ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPLICATION IN FOOD PROCESSING AND ITS POTENTIAL IN INDUSTRY 4.0
Keywords:
AI In Food Industry, Machine Learning, Smart Farming, Internet Of Things, Sustainable Management, Food Quality;, Food SafetyAbstract
Food is essential for human survival. Some of the most crucial factors to think about are minimising food waste, optimising the supply chain, and enhancing food logistics, delivery, and safety. The achievement of these goals is greatly aided by the application of AI and ML. Proliferation of ever-more-powerful and pervasive computer networks has enabled modern logistical and industrial systems. Within these networks, there is a continual influx of fresh data from many sources, generated by sensors, equipment, systems, intelligent devices, and humans. Thanks to computers' ever-improving capabilities, we can now analyse Big Data more quickly, thoroughly, and in-depth than ever before. These developments have rejuvenated and ushered in a new era called Industry 4.0 or the Smart Factory, which has increased the importance of artificial information technology (AI). Machine learning and artificial intelligence are discussed in this article within the context of the food industry and business. Important uses of this technique include supply chain optimisation, crop selection, logistics, food distribution, and processing plant predicting maintenance.
References
Misra N., Dixit Y., Al-Mallahi A., Bhullar M.S., Upadhyay R., Martynenko A. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Int. Things J. 2020;9:6305–6324. doi: 10.1109/JIOT.2020.2998584. [CrossRef] [Google Scholar]
Sahu M., Gupta R., Ambasta R.K., Kumar P. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. Prog. Mol. Biol. Transl. Sci. 2022;190:57–100. doi: 10.1016/bs.pmbts.2022.03.002. [PubMed] [CrossRef] [Google Scholar]
Ramakrishna R.R., Abd Hamid Z., Zaki W.M.D.W., Huddin A.B., Mathialagan R. Stem cell imaging through convolutional neural networks: Current issues and future directions in artificial intelligence technology. PeerJ. 2020;8:e10346. doi: 10.7717/peerj.10346. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
Serazetdinova L., Garratt J., Baylis A., Stergiadis S., Collison M., Davis S. How should we turn data into decisions in AgriFood? J. Sci. Food Agric. 2019;99:3213–3219. doi: 10.1002/jsfa.9545. [PubMed] [CrossRef] [Google Scholar]
Ben Ayed R., Hanana M. Artificial Intelligence to Improve the Food and Agriculture Sector. J. Food Qual. 2021;2021:7. doi: 10.1155/2021/5584754.
Qian C., Murphy S., Orsi R., Wiedmann M. How can AI help improve food safety? Annu. Rev. Food Sci. Technol. 2023;14:517–538. doi: 10.1146/annurev-food-060721-013815. [PubMed] [CrossRef] [Google Scholar]
Camarena S. Artificial intelligence in the design of the transitions to sustainable food systems. J. Clean. Prod. 2020;271:122574. doi: 10.1016/j.jclepro.2020.122574. [CrossRef] [Google Scholar]
Mavani N.R., Ali J.M., Othman S., Hussain M., Hashim H., Rahman N.A. Application of artificial intelligence in food industry—A guideline. Food Eng. Rev. 2022;14:134–175. doi: 10.1007/s12393-021-09290-z.
Medus, L.; Saban, M.; Francés-Víllora, J.; Bataller-Mompeán, M.; Rosado-Muñoz, A. Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control 2021, 125, 107962. [CrossRef]
Benouis, M.; Medus, L.; Saban, M.; Łabiak, G.; Rosado-Muñoz, A. Food tray sealing fault detection using hyperspectral imaging and PCANet. IFAC Pap. 2020, 53, 7845–7850.
Sharma, A.; Zanotti, P.; Musunur, L. Drive through robotics: Robotic automation for last mile distribution of food and essentials during pandemics. IEEE Access 2020, 8, 127190–127219. [CrossRef]
Wardah, S.; Djatna, T.; Marimin, M.; Yani, M. New product development in coconut-based agro-industry: Current research progress and challenges. IOP Conf. Ser. Earth Environ. Sci. 2020, 472, 012053. [CrossRef]
Bo, W.; Qin, D.; Zheng, X.; Wang, Y.; Ding, B.; Li, Y.; Liang, G. Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network. Food Res. Int. 2022, 153, 110974. [CrossRef]
Zhang, P.; Guo, Z.; Ullah, S.; Melagraki, G.; Afantitis, A.; Lynch, I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nat. Plants 2021, 7, 864–876. [CrossRef]
Ben Ayed, R.; Hanana, M. Artificial intelligence to improve the food and agriculture sector. J. Food Qual. 2021, 2021, 5584754. [CrossRef]
Subeesh, A.; Mehta, C.R. Automation and digitization of agriculture using artificial intelligence and internet of things. Artif. Intell. Agric. 2021, 5, 278–291.
Talaviya, T.; Shah, D.; Patel, N.; Yagnik, H.; Shah, M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif. Intell. Agric. 2020, 4, 58–73.
Kakani, V.; Nguyen, V.H.; Kumar, B.P.; Kim, H.; Pasupuleti, V.R. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2022, 2, 100033.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Jeshwanth Reddy Machireddy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
