The Place and Importance of Artificial Intelligence in the Gastronomy Sector

Year 2022, Volume: 10 Issue: 4, 1070 - 1082, 30.12.2022

Hatice Türkoğlu , Gül Yılmaz

https://doi.org/10.29109/gujsc.1199093

Cited By: 2

Abstract

The demand for artificial intelligence in the world is increasing day by day in the gastronomy sector. In this study, the importance of artificial intelligence in the gastronomy sector is emphasized and the studies on this subject are mentioned. Since the studies in the literature are limited, studies on all sub-branches related to artificial intelligence-based gastronomy and tourism are mentioned. The number of studies related to gastronomy and artificial intelligence in the literature is limited. Therefore, in this study, artificial intelligence applications used in the gastronomy sector are detailed under the subheadings of cuisine, promotion, health, and forecasting. Accordingly, approximately 40 articles were analyzed considering these sub-headings. In light of the information obtained from these studies, artificial intelligence systems to be developed in the gastronomy sector will provide great gains. Moreover, it is estimated that meeting consumer expectations and using innovative technologies in the gastronomy sector will increase the popularity of gastronomy science.

Keywords

Gastronomy, Tourism, Smart Systems, artificial intelligence, deep learning

References

• [1] Eren D, Eroğlu S. Niğde ilinin gastronomi turizmi potansiyelinin değerlendirilmesi. Nevşehir HBV Üniversitesi Turizm Fakültesi, 122, (2019).
• [2] Uzun D. Bingöl İlindeki Turizm İşletme Belgeli Otel Restoranlarının Menü Mühendisliği Analizi. To & Re 2019, 1 (1) 9-14, (2019).
• [3] Lilholt, A. (2015). Entomological Gastronomy. Addison Lilholt.
• [4] Uzan ŞB, Sevimli Y. Gastronomideki robotik uygulamalar ve yapay zekâ. Tourism and Recreation, 2(2), 46-58, (2020).
• [5] Oktay S, Sadıkoğlu S. The Gastronomic cultures' impact on the African cuisine. Journal of Ethnic Foods, 5(2), 140-146, (2018).
• [6] Şahin EÖ, Ağaoğlu B. Gastronomi Alanında Bulanık Mantık Kullanarak Etin Pişme Oranını Tahmin Eden Sistem Tasarımı, Journal of Tourism and Gastronomy Studies, 2020, Special Issue (4), 334-346, (2020).
• [7] Çavuşoğlu M. Gastronomi turizmi ve Kıbrıs mutfak kültürü üzerine bir araştırma. N. Avcı ve Ö. Kürşat (Ed.), I. Ulusalararası IV. Ulusal Eğridir Turizm Sempozyumu Bildiriler Kitabı (ss. 527-538), (2011).
• [8] Birdir K, Akgöl Y. Gastronomi turizmi ve Türkiye’yi ziyaret eden yabancı turistlerin gastronomi deneyimlerinin değerlendirilmesi. İşletme ve İktisat Çalışmaları Dergisi, 3(2), 57-68, (2015).
• [9] Baysal, A., and Küçükaslan, N. (2009). Beslenme İlkeleri ve Menü Planlaması. Bursa: Ekin Yayınevi.
• [10] Çerkez M, Kızıldemir Ö. Yiyecek–İçecek İşletmelerinde Yapay Zekâ Kullanımı. Türk Turizm Araştırmaları Dergisi, 4(2), 1264-1278, (2020).
• [11] Ardatürk ÖÜAŞ. Tasarımcı Zihninin Bir Yansıması Olarak; “Yapay Zeka”. Online Journal of Art and Design, 10(4), (2022).
• [12] Arslan K. Eğitimde Yapay Zekâ Uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71-80, (2020).
• [13] Kuşçu E. Çeviride Yapay Zekâ Uygulamaları. Atatürk Üniversitesi Kazım Karabekir Eğitim Fakültesi Dergisi (30), 45-58, (2015).
• [14] Kamble R, Shah D. Applications of Artificial Intelligence in Human Life. International Journal of Research, 6(6), 178-188, (2018).
• [15] Yağcı, C., Gökçe, İ., Bozüyük, T., ve Akar, G. (2005). Yapay Zeka Teknolojisinin Endüstrideki Uygulamaları. Bitirme Projesi, M. Ü, Teknik Bilimler MYO Temmuz, 27, 2016.
• [16] Choudhary S, Arba H, Patkar U. An Innovative Study on Artificial Intelligence and Robotics. International Journal of Innovative Research in Computer and Communication Engineering, 4(3), 3292-3296, (2016).
