NutrifyAI: An AI-Powered System for Real-Time Food Detection, Nutritional Analysis, and Personalized Meal Recommendations

TL;DR

Preliminary results showcase the system's effectiveness by providing immediate, accurate dietary insights, with a demonstrated food recognition accuracy of nearly 80%, making it a valuable tool for users to make informed dietary decisions.

Abstract

With diet and nutrition apps reaching 1.4 billion users in 2022 [1], it's not surprise that popular health apps, MyFitnessPal, Noom, and Calorie Counter, are surging in popularity. However, one major setback [2] of nearly all nutrition applications is that users must enter food data manually, which is time-consuming and tedious. Thus, there has been an increasing demand for applications that can accurately identify food items, analyze their nutritional content, and offer dietary recommendations in real-time. This paper introduces a comprehensive system that combines advanced computer vision techniques with nutritional analysis, implemented in a versatile mobile and web application. The system is divided into three key concepts: 1) food detection using the YOLOv8 model, 2) nutrient analysis via the Edamam Nutrition Analysis API, and 3) personalized meal recommendations using the Edamam Meal Planning and Recipe Search APIs. Preliminary results showcase the system's effectiveness by providing immediate, accurate dietary insights, with a demonstrated food recognition accuracy of nearly 80%, making it a valuable tool for users to make informed dietary decisions.

Figures (8)

• Figure 1: End-to-end User Pipeline for NutrifyAI
• Figure 2: Comparison of mAP results across YOLO model variations at 0.5 IoU [5]
• Figure 3: Food tracking example in Google Sheets
• Figure 4: Nutrient analysis chart on web-app interface
• Figure 5: Meal Recommendation Prompting for User