arXiv:2511.15728cs.CYcs.AI2025-11

AI正重塑食品制造,从农场到餐桌实现智能优化。

The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing

  • 整合多源数据与AI模型,打通食品全产业链
  • 推动营养健康、感官预测等五大领域落地应用
  • 强调跨学科协作与可解释性,保障技术伦理

人工智能正在加速食品创新的新时代,通过连接从农场到消费者的全流程数据,提升配方设计、加工工艺与健康效果。深度学习、自然语言处理和多组学整合的进展使食品系统优化达到前所未有的深度。然而,食品行业中的AI应用仍不均衡,受限于数据异构性、模型与系统互操作性不足,以及数据科学与食品专家之间的技能鸿沟。为应对挑战并推动负责任的创新,下一代食品系统人工智能研究所(AIFS)于2025年10月在加州大学戴维斯分校举办了首届食品产品开发人工智能研讨会。本白皮书整合了会议见解,围绕五个近期影响最大的领域展开:供应链、配方与加工、消费者洞察与感官预测、营养与健康、教育与人才发展。各领域均强调互操作数据标准、透明可解释模型及跨行业合作的重要性,以加速科研成果向实践转化。讨论还突出需建设稳健数字基础设施、隐私保护的数据共享机制,以及融合AI素养与领域专长的跨学科培养路径。总体而言,这些优先事项勾勒出一条将AI融入食品制造的路线图,旨在提升创新力、可持续性与人类福祉,同时确保技术进步根植于伦理、科学严谨性与社会价值。

原文摘要 · Abstract (English)

Artificial intelligence is accelerating a new era of food innovation, connecting data from farm to consumer to improve formulation, processing, and health outcomes. Recent advances in deep learning, natural language processing, and multi-omics integration make it possible to understand and optimize food systems with unprecedented depth. However, AI adoption across the food sector remains uneven due to heterogeneous datasets, limited model and system interoperability, and a persistent skills gap between data scientists and food domain experts. To address these challenges and advance responsible innovation, the AI Institute for Next Generation Food Systems (AIFS) convened the inaugural AI for Food Product Development Symposium at University of California, Davis, in October 2025. This white paper synthesizes insights from the symposium, organized around five domains where AI can have the greatest near-term impact: supply chain; formulation and processing; consumer insights and sensory prediction; nutrition and health; and education and workforce development. Across the areas, participants emphasized the importance of interoperable data standards, transparent and interpretable models, and cross-sector collaboration to accelerate the translation of AI research into practice. The discussions further highlighted the need for robust digital infrastructure, privacy-preserving data-sharing mechanisms, and interdisciplinary training pathways that integrate AI literacy with domain expertise. Collectively, the priorities outline a roadmap for integrating AI into food manufacturing in ways that enhance innovation, sustainability, and human well-being while ensuring that technological progress remains grounded in ethics, scientific rigor, and societal benefit.

食品AI智能制造多模态融合产业转型

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