• [17] Tutorials Point (2015). Artificial Intelligence. Haydarabad: Tutorials Point.
• [18] Ertürk FE, Yayan G. Bilim ve Sanatı Birleştiren İki Usta. Batman Üniversitesi Yaşam Bilimleri Dergisi, 1(1), 453-464, (2012).
• [19] Yülek, M. (2018). 11. Kalkınma Planı ve Türkiye’nin robotları. [Online] https://www.dunya.com/kose-yazisi/11-kalkinma-plani-ve-turkiyenin-robotlari/401624#> [Erişim Tarihi: 15.03.2022].
• [20] Aydın, Ş. E. (2017). Yapay Zekâ Teknolojisi (Yapay Zekaların Dünü Bugünü Yarını). Adana.
• [21] Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, Eirug A. Artificial ıntelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and Policy. International Journal of Information Management, 101-994, (2019).
• [22] Ergün ÖÖ, Özturk B. Türk mutfaǧi için ontoloji tabanli semantik gösterim. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4), (2018).
• [23] Çuhadar M. Türkiye’ye yönelik diş turizm talebinin MLP, RBF ve TDNN yapay sinir aği mimarileri ile modellenmesi ve tahmini: karşilaştirmali bir analiz. Yaşar Üniversitesi E-Dergisi, 8(31), 5274-5295, (2013).
• [24] Çifçi, O. (2019). Türkiyedeki gastronomi ve mutfak sanatları eğitimi alan öğrencilerin profesyonel mutfak yeterliliklerinin belirlenmesi, Yüksek Lisans Tezi, İstanbul Üniversitesi Sosyal Bilimleri Enstitüsü, İstanbul.
• [25] Ignatov E, Smith S. Segmenting Canadian culinary tourists. Current issues in tourism, 9(3), 235, (2006).
• [26] Santich B. The Study of Gastronomy and Its Relevance to Hospitality Education and Training, International Journal of Hospitality Management, 23, 15-24, (2004).
• [27] Sugiura Y, Sakamoto D, Withana A, Inami M, Igarashi T. Cooking with robots: designing a household system working in open environments. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2427-2430), (2010).
• [28] Varol, A. (2000). Robotik. Ankara: Milli Eğitim Bakanlığı Yayınları.
• [29] Master chef, https://moley.com/, [Erişim Tarihi: 11.10.2022].
• [30] Samsung Newsroom (2019), Get a Glimpse of the Next-generation Innovations on Display at Samsung’s Technology Showcase, https://news.samsung.com/global/get-a-glimpse-of-the-next-generation-innovations-on-display-at-samsungs-technology-showcase, [Erişim Tarihi: 21.07.2022].
• [31] Ayyildiz AY, Eroğlu E. Restoranlarda Kullanılan Akıllı Teknolojiler ve Robot Restoranlar Hakkında Tripadvisor’da Yapılan Yorumların Değerlendirilmesi (Evaluation of Tripadvisor). Journal of Tourism and Gastronomy Studies, 9(2), 1102-1122, (2021).
• [32] FAH THAI (2016), Robot Chefs, http://fahthaimag.com/ausca-robot-chefs-rollout-singapore/, [Erişim Tarihi: 04.08.2022].
• [33] Oğan, Y. (2021). Gastronomi Araştırmaları. Çizgi Kitabevi Yayınları (e-kitap).
• [34] Yılmaz, G. (2020). Turizm-Gastronomi Turizmi Ve Gastronomik Seyahatler, Detay Yayıncılık.
• [35] Chen X, Zhu Y, Zhou H, Diao L, Wang D. Chinesefoodnet: A large-scale image dataset for chinese food recognition. arXiv preprint arXiv:1705.02743, (2017).
• [36] Razali MN, Moung EG, Yahya F, Hou CJ, Hanapi R, Mohamed R, Hashem IAT. Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics. Information, 12(8), 322, (2021).
• [37] Jiang S, Min W, Liu L, Luo Z. Multi-scale multi-view deep feature aggregation for food recognition. IEEE Transactions on Image Processing, 29, 265-276, (2019).
• [38] Khan R, Kumar S, Dhingra N, Bhati N. The use of different image recognition techniques in food safety: a study. Journal of Food Quality, (2021).
• [39] Chen J, Zhu B, Ngo CW, Chua TS, Jiang YG. A study of multi-task and region-wise deep learning for food ingredient recognition. IEEE Transactions on Image Processing, 30, 1514-1526, (2020).
