Aims
Intelligent Food Systems is an international, peer-reviewed journal dedicated to advancing the integration of artificial intelligence, data science, and intelligent technologies across the entire food system. The journal addresses a critical gap: while AI has transformed fields such as drug discovery and autonomous systems, its application to food science remains fragmented, often confined to narrow tasks like sensory prediction or adulteration detection, without addressing the fundamental challenges of data scarcity, multimodal complexity, and cross-disciplinary translation that characterize food systems research.
The journal's central premise is that the challenges confronting modern food systems - sustainability, food security, nutritional equity, and supply chain resilience - are fundamentally problems of complexity and uncertainty. These are precisely the domains where intelligent systems can offer transformative solutions. Intelligent Food Systems provides a dedicated forum for research that bridges food science, engineering, and computational intelligence, moving beyond isolated AI applications toward integrated, adaptive, and intelligence-driven food systems.
The revised positioning further emphasizes system-level intelligence and emerging directions such as multimodal intelligence, foundation models, generative and agentic AI, digital twins, intelligent robotics, and autonomous food systems, with the aim of advancing from data analysis and prediction toward intelligent decision-making, adaptation, and autonomous optimization.
Scope
The journal welcomes original research, reviews, and perspectives that advance the science, technologies, and applications of intelligent food systems. Contributions may span the entire food system and should demonstrate how intelligent and computational approaches can deepen scientific understanding, enable intelligent decision-making, or support the development of integrated, adaptive, and autonomous food systems.
Topics include, but are not limited to, the following areas:
- Intelligent food discovery and design: AI- and data-driven discovery and design of food ingredients, formulations, structures, and functional components; predictive modeling of food matrices; and high-throughput screening of bioactive compounds and peptides.
- Intelligent sensing, data, quality, and safety: Intelligent sensing, computer vision, spectroscopy, electronic nose and tongue systems, multimodal data integration, knowledge systems, and AI-enabled approaches for food quality, safety, authenticity, composition, and process monitoring.
- Smart manufacturing and autonomous food systems: Digital twins, intelligent process modeling, real-time optimization, adaptive control, robotics, foundation models, generative AI, agentic AI, human-in-the-loop systems, and autonomous operation of food production and processing systems.
- Supply chain and system intelligence: AI, IoT, blockchain, predictive analytics, and intelligent decision-making for traceability, logistics, demand forecasting, waste reduction, resource optimization, and supply chain resilience.
- Personalized nutrition and consumer intelligence: AI-driven dietary recommendations, personalized nutrition, digital phenotyping, consumer behavior modeling, and data-driven understanding of food preferences and health-related outcomes.
- Responsible and human-centered intelligence: Explainability, robustness, safety, data privacy, algorithmic bias, governance, regulation, and human–AI collaboration in food systems.
Article Types
The journal publishes Research Article, Reviews, Perspectives, etc. Reviews should provide in-depth interpretive coverage of narrowly defined topics with critical evaluation of existing literature. Perspectives should offer forward-looking analyses of emerging areas or controversial issues.
Audience
Intelligent Food Systems serves researchers, engineers, and practitioners at the intersection of food science, artificial intelligence, data science, and food engineering. The journal is committed to international and interdisciplinary representation, encouraging contributions that bridge methodological innovation with real-world food system challenges.
