Automated Nutrient Level Determination Using Machine Learning
- DOI
- 10.2991/978-94-6463-196-8_53How to use a DOI?
- Keywords
- Nutrient Level Determination; Food Freshness; Python; Machine Learning
- Abstract
In present scenario, people look health issues as non-trivial. The primary agenda of life is to have better nutrient food. The calories and nutrition intake as proved harmful worldwide, as it has led to many disease’s however dieticians have mistaken that the standard intake of number of calories is essential to maintain the right balance of nutrition and calorie context in the human body. The common people (literate or illiterate) may not able to analyze and decide about nutrient levels in packed food items and even unable to identify the freshness of the food. This system is an aid in such scenarios where if the ingredient list is given, then the system gives a suggestion regarding the nutrition level based on the input. Since the system is to be used by the common (non-technical) people the input to the system is the image of the nutrient list (available at rear end of packaged food items).So our system involves both image processing and machine learning (for automated suggestions based on the input ingredient list).
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shivanand S. Gornale AU - A. C. Nuthan AU - C Sumitha PY - 2023 DA - 2023/08/10 TI - Automated Nutrient Level Determination Using Machine Learning BT - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022) PB - Atlantis Press SP - 701 EP - 710 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-196-8_53 DO - 10.2991/978-94-6463-196-8_53 ID - Gornale2023 ER -