Research Article
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Food styling and food photography with generative AI

Year 2024, Volume: 6 Issue: 2, 90 - 103, 07.01.2025
https://doi.org/10.48119/toleho.1573824

Abstract

The objective of this study is to evaluate the aesthetic suitability of generative AI food images and to examine the potential role of AI in food styling and photography, including its strengths, weaknesses, opportunities, and threats. In this research, eight dishes from Turkish cuisine, Imambayıldı and Zeytinyağlı enginar (artichoke with extra virgin olive oil) for the olive oil theme, Adana kebab and Hünkâr beğendi for the main course theme, fırında sütlaç (baked rice pudding) and pumpkin dessert for the dessert theme, çay (Turkish tea) and Turkish coffee for the beverage theme, were produced separately using Adobe Firefly 3 and DALL-E 3 Artificial Intelligence (AI) applications. Real food photographs were also included for comparison. Thirty-one professional food stylists and photographers volunteered and participated in the study. Consequently, a total of 24 food images were created and evaluated by professionals according to six aesthetic criteria: lighting, color, composition, presentation, appropriateness of the props and background, and the creation of a mouth-watering sensation. The findings reveal no significant difference between the food photographs produced using the AI 1 application and real food photographs. Half of the images created by the AI 2 application also showed no significant differences compared to real images. However, significant differences were observed in five images between the two AI applications. Participants highlighted low costs, fast production, and flexibility as strengths of AI applications in food styling and photography. Conversely, weaknesses included the production of surreal images and aesthetic concerns. Opportunities were identified in fostering innovation, creativity, and new perspectives, while potential threats involved ethical and copyright concerns, overdependence on AI tools, and potential job displacement.

Ethical Statement

Ethical approval was given by the Ankara Hacı Bayram Veli University Ethics Commission on 17.07.2024, with the number 280014.

Thanks

This study was presented orally at the 8th UGTAK 2024 congress.

