Unleashing the Potential of Digitalization in the Agri-Food Chain for Integrated Food Systems

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

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.

Transforming Agricultural Waste from Mediterranean Fruits into Renewable Materials and Products with a Circular and Digital Approach

The Mediterranean area is one of the major global producers of agricultural food. However, along the entire supply chain-from farming to food distribution and consumption-food waste represents a significant fraction. Additionally, plant waste residues generated during the cultivation of specific fruits and vegetables must also be considered. This heterogeneous biomass is a valuable source of bioactive compounds and materials that can be transformed into high-performance functional products. By analyzing technical and scientific literature, this review identifies extraction, composite production, and bioconversion as the main strategies for valorizing agricultural by-products and waste. The advantages of these approaches as well as efficiency gains through digitalization are discussed, along with their potential applications in the Mediterranean region to support new research activities and bioeconomic initiatives. Moreover, the review highlights the challenges and disadvantages associated with waste valorization, providing a critical comparison of different studies to offer a comprehensive perspective on the topic. The objective of this review is to evaluate the potential of agricultural waste valorization, identifying effective strategies while also considering their limitations, to contribute to the development of sustainable and innovative solutions in Mediterranean bioeconomy.

Digital Twin Methodology in Food Processing: Basic Concepts and Applications

PURPOSE OF REVIEW

The goal of this article is to present a concise review about digital twin (DT) methodology and its application in food processing. We aim to identify the building blocks, current state and bottlenecks, and to discuss future developments of this approach.

RECENT FINDINGS

DT methodology appears as a powerful approach for digital transformation of food production, via integration of modelling and simulation tools, sensors, actuators and communication platforms. This methodology allows developing virtual environments for real-time monitoring and controlling of processes, as well as providing actionable metrics for decision-making, which are not possible to obtain by physical sensors. So far, main applications were focused on refrigerated transport and storage of fresh produces, and thermal processes like cooking and drying. DT methodology can provide useful solutions to food industry towards productivity and sustainability, but requires of multidisciplinary efforts. Wide and effective implementation of this approach will largely depend on developing high-fidelity digital models with real-time simulation capability.

A Conceptual Model Relationship between Industry 4.0-Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps

With the expected colonization of human daily life by artificial intelligence, including in industry productivity, the deployment of Industry 4.0 (I4) in the food agriculture industry (FAI) is expected to revolutionize and galvanize food production to increase the efficiency of the industry's production and to match, in tandem, a country's gross domestic productivity. Based on a literature review, there have been almost no direct relationships between the I4-Food-Agriculture (I4FA) Nexus and the agroecosystem. This study aimed to evaluate the state-of-the-art relationships between the I4FA Nexus and the agroecosystem and to discuss the challenges in the sustainable FAI that can be assisted by the I4 technologies. This objective was fulfilled by (a) reviewing all the relevant publications and (b) drawing a conceptual relationship between the I4FA Nexus and the agroecosystem, in which the I4FA Nexus is categorized into socio-economic and environmental (SEE) perspectives. Four points are highlighted in the present review. First, I4 technology is projected to grow in the agricultural and food sectors today and in the future. Second, food agriculture output may benefit from I4 by considering the SEE benefits. Third, implementing I4 is a challenging journey for the sustainable FAI, especially for the small to medium enterprises (SMEs). Fourth, environmental, social, and governance (ESG) principles can help to manage I4's implementation in agriculture and food. The advantages of I4 deployment include (a) social benefits like increased occupational safety, workers' health, and food quality, security, and safety; (b) economic benefits, like using sensors to reduce agricultural food production costs, and the food supply chain; and (c) environmental benefits like reducing chemical leaching and fertilizer use. However, more studies are needed to address social adaptability, trust, privacy, and economic income uncertainty, especially in SMEs or in businesses or nations with lower resources; this will require time for adaptation to make the transition away from human ecology. For agriculture to be ESG-sustainable, the deployment of I4FA could be an answer with the support of an open-minded dialogue platform with ESG-minded leaders to complement sustainable agroecosystems on a global scale.