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1. TYPE THE SUBJECT NAME HERE
SUBJECT CODE
II III
MONITORING NON-NUTRIENT LEVEL
IN PACKAGED FOOD THROUGH
DEEP LEARNING
DATE : 26/02/2022
STUDENT MEMBER : NITHYA S
REGISTER NUMBER : 412520403004
SUPERVISOR: Dr. J. RAJA
SD GOALS: 3
SDG ACTION PROGRAM:
SAP030401,SAP030D01
20PCOPJ301
Project Work – Phase I
2. OVERVIEW
● Problem definition
● Objective
● Literature Review
● Existing System
● Proposed System and Design Flow
● Work Flow of CNN
● Results
● Sustainable Development Goals – Category
● Conclusion
● Future Scope
● References
2
20PCOPJ301
Project Work Phase 1
3. 3
PROBLEM DEFINITION
• India is one of the lowest ranking country in
consuming unhealthiest packaged food.
• There is an evident growing trend in consuming
un-healthy packaged food such as noodles, savory
snacks, biscuits etc..
• The Indian packaged food market is expected to be
double and grow up to USD 70 billion in the next 5-
10 years. Source: Business Standard (BS- Feb, 2021)
20PCOPJ301
Project Work Phase 1
4. 4
OBJECTIVE
PROJECT WORK
• The objective of this project is to monitor the level of Fat, Transfat through consumption of
packaged food.
• This project will use the most powerful machine learning technique from the field of deep
learning to recognize and classify the nutritional facts in each of the Packaged food
images using a pre trained Convolutional Neural Network (CNN) as a feature extractor to
train an image category classifier.
• The user will capture the packaged food image through his/her mobile phone and the
image will be pre-processed, segmented, and classified to determine the data such as Fat,
Trans-Fat.
• The fat level on the packaged food will be compared with the permissible limit as advised
by the WHO (based on Age & Gender). The user will be informed on the fat level through
email/blynk app.
20PCOPJ301
Project Work Phase 1
5. LITERATURE REVIEW
S. No Paper title Publication & Year Author
Inference used
for project
1
An Efficient CNN
Architecture for
Image
Classification
IEEE, 04 October
2018
Divya Kiran,
Hariharan
Ramasangu,
Shahmustafa
Mujawar
Study &
understanding
of CNN In
image
processing
2
Review of Deep
Learning
Algorithms and
Architectures
IEEE, 22 April 2019
Ajay Shrestha,
Ausif
Mahmood
Study &
understanding
of Deep
Learning
3 Machine learning
methods for IoT
and their Future
Applications
IEEE, 30 January
2020
Mansi Jindal,
Jatin Gupta,
Bharat
Bhushan
ML-IoT
interface and
application
4 Table Detection
using Foreground
and Background
Features
IEEE, 01 December
2018
Saman Arif,
Faisal Shafait
Study of table
detection in
document
images
20PCOPJ301
Project Work Phase 1
6. 6
EXISTING SYSTEM
PROJECT WORK
The existing system classifies & recognise only the food type using the following
segmentation techniques:
• Background colour of the food items.
• Image boundary Detection.
• Weight Estimation.
20PCOPJ301
Project Work Phase 1
13. 13
CONCLUSION
PROJECT WORK
The proposed system will facilitate the user to understand the Fat level in each food item.
Also, the user can categorize the food based on the Fat level to optimize the food pattern.
This project provides real-time feedback to user on the fat level in the food to establish a
tool for recording dietary intakes.
20PCOPJ301
Project Work Phase 1
14. 14
FUTURE SCOPE
PROJECT WORK
• Initial phase which is implemented has focused on identifying fat content in the food items
in an image using image analysis techniques.
• We are looking to create 3D food models to assist in pattern matching and to render our
estimated sizes of the food back into the image so that we can have the user adjust the
portion size to make it more accurate larger, smaller, etc.
• We intend to extend the segmentation and classification for overlapped food items.
• Also, with the help of GSR sensor the real-time fat accumulation will be monitored for
individual user.
20PCOPJ301
Project Work Phase 1