References

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  • Bhattacharjee, G. (2023). Art and photography in the age of artificial intelligence. In 12th International Photographic Conference of PAD, Kolkata.
  • Brady, E., & Prior, J. (2020). Environmental aesthetics: A synthetic review. People and Nature, 2(2), pp. 254-266.
  • Boddy, J.R. (2016). Sample size for qualitative research. Qualitative Market Research, 19(4), pp. 426-432. doi:10.1108/QMR-06-2016-0053.
  • Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), pp. 233-241.
  • Cankul, D., Ari, O.P., & Okumus, B. (2021). The current practices of food and beverage photography and styling in food business. Journal of Hospitality and Tourism Technology, 12(2), pp. 287-306.
  • Califano, G., Zhang, T., & Spence, C. (2024). Would you trust an AI chef? Examining what people think when AI becomes creative with food. International Journal of Gastronomy and Food Science, 100973. https://doi.org/10.1016/j.ijgfs.2024.100973.
  • Califano, G., & Spence, C. (2024). Assessing the visual appeal of real/AI-generated food images. Food Quality and Preference, 116, 105149. https://doi.org/10.1016/j.foodqual.2024.105149.
  • Conolly, O., & Haydar, B. (2003). Aesthetic principles. The British Journal of Aesthetics, 43(2), pp. 114-125.
  • Custer, D. (2010). Food styling: The art of preparing food for the camera. John Wiley & Sons.
  • Chen, Y. (2024). Artificial intelligence technology in photography and future challenges and reflections. The Frontiers of Society, Science and Technology. ISSN 2616-7433 6(6), (pp. 24-30), doi: 10.25236/FSST.2024.060605.
  • Değerli, A.H., & Tatalısu, N.B. (2023). Cooking with ChatGPT and Bard: A study on competencies of AI tools on recipe correction, adaption, time management and presentation. Journal of Tourism & Gastronomy Studies, 11(4), 2658-2673.
  • Denecke, K., Glauser, R., & Reichenpfader, D. (2023). Assessing the potential and risks of AI-based tools in higher education: Results from an eSurvey and SWOT analysis. Trends in Higher Education, 2(4), pp. 667-688.
  • Dujardin, H. (2011). Plate to pixel: Digital food photography and styling. John Wiley & Sons.
  • Eker, E. (2020). Türk mutfağı asırlık tariflerle, Turkish cuisine with timeless recipes. Kültür ve Turizm Bakanlığı Yayınları.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), pp. 111-126.
  • Gambetti, A., & Han, Q. (2022). Camera eats first: Exploring food aesthetics portrayed on social media using deep learning. International Journal of Contemporary Hospitality Management, 34(9), pp. 3300-3331.
  • Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486.
  • Greif, L., Kimmig, A., El Bobbou, S., Jurisch, P., & Ovtcharova, J. (2024). Strategic view on the current role of AI in advancing environmental sustainability: a SWOT analysis. Discover Artificial Intelligence, 4(1), 45.
  • Gross, E.C. (2024). The art of AI: Perspectives on artificial intelligence in photography. Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences, Law, pp. 65-70.
  • Goldman Sachs (2023). Generative AI could raise global GDP by 7%. https://www.goldmansachs.com/insights/pages/generative-aicould-raise-global-gdp-by-7-percent.html (Retrieved June 23, 2024).
  • Göktaş, L.S. (2023). The role of ChatGPT in vegetarian menus. Tourism and Recreation, 5(2), pp. 79-86.
  • Haradhan, M. (2018). Qualitative research methodology in social sciences and related subjects. Journal of Economic Development, Environment and People, 7(1), pp. 23-48.
  • Karhan, J. (2021). Toplumsal ve kültürel bir içecek: “Türk Kahvesi”. A Societal and Cultural Beverage “Turkish Coffee”. Karadeniz Uluslararası Bilimsel Dergi, (52), pp. 149-165.
  • Khan, A.S., & Hoffmann, A. (2003). An advanced artificial intelligence tool for menu design. Nutrition and health, 17(1), pp. 43-53.
  • Kul, S. (2014). Uygun İstatistiksel test seçim klavuzu/ Guideline for suitable statistical test selection. Plevra Bülteni, 8(2), 26.
  • Kolides, A., Nawaz, A., Rathor, A., Beeman, D., Hashmi, M., Fatima, S., Berdik, D., Al-Ayyoub, M., & Jararweh, Y. (2023). Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts. Simulation Modelling Practice and Theory, 126. https://doi.org/10.1016/j.simpat.2023.102754
  • Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021. https://doi.org/10.1155/2021/4535567
  • Liu, B., Norman, W.C., Backman, S.J., Cuneo, K. and Condrasky, M. (2012). Shoot, taste and post: An Exploratory study of food and tourism experiences in an online image share community. e-Review of Tourism Research, 10(6), pp. 917-922.
  • Marr, B. (2023). 5 Amazing ways Meta (Facebook) is using generative AI. https://www.forbes.com/sites/bernardmarr/2023/05/02/5-amazing-ways-how-meta-facebook-is-using-generative-ai/ (Retrieved August 29, 2024).
  • Merriam, S.B., & Tisdell, E.J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
  • Michel, C., Velasco, C., Gatti, E., & Spence, C. (2014). A taste of Kandinsky: Assessing the influence of the artistic visual presentation of food on the dining experience. Flavour, 3,7.
  • Michel, C., Woods, A.T., Neuhäuser, M., Landgraf, A., & Spence, C. (2015). Rotating plates: Online study demonstrates the importance of orientation in the plating of food. Food Quality and Preference, 44, pp. 194-202.
  • Mingjing, Q.U. (2024). SWOT analysis of customer perceptions towards AI robot services in Bangkok restaurants. Journal of System and Management Sciences, 14(2), pp. 323-338.
  • Niszczota, P., & Rybicka, I. (2023). The credibility of dietary advice formulated by ChatGPT: Robo-diets for people with food allergies. Nutrition, 112, 11207.
  • Oktay, S. and Guden, N. (2021). The gastronomic cultural reflection of Greek, Turkish and Cyprus culinary. Journal of Gastronomy, Hospitality, and Travel, 4(2) – 2021.
  • Paden, R., Harmon, L. K., & Milling, C. R. (2013). Philosophical histories of the aesthetics of nature. Environmental Ethics, 35(1), pp. 57–77. https://doi.org/10.5840/envir oethi cs201 33516
  • Ponzo, V., Goitre, I., Favaro, E., Merlo, F.D., Mancino, M.V., Riso, S., & Bo, S. (2024). Is ChatGPT an effective tool for providing dietary advice? Nutrients, 16(4), 469.
  • Rony, M.K.K., Akter, K., Debnath, M., Rahman, M.M., tuj Johra, F., Akter, F., ... & Parvin, M. R. (2024). Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care. Journal of Medicine, Surgery, and Public Health, 3, 100113.
  • Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18(2), pp. 179-183. doi:10.1002/nur.477018021
  • Sisti, A., Aryan, N., & Sadeghi, P. (2021). What is beauty? Aesthetic Plastic Surgery, 45(5), pp. 2163-2176.
  • Sperlich, B., Düking, P., Leppich, R., & Holmberg, H. C. (2023). Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis. Frontiers in Sports and Active Living, 5, 1258562.
  • Şener, E., & Ulu, E. K. (2024). Culinary innovation: Will the future of chefs' creativity be shaped by AI technologies? Tourism: An International Interdisciplinary Journal, 72(3), pp. 340-352.
  • Tabachnick, B. G. & Fidell, L. S. (2013). Using multivariate statistics (6th ed). Boston, MA: Pearson.
  • Tang, Z. (2023). The transformation of photography by artificial intelligence generative AI technology. Journal of Artificial Intelligence Practice, 6(8), pp. 57-62.
  • Turkish Foodie (2024a). Turkish Foodie. https://turkishfoodie.com/adana-kebab/ (Retreived August 17, 2024).
  • Turkish Foodie (2024b). Turkish Foodie. https://turkishfoodie.com/hunkar-begendi/ (Retreived August 19, 2024).
  • Ulu, E.K. (2024). Gastronomi Alanında Yapay Zekâ Uygulamaları: Bing Image Creator. Sofradaki bilim ve lezzetteki sanat: Gastronomiye kapsamlı bir bakış. Detay Yayıncılık. Ankara.
  • Yavuz, O. (2021). Novel paradigm of cameraless photography: methodology of AI-generated photographs. In Proceedings of EVA London 2021 (pp. 207-213). BCS Learning & Development.
  • Yıldırım, Y., & Yıldırım, H. (2022). Dijital sınırların sonsuzluğu: Günlük hayattan somut örnekler. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 10(4), pp. 1838-1864.
  • Young, N. S. (2011). Food photography: From snapshots to great shots. Peachpit Press.
  • Zaman, M. (2023). ChatGPT for healthcare sector: SWOT analysis. International journal of research in IE, 12(3), pp. 221-233.
Year 2024, Volume: 6 Issue: 2, 90 - 103, 07.01.2025
https://doi.org/10.48119/toleho.1573824

