AI in Food publishes high-quality research that leverages artificial intelligence and data-driven methodologies to transform the food system. We focus on the integration of computational intelligence with food science to accelerate the discovery of food materials, optimize complex manufacturing processes, and ensure the sustainability and safety of the global food supply chain.

Topics covered by the journal include but are not limited to:

• Foundational AI for Food: Core algorithms, large-scale foundation models, and robust data infrastructure tailored for food applications.

• AI-Enabled Optimization of Postharvest Handling and Primary Processing: Optimization of postharvest handling and primary processing empowered by artificial intelligence.

• Smart Modeling and Autonomous Control in Food Processing and Manufacturing: Intelligent modeling and autonomous control technologies throughout food processing and manufacturing operations.

• Machine Learning for Food Safety and Quality Control: Applications of machine learning in food safety surveillance, quality assurance, and authenticity verification.

• Computational Intelligence in Nutrition and Health: Deployment of computational intelligence for nutritional analysis, health informatics, and personalized dietetics.

• Data-Driven Supply Chain and Smart Preservation: Data-driven food supply chain management, predictive logistics, and smart preservation technologies.

• Consumer Insight and Life Cycle Assessment: Data-analytics-based consumer behavior insights and life cycle sustainability assessments.

• AI-Driven Discovery of Food Resources and Structure-Function Deciphering: AI-powered exploration of novel food resources and elucidation of structure-function relationships.

• Generative AI and LLM Interaction: Applications of Large Language Models (LLMs), generative AI, and intelligent human-machine interaction in food domains.

• Digital Twins and Autonomous Scientific Discovery: Digital twins, autonomous experimentation, and AI-driven scientific discovery (AI4S) in food research.

• Explainable AI and Causal Decision-Making: Explainable AI (XAI), causal inference, and transparent decision-making mechanisms within food systems.

• Algorithmic Governance and Ethical Implications: Algorithmic governance, ethical frameworks, and the broader societal impact of AI deployment in the food sector.

• Food AI Agents and Embodied Intelligence: Autonomous AI agents, multi-agent collaboration, and task orchestration in food processing, quality control, and supply chains; Embodied Artificial Intelligence (EAI) enabling perception-decision-action loops in postharvest sorting, flexible grasping, intelligent cooking, and cold-chain operations; agent-based digital employees, autonomous economic agents, and human-agent collaboration paradigms in food systems.

The journal welcomes original research articles, review articles, perspective papers and short communications. The journal's editorial leadership welcome suggestions and proposals for special issues.