AI-Driven Autonomous Decision Intelligence for Smart Food Manufacturing Cyber-Physical Systems
DOI:
https://doi.org/10.68104/ijasit.v1.i3.24Keywords:
Artificial Intelligence (AI), Cyber-Physical Systems (CPS), Smart Food Manufacturing, Industrial Internet of Things (IIoT)Abstract
The food manufacturing industry is undergoing a rapid transformation through the adoption of Industry 4.0 technologies, which include Cyber Physical System (CPS), Artificial Intelligence (AI) and Industrial Internet of Things (IIOT). Despite of these developments, modern food manufacturers continue to face challenges which are related to quality consistency, food safety assurance, process optimization and real-time decision making. The large amount of data generated by numerous sensors and production systems often remain underutilized because of the limited intelligent decision-making capability of the system. Simultaneously, the manufacturers identify production inefficiencies, resource wastage, increased operational cost and difficulties in maintaining a uniform product quality. To address these challenges, Autonomous System, Artificial Intelligence (AI) and Decision Intelligence are introduced as transformative technologies which are capable of converting a raw data into an actionable insight. This process includes by integrating the Machine Learning, Computer Vision and Predictive Analytics enables real-time monitoring, predictive maintenance and adaptive process control. Therefore, these capabilities support a data-driven decision making, improve the resource utilization and enhance food-safety and sustainability. Furthermore, the autonomous system can dynamically respond to the changing production conditions enabling a flexible manufacturing operation. This paper explores the role of AI-driven autonomous system and decision intelligence in advancing smart food manufacturing CPS. It discusses all the key enabling technologies, implementation challenges and industrial application while highlighting their potential to transform traditional manufacturing environments into an intelligent and a self-optimizing system. This paper aims to provide an insight into the development of efficient, reliable and a sustainable next-generation food manufacturing ecosystem.
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N. R. Mavani, J. M. Ali, S. Othman, M. A. Hussain, H. Hashim, and N. A. Rahman, “Application of Artificial Intelligence in Food Industry—a Guideline,” Food Engineering Reviews, vol. 14, no. 1, pp. 134–175, Mar. 2022, doi: 10.1007/s12393-021-09290-z.
V. Zatsu et al., “Revolutionizing the food industry: The transformative power of artificial intelligence-a review,” Food Chem. X, vol. 24, p. 101867, Dec. 2024, doi: 10.1016/j.fochx.2024.101867.
K. Agrawal, P. Goktas, M. Holtkemper, C. Beecks, and N. Kumar, “AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance,” Front. Nutr., vol. 12, Mar. 2025, doi: 10.3389/fnut.2025.1553942
K. Agrawal, P. Goktas, N. Kumar, and M.-F. Leung, “Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions,” Front. Nutr., vol. 12, Jul. 2025, doi: 10.3389/fnut.2025.1636980.
Zohra Dakhia, Mariateresa Russo, and Massimo Merenda, “AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions,” Sensors, 2025.
D. Saha, M. Padhiary, and N. Chandrakar, “AI vision and machine learning for enhanced automation in food industry: A systematic review,” Food and Humanity, vol. 4, p. 100587, May 2025, doi: 10.1016/j.foohum.2025.100587.
P. Radanliev, D. De Roure, R. Nicolescu, M. Huth, and O. Santos, “Artificial Intelligence and the Internet of Things in Industry 4.0,” CCF Transactions on Pervasive Computing and Interaction, vol. 3, no. 3, pp. 329–338, Sep. 2021, doi: 10.1007/s42486-021-00057-3.
A. Frankó, G. Hollósi, D. Ficzere, and P. Varga, “Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability,” Sensors, vol. 22, no. 23, p. 9148, Nov. 2022, doi: 10.3390/s22239148.
K. S. Vedantam, S. K. Jain, N. L. Panwar, J. Sunil, N. Wadhawan, and A. Kumar, “Emergence of Internet of Things technology in food and agricultural sector: A review,” J. Food Process Eng., vol. 47, no. 8, Aug. 2024, doi: 10.1111/jfpe.14698.
S. J. Oks et al., “Cyber-Physical Systems in the Context of Industry 4.0: A Review, Categorization and Outlook,” Information Systems Frontiers, vol. 26, no. 5, pp. 1731–1772, Oct. 2024, doi: 10.1007/s10796-022-10252-x.
H. Yang, S. Kumara, S. T. S. Bukkapatnam, and F. Tsung, “The internet of things for smart manufacturing: A review,” IISE Trans., vol. 51, no. 11, pp. 1190–1216, Nov. 2019, doi: 10.1080/24725854.2018.1555383.
M. Andronie, G. Lăzăroiu, R. Ștefănescu, C. Uță, and I. Dijmărescu, “Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review,” Sustainability, vol. 13, no. 10, p. 5495, May 2021, doi: 10.3390/su13105495.
