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Calories Estimation of Food and Beverage using Deep Learning-based Image Analysis

Author(s) E. Mahammad Irfan, B. Priyanka, C. Uday Kiran, R. Vinod Kumar, K. Mahesh
Country India
Abstract Accurate monitoring of caloric consumption is crucial for sustaining a healthy lifestyle and mitigating the risk of lifestyle-related ailments, including obesity and diabetes. Conventional methods for estimating caloric intake necessitate manual food logging, a process that is both time-intensive and frequently imprecise. To overcome this limitation, this study introduces a deep learning-based methodology for the automated classification of food items and subsequent caloric estimation, leveraging food images. The proposed system employs Convolutional Neural Networks (CNNs) in conjunction with image processing techniques to analyse food photographs taken under diverse conditions. Initially, a comprehensive dataset, encompassing images of food and beverages from various cuisines and presentation styles, is gathered and subjected to preprocessing, including resizing, normalisation, and segmentation techniques.A deep convolutional neural network (CNN) model is utilised to classify food items by extracting hierarchical visual features from raw pixel data.Subsequently, a regression model is employed to compute calorie values, utilising the features derived from the trained CNN. The results from the experiment show that the method proposed has a very high accuracy in food classification and a very good consistency in calorie prediction. In addition, the system allows for the tracking of weekly calorie intake the user’s way, so the user can very well keep an eye on their eating habits. As a result, the suggested solution offers a practical and automated tool for nutritional evaluation, aiding individuals, dietitians, and healthcare professionals in promoting healthier eating habits.
Keywords Food Image Classification, Calorie Estimation, Deep Learning, Convolutional Neural Networks, Image Processing, Dietary Monitoring
Field Engineering
Published In Volume 7, Issue 4, April 2026
Published On 2026-04-04
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