Abstract
This study is one of many that investigate the relationship between determining the nutritional ingredients in food and calculating the calories using data analysis utilizing machine learning techniques. Due to the availability of multifood photos, which must be cropped before processing, the Indian food recipes database is used for the research. The study uses a large dataset of various food photos to train state-of-the-art deep convolutional neural networks (CNNs) to recognize and categorize distinct food items with an amazing 99.89% accuracy. This study’s applicability spans several sectors in addition to food recognition, including calorie measurement, meal planning services, and nutritional monitoring systems. The solution is widely available to a wide range of users thanks to a user-friendly web interface. The system’s 99.89% accuracy in food detection and calorie measurement demonstrates its dependability and distinguishes it from competing options. Its ability to improve individual health, fight obesity, and encourage healthy eating habits makes it a vital tool in today’s health-conscious culture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Katiyar B et al (2019) Food calorie estimation using deep learning and mobile images. In: Research gate conference
Memis S, Arslan B, Batur OZ, Sonmez EB (2020) A comparative study of deep learning methods on food classification problem. In: Proceedings of the 2020 innovations in intelligent systems and applications conference (ASYU), pp 1–4
Indian food dataset from “Kaggle”. https://www.kaggle.com/datasets/nehaprabhavalkar/indian-food-101
Matsuda Y, Hoashi H, Yanai K (2012) Recognition of multiple-food images by detecting candidate regions. In: Proceedings of the 2012 IEEE international conference on multimedia and expo, pp 25–30
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), June 2016
Kawano Y, Yanai K (2014) Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. In: Proceedings of the European conference on computer vision. Springer, pp 3–17
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. Association for Computing Machinery, New York, NY, USA
Hassannejad H, Matrella G, Ciampolini P, De Munari I, Mordonini M, Cagnoni S (2016) Food image recognition using very deep convolutional networks. In: Proceedings of the 2nd international workshop on multimedia assisted dietary management, pp 41–49
Martinel N, Piciarelli C, Micheloni C (2016) A supervised extreme learning committee for food recognition. Comput Vis Image Underst 148:67–86
Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Proceedings of the British machine vision conference (BMVC), pp 87.1–87.12
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1. Ser. NIPS 12. Curran Associates Inc., pp 1097–1105
Jiang S, Min W, Liu L, Luo Z (2020) Multi-scale multi view deep feature aggregation for food recognition. IEEE Trans Image Process 29:265–276
Yanai K, Kawano Y (2015) Food image recognition using deep convolutional networks with pre-training and fine-tuning. In: Proceedings of the IEEE international conference on multimedia expo workshops (ICMEW), pp 1–6
Rodriguez P, Gonfaus JM, Cucurull G, XavierRoca F, Gonzalez J (2018) Attend and rectify: gated attention mechanism for fine grained recovery. In: Proceedings of the European conference on computer vision (ECCV)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vasudha, M., Rashmi, D., Mahalakshmi Jain, B.A. (2024). Food Recognition and Calorie Measurement Using Machine Learning. In: Devi, B.R., Kumar, K., Raju, M., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fifth International Conference on Computer and Communication Technologies. IC3T 2023. Lecture Notes in Networks and Systems, vol 897. Springer, Singapore. https://doi.org/10.1007/978-981-99-9704-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-9704-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9703-9
Online ISBN: 978-981-99-9704-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)