Skip to content
2000
image of The Role of AI and ML in Shaping the Future of the Food Industry

Abstract

With the integration of Machine Learning (ML) and Artificial Intelligence (AI) in the food industry, from production to retail, the sector is undergoing a transformative shift. These technologies enhance efficiency through better decision-making and increased automation, helping overcome some of the current challenges in terms of sustainability, food security, and cost reduction. AI and ML are being applied in food processing to optimize production processes, monitor food quality, and detect contaminants, thus achieving higher safety standards and reducing waste. In the supply chain, these technologies enable real-time tracking, predictive analytics for demand forecasting, and optimization of distribution networks to ensure that food reaches consumers in a timely and cost-effective manner. Another area is the use of AI to revolutionize inventory control in retail, helping retailers anticipate consumer demand and prevent food spoilage, thereby maintaining effective stock control. In addition, the current consumer interest in AI-assisted nutrition analysis and personalized recipe recommendations suggests that they are eating healthier. The potential for even greater impacts is vast with the advancement of these technologies. The future directions to be taken forward in this area of concern include precision agriculture, AI for food traceability, and machine learning for predictive models, with further advancements in data science to enhance efficiency, minimize carbon footprints, and maximize security from contamination. The future of food indeed depends significantly on AI and ML; hence, the future is also daunting in its prospects.

Loading

Article metrics loading...

/content/journals/cnf/10.2174/0115734013371588250815061734
2025-08-27
2025-09-24
Loading full text...

Full text loading...

