Calorie Estimation of Food and Beverages using Deep Learning
IEEE BASE PAPER TITLE:
Calorie Estimation of Food and Beverages using Deep Learning
(or)
OUR PROPOSED PROJECT TITLE:
SmartBite: Deep Learning-driven Food Recognition and Calorie Estimation for Intelligent Diet Monitoring
IEEE BASE PAPER ABSTRACT:
Obesity, a serious chronic disease, is on the rise as a result of how easily food can be brought to our door steps. People’s need for food grew, and at the same time, their anxiety about their nutrition also grew. This study offers an image-based calorie estimation system that asks the user to upload an image of a food item in order to calculate the estimated number of calories in the image. It is a multitasking system that displays weekly information on a user’s calorie consumption and the number of calories that must be ingested to prevent obesity-related illnesses like cancer, heart attack, etc. To recognize complex pictures, a collection of food images with 20 classes and 500 images are built in each class. This study has developed a six-layer Convolutional Neural Network (CNN) architecture for the purpose of extracting the traits and classifying the images. The proposed food identification trials had an accuracy of 78.7% during testing and 93.29% throughout training. By using software designed to accurately estimate food calories from still images, users and heal thcare experts may be able to more rapidly detect dietary practices and food choices connected to health and health concerns. Calorie calculation has been done by using photographs, however, it is difficult, and there is presently no publicly available program that can conduct both food estimation using images and provide health information about the individual.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
MobileNet Architecture.
OUR PROPOSED ABSTRACT:
The project “SmartBite: Deep Learning-driven Food Recognition and Calorie Estimation for Intelligent Diet Monitoring” presents a novel approach to enhance diet monitoring and promote healthy eating habits using deep learning techniques.
The primary objective of this project is to accurately recognize different food items and estimate their calorie content in real-time, providing users with intelligent and personalized diet monitoring capabilities. To achieve this, we utilized the Python programming language and employed the MobileNet architecture model for food recognition and calorie estimation.
The project was trained and evaluated using the Food 101 dataset, consisting of 37,046 food images across 101 distinct food classes. Through an extensive training process, our model achieved remarkable results, obtaining a training accuracy of 97.00% and a validation accuracy of 98.00%. This high level of accuracy demonstrates the effectiveness of the proposed approach in accurately recognizing and categorizing various food items.
The SmartBite system offers several key features to users. By leveraging deep learning algorithms, it analyzes the input image given in the web framework to identify the specific food item within seconds. Furthermore, the system estimates the calorie content of the recognized food, providing users with crucial information to monitor their dietary intake effectively.
The intelligent diet monitoring capabilities of SmartBite empower users to make informed decisions about their food choices. By tracking and analyzing their daily food intake, users can gain insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a balanced and healthy diet.
SmartBite exemplifies the successful implementation of deep learning techniques, specifically employing the MobileNet architecture, for food recognition and calorie estimation. With its high accuracy, real-time processing, and intelligent diet monitoring capabilities, SmartBite has the potential to revolutionize the way individuals track and manage their dietary habits, promoting healthier lifestyles and well-being.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 6 GB
SOFTWARE REQUIREMENTS:
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.8.
- Web Framework : Flask
- Frontend : HTML, CSS, JavaScript.
REFERENCE:
Parimala Gandhi A; Sapna S; Yaswanth K M; Praveen Kumar M, “Calorie Estimation of Food and Beverages using Deep Learning”, 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), IEEE CONFERENCE, 2023.
👉CLICK HERE TO BUY THIS PROJECT SOURCE CODE👈
