SmartCal: Calorie Estimation of Local Nepali Cuisine with Deep Learning-Powered Food Detection

Abstract

Diet observation is a critical component of preventive healthcare, yet traditional manual calorie tracking methods are often inaccurate and time-consuming. This paper presents SmartCal, an automated system for calorie estimation of local Nepali cuisine using deep learning. The system focuses on foods like rice, lentils, spinach, and boiled eggs, which are staples in Nepali diets. It employs an ESP32 Camera Module to capture food images, which are processed by a Mask R-CNN (Region-Based Convolutional Neural Network) model trained specifically for Nepali cuisine. The model achieves a mean Average Precision at 50 (mAP50) of 99%, demonstrating high accuracy in food detection. Calorie estimation is performed based on standard cooking conditions and fixed portion sizes. Results are displayed via a web application, where users can set daily calorie targets and track their intake history. This system provides a practical, accurate, and user-friendly solution for dietary monitoring, particularly suited to the dietary habits of Nepali individuals.