References

  1. Amore A. Philip S. Artificial intelligence in food biotechnology: Trends and perspectives. Frontiers in Industrial Microbiology 2023 1 1255505 10.3389/finmi.2023.1255505
    [Google Scholar]
  2. Bhatt S. Food quality control & assurance using artificial intelligence: A review paper. Int. J. Res. Appl. Sci. Eng. Technol. 2023 11 10 6 11 10.22214/ijraset.2023.55898
    [Google Scholar]
  3. Chen T-C. Yu S-Y. The review of food safety inspection system based on artificial intelligence, image processing, and robotic. Food Sci Technol 2022 42 1 10.1590/fst.35421
    [Google Scholar]
  4. Chidinma-Mary-Agbai Application of artificial intelligence (AI) in food industry. GSC Biol. Pharm. Sci. 2020 13 1 171 178 10.30574/gscbps.2020.13.1.0320
    [Google Scholar]
  5. Choudhary A. Singh J. Bhat A. Gupta N. Reshi M. Kour D. Artificial intelligence and its applications in the food. Pharma Innov. 2023 SP-12 7 1351 1355
    [Google Scholar]
  6. Deokar S.R. Sonawane G.S. Dighe P.S. Sali N.D. Machine learning (ML), data science & artificial intelligence (AI) in food industry: Opportunities, solutions & rising potential. J. Emerg. Technol. Innov. Res. 2023 10 1
    [Google Scholar]
  7. Friedlander A. Zoellner C. Artificial intelligence opportunities to improve food safety at retail. Food Prot. Trends 2020 40 272 278
    [Google Scholar]
  8. Ikram A. Mehmood H. Arshad M.T. Rasheed A. Noreen S. Gnedeka K.T. Applications of artificial intelligence (AI) in managing food quality and ensuring global food security. CYTA J. Food 2024 22 1 2393287 10.1080/19476337.2024.2393287
    [Google Scholar]
  9. Khan R. Artificial intelligence and machine learning in food industries: A study. J Food Chem Nanotechnol 2021 7 3 60 67 10.17756/jfcn.2021‑114
    [Google Scholar]
  10. Kumar I. Rawat J. Mohd N. Husain S. Opportunities of artificial intelligence and machine learning in the food industry. J. Food Qual. 2021 2021 1 10 10.1155/2021/4535567
    [Google Scholar]
  11. Liu Z. Wang S. Zhang Y. Feng Y. Liu J. Zhu H. Artificial intelligence in food safety: A decade review and bibliometric analysis. Foods 2023 12 6 1242 10.3390/foods12061242 36981168
    [Google Scholar]
  12. Machireddy J.R. Artificial intelligence and machine learning application in food processing and its potential in Industry 4.0. Int J Artif Intell Mach Learn 2024 3 2 40 53 10.5281/zenodo.13306484
    [Google Scholar]
  13. Mavani N.R. Ali J.M. Othman S. Hussain M.A. Hashim H. Rahman N.A. Application of artificial intelligence in food industry-A guideline. Food Eng. Rev. 2022 14 1 134 175 10.1007/s12393‑021‑09290‑z 40477643
    [Google Scholar]
  14. Menichetti G. Ravandi B. Mozaffarian D. Barab A. ¬si AL. Machine learning prediction of the degree of food processing. Nat. Commun. 2023 14 1 2312 10.1038/s41467‑023‑37457‑1 37085506
    [Google Scholar]
  15. Nikolola-Alexieva V. Valeva K. Pashev S. Artificial intelligence in the food industry. BIO Web Conf 2024 102 04002 10.1051/bioconf/202410204002
    [Google Scholar]
  16. Palakurti N.R. AI applications in food safety and quality control. ESP J Eng Technol Adv 2022 2 3 48 61 10.56472/25832646/JETA‑V2I3P111
    [Google Scholar]
  17. Patil B. Morbale R. Nripesh N. Applications and roles of artificial intelligence and machine learning in food industry. Int J Mod Eng Technol Sci 2022 4 1 965 974
    [Google Scholar]
  18. Paul A. Gaur S. Ahamed M. Artificial intelligence: Importance in food chain industry. SSRN Electron J 2021 10.2139/ssrn.3855252
    [Google Scholar]
  19. Qian C. Murphy S.I. Orsi R.H. Wiedmann M. How can AI help improve food safety? Annu. Rev. Food Sci. Technol. 2023 14 1 517 538 10.1146/annurev‑food‑060721‑013815 36542755
    [Google Scholar]
  20. Saxena V. Gautam A. Machine learning and artificial intelligence in food industry. Int Res J Mod Eng Technol Sci 2021 3 9 585 602
    [Google Scholar]
  21. Sharma P. Sharma A. Artificial intelligence in food industry: A comprehensive review. Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2022 10.2139/ssrn.4024154
    [Google Scholar]
  22. Sharma S. Gahlawat V.K. Rahul K. Mor R.S. Malik M. Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics 2021 5 4 66 10.3390/logistics5040066
    [Google Scholar]
  23. Sonawane G.S. Deokar S.R. Sali N.D. Dighe P.S. Machine learning, data science and AI in food industry: Opportunities, solutions and potential. J. Manag. Res. 2023 12 2 58 67 10.17697/ibmrd/2023/v12i2/173223
    [Google Scholar]
  24. Tajkarimi M. Food safety and quality data management using artificial intelligence. Food Prot. Trends 2020 40 6 464 10.4315/1541‑9576‑40.6.464
    [Google Scholar]
  25. Taneja A. Nair G. Joshi M. Artificial intelligence: Implications for the agri-food sector. Agronomy 2023 13 5 1397 10.3390/agronomy13051397
    [Google Scholar]
