ReviewSensors (Basel, Switzerland)2021
Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Towards a Sustainable Food Supply Chain: Digital Transformation via Blockchain, IoT and AI.Food science & nutrition · 2026Review
- AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2026Review
- Advances in Food Quality Management Driven by Industry 4.0: A Systematic Review-Based Framework.Foods (Basel, Switzerland) · 2025Review
- AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Transforming Agricultural Waste from Mediterranean Fruits into Renewable Materials and Products with a Circular and Digital Approach.Materials (Basel, Switzerland) · 2025Review
- Digital twin-based applications in crop monitoring.Heliyon · 2025Review
- Digital Twins in Agriculture: Orchestration and Applications.Journal of agricultural and food chemistry · 2024Review
- Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems.Biomimetics (Basel, Switzerland) · 2023Review
- Transforming agrifood production systems and supply chains with digital twins.NPJ science of food · 2022Article
- Design, Modeling and Implementation of Digital Twins.Sensors (Basel, Switzerland) · 2022Article
- Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.