Vol. 36 No. 2 (2023): Revista ION
Articles

Technological innovation horizons in the food industry: synthetic data and quantum computing for the near and long-term future

PDF (Español (España))
  • Camilo Andres Castro Lopez
Camilo Andres Castro Lopez
AlianzaTeam USA
DOI: https://doi.org/10.18273/revion.v36n2-2023007

Published 2023-09-30

Keywords

  • Artificial intelligence,
  • Technological transformation,
  • Food technology

How to Cite

Castro Lopez, C. A. (2023). Technological innovation horizons in the food industry: synthetic data and quantum computing for the near and long-term future. Revista ION, 36(2), 101–107. https://doi.org/10.18273/revion.v36n2-2023007

Copyright (c) 2023 Camilo Andres Castro Lopez

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The article provides an opinion-based overview of the technological transformation in the food industry, highlighting the positive influence of artificial intelligence and synthetic data generation in enhancing product quality and process optimization. It addresses the challenges of implementing AI, alongside the promising application of quantum computing in tackling complex problems. Concrete examples of synthetic data generation in disease detection in plants and real-time temperature prediction in the cold chain are presented. The importance of these technologies in supporting decision-making, improving food safety and quality is underscored, with recognition that despite current challenges, quantum computing holds the potential to be a revolutionary tool in the food industry.

PDF (Español (España))

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References

  1. Monteiro J, Barata J. Artificial Intelligence in Extended Agri-Food Supply Chain: A Short Review Based on Bibliometric Analysis. Procedia Computer Science. 2021;192:3020- 3029. doi.org/10.1016/j.procs.2021.09.074
  2. Ramirez E, Vilchez J, Thakar C, Phasinam K, Kassanuk T, Naved M. A review on role of artificial intelligence in food processing and manufacturing industry. Materials Today: Proceedings. 2022;51:2462-2465. doi. org/10.1016/j.matpr.2021.11.616
  3. Kudashkina K, Corradini MG, Thirunathan P, Yada RY, Fraser E. Artificial Intelligence technology in food safety: A behavioral approach. Trends in Food Science & Technology. 2022;123:376- 381. doi.org/10.1016/j.tifs.2022.03.021
  4. Khan R. Artificial Intelligence and Machine learning in Food Industries: A Study. Journal of Food Chemistry & Nanotechnology. 2022;7:60- 67.
  5. Webber M, Elfving V, Weidt S, Hensinger WK. The Impact of Hardware Specifications on Reaching Quantum Advantage in the Fault Tolerant Regime. AVS Quantum Computing. 2022;4:1-22. doi.org/10.1116/5.0073075
  6. Byrum J. Quantum computing may help to solve the global food security problem (sitio en internet). AgFunder News. Disponible en https:// agfundernews.com/quantum-computing-answer-to-the-global-food-security-problem. Acceso el 30 de septiembre de 2023.
  7. Nikolenko SI. Synthetic Data for Deep Learning. Springer International Publishing; 2021. doi. org/10.1007/978-3-030-75178-4
  8. Aldoseri A, Al-Khalifa KN, Hamouda AM. (2023). Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Appl. Sci. 2023;13:7082-7115. doi.org/10.20944/preprints202305.1565.v1
  9. Padmanabhuni SS, Gera P. Synthetic Data Augmentation of Tomato Plant Leaf using Meta Intelligent Generative Adversarial Network: Milgan. International Journal of Advanced Computer Science and Applications, 2022;13:230-238. doi.org/10.14569/ IJACSA.2022.0130628
  10. Loisel J, Cornuéjols A, Laguerreb O, Tardetc M, Cagnon D, Duchesne O, et al. Machine learning for temperature prediction in food pallet along a cold chain: Comparison between synthetic and experimental training dataset. Journal of Food Engineering. 2022;335:111156-111167. doi. org/10.1016/j.jfoodeng.2022.111156
  11. Wisnosky D, Asaithambi A. Solving Classical Computing Problems Via Quantum Computing – SOARS 2021. SOARS Virtual Conference. Disponible en: https://unfsoars.domains.unf. edu/2021/posters/solving-classical-computing-problems-via-quantum-computing/. Acceso el 30 de septiembre de 2023.
  12. Ajagekar, A. New frontiers of quantum computing in chemical engineering. Korean Journal of Chemical Engineering. 2022;39:811- 820. doi.org/10.1007/s11814-021-1027-6
  13. Faro I, Johnson B. IBM Quantum delivers 120x speedup of quantum workloads with Qiskit Runtime. IBM.com. Disponible en https://research.ibm.com/blog/120x-quantum-speedup?lnk=ushpv18re2. Acceso el 30 de septiembre de 2023.