  26. Aravind S. Sivakumar V. A survey on drug suggestion mechanisms using machine learning algorithm. 7th International Conference on Intelligent Computing and Control Systems (ICICCS) Madurai, India 17-19 May 2023 187 191 10.1109/ICICCS56967.2023.10142523
    [Google Scholar]
  27. Venu S. Rahman ZAMJM Energy and cluster based efficient routing for broadcasting in mobile ad hoc networks. Cluster Comput. 2019 22 S1 661 671 10.1007/s10586‑018‑2255‑3
    [Google Scholar]
  28. Tao Y. Wang D. Recent advances of artificial intelligence applications in food. IUFoST Scientific Information Bulletin. SIB 2020 10.13140/RG.2.2.25942.27203
    [Google Scholar]
  29. Sivakumar V. Sushruth L.T. Suthersen J.A.R. Gugan S.K. Weed detection in farmlands using RCNN. 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) Bengaluru, India 2025 1485 1491 10.1109/IDCIOT64235.2025.10915039
    [Google Scholar]
  30. Johri S. Jain D. Gupta A. LangBridge: Computational AI solutions for global learning. 2024 International Conference on Emerging Research in Computational Science (ICERCS) Coimbatore, India 2024 1 8 10.1109/ICERCS63125.2024.10895947
    [Google Scholar]
  31. Venu S. Kumar R.G. Kumar M.K. Prasad T.G. Suresh B. Neelima P. An intelligent and service based smart agriculture recommendation system. Int J Intell Syst Appl Eng 2023 12 7s 153 158
    [Google Scholar]
  32. Sivakumar V. Reddy A.H. Varma N.A. GRU in anomaly detection for IoT: A comparative study. 2025 IEEE International Students Conference on Electrical, Electronics and Computer Science (SCEECS) Bhopal, India 2025 1 7 10.1109/SCEECS64059.2025.10940416
    [Google Scholar]
  33. Rao K.V. Sivakumar V. Swathi R. Generative AI and secure IoMT Systems for enhanced real-time health monitoring and security. In: Utilizing AI of medical things for healthcare security and sustainability. IGI Global Scientific Publishing 2025 469 502 10.4018/979‑8‑3373‑0690‑2.ch015
    [Google Scholar]
  34. Augustine M.M. Sivakumar V. Swathi R. Precision fitness instruction system using vector database. In: Harnessing AI and machine learning for precision wellness. IGI Global Scientific Publishing 2025 227 242 10.4018/979‑8‑3693‑9521‑9.ch009
    [Google Scholar]
  35. Bossard L. Guillaumin M. Van Gool L. Food-101 – Mining discriminative components with random forests. European Conference on Computer Vision (ECCV) 446 461 10.1007/978‑3‑319‑10599‑4_29
    [Google Scholar]
  36. Chen J. Deng Z. Zhu L. Han J. PFID: Pittsburgh fast-food image dataset. 16th IEEE International Conference on Image Processing (ICIP) Cairo, Egypt 2009 289 292 10.1109/ICIP.2009.5413511
    [Google Scholar]
  37. Marin J. Biswas A. Ofli F. Recipe1M+: A dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Trans. Pattern Anal. Mach. Intell. 2019 41 1 1 14 10.1109/TPAMI.2018.2858820 31295105
    [Google Scholar]
  38. Yin X. Jiang L. Li S. Xu C. FoodLMM: A vision-language model for food understanding. CVPR 2023 2385 2393 10.1109/CVPR52688.2023.00236
    [Google Scholar]
  39. Rodrigues A. Patel S. Lee K. Recipe I.S. Interactive cooking assistant using AI and NLP. Proc. ACM Hum. Comput. Interact. 2023 7 1 1 19 10.1145/3616749
    [Google Scholar]
  40. Vaswani A. Shazeer N. Parmar N. Attention is All You Need. Adv Neural Inf Process Syst 2017 30 5998 6008 10.48550/arXiv.1706.03762
    [Google Scholar]
  41. Akbari H Yuan L Qian R Yang T Sun M Zhou J. VLMo: Unified vision-language pretraining with mixture-of-modality experts. NeurIPS 2021
    [Google Scholar]
  42. Noever D. Williams J. Personalized AI-based recipe generation using Food AI. J. Artif. Intell. Res. 2023 67 89 105 10.1613/jair.1.12624
    [Google Scholar]
  43. Pereira R.C. Santos F. Costa M. AI in culinary science: Enhancing nutrition and personalization. J. Food Sci. Technol. 2022 59 6 2473 2488 10.1007/s13197‑021‑05102‑7
    [Google Scholar]
  44. Salvador A. Hynes N. Aytar Y. de Freitas J. Torralba A. Inverse cooking: Recipe generation from food images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1040 4049 10.1109/CVPR.2019.01070
    [Google Scholar]
  45. Chen D. Han Y. Wang Z. Zhang L. Food A.I. Advancing automatic recipe generation using multimodal learning. IEEE Trans. Multimed. 2022 25 1637 1649 10.1109/TMM.2022.3142012
    [Google Scholar]
  46. Gupta R. Singh P. Chaturvedi A. AI-based interactive cooking assistants: Bridging the gap between vision and language. J. Comput. Sci. 2023 98 102527 10.1016/j.jocs.2023.102527
    [Google Scholar]
  47. Brown M. Culinary AI: The Future of Smart Kitchens. London Oxford University Press 2020
    [Google Scholar]
  48. Kumar A. White T. AI in food technology: Enhancing efficiency and creativity. In: Smith R, Zhao P, Eds. AI and Modern Food Science. New York Springer 2021 145 162
    [Google Scholar]
  49. The role of AI in food security. 2023 Available from: https://www.fao.org/ai-in-food-security
  50. Bossard L. Guillaumin M. Van Gool L. Food-101 dataset. Mendeley Data 2014
    [Google Scholar]