• [40] Min W, et al. Large scale visual food recognition. arXiv preprint arXiv:2103.16107, (2021).
• [41] Kumar Dey S, Akter L, Saha D, Akter M, Rahman M. DeshiFoodBD: Development of a Bangladeshi Traditional Food Image Dataset and Recognition Model Using. In Machine Intelligence and Data Science Applications, Springer, Singapore, (pp. 639-648), (2022).
• [42] Shifat SM, Parthib T, Pyaasa ST, Chaity NM, Kumar N, Morol M. A Real-time Junk Food Recognition System based on Machine Learning. arXiv preprint arXiv:2203.11836, (2022).
• [43] CALORIE MAMA (2017), Instant Food Recognition, https://www.caloriemama.ai/, [Erişim Tarihi: 15.08.2022].
• [44] https://www.yummly.com/, [Erişim Tarihi: 26.09.2022].
• [45] LogMeal (2022), Artificial Intelligence and Deep Learning Solutions for Food Recognition, https://www.logmeal.es/, [Erişim Tarihi: 14.09.2022].
• [46] Tellspec (2015), Empowering a Healthier World with Real-Time AI-Analysis Using Portable Low-Cost Sensors, https://tellspec.com/, [Erişim Tarihi: 02.10.2022].
• [47] Pouladzadeh P, Kuhad P, Peddi SVB, Yassine A, Shirmohammadi S. Mobile cloud based food calorie measurement. In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 1-6). IEEE, (2014, July).
• [48] Tanno R, Ege T, Yanai K. AR DeepCalorieCam V2: Food calorie estimation with cnn and ar-based actual size estimation. In Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology (pp. 1-2), (2018).
• [49] Ege T, Yanai K. Multi-task learning of dish detection and calorie estimation. In Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management (pp. 53-58), (2018).
• [50] Ege T, Yanai K. Image-based food calorie estimation using recipe information. IEICE TRANSACTIONS on Information and Systems, 101(5), 1333-1341, (2018).
• [51] Naritomi S, Yanai K. CalorieCaptorGlass: Food calorie estimation based on actual size using hololens and deep learning. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 818-819). IEEE, (2020).
• [52] Kumar RD, Julie EG, Robinson YH, Vimal S, Seo S. Recognition of food type and calorie estimation using neural network. The Journal of Supercomputing, 77(8), 8172-8193, (2021).
• [53] Shimoda W, Yanai K. CNN-based food image segmentation without pixel-wise annotation. In International Conference on Image Analysis and Processing, Springer, Cham, (pp. 449-457), (2015, September). [54] Chokr M, Elbassuoni S. Calories prediction from food images. In Twenty-Ninth IAAI Conference, (2017, February).
• [55] Ege T, Ando Y, Tanno R, Shimoda W, Yanai K. Image-based estimation of real food size for accurate food calorie estimation. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 274-279). IEEE, (2019, March).
• [56] Turmchokkasam S, Chamnongthai K. The design and implementation of an ingredient-based food calorie estimation system using nutrition knowledge and fusion of brightness and heat information. IEEE Access, 6, 46863-46876, (2018).
• [57] Hu H, Zhang Q, Chen Y. NIRSCAM: A Mobile Near-Infrared Sensing System for Food Calorie Estimation. IEEE Internet of Things Journal, (2022).
• [58] Zengin B, Uyar H, Erkol G. Gastronomi turizmi üzerine kavramsal bir inceleme. Ulusal Turizm Kongresi, 1, 16, (2015).
• [59] Pai PF, Hung KC, Lin KP. Tourism demand forecasting using novel hybrid system. Expert Systems with applications, 41(8), 3691-3702, (2014).
• [60] Law R, Li G, Fong DKC, Han X. Tourism demand forecasting: A deep learning approach. Annals of tourism research, 75, 410-423, (2019).
• [61] Li X, Pan B, Law R, Huang X. Forecasting tourism demand with composite search index. Tourism management, 59, 57-66, (2017).
• [62] Chen KY, Wang CH. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism management, 28(1), 215-226, (2007).