Food Recognition and Calorie Estimation Using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802001Keywords:
Food Recognition, Calorie Estimation, Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Image Processing, Computer Vision, Nutrition Analysis, Health Monitoring, Diet Tracking SystemAbstract
The rapid growth of health awareness and fitness tracking, accurate monitoring of daily food intake and calorie consumption has become increasingly important. Traditional methods of calorie tracking rely on manual data entry, which is time-consuming, error-prone, and inconvenient for users. To address these limitations, this project proposes a Food Recognition and Calorie Estimation System using Machine Learning, which automatically identifies food items from images and estimates their corresponding calorie values.
The system uses computer vision and deep learning techniques to recognize different types of food from user-captured images. A convolutional neural network (CNN) model is trained on a labeled food image dataset to classify food items with high accuracy. Once a food item is recognized, its nutritional information, including calorie content, is retrieved from a predefined nutrition database. The system further estimates portion size using image-based features such as object area and volume approximation, enabling more accurate calorie calculation.
The application provides a user-friendly interface where users can upload or capture food images, view recognized food names, and receive instant calorie estimates. This system is designed to support healthy lifestyle management by helping users track their daily calorie intake effortlessly. The proposed solution has potential applications in diet planning, fitness monitoring, and healthcare management. Experimental results demonstrate that the system achieves satisfactory recognition accuracy and reliable calorie estimation, making it a practical and efficient tool for real-world use.
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