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.

翻译：粮食安全作为全球性议题，亟需精准多元的数据驱动方案应对其复杂挑战。本文探索将人工智能基础模型集成于各类粮食安全应用场景，通过整合异构数据类型，突破当前深度与机器学习方法的局限性。具体而言，本研究聚焦其在作物类型制图、农田边界提取、耕地地块划分及作物产量预测中的应用。通过融合多光谱影像、气象数据、土壤属性、历史记录及高分辨率卫星影像，人工智能基础模型展现出多维度适配能力。研究表明，该模型能通过提供精准预测、优化资源配置及支撑科学决策，有效增强粮食安全举措。此类模型作为应对全球粮食安全瓶颈的变革性力量，标志着向可持续与可靠粮食未来迈出关键跨越。