Calorie estimation from the food using machine learning

1. Project Title: Food Recognition and Calorie Estimation
Using Machine Learning.
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2. Overview:
• Abstract
• Introduction
• Existing System
• Drawbacks of Existing System
• Proposed System
• Advantages of Proposed System
• Software Requirements
• Hardware Requirements
Department of CSE(AI&ML) 2

3. Abstract:
This project focuses on developing an advanced "smart system" designed to automatically
identify food items from images and estimate their corresponding calorie content. Leveraging
deep learning techniques, particularly Convolutional Neural Networks (CNN), the system
processes food images to accurately recognize various food types. The identified items are then
cross-referenced with a comprehensive nutrition database to provide real-time estimates of their
caloric value. The primary goal is to offer users an efficient, user-friendly method of tracking
their dietary intake through simple photos, eliminating the need for manual input. Ultimately, the
system aims to support healthier lifestyle choices by providing automated and accurate calorie
tracking, helping users monitor and optimize their nutrition as part of their fitness journey.
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Department of CSE(AI&ML)

4. Introduction:
• Healthy eating and calorie awareness are essential for maintaining fitness and preventing
lifestyle diseases.
• Traditional calorie tracking apps rely on manual entry, which is time-consuming and prone to
errors.
• This project introduces an AI-powered system that can:
• Recognize food items from images using Convolutional Neural Networks (CNN) and transfer
learning.
• Automatically estimate calorie content based on recognized food type and quantity.
• Track daily intake and provide personalized dietary recommendations.
• The goal is to make diet tracking faster, more accurate, and user-friendly through automation
and machine learning.
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Department of CSE(AI&ML)

5. Existing System:
1. Current dietary tracking apps rely on manual input of food items and portion sizes, which is time-
consuming, cumbersome, and prone to human error, leading to inaccurate calorie tracking.
2. These apps lack visual recognition, forcing users to manually search or type food names, which can
be tedious and inefficient, especially for culturally specific foods like Indian cuisine.
3. Most calorie-tracking tools do not offer intelligent, automatic calorie estimation, leaving users to
manually calculate nutritional content, which can lead to errors based on portion size and preparation
methods.
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Department of CSE(AI&ML)

6. Drawbacks of Existing System:
• Manual and Time-Consuming – Requires users to enter food names and portion sizes for every meal.
• Prone to Inaccuracy – Errors in spelling, portion estimation, or food description affect calorie counts.
• No Image Recognition – Lacks automation for identifying food items from photos.
• Lack of Intelligent Calorie Estimation – Users must manually calculate nutritional values, leading to
inconsistent results.
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Department of CSE(AI&ML)

7. Proposed System:
1. The system accepts image input from users (e.g., food photos), using Convolutional Neural
Networks (CNN) and transfer learning to accurately identify the food category.
2. It estimates calorie content by referencing predefined calorie values and approximating food
type and quantity based on the image, providing accurate calorie tracking.
3. The system tracks daily intake, generates progress logs, and offers personalized dietary
suggestions, including visualizations of progress to help users stay on track with their health goals.
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Department of CSE(AI&ML)

8. Advantages of Proposed System:
• Automated Food Recognition – Identifies food items from images using CNN and transfer learning,
reducing manual effort.
• Accurate Calorie Estimation – Calculates calorie content based on recognized food type and portion
size for reliable tracking.
• Personalized Health Insights – Tracks daily intake, generates logs, and provides tailored dietary
recommendations.
• Visual Progress Monitoring – Offers charts and visual feedback to keep users motivated toward their
health goals
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Department of CSE(AI&ML)

9. Software Requirements:
• Programming Language: Python
• Libraries & Frameworks: TensorFlow, Keras, OpenCV, Pandas, NumPy, Matplotlib/Seaborn
• Web Framework: Flask or Streamlit (for UI)
• Database: SQLite or MongoDB
• IDE: Jupyter Notebook / VS Code
• Operating System: Windows / macOS / Linux
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Department of CSE(AI&ML)

10. Hardware Requirements:
• Processor: Intel i5 / Ryzen 5 or higher
• RAM: Minimum 8 GB (16 GB recommended for faster training)
• GPU: NVIDIA GPU (e.g., GTX 1050 Ti or better) for deep learning acceleration
• Storage: Minimum 256 GB SSD (for dataset and model storage)
• Camera: Integrated or external camera for image input
• Internet Connection: Required for dataset download and model updates
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Department of CSE(AI&ML)

11. Thank You
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Department of CSE(AI&ML)