Calorie Measurement and Food Recognition Using Machine Learning

This paper proposes a novel approach leveraging machine learning for accurate calorie estimation and food recognition. The system employs computer vision techniques to identify and classify food items from images, utilizing convolutional neural networks (CNNs) trained on diverse food datasets. Subsequently, it integrates this recognition with nutritional databases to estimate calorie content based on portion sizes and ingredients. K Nearest Neighbour, VGG16 Model, and image processing are used to enhance accuracy in recognizing various food items and their nutritional composition. The system aims to provide a user-friendly interface for individuals to track dietary intake, promote healthier eating habits, and facilitate more precise nutritional analysis. Experimental results demonstrate the efficacy of the proposed method in accurately identifying and quantifying food items, enabling more efficient and reliable calorie measurement for dietary management and health monitoring.