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AI for food: Accelerating and democratizing discovery and innovation

Kuhl, Ellen·2025·npj Science of Food
DOI 10.1038/s41538-025-00441-8PMC PMC12098880
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Abstract

By 2050, feeding nearly 10 billion people will require transformative changes to ensure nutritious, sustainable food for all. Our current food system is inefficient and unsustainable. Traditional attempts to transform the global food system are too slow to drive innovation at scale. Here we explore the potential of artificial intelligence to reshape the future of food. We review the state of the art in food development, discuss the data needed to define a new food product, and highlight seven challenges where AI can help us design nutritious, delicious, and sustainable foods for all. By leveraging AI to democratize food innovation, we can accelerate the transition to resilient global food systems that meet the urgent challenges of food security, climate change, and planetary health.

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  1. [2]Seppelt, R., Klotz, S., Peiter, E. & Volk, M. Agriculture and food security under a changing climate: An underestimated challenge. iScience 25, 105551 (2022). 36458255 PMC9706706 · DOI
  2. [4]Hong, C. et al. Global and regional drivers of land-use emissions in 1961–2017. Nature 589, 554–561 (2021). 33505037 · DOI
  3. [5]Friedrich, B. Transforming a 12,000-year-old technology. Nat. Food 3, 807–808 (2022). 37117888 · DOI
  4. [9]Eisen, M. B. & Brown, P. O. Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO 2 emissions this century. PLOS Clim. 1, e0000010 (2022). · DOI
  5. [10]Akkem, Y., Biswas, S. K. & Varanasi, A. Smart farming using artificial intelligence: A review. Eng. Appl. Artif. Intell. 120, 105899 (2023). · DOI
  6. [11]Alasi, S. O., Sanusi, S. M., Sunmonu, M. O., Odewole, M. M. & Adepoju, A. L. Exploring recent developments in novel technologies and AI integration for plant-based protein functionality: A review. J. Agric. Food Res. 15, 101036 (2024).
  7. [12]Al-Sarayreh, M., Gomes Reis, M., Carr, A. & Martins dos Reis, M. Inverse design and AI/Deep generative networks in food design: A comprehensive review. Trends Food Sci. Technol. 138, 215–228 (2023). · DOI
  8. [13]Barthwal, R., Kathuria, D., Joshi, S., Kaler, R. S. S. & Singh, N. New trends in the development and application of artificial intelligence in food processing. Innov. Food Sci. Emerg. Technol. 92, 103600 (2024). · DOI
  9. [14]Cui, Z. et al. Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development. Compr. Rev. Food Sci. Food Saf. 24, e70068 (2025). 39783879 · DOI
  10. [15]Datta, A. et al. Computer-aided food engineering. Nat. Food 3, 894–904 (2022). 37118206 · DOI
  11. [16]Esmaeily, R., Rzavi, M. A. & Razavi, S. H. A step forward in food science, technology and industry using artificial intelligence. Trends Food Sci. Technol. 143, 104286 (2024). · DOI
  12. [17]Hagendorff, T. How artificial intelligence can support veganism: an exploratory analysis. J. Anim. Ethics 13, 142–149 (2023). · DOI
  13. [18]Kakania, V., Nguyen, V. H., Kumar, B. P., Kima, H. & Pasupuleti, V. P. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2, 100033 (2020).
  14. [19]Kumar, I., Rawat, J., Mohd, N. & Husain, S. Opportunities of artificial intelligence and machine learning in the food industry. J. Food Qual. 2021, 4535567 (2021). · DOI
  15. [20]Menichetti, G., Ravandi, B., Mozaffarian, D. & Barabasi, A. L. Machine learning prediction of the degree of food processing. Nat. Commun. 14, 2312 (2023). 37085506 PMC10121643 · DOI
  16. [21]Mu, W. et al. Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Compr. Rev. Food Sci. Food Saf. 23, e13296 (2024). 38284601 · DOI
