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

• URL: http://arxiv.org/abs/2310.20301v1
• Date: Tue, 31 Oct 2023 09:15:35 GMT
• Title: Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions
• Authors: Mohamed R. Shoaib, Heba M. Emara, Jun Zhao
• Abstract summary: This paper explores the integration of AI foundation models across various food security applications. We investigate their utilization in crop type mapping, cropland mapping, field delineation and crop yield prediction.
• Score: 8.017557640367938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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