food future.

Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions