Food Recognition and Nutrition Estimator Using Computer Vision and Machine Learning

Vijayaraghavan R, Dr and V. S. Anita Sofia

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 5, 523-536

Abstract: Food logging works when it is simple enough to become a habit. Traditional nutrition programs often require the user to browse a list of foods, choose a food, and guess at a portion size, and then repeat the procedure for each food item in a meal. CrunchUP resolves that friction by using a photo of a meal to create a nutrition record. The system is a complete web application, where a user uploads an image or takes a picture using their camera, a FastAPI service checks and standardizes the image, and the Google Gemini vision model identifies the foods visible in the image and estimates the quantity, calories, protein, carbohydrates, fat, and fibre. The response is validated, converted into application objects, displayed to the user and optionally stored in PostgreSQL. The information on a personal profile includes height, weight, age, gender, activity level, and fitness goal. These values are used for the calculation of BMI, basal metabolic rate according to Mifflin–St Jeor, total daily energy expenditure, calorie adjustment which is goal oriented and macronutrient targets. The work is important because it brings together recognition, nutrition estimation, personal targets, meal history, trends and feedback for decision making in a single stream. The report describes the requirements, literature background, architecture, database design, algorithms, security measures, implementation, verification strategy, restrictions and extensions for the future. Separation of concerns is evident in the repository: the client is responsive (React, Vite); the service and data are implemented (FastAPI, Pydantic, SQLAlchemy); access is protected (JWT, bcrypt); images are normalized (Pillow); and multimodal output is schema shaped (Gemini 2.5 Flash). The dashboard consolidates intake and translates progress data into easy-to-understand recommendations. A key finding is that a well-defined vision-language service can help ease recording effort in the diet, but needs to be interpreted as guidance, not a clinical measurement. There is a need for future work including calibrated portion references, verified food databases, confidence reporting, culturally wider evaluation, personalised meal recommendation, and the development of a mobile app in the native application platform "React Native".

Keywords: food recognition; nutrition estimation; computer vision; machine learning; Gemini; FastAPI; React; dietary assessment (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612569
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i5:id:2198

DOI: 10.32628/CSEIT2612569

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