The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing
- URL: http://arxiv.org/abs/2511.15728v1
- Date: Mon, 17 Nov 2025 20:17:55 GMT
- Title: The Future of Food: How Artificial Intelligence is Transforming Food Manufacturing
- Authors: Xu Zhou, Ivor Prado, AIFPDS participants, Ilias Tagkopoulos,
- Abstract summary: 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.<n>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.<n>This white paper synthesizes insights from the symposium, organized around five domains where AI can have the greatest near-term impact.
- Score: 3.6212098394171606
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Transforming Science Learning Materials in the Era of Artificial Intelligence [0.9851520275517003]
The integration of artificial intelligence into science education is transforming the design and function of learning materials.<n>This chapter examines how AI technologies are transforming science learning materials across six interrelated domains.
arXiv Detail & Related papers (2026-02-08T23:57:49Z) - AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
arXiv Detail & Related papers (2025-11-26T02:10:28Z) - AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock [77.95897723270453]
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population.<n> Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI)<n>This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques, and recent vision-language foundation models.
arXiv Detail & Related papers (2025-07-29T17:59:48Z) - Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) [49.142289900583705]
We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
arXiv Detail & Related papers (2025-05-22T12:52:34Z) - Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence [54.14779179869007]
We highlight key areas where AI is driving innovation, from data analysis to new biological insights.<n>These include developing an AI-friendly ecosystem for data generation, sharing, and analysis.
arXiv Detail & Related papers (2025-02-21T13:20:33Z) - Artificial Intelligence in Sustainable Vertical Farming [0.0]
The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming.
The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics.
The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security.
arXiv Detail & Related papers (2023-11-17T22:15:41Z) - From Plate to Production: Artificial Intelligence in Modern
Consumer-Driven Food Systems [32.55158589420258]
Global food systems confront supplying, nutritious diets in the face of escalating demands.
The advent of Artificial Intelligence is bringing in a personal choice revolution, wherein AI-driven individual decisions transform food systems.
This paper explores AI promise and challenges it poses within the food domain.
arXiv Detail & Related papers (2023-11-04T13:13:44Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.