Published Paper Details:

This study pioneers the development of an advanced digital tool utilizing artificial intelligence (AI) for the classification and informational enrichment of a wide array of food items, transcending the initial focus on fruits and vegetables. Aimed at revolutionizing the way individuals' access and interact with nutritional information, our project leverages a sophisticated convolutional neural network (CNN) alongside natural language processing (NLP) technologies. These are designed not only to accurately identify diverse food items from images but also to generate detailed, user-friendly content regarding their nutritional content, health benefits, and culinary uses. The research embarked with a comprehensive dataset, initially comprising over 10,000 images of 500 different fruits and vegetables, achieving an impressive 98% accuracy in image classification. This success laid the groundwork for the subsequent expansion of our dataset to include a broader spectrum of food items, addressing the critical need for a more inclusive and versatile nutritional information tool. Our findings, underscored by a pilot user study with 50 participants, indicate significant engagement and positive feedback, suggesting the tool's potential to significantly impact public health by enhancing nutritional awareness and food literacy across diverse populations. Moreover, the planned expansion to include a wider variety of food items reflects our commitment to adapt and respond to the evolving dietary needs and preferences of the global community. This research not only demonstrates the feasibility of applying AI in the domain of nutritional education and public health but also highlights the potential for such technologies to foster informed food choices, support dietary planning, and contribute to the broader goals of improving public health and nutritional awareness on a global scale.