Abstract

References

  • Baştürk, S., & Taştepe, M. (2013). Evren ve Örneklem. (Ed. S.Baştürk). Bilimsel araştırma yöntemleri, Ankara:Vize Yayıncılık.
  • Bhattacharjee, G. (2023). Art and photography in the age of artificial intelligence. In 12th International Photographic Conference of PAD, Kolkata.
  • Brady, E., & Prior, J. (2020). Environmental aesthetics: A synthetic review. People and Nature, 2(2), pp. 254-266.
  • Boddy, J.R. (2016). Sample size for qualitative research. Qualitative Market Research, 19(4), pp. 426-432. doi:10.1108/QMR-06-2016-0053.
  • Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), pp. 233-241.
  • Cankul, D., Ari, O.P., & Okumus, B. (2021). The current practices of food and beverage photography and styling in food business. Journal of Hospitality and Tourism Technology, 12(2), pp. 287-306.
  • Califano, G., Zhang, T., & Spence, C. (2024). Would you trust an AI chef? Examining what people think when AI becomes creative with food. International Journal of Gastronomy and Food Science, 100973. https://doi.org/10.1016/j.ijgfs.2024.100973.
  • Califano, G., & Spence, C. (2024). Assessing the visual appeal of real/AI-generated food images. Food Quality and Preference, 116, 105149. https://doi.org/10.1016/j.foodqual.2024.105149.
  • Conolly, O., & Haydar, B. (2003). Aesthetic principles. The British Journal of Aesthetics, 43(2), pp. 114-125.
  • Custer, D. (2010). Food styling: The art of preparing food for the camera. John Wiley & Sons.
  • Chen, Y. (2024). Artificial intelligence technology in photography and future challenges and reflections. The Frontiers of Society, Science and Technology. ISSN 2616-7433 6(6), (pp. 24-30), doi: 10.25236/FSST.2024.060605.
  • Değerli, A.H., & Tatalısu, N.B. (2023). Cooking with ChatGPT and Bard: A study on competencies of AI tools on recipe correction, adaption, time management and presentation. Journal of Tourism & Gastronomy Studies, 11(4), 2658-2673.
  • Denecke, K., Glauser, R., & Reichenpfader, D. (2023). Assessing the potential and risks of AI-based tools in higher education: Results from an eSurvey and SWOT analysis. Trends in Higher Education, 2(4), pp. 667-688.
  • Dujardin, H. (2011). Plate to pixel: Digital food photography and styling. John Wiley & Sons.
  • Eker, E. (2020). Türk mutfağı asırlık tariflerle, Turkish cuisine with timeless recipes. Kültür ve Turizm Bakanlığı Yayınları.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66(1), pp. 111-126.
  • Gambetti, A., & Han, Q. (2022). Camera eats first: Exploring food aesthetics portrayed on social media using deep learning. International Journal of Contemporary Hospitality Management, 34(9), pp. 3300-3331.
  • Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486.
  • Greif, L., Kimmig, A., El Bobbou, S., Jurisch, P., & Ovtcharova, J. (2024). Strategic view on the current role of AI in advancing environmental sustainability: a SWOT analysis. Discover Artificial Intelligence, 4(1), 45.
  • Gross, E.C. (2024). The art of AI: Perspectives on artificial intelligence in photography. Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences, Law, pp. 65-70.
  • Goldman Sachs (2023). Generative AI could raise global GDP by 7%. https://www.goldmansachs.com/insights/pages/generative-aicould-raise-global-gdp-by-7-percent.html (Retrieved June 23, 2024).
  • Göktaş, L.S. (2023). The role of ChatGPT in vegetarian menus. Tourism and Recreation, 5(2), pp. 79-86.
  • Haradhan, M. (2018). Qualitative research methodology in social sciences and related subjects. Journal of Economic Development, Environment and People, 7(1), pp. 23-48.
  • Karhan, J. (2021). Toplumsal ve kültürel bir içecek: “Türk Kahvesi”. A Societal and Cultural Beverage “Turkish Coffee”. Karadeniz Uluslararası Bilimsel Dergi, (52), pp. 149-165.
  • Khan, A.S., & Hoffmann, A. (2003). An advanced artificial intelligence tool for menu design. Nutrition and health, 17(1), pp. 43-53.
  • Kul, S. (2014). Uygun İstatistiksel test seçim klavuzu/ Guideline for suitable statistical test selection. Plevra Bülteni, 8(2), 26.
  • Kolides, A., Nawaz, A., Rathor, A., Beeman, D., Hashmi, M., Fatima, S., Berdik, D., Al-Ayyoub, M., & Jararweh, Y. (2023). Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts. Simulation Modelling Practice and Theory, 126. https://doi.org/10.1016/j.simpat.2023.102754
  • Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021. https://doi.org/10.1155/2021/4535567