  51. Marin J. Salvado J. Hynes N. Aytar Y. de Freitas J. Torralba A. Recipe1M+ dataset. Mendeley Data 2019
    [Google Scholar]
  52. Yin X. Jiang L. Li S. Xu C. FoodLMM dataset for multimodal food analysis. Mendeley Data 2023
    [Google Scholar]
  53. Coon E. Berndt M. Jan A. Advanced Terrestrial Simulator (ATS). Zenodo 2020 10.5281/zenodo.3727209
    [Google Scholar]
  54. Liu T Zhao W Chen H. AI-powered food generation models: A review of recent advancements. arXiv 2024
    [Google Scholar]
  55. Fernandez L Patel R Wang Y Vision-language models for ingredient understanding: A comparative study. arXiv 2023
    [Google Scholar]
  56. Zhang G J Ma L F Wang X Q Zhou X G Secondary structure and contact guided differential evolution for protein structure prediction. IEEE/ACM Trans Comput Biol Bioinform 2020 17 3 1068 81 10.1109/TCBB.2018.2873691
    [Google Scholar]
  57. Zhong W. Gu F. Predicting local protein 3D structures using clustering deep recurrent neural network. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2022 19 1 593 604 10.1109/TCBB.2020.3005972 32750880
    [Google Scholar]
  58. Tripathi A. Mondal R. Lahiri T. Chaurasiya D. Pal M.K. TemPred: A novel protein template search engine to improve protein structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 3 2112 2121 10.1109/TCBB.2022.3233846 37018272
    [Google Scholar]
  59. Bhuvaneswari S Deepakraj R Urooj S Sharma N Pathak N. Computational analysis: Unveiling the quantum algorithms for protein analysis and predictions. IEEE Access 2023 11 94023 33 10.1109/ACCESS.2023.3310812
    [Google Scholar]
  60. Wang J. Wang W. Shang Y. Protein loop modeling using AlphaFold2. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 5 3306 3313 10.1109/TCBB.2023.3264899 37037235
    [Google Scholar]
  61. Zhou S Zou H Liu C Zang M Liu T Combining deep neural networks for protein secondary structure prediction. IEEE Access 2020 8 84362 70 10.1109/ACCESS.2020.2992084
  62. Zhang F. Zhang Y. Zhu X. Chen X. Lu F. Zhang X. DeepSG2PPI: A protein-protein interaction prediction method based on deep learning. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 5 2907 2919 10.1109/TCBB.2023.3268661 37079417
    [Google Scholar]
  63. Yang J. Li Y. Wang G. Chen Z. Wu D. An end-to-end knowledge graph fused graph neural network for accurate protein-protein interactions prediction. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2024 21 6 2518 2530 10.1109/TCBB.2024.3486216 39446541
    [Google Scholar]
  64. Jha K. Saha S. Analyzing effect of multi-modality in predicting protein-protein interactions. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 1 162 173 10.1109/TCBB.2022.3157531 35259112
    [Google Scholar]
  65. Zhong J. Zhao H. Zhao Q. RGCNPPIS: A residual graph convolutional network for protein-protein interaction site prediction. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2024 21 6 1676 1684 10.1109/TCBB.2024.3410350 38843057
    [Google Scholar]
  66. Lakshmi P Manikandan P Ramyachitra D An improved bagging of machine learning algorithms to predict motif structures from protein-protein interaction networks. IEEE Access 2025 13 45077 88 10.1109/ACCESS.2025.3549880
    [Google Scholar]
  67. Yang S. Cheng P. Liu Y. Feng D. Wang S. Exploring the knowledge of an outstanding protein to protein interaction transformer. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2024 21 5 1287 1298 10.1109/TCBB.2024.3381825 38536676
    [Google Scholar]
  68. Zhao Y. Yang Z. Hong Y. Protein function prediction with functional and topological knowledge of gene ontology. IEEE Trans. Nanobiosci. 2023 22 4 755 762 10.1109/TNB.2023.3278033 37204950
    [Google Scholar]
  69. Yang Z. Zhong W. Lv Q. Dong T. Chen G. Chen C.Y.C. Interaction-based inductive bias in graph neural networks: Enhancing protein-ligand binding affinity predictions from 3D structures. IEEE Trans. Pattern Anal. Mach. Intell. 2024 46 12 8191 8208 10.1109/TPAMI.2024.3400515 38739515
    [Google Scholar]
  70. Dewan R. Rangaswamy S. Venus S. A deep dive into detecting and investigating fileless malware. Int. J. Sensors Wirel. Commun. Control 2025 15 22103279377106 10.2174/0122103279377106250507052410
    [Google Scholar]
  71. Zaki M. Sivakumar V. Shrivastava S. Gaurav K. Cybersecurity framework for healthcare industry using NGFW. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) Tirunelveli, India 2021 196 200 10.1109/ICICV50876.2021.9388455
    [Google Scholar]
  72. Wang H. Wang S. Ouyang X. Zhao J. He Z. Gao T. Predicting protein-ligand binding affinity with multi-scale structural features. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Istanbul, Turkiye 2023 63 68 10.1109/BIBM58861.2023.10385328
    [Google Scholar]
/content/journals/cnf/10.2174/0115734013371588250815061734
Loading
/content/journals/cnf/10.2174/0115734013371588250815061734
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test