Contact

AI Calorie Calculator and Food Recognition

Azati’s data scientists successfully developed a prototype for a calorie-counting application that uses AI to estimate the calorie content of complex dishes through image analysis. This solution can be applied in industries like agriculture, catering, sports, or for everyday life, making calorie tracking faster and more accurate.

Discuss an idea

All Technologies Used

Python
Python
Caffe
Caffe
OpenCV
OpenCV

Motivation

The objective was to develop a more efficient and accurate solution for calorie counting by using deep learning and computer vision. Unlike current calorie tracking applications that rely on manual data entry, Azati aimed to automate the process of estimating calorie content through the analysis of food images.

Main Challenges

Challenge 1
Training Data Quality

One of the key challenges was ensuring a sufficient amount of high-quality training data. Different products and cooking methods resulted in varied calorie counts, and Azati had to collect diverse images of dishes to account for all possible variations. For example, the calorie content of a boiled egg differs from a fried one, and this needed to be captured in the data.

Challenge 2
Image Quality and Angle Issues

The AI algorithm struggled with inconsistencies in lighting, angles, and dish sizes. To overcome this, Azati had to account for situations like partial food images or varying plate sizes. Manual adjustments were made to compare and calculate data accurately, ensuring that the algorithm could recognize and estimate calorie content correctly.

Key Features

  • AI-Based Food Recognition: The prototype uses AI and computer vision to automatically recognize food components in images, including complex dishes with multiple ingredients. The system estimates the calorie content of each component and calculates the total calories for the dish.
  • Visual Representation of Calories: The application visually displays the calorie count for each individual component of a dish, using frames to circle the recognized food items. This provides users with an easy-to-understand breakdown of their meal's nutritional content.
  • High Accuracy through Manual Mapping: To maximize accuracy, Azati’s team manually mapped various food components, such as separating the garnish from the meat. This ensures that the system delivers precise calorie counts even for dishes with multiple ingredients.
  • Real-Time Calculation: The AI-powered system processes images in real-time, providing users with an instant estimate of their meal’s calorie content, making it faster and more efficient than traditional manual input methods.

Our Approach

Market Research
Azati began by analyzing existing software in the market to understand basic operational principles and identify areas for improvement. This step ensured the team could build on existing technologies and enhance the user experience.
Algorithm Development
After studying the necessary documentation and tools, Azati’s team developed a custom algorithm to process food images and estimate calorie content. The algorithm was designed to handle complex dishes and multiple ingredients.
Data Collection and Preparation
Azati collected and prepared high-quality images of dishes for training the AI model. The process involved gathering images from open sources and creating new ones manually to ensure a wide variety of food types and cooking methods were represented.
Machine Learning Training
Once the images were prepared, Azati trained the machine learning model to automatically recognize and categorize food components. The team manually mapped categories, ensuring the AI could distinguish between different parts of a dish, such as garnish and meat.
Template Creation
For each group of food components, Azati created templates that helped the system process the images with maximum accuracy. These templates were used to analyze all images within their respective categories, ensuring consistent results.
Iterative Refinement
The system underwent multiple rounds of processing to refine the data. Each round improved the accuracy of the calorie estimations, resulting in a highly functional prototype.

Project Impact

Azati successfully developed a prototype that simplifies calorie counting by automating the process through AI and computer vision.

The solution makes it easier for users to track their daily calorie intake, offering applications in healthcare, fitness, and everyday nutrition.

By automating food recognition and calorie estimation, this system provides a faster, more efficient way for users to stay on top of their nutritional goals.

Ready To Get Started

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.