  • Liu, B., Norman, W.C., Backman, S.J., Cuneo, K. and Condrasky, M. (2012). Shoot, taste and post: An Exploratory study of food and tourism experiences in an online image share community. e-Review of Tourism Research, 10(6), pp. 917-922.
  • Marr, B. (2023). 5 Amazing ways Meta (Facebook) is using generative AI. https://www.forbes.com/sites/bernardmarr/2023/05/02/5-amazing-ways-how-meta-facebook-is-using-generative-ai/ (Retrieved August 29, 2024).
  • Merriam, S.B., & Tisdell, E.J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.
  • Michel, C., Velasco, C., Gatti, E., & Spence, C. (2014). A taste of Kandinsky: Assessing the influence of the artistic visual presentation of food on the dining experience. Flavour, 3,7.
  • Michel, C., Woods, A.T., Neuhäuser, M., Landgraf, A., & Spence, C. (2015). Rotating plates: Online study demonstrates the importance of orientation in the plating of food. Food Quality and Preference, 44, pp. 194-202.
  • Mingjing, Q.U. (2024). SWOT analysis of customer perceptions towards AI robot services in Bangkok restaurants. Journal of System and Management Sciences, 14(2), pp. 323-338.
  • Niszczota, P., & Rybicka, I. (2023). The credibility of dietary advice formulated by ChatGPT: Robo-diets for people with food allergies. Nutrition, 112, 11207.
  • Oktay, S. and Guden, N. (2021). The gastronomic cultural reflection of Greek, Turkish and Cyprus culinary. Journal of Gastronomy, Hospitality, and Travel, 4(2) – 2021.
  • Paden, R., Harmon, L. K., & Milling, C. R. (2013). Philosophical histories of the aesthetics of nature. Environmental Ethics, 35(1), pp. 57–77. https://doi.org/10.5840/envir oethi cs201 33516
  • Ponzo, V., Goitre, I., Favaro, E., Merlo, F.D., Mancino, M.V., Riso, S., & Bo, S. (2024). Is ChatGPT an effective tool for providing dietary advice? Nutrients, 16(4), 469.
  • Rony, M.K.K., Akter, K., Debnath, M., Rahman, M.M., tuj Johra, F., Akter, F., ... & Parvin, M. R. (2024). Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care. Journal of Medicine, Surgery, and Public Health, 3, 100113.
  • Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18(2), pp. 179-183. doi:10.1002/nur.477018021
  • Sisti, A., Aryan, N., & Sadeghi, P. (2021). What is beauty? Aesthetic Plastic Surgery, 45(5), pp. 2163-2176.
  • Sperlich, B., Düking, P., Leppich, R., & Holmberg, H. C. (2023). Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis. Frontiers in Sports and Active Living, 5, 1258562.
  • Şener, E., & Ulu, E. K. (2024). Culinary innovation: Will the future of chefs' creativity be shaped by AI technologies? Tourism: An International Interdisciplinary Journal, 72(3), pp. 340-352.
  • Tabachnick, B. G. & Fidell, L. S. (2013). Using multivariate statistics (6th ed). Boston, MA: Pearson.
  • Tang, Z. (2023). The transformation of photography by artificial intelligence generative AI technology. Journal of Artificial Intelligence Practice, 6(8), pp. 57-62.
  • Turkish Foodie (2024a). Turkish Foodie. https://turkishfoodie.com/adana-kebab/ (Retreived August 17, 2024).
  • Turkish Foodie (2024b). Turkish Foodie. https://turkishfoodie.com/hunkar-begendi/ (Retreived August 19, 2024).
  • Ulu, E.K. (2024). Gastronomi Alanında Yapay Zekâ Uygulamaları: Bing Image Creator. Sofradaki bilim ve lezzetteki sanat: Gastronomiye kapsamlı bir bakış. Detay Yayıncılık. Ankara.
  • Yavuz, O. (2021). Novel paradigm of cameraless photography: methodology of AI-generated photographs. In Proceedings of EVA London 2021 (pp. 207-213). BCS Learning & Development.
  • Yıldırım, Y., & Yıldırım, H. (2022). Dijital sınırların sonsuzluğu: Günlük hayattan somut örnekler. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 10(4), pp. 1838-1864.
  • Young, N. S. (2011). Food photography: From snapshots to great shots. Peachpit Press.
  • Zaman, M. (2023). ChatGPT for healthcare sector: SWOT analysis. International journal of research in IE, 12(3), pp. 221-233.
There are 52 citations in total.

Details

Primary Language English
Subjects Tourism (Other)
Journal Section Peer-reviewed Articles
Authors

Hakan Güleç 0000-0002-3790-3911

Publication Date January 7, 2025
Submission Date October 25, 2024
Acceptance Date December 20, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Güleç, H. (2025). Food styling and food photography with generative AI. Journal of Tourism Leisure and Hospitality, 6(2), 90-103. https://doi.org/10.48119/toleho.1573824

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