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Enabling the Digital Food Supply Chain from Farm to Fork...
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Enabling the Digital Food Supply Chain from Farm to Fork with Smart Sensors, Edge Computing, and Artificial Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 22024

Special Issue Editors

Prof. Dr. Christian Krupitzer

E-Mail Website
Guest Editor
Department of Computer Science, University of Würzburg, Würzburg, Germany
Interests: food informatics; Industry 4.0; adaptive software systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anthony Stein

E-Mail Website
Guest Editor
Department of Artificial Intelligence in Agricultural Engineering, University of Hohenheim, Stuttgart, Germany
Interests: artificial intelligence; evolutionary computation; digital agriculture; complex system engineering
Prof. Dr. Janick Edinger

E-Mail Website
Guest Editor
Department of Informatics, University of Hamburg, Hamburg, Germany
Interests: edge computing; code offloading; assistive technologies; smart cities

Special Issue Information

Dear Colleagues,

A representative survey by the digital association Bitkom and the Federation of German Food and Drink Industries (BVE) shows that 70% of more than 300 German companies from the food industry surveyed consider “end-to-end traceability and information retrieval from the origin of goods to the customer” to be an important scenario for the current decade. This not only supports quality assurance but also provides an interface for consumers to obtain information about the origin of food and offers the possibility to optimize all processes from farm to fork. The required technologies, such as big data analytics or edge computing for decentralized data analysis to provide data sovereignty, are currently finding their way into the food industry. However, the potential is often not exploited; in particular, the real-time analysis of sensor information on the edge and corresponding adaptation of the process or the involved systems are often missing, and the data are only analyzed retrospectively in the cloud.

This Special Issue addresses all facets of research in the area of the digitalization of the food supply chain from farm to fork, i.e., it explicitly includes the domains of agriculture for food production and food processing systems. We mainly focus on the aspects of smart sensing of data as well as the (real-time) analysis of these data using methods from the domains of artificial intelligence, including machine and deep learning, problem solving, reasoning and planning, and edge AI, as well as intelligent, adaptive system technology for continuous self-optimization and -configuration through, e.g., automated code offloading, in order to support process optimization, system adaptation to changes, food alerts, or traceability; however, the Special Issue is not limited to those topic areas.

In addition to applications and case studies, we seek for fundamental science and theory, including surveys and works on objectives, metrics, tools, procedure, methodologies, reference systems, benchmarks, pointing toward open challenges, and future research directions.

Prof. Dr. Christian Krupitzer
Prof. Dr. Anthony Stein
Prof. Dr. Janick Edinger
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Aurora Tang via <aurora.tang@mdpi.com> directly.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food supply chain
  • smart sensors
  • edge computing
  • edge AI
  • AI-supported agriculture
  • food processing systems

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Published Papers (4 papers)