  17. [22]Nunes, C. A. et al. Artificial intelligence in sensory and consumer studies of food products. Curr. Opin. Food Sci. 50, 101002 (2023). · DOI
  18. [23]Park, J., Beck, B. R., Kim, H. H., Lee, S. & Kang, K. A brief review of machine learning-based bioactive compound research. Appl. Sci. 12, 2906 (2022). · DOI
  19. [24]Queiroz, L. P., Nogueira, I. B. R. & Ribeiro, A. M. Flavor engineering: a comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics. Food Res. Int. 196, 115100 (2024). 39614513 · DOI
  20. [25]Sak, J. & Suchodolska, M. Artificial intelligence in nutrients science research: A review. Nutrients 12, 322 (2021). PMC7911928 33499405 · DOI
  21. [26]Zatsu, V. et al. Revolutionizing the food industry: The transformative power of artificial intelligence-a review. Food Chem. X 24, 101867 (2024). 39431210 PMC11488428 · DOI
  22. [27]Barabasi, A. L., Menichetti, G. & Loscalzo, J. The unmapped chemical complexity of our diet. Nat. Food 1, 33–37 (2020). · DOI
  23. [28]Decardi-Nelson, B. & You, F. Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production. Nat. Food 5, 869–881 (2024). 39251762 · DOI
  24. [29]Lurie-Luke, E. Alternative protein sources: science powered startups to fuel food innovation. Nat. Commun. 15, 4425 (2024). 38806477 PMC11133469 · DOI
  25. [30]Fernqvist, F., Spenrup, S. & Tellström, R. Understanding food choice: A systematic review of reviews. Heliyon 10, e32492 (2024). 38952383 PMC11215270 · DOI
  26. [31]Prabhakaran, V., Qadri, R. & Hutchinson, B. Cultural incongruencies in artificial intelligence. arXiv10.48550/arXiv.2211.13069 (2022).
  27. [32]Rudolph, M. J. The food product development process. Br. Food J. 97, 3–11 (1995). · DOI
  28. [33]Earle, M. D. Changes in the food product development process. Trends Food Sci. Techol. 8, 19–24 (1997). · DOI
  29. [35]Wee, M. S. M., Goh, A. T., Stiegerb, M. & Forde, C. G. Correlation of instrumental texture properties from textural profile analysis (TPA) with eating behaviours and macronutrient composition for a wide range of solid foods. Food Funct. 9, 5301–5312 (2018). 30256358 · DOI
  30. [36]Ribeiro, M. N., Carvalho, I. A., Ferreira, D. D. & Marques Pinheiro, A. C. A comparison of machine learning algorithms for predicting consumer responses based on physical, chemical, and physical–chemical data of fruits. J. Sens. Stud. 37, e12738 (2022). · DOI
  31. [37]Zeni, C. et al. MatterGen: a generative model for inorganic materials design. Nature10.1038/s41586-025-08628-5 (2025). PMC11922738 39821164
  32. [39]Campos, S., Doxey, J. & Hammond, D. Nutrition labels on pre-packaged foods: a systematic review. Public Health Nutr. 14, 1496–1506 (2011). 21241532 · DOI
  33. [40]Nishinari, K. & Fang, Y. Perception and measurement of food texture: Solid foods. J. Texture Stud. 49, 160–201 (2018). 29437224 · DOI
  34. [41]Reineccius, G. Flavor Chemistry and Technology. CRC Press, Boca Raton, Florida (1986).
  35. [42]Huang, J., Zhang, M., Mujumdar, A. S. & Li, C. AI-based processing of future prepared foods: Progress and prospects. Food Res. Int. 201, 115675 (2025). 39849794 · DOI
  36. [43]Bohn, K. et al. Estimating food ingredient compositions based on mandatory product labeling. J. Food Compos. Anal. 110, 104508 (2022). · DOI
  37. [44]Siddique, A., Gupta, A., Sawyer, J. T., Huang, T. S. & Morey, A. Big data analytics in food industry: a state-of-the-art literature review. npj Sci. Food 9, 36 (2025). 40118924 PMC11928524 · DOI
  38. [45]Morales-Garzón, A., Gómez-Romero, J. & Martín-Bautista, M. J. A word embedding-based method for unsupervised adaptation of cooking recipes. IEEE Access 9, 27389–27404 (2021). · DOI
  39. [46]Queiroz, L. P. et al. A reinforcement learning framework to discover natural flavor molecules. Foods 12, 1147 (2023). 36981074 PMC10048107 · DOI
