Intelligent Food System: Generative AI for Smarter, Healthier Choices

Abstract

In the modern era, maintaining a healthy lifestyle has become increasingly challenging. Ensuring food safety and making informed dietary choices are crucial due to the rising threats of food adulteration and health-related concerns. Our research focuses on developing a Generative AI-based model capable of tasks such as ingredient analysis, food adulteration detection, nutritional assessment, and personalized dietary guidance. Leveraging cutting-edge Generative AI technologies, including Large Language Models (LLMs), Bidirectional Encoder Representations from Transformers (BERT), and Generative Adversarial Networks (GANs), the system delivers accurate and real-time evaluations. The approach involves assessing nutritional content, identifying adulterants, interpreting ingredient lists, and providing tailored dietary suggestions. The system employs advanced AI tools like GANs for precise food recognition and adulteration detection, LLMs for processing ingredient information, and BERT with cross-attention mechanisms for comparing product ingredients. By offering reliable and actionable insights, this approach promotes healthier eating habits. The methodology marks a significant step forward in enhancing overall well-being.