Abstract: Maintaining a healthy body in modern society necessitates careful monitoring of calorie intake to achieve and sustain an optimal Body Mass Index (BMI). Traditional methods of calorie estimation, which are often manual and cumbersome, hinder the feasibility of regular use. This project introduces a novel, automated approach to calorie estimation using deep learning algorithms, specifically convolutional neural networks (CNNs), to classify and estimate the calorie content of food items from images. By leveraging Tensor Flow, a robust machine learning framework, the system is capable of detecting various food types, such as fruits and vegetables, and calculating their respective calorie values. Additionally, the integration of Google's Generative AI (Gemini) enhances the system by providing comprehensive nutritional information beyond calorie content, offering users insights into macronutrient composition and other health-related data. Techniques like prompt engineering and template patterns are employed to ensure that the generated information is accurate and contextually relevant to the user's dietary needs.
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DOI:
10.17148/IJARCCE.2024.13859
