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Nutrient Net: A Multi-Layered Deep Learning Approach for Calorie and Macro nutrient Estimation from Food Images
Subject area: Science,Engineering and Technology · Area of research: Deep Learning and Health Informatics
DOI: https://doi.org/10.64388/IREV9I10-1715946
Abstract
In order to preserve our dieting habits and lead a healthy lifestyle, it is necessary to calculate the value of calorie content and macro-nutrient(protein, fat, carbohydrates) value of food. For monitoring our daily food consumption, there are some other methods available homemade systems, but sometimes these methods are not very efficient and often result in errors as it takes a lot of time to accomplish the task. To resolve the above-mentioned problem, our advanced approach is Nutrient Net, which is a system used to predict the total calorie content and macro-nutrient content of the food item from the image using a multi-layered deep learning technique. To classify and calculate the food item, this system uses Convolution Neural Networks (CNN). CNN is a deep learning technique which is specifically designed for images, it scans the food image and easily identifies the food item(for example: rice, pizza, apple, etc.) After identifying the food item, Long Short-Term Memory (LSTM) network assists to calculate the portion size(how much food) and nutrient contents(protein, fat, carbohydrate, etc.) of the food item. This app supports both real-time images and saved images from the gallery. Once the food item has been identified, the Nutrient Net System provides information about each food item. With the help of this, we can manage our diet and food habits effectively.
Keywords
Nutrient Net, Deep Learning, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Food Image Classification, Portion Size Estimation, , Nutritional Analysis.
How to cite this paper
@article{1715946,
author = {Canosa R Krucy, Enugula Vyshnavi},
title = {Nutrient Net: A Multi-Layered Deep Learning Approach for Calorie and Macro nutrient Estimation from Food Images },
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {301-309},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715946.pdf},
abstract = {In order to preserve our dieting habits and lead a healthy lifestyle, it is necessary to calculate the value of calorie content and macro-nutrient(protein, fat, carbohydrates) value of food. For monitoring our daily food consumption, there are some other methods available homemade systems, but sometimes these methods are not very efficient and often result in errors as it takes a lot of time to accomplish the task. To resolve the above-mentioned problem, our advanced approach is Nutrient Net, which is a system used to predict the total calorie content and macro-nutrient content of the food item from the image using a multi-layered deep learning technique. To classify and calculate the food item, this system uses Convolution Neural Networks (CNN). CNN is a deep learning technique which is specifically designed for images, it scans the food image and easily identifies the food item(for example: rice, pizza, apple, etc.) After identifying the food item, Long Short-Term Memory (LSTM) network assists to calculate the portion size(how much food) and nutrient contents(protein, fat, carbohydrate, etc.) of the food item. This app supports both real-time images and saved images from the gallery. Once the food item has been identified, the Nutrient Net System provides information about each food item. With the help of this, we can manage our diet and food habits effectively.},
keywords = {Nutrient Net, Deep Learning, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Food Image Classification, Portion Size Estimation, , Nutritional Analysis.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1715946}
}