  40. [47]Ruiz-Capillas, C. & Herrero, A. M. Sensory analysis and consumer research in new product development. Foods 10, 582 (2021). 33802030 PMC8001375 · DOI
  41. [48]Li, P., Huang, X., Tian, Y. & Chawla, N. V. ChefFusion: Multimodal foundation model integrating recipe and food image generation. arXiv10.48550/arXiv.2409.12010 (2024).
  42. [50]Kraemer, M. V. S. et al. Is the list of ingredients a source of nutrition and health information in food labeling? A scoping review. Nutrients 21, 4513 (2023). PMC10647387 37960166 · DOI
  43. [51]Monteiro, C. A. et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 22, 936–941 (2019). 30744710 PMC10260459 · DOI · Paper page
  44. [52]Cowburn, G. & Stockley, L. Consumer understanding and use of nutrition labelling: a systematic review. Public Health Nutr. 8, 21–28 (2005). 15705241 · DOI
  45. [53]Christoph, M. J., Larson, N., Laska, M. N. & Neumark-Sztainer, D. Nutrition facts panels: Who uses them, what do they use, and how does use relate to dietary intake? J. Acad. Nutr. Diet. 118, 217–228 (2018). 29389508 PMC5797995 · DOI
  46. [54]Dioszegi, J., Llanaj, E. & Adany, R. Genetic background of taste perception, taste preferences, and its nutritional implications: a systematic review. Front. Genet. 10, 1272 (2019). 31921309 PMC6930899 · DOI
  47. [55]Lawless, H. & Heymann, H. Sensory Evaluation of Food: Principles and Practices. Springer Science + Business Media, New York (1998).
  48. [56]Androutsos, L. et al. Predicting multiple taste sensations with a multiobjective machine learning method. npj Sci. Food 8, 47 (2024). 39054312 PMC11272927 · DOI
  49. [60]Szczesniak, A. S. Texture is a sensory property. Food Qual. Prefer. 13, 215–225 (2002). · DOI · Paper page
  50. [61]Christensen, C. M. Food texture perception. Adv. Food Res. 29, 159–199 (1984). · DOI
  51. [62]Chang, S. K. C. & Liu, Z. Soymilk and tofu manufacturing. Handb. Plant-Based Ferment. Food Beverage Technol. 8, 139–161 (2012). · DOI
  52. [64]Friedman, H. H., Whitney, J. E. & Szczesniak, A. The texturometer–A new instrument for objective texture measurement. J. Food Sci. 28, 390–395 (1963). · DOI
  53. [66]St. Pierre, S. R. & Kuhl, E. Mimicking mechanics: A comparison of meat and meat analogs. Foods 13, 3495 (2024). 39517278 PMC11545010 · DOI
  54. [67]Dunne, R. A. et al. Texture profile analysis and rheology of plant-based and animal meat. Food Res. Int. 205, 115876 (2025). 40032452 PMC11885209 · DOI
  55. [68]Chen, J. & Stokes, J. R. Rheology and tribology: Two distinctive regimes of food texture sensation. Trends Food Sci. Technol. 25, 4–12 (2012). · DOI
  56. [69]Rao, M. A. Rheology of Fluid and Semisolid Foods. Principles and Applications. Food Engineering Series Springer Science + Business Media, New York (1998).
  57. [71]Goodfellow, I. J. et al. Generative adversarial nets. arXiv10.48550/arXiv.1406.2661 (2014).
  58. [72]Vaswani, A. et al. Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017) Long Beach, CA, USA (2017).
  59. [73]Timm, M., Offringa, L. C., van Klinken, B. J. W. & Slavin, J. Beyond insoluble dietary fiber: Bioactive compounds in plant foods. Nutrients 15, 4138 (2023). 37836422 PMC10574517 · DOI
  60. [74]Flores Martinez, K. E., Bloszies, C. S., Bolino, M. J., Henrick, B. M. & Frese, S. A. Hemp hull fiber and two constituent compounds, N-trans-caffeoyltyramine and N-trans-feruloyltyramine, shape the human gut microbiome in vitro. Food Chem.: X 23, 101611 (2024). 39113742 PMC11304861
  61. [75]van Klinken, B. J. W., Steward, M. L., Kalgaonkar, S. & Chae, L. Health-promoting opportunities of hemp hull: The potential of bioactive compounds. J. Diet. Suppl. 21, 543–557 (2024). 38303514 · DOI
