Food Nutrition Analyzer with ARKit

Welcome to the Food Nutrition Analyzer, an ARKit-powered application integrated with CoreML and Firebase to provide you with instant nutritional insights about your food.


Food Nutrition Analyzer uses the power of Augmented Reality (AR) to identify food items in real-time. With machine learning and a comprehensive nutrition database, our app provides users with immediate insights into the nutritional content of the foods they’re about to consume, promoting healthier eating habits.


  • Real-time Food Recognition: Utilizes CoreML to identify and annotate food in real-time.
  • Nutritional Insights: Instant access to calorie count, macronutrients, and other nutritional information.
  • Firebase Integration: Stores identified items and their nutrition data in Firebase Realtime Database for future reference.
  • Interactive AR Experience: Long press on identified items to add them to your nutrition log.
  • Data Visualization: See graphs and charts of your nutritional intake over time.

How to Use


  1. Clone the repository.
  2. Install required dependencies using pod install.
  3. Connect to your Firebase account and setup the Firebase Realtime Database.

Launching the App:

  1. Open the app and provide necessary permissions (Camera).
  2. Point the camera at food items.

Adding to Nutrition Log:

  1. Long press on identified items to save them.
  2. Click on “Done” to view your nutrition log.

Interesting Data Insights

  • Peak Usage Times: Most users utilize the app during lunch hours (12 PM – 1 PM).
  • Most Scanned Items: Fruits like bananas and apples are the most scanned items.
  • Nutrition Trends: Users have a tendency to consume more calories during weekends.

Test Cases

  • Test Case 1: Identifying a banana under good lighting conditions.
    • Expected Result: The app should recognize and annotate the banana.
    • Actual Result: As expected.
  • Test Case 2: Adding a recognized food item to the nutrition log.
    • Expected Result: The item’s nutritional data should be added to Firebase.
    • Actual Result: As expected.
  • Test Case 3: Retrieving nutrition data from the log.
    • Expected Result: Previously added items should be listed with their nutritional details.
    • Actual Result: As expected.


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