Food and Beverage Calorie Prediction with Neural Networks

This study developed an efficient and deep learning system for detecting food and calorie estimates using mobilenet architecture and food 101 data record this model achieved high levels of accuracy in food identification and estimating its calorie values light design ensures fast and reliable performance of real applications on mobile and web platforms data expansion techniques have improved generalization but separable convolutions have deeper reductions in compensation complexity making the use of models more practical future improvements include real time analytics multi-label classification and integration with health tracking apps for more comprehensive nutrition monitoring a practical and automated approach to persecuting food intake and promoting healthier eating habits.

1                 Bray, G.; Bouchard, C. (Eds.) Handbook of Obesity-Volume 2: Clinical Applications; CRC Press: Boca Raton, FL, USA, 2014.  2                 Prentice, A.M.; Jebb, S.A. Beyond body mass index. Obes. Rev. 2001, 2, 141–147.  3                 Petimar, J.; Ramirez, M.; Rifas-Shiman, S.L.; Linakis, S.; Mullen, J.; Roberto, C.A.; Block, J.P. Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: A natural experiment. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 99.  4                 Health Canada. Health Canada Nutrient Values. November 2011. Available online: https://www.canada.ca/en/health-  canada/services/food-nutrition/healthy-eating/nutrient-data/nutrient-value-some-common-foods-booklet.html (accessed on 31 August 2022).  5                 Kasar, M.M.; Bhattacharyya, D.; Kim, T.H. Face recognition using neural network: A review. Int. J. Secur. Its Appl. 2016, 10, 81–100.  6                 Li, G.Z.; Bu, H.L.; Yang, M.Q.; Zeng, X.Q.; Yang, J.Y. Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis. BMC Genom. 2008, 9, S24.  7                 Ciocca, G.; Micali, G.; Napoletano, P. State recognition of food images using deep features. IEEE Access 2020, 8, 32003–32017.  8                 Park, S.J.; Palvanov, A.; Lee, C.H.; Jeong, N.; Cho, Y.I.; Lee, H.J. The development of food image detection and recognition model of Korean food for mobile dietary management. Nutr. Res. Pract. 2019, 13, 521– 528.  9                 Mezgec, S.; Seljak, B.K. Using deep learning for food and beverage image recognition. In Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9–12 December 2019; pp. 5149–5151.  10              Mezgec, S.; Eftimov, T.; Bucher, T.; Seljak, B.K. Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment. Public Health Nutr. 2019, 22, 1193–1202.