  62. [76]Gastaldello, A. et al. The rise of processed meat alternatives: A narrative review of the manufacturing, composition, nutritional profile and health effects of newer sources of protein, and their place in healthier diets. Trends Food Sci. Technol. 127, 263–271 (2022). · DOI
  63. [77]Boukid, F. Plant-based meat analogues: from niche to mainstream. Eur. Food Res. Technol. 247, 297–308 (2021). · DOI
  64. [78]Lane, M. M. et al. Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. Br. Med. J. 284, e077310 (2024). PMC10899807 38418082 · DOI
  65. [79]Ravandi, B. et al. Prevalence of processed foods in major US grocery stores. Nature Food10.1038/s43016-024-01095-7 (2025). PMC12579494 39806219
  66. [80]Rita, L., Southern, J., Laponogov, I., Higgins, K. & Veselkov, K. Optimizing ingredient substitution using Large Language Models to enhance phytochemical content in recipes. Mach. Learn. Knowl. Extract. 6, 2738–2752 (2024). · DOI
  67. [81]Kim, H., Venkataramanan, R. & Sheth, A. A survey on food ingredient substitutions. arXiv 2501.01958v1 (2024). · arXiv
  68. [82]Doherty, A., Wall, A., Khaldi, N. & Kussmann, M. Food ingredient discovery and characterisation: A focus on bioactive plant and food peptides. Front. Genet. 12, 768979 (2021). 34868255 PMC8640466 · DOI
  69. [83]Loesch, J. et al. Automated identification of healthier food substitutions through a combination of graph neural networks and nutri-scores. J. Food Compos. Anal. 125, 105829 (2024). · DOI
  70. [85]Hilgendorf, K., Wang, Y., Miller, M. J. & Jin, Y. S. Precision fermentation for improving the quality, flavor, safety, and sustainability of foods. Curr. Opin. Biotechnol. 86, 103084 (2024). 38394936 · DOI
  71. [86]St Pierre, S. R. et al. Discovering the mechanics of artificial and real meat. Comput. Methods Appl. Mech. Eng. 415, 116236 (2023). · DOI
  72. [87]St. Pierre, S. R. et al. The mechanical and sensory signature of plant-based and animal meat. Sci. Food 8, 94 (2024). PMC11568319 39548076
  73. [88]Linka, K. & Kuhl, E. A new family of Constitutive Artificial Neural Networks towards automated model discovery. Comput. Methods Appl. Mech. Eng. 403, 115731 (2023). · DOI
  74. [89]Linka, K., Cavinato, C., Humphrey, J. D. & Cyron, C. J. Predicting and understanding arterial elasticity from key microstructural features by bidirectional deep learning. Acta Biomater. 147, 63–72 (2022). 35643194 · DOI
  75. [90]Wang, Y. et al. Flavor challenges in extruded plant-based meat alternatives: A review. Compr. Rev. Food Sci. Food Saf. 21, 2898–2929 (2022). 35470959 · DOI
  76. [93]Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. International Conference on Learning Representations (ICLR) arXiv:1312.6114 (2014). · arXiv
  77. [94]Sardeshmukh, A., Reddy, S., Gautham, B. P. & Bhattacharyya, P. Material microstructure design using VAE-regression with a multimodal prior. Advances in Knowledge Discovery and Data Mining Springer Nature, Singapore, 29–41 (2024)
  78. [96]Ma, P. et al. Large language models in food science: Innovations, applications, and future. Trends Food Sci. Technol. 148, 104488 (2024). · DOI
  79. [97]St. Pierre, S.R. et al. Biaxial testing and sensory texture evaluation of plant-based and animal deli meat. bioRxiv10.1101/2025.02.19.639170 (2025). PMC12173621 40529651

Cited by

Sorted by citations

Authors
Kuhl, E.
Venue
npj Science of Food
Publisher
Springer Science and Business Media LLC
Published
2025-05-22
Volume
9
Issue
1
ISSN
2396-8370
DOI
10.1038/s41538-025-00441-8
PMC
PMC12098880
Cited by
68
References
4
Language
en
Licence
Funders
  • HORIZON EUROPE European Research Council (Advanced Grant 101141626)
  • National Science Foundation, United States (CMMI 2320933)
  • Food System Innovations (Seed Grant)
  • Stanford Plant-Based Diet Initiative (Seed Grant)

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