A Multilingual, Generative AI-Based Food Calorie Estimation Method: Algorithms, Advantages, and Comparative Analysis

Abstract

The rapid evolution of artificial intelligence (AI) and deep learning has transformed the field of nutritional analysis, offering significant improvements over traditional methods in food recognition and calorie estimation. Conventional techniques based on convolutional neural networks (CNNs) have shown promise yet remain limited by extensive data requirements, language dependence, and inadequate nutritional insights. In this paper, we propose a novel, multilingual, generative AI-based approach that leverages large multimodal models (LMMs) such as Google’s Gemini Pro Vision and OpenAI’s GPT-4 Vision. Our solution integrates robust image validation, dynamic prompt engineering, and multilingual natural language processing to deliver detailed calorie estimates and nutritional breakdowns while overcoming the challenges inherent in CNN-based systems. We detail the underlying algorithms, provide a conceptual system flowchart, and present comparative analyses against traditional approaches. Finally, our consolidated “Proposed Solution and Future Directions” section describes the system architecture, implementation details, and outlines the future research agenda.