AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 7; p. 2147
• Main Authors: Dakhia, Zohra, Russo, Mariateresa, Merenda, Massimo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.03.2025; MDPI
• Subjects: Artificial Intelligence; artificial intelligence (AI); Computational linguistics; Food; food computing; Food industry; Food quality; food traceability; Humans; Innovations; Internet of Things; internet of things (IoT); Language processing; Logistics; Machine vision; Marketing research; Natural language interfaces; Nutrition; Requirements; Review; Sensors; supply chain optimization; United States; Italy; New Jersey; supply chain optimization; food traceability; internet of things (IoT); food computing; artificial intelligence (AI)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25072147

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven approaches, to address various challenges in the food sector. It encompasses a wide range of technologies that improve the efficiency, safety, and sustainability of food systems, from production to consumption. It represents a transformative approach to addressing challenges in the food sector by integrating AI, the IoT, and data-driven methodologies. Unlike traditional food systems, which primarily focus on production and safety, food computing leverages AI for intelligent decision making and the IoT for real-time monitoring, enabling significant advancements in areas such as supply chain optimization, food safety, and personalized nutrition. This review highlights AI applications, including computer vision for food recognition and quality assessment, Natural Language Processing for recipe analysis, and predictive modeling for dietary recommendations. Simultaneously, the IoT enhances transparency and efficiency through real-time monitoring, data collection, and device connectivity. The convergence of these technologies relies on diverse data sources, such as images, nutritional databases, and user-generated logs, which are critical to enabling traceability and tailored solutions. Despite its potential, food computing faces challenges, including data heterogeneity, privacy concerns, scalability issues, and regulatory constraints. To address these, this paper explores solutions like federated learning for secure on-device data processing and blockchain for transparent traceability. Emerging trends, such as edge AI for real-time analytics and sustainable practices powered by AI–IoT integration, are also discussed. This review offers actionable insights to advance the food sector through innovative and ethical technological frameworks.