R. Kumar and N. Agrawal, “Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge–Fog–Cloud based architectural frameworks : A survey on current state and research challenges,” J. Ind. Inf. Integr., vol. 35, p. 100504, Oct. 2023, doi: 10.1016/j.jii.2023.100504.
G. S. S. Chalapathi, V. Chamola, A. Vaish, and R. Buyya, “Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing: A Review and Future Directions,” 2021, pp. 293–325. doi: 10.1007/978-3-030-57328-7_12.
S. Gbashi and P. B. Njobeh, “Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review,” Applied Sciences, vol. 14, no. 8, p. 3421, Apr. 2024, doi: 10.3390/app14083421.
L. Zhang, R. M. Boom, and Y. Ma, “Machine Learning in Automated Food Processing: A Mini Review,” Annu. Rev. Food Sci. Technol., vol. 16, no. 1, pp. 25–37, Apr. 2025, doi: 10.1146/annurev-food-111523-122039.
M. Canatan, N. Alkhulaifi, N. Watson, and Z. Boz, “Artificial Intelligence in Food Manufacturing: A Review of Current Work and Future Opportunities,” Food Engineering Reviews, vol. 17, no. 2, pp. 189–219, Jun. 2025, doi: 10.1007/s12393-024-09395-1.
Georgios Sofianidis, Jože M. Rožanec, Dunja Mladenić, and Dimosthenis Kyriazis, “A Review of Explainable Artificial Intelligence in Manufacturing,” ArXiv, 2021.
Y. Huang, A. Ghadge, and N. Yates, “Implementation of digital twins in the food supply chain: a review and conceptual framework,” Int. J. Prod. Res., vol. 62, no. 17, pp. 6400–6426, Sep. 2024, doi: 10.1080/00207543.2024.2305804.
A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital Twin: Enabling Technologies, Challenges and Open Research,” IEEE Access, vol. 8, pp. 108952–108971, 2020, doi: 10.1109/ACCESS.2020.2998358.
A. Thelen et al., “A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies,” Structural and Multidisciplinary Optimization, vol. 65, no. 12, p. 354, Dec. 2022, doi: 10.1007/s00158-022-03425-4.
P. Visconti, G. Rausa, C. Del-Valle-Soto, R. Velázquez, D. Cafagna, and R. De Fazio, “Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review,” Future Internet, vol. 16, no. 11, p. 394, Oct. 2024, doi: 10.3390/fi16110394.
J. B. Hussein, T. S. Workneh, A. Kassim, K. Ntsowe, S. F. Melesse, and H. S. El-Mesery, “Exploring Trends and Future Developments in the Application of Artificial Intelligence in Food Processing and Preservation,” Food Engineering Reviews, vol. 18, no. 2, p. 16, Jun. 2026, doi: 10.1007/s12393-026-09445-w.
X. Song et al., “AI in food industry automation: applications and challenges,” Front. Sustain. Food Syst., vol. 9, Apr. 2025, doi: 10.3389/fsufs.2025.1575430.
A. Hassoun et al., “From Food Industry 4.0 to Food Industry 5.0: Identifying technological enablers and potential future applications in the food sector,” Compr. Rev. Food Sci. Food Saf., vol. 23, no. 6, Nov. 2024, doi: 10.1111/1541-4337.70040.
P. Roy, D. Das, B. Prasad, A. Sen, and A. K. Mandal, “AI in Food Safety and Traceability: Legal and Ethical Dimensions of Next-Generation Food Systems,” Curr. Opin. Food Sci., p. 101436, Jul. 2026, doi: 10.1016/j.cofs.2026.101436.
M. R. Sebti, U. McCarthy, A. Ktenioudaki, M. Russo, and M. Merenda, “Traceability and Anti-Counterfeiting in Agri-Food Supply Chains: A Review of RFID, IoT, Blockchain, and AI Technologies,” Sensors, vol. 26, no. 5, p. 1685, Mar. 2026, doi: 10.3390/s26051685.
E. Ramirez-Asis, J. Vilchez-Carcamo, C. M. Thakar, K. Phasinam, T. Kassanuk, and M. Naved, “A review on role of artificial intelligence in food processing and manufacturing industry,” Mater. Today, Proc., vol. 51, pp. 2462–2465, 2022, doi: 10.1016/j.matpr.2021.11.616.
J. Azeta et al., “Artificial intelligence and robotics in predictive maintenance: a comprehensive review,” Front. Mech. Eng., vol. 11, Jan. 2026, doi: 10.3389/fmech.2025.1722114.
K. A. Singh, F. Patra, T. Ghosh, N. K. Mahnot, H. Dutta, and R. K. Duary, “Advancing food systems with industry 5.0: A systematic review of smart technologies, sustainability, and resource optimization,” Sustainable Futures, vol. 9, p. 100694, Jun. 2025, doi: 10.1016/j.sftr.2025.100694.
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