Food and Beverage Calorie Prediction with Neural Networks | IJCSE Volume 10 – Issue 6 | IJCSE-V10I6P52
IJCSE
International Journal of Computer Science Engineering Techniques
ISSN 2455-135X · Peer-Reviewed · Open Access
📚 Volume 10, Issue 6
📅 November 13, 2026
📄 Pages 434–445
🔖 ID: IJCSE-V10I6P52
Food and Beverage Calorie Prediction with Neural Networks
Author(s)
D.Ramanjaneyulu, Raman kumar mandal, PriyaRanjan Raj, K.Ravi Naik, Anjanadevi Dayinaboyina
Abstract
This observe introduces a revolutionary approach for enhancing nutritional monitoring and selling healthful consuming behavior via deep analyzing technology. The primary objective is to correctly come across several meals objects and estimate their calorie content cloth in actual-time, offering customers a smart and customized technique to nutritional tracking performed using Python, the gadget makes use of the cellular net architecture for food category and calorie estimation. The proposed gadget methods meals snap shots through a web based totally interface, figuring out gadgets hastily and supplying customers with a accurate calorie estimate. This machine empowers people with treasured insights into their nutritional behavior, permitting them to song meals intake, set dietary dreams, and make knowledgeable picks. via way of leveraging deep studying for green and correct calorie estimation, this approach offers a transformative solution for nutritional control. With its excessive precision, real-time evaluation, and clever nutritional monitoring, this system has the ability to redefine how humans monitor and optimize their dietary conduct, fostering greater healthy lifestyles and average well-being.
Keywords
Deep Learning, Food Calorie Estimation, Image Processing, CNN, Multi-Label Classification, Computer Vision
Conclusion
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.
References
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3Petimar, 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.
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6Li, 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.
7Ciocca, G.; Micali, G.; Napoletano, P. State recognition of food images using deep features. IEEE Access 2020, 8, 32003–32017.
8Park, 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.
9Mezgec, 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.
10Mezgec, 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.
11Liang, H.; Gao, Y.; Sun, Y.; Sun, X. CEP: Calories estimation from food photos. Int. J. Comput. Appl. 2020, 42, 569–577. [12] Poply, P. An Instance Segmentation approach to Food Calorie Estimation using Mask R-CNN. In Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, Beijing, China, 22–24 October 2020; pp. 73–78.
13Hoashi, H.; Joutou, T.; Yanai, K. Image recognition of 85 food categories by feature fusion. In Proceedings of the Proceedings of the 2010 IEEE International Symposium on Multimedia, Taichung, Taiwan, 13–15 December 2010; pp. 296–301.
14Pouladzadeh, P.; Shirmohammadi, S.; Al-Maghrabi, R. Measuring calorie and nutrition from food image. IEEE Trans. Instrum. Meas. 2014, 63, 1947–1956.
15Liang, Y.; Li, J. Computer vision-based food calorie estimation: Data set, method, and experiment. arXiv 2017, arXiv:1705.07632.
16 Latif, G.; Alsalem, B.; Mubarky, W.; Mohammad, N.; Alghazo, J. Automatic Fruits Calories Estimation through Convolutional Neural
Networks. In Proceedings of the 2020 6th International Conference on
Computer and Technology Applications, Antalya, Turkey, 14–16 April 2020; pp. 17–21.
17Shen, Z.; Shehzad, A.; Chen, S.; Sun, H.; Liu, J. Machine learning based approach on food recognition and nutrition estimation. Procedia Comput. Sci. 2020, 174, 448–453.
18Okamoto, K.; Adachi, K.; Yanai, K. Region-Based Food Calorie Estimation for Multiple-Dish Meals. In Proceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities, Taipei, Taiwan, 16–19 November 2021; pp. 17–24.
19Ruede, R.; Heusser, V.; Frank, L.; Roitberg, A.; Haurilet, M.; Stiefelhagen, R. Multi-task learning for calorie prediction on a novel large-scale recipe data set enriched with nutritional information. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10–15 January 2021; pp. 4001–4008.
20Naritomi, S.; Yanai, K. CalorieCaptorGlass: Food calorie estimation based on actual size using hololens and deep learning. In Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Atlanta, GA, USA, 22–26 March 2020; pp. 818–819
2Prentice, A.M.; Jebb, S.A. Beyond body mass index. Obes. Rev. 2001, 2, 141–147.
3Petimar, 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.
4Health 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).
5Kasar, M.M.; Bhattacharyya, D.; Kim, T.H. Face recognition using neural network: A review. Int. J. Secur. Its Appl. 2016, 10, 81–100.
6Li, 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.
7Ciocca, G.; Micali, G.; Napoletano, P. State recognition of food images using deep features. IEEE Access 2020, 8, 32003–32017.
8Park, 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.
9Mezgec, 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.
10Mezgec, 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.
11Liang, H.; Gao, Y.; Sun, Y.; Sun, X. CEP: Calories estimation from food photos. Int. J. Comput. Appl. 2020, 42, 569–577. [12] Poply, P. An Instance Segmentation approach to Food Calorie Estimation using Mask R-CNN. In Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, Beijing, China, 22–24 October 2020; pp. 73–78.
13Hoashi, H.; Joutou, T.; Yanai, K. Image recognition of 85 food categories by feature fusion. In Proceedings of the Proceedings of the 2010 IEEE International Symposium on Multimedia, Taichung, Taiwan, 13–15 December 2010; pp. 296–301.
14Pouladzadeh, P.; Shirmohammadi, S.; Al-Maghrabi, R. Measuring calorie and nutrition from food image. IEEE Trans. Instrum. Meas. 2014, 63, 1947–1956.
15Liang, Y.; Li, J. Computer vision-based food calorie estimation: Data set, method, and experiment. arXiv 2017, arXiv:1705.07632.
16 Latif, G.; Alsalem, B.; Mubarky, W.; Mohammad, N.; Alghazo, J. Automatic Fruits Calories Estimation through Convolutional Neural
Networks. In Proceedings of the 2020 6th International Conference on
Computer and Technology Applications, Antalya, Turkey, 14–16 April 2020; pp. 17–21.
17Shen, Z.; Shehzad, A.; Chen, S.; Sun, H.; Liu, J. Machine learning based approach on food recognition and nutrition estimation. Procedia Comput. Sci. 2020, 174, 448–453.
18Okamoto, K.; Adachi, K.; Yanai, K. Region-Based Food Calorie Estimation for Multiple-Dish Meals. In Proceedings of the 13th International Workshop on Multimedia for Cooking and Eating Activities, Taipei, Taiwan, 16–19 November 2021; pp. 17–24.
19Ruede, R.; Heusser, V.; Frank, L.; Roitberg, A.; Haurilet, M.; Stiefelhagen, R. Multi-task learning for calorie prediction on a novel large-scale recipe data set enriched with nutritional information. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10–15 January 2021; pp. 4001–4008.
20Naritomi, S.; Yanai, K. CalorieCaptorGlass: Food calorie estimation based on actual size using hololens and deep learning. In Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Atlanta, GA, USA, 22–26 March 2020; pp. 818–819
📋 How to Cite This Paper
D.Ramanjaneyulu, Raman kumar mandal, PriyaRanjan Raj, K.Ravi Naik, Anjanadevi Dayinaboyina (2026). Food and Beverage Calorie Prediction with Neural Networks. International Journal of Computer Science Engineering Techniques, 10(6), 434–445. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.24199569
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