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Research

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16 pages, 2669 KiB  
Article
Intelligent Tools to Monitor, Control and Predict Wastewater Reclamation and Reuse
by Dimitris Ntalaperas, Christophoros Christophoridis, Iosif Angelidis, Dimitri Iossifidis, Myrto-Foteini Touloupi, Danai Vergeti and Elena Politi
Sensors 2022, 22(8), 3068; https://doi.org/10.3390/s22083068 - 16 Apr 2022
Cited by 4 | Viewed by 2650
Abstract
Contemporary wastewater reclamation units entail several diverse treatment and extraction processes, with a multitude of monitored quality characteristics, controlled by a variety of key operational parameters directly affecting the efficiency of treatment. The conventional optimization of this highly complex system is time- and [...] Read more.
Contemporary wastewater reclamation units entail several diverse treatment and extraction processes, with a multitude of monitored quality characteristics, controlled by a variety of key operational parameters directly affecting the efficiency of treatment. The conventional optimization of this highly complex system is time- and energy- consuming, frequently relying on intuitive decision making by operators, and does not predict or forecast efficiency changes and system maintenance. In this paper, we introduce intelligent solutions to enhance the operational control of the unit with minimal human intervention and to develop an AI-powered DSS that is installed atop the sensors of a water treatment module. The DSS uses an expert model, both to assess the quality of water and to offer suggestions based on current values and future trends. More specifically, the quality of the produced water was successfully visualized, assessed and rated, based on a set of input operational variables (pH, TOC for this case), while future values of monitored sensors were forecasted. Additionally, monitoring services of the DSS were able to identify unexpected events and to generate alerts in the case of observed violation of operational limits, as well as to implement changes (automatic responses) to operational parameters so as to reestablish normal operating conditions and to avoid such events in the future. Up to now, the DSS suggestion and forecasting services have proven to be adequately accurate. Though data are still being collected from early adopters, the solution is expected to provide a complete water treatment solution that can be adopted by a vast range of parties. Full article
(This article belongs to the Special Issue Enabling the Digital Food Supply Chain from Farm to Fork with Smart Sensors, Edge Computing, and Artificial Intelligence)
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14 pages, 272 KiB  
Article
Digital Strategy Decision Support Systems: Agrifood Supply Chain Management in SMEs
by Maria Kamariotou, Fotis Kitsios, Chrysanthi Charatsari, Evagelos D. Lioutas and Michael A. Talias
Sensors 2022, 22(1), 274; https://doi.org/10.3390/s22010274 - 30 Dec 2021
Cited by 11 | Viewed by 3528
Abstract
The specific attributes of agrifood supply chains, along with their importance for the economy and society, have led to an increased interest in the parameters that enhance their effectiveness. Recently, numerous digital tools aimed at improving supply chain effectiveness have been developed. The [...] Read more.
The specific attributes of agrifood supply chains, along with their importance for the economy and society, have led to an increased interest in the parameters that enhance their effectiveness. Recently, numerous digital tools aimed at improving supply chain effectiveness have been developed. The majority of existing research focuses on optimizing individual processes rather than the overall growth of a food supply chain. This study aims to identify the stages of the information systems planning (ISP) process that affect the success of developing a strategic decision support system (DSS) for improving the decision-making process in the agrifood supply chains. Data were collected from 66 IT executives from Greek small and medium-sized enterprises (SMEs) in the agrifood sector and analyzed using regression analysis. The results revealed that situation analysis is the only stage of ISP that predicts ISP success. These findings can assist managers in appreciating the critical role of ISP for improving the performance of agrifood supply chain operations. Implementing the most appropriate information systems (IS) and digital tools results in increased competitive advantage, cost savings, and increased customer value. Full article
(This article belongs to the Special Issue Enabling the Digital Food Supply Chain from Farm to Fork with Smart Sensors, Edge Computing, and Artificial Intelligence)
34 pages, 7707 KiB  
Article
A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective
by Juan S. Angarita-Zapata, Ainhoa Alonso-Vicario, Antonio D. Masegosa and Jon Legarda
Sensors 2021, 21(20), 6910; https://doi.org/10.3390/s21206910 - 18 Oct 2021
Cited by 20 | Viewed by 6574
Abstract
In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more [...] Read more.
In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC. Full article
(This article belongs to the Special Issue Enabling the Digital Food Supply Chain from Farm to Fork with Smart Sensors, Edge Computing, and Artificial Intelligence)
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Review

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27 pages, 854 KiB  
Review
Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry
by Elia Henrichs, Tanja Noack, Ana María Pinzon Piedrahita, María Alejandra Salem, Johnathan Stolz and Christian Krupitzer
Sensors 2022, 22(1), 115; https://doi.org/10.3390/s22010115 - 24 Dec 2021
Cited by 48 | Viewed by 8081
Abstract
The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data [...] Read more.
The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods. Full article
(This article belongs to the Special Issue Enabling the Digital Food Supply Chain from Farm to Fork with Smart Sensors, Edge Computing, and Artificial Intelligence)
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