Diet Engine: A real-time food nutrition assistant system for personalized dietary guidance - Asim Moin Saad; Md. Raihanul Haque Rahi; Md. Manirul Islam; Gulam Rabbani (Food Chemistry Advances)
In an era where intelligent technologies are rapidly shaping our lives, a Real-Time Nutrition Assistant System emerges as an essential tool for maintaining a healthy lifestyle and promoting awareness. A Real-Time Nutrition Assistant System advances nutrition and healthcare technologies to improve public health by offering quick insight into the nutritional content of our meals. This study introduces Diet Engine, an innovative smartphone application powered by machine learning that enhances health outcomes by providing immediate food classification and personalized dietary suggestions. The system features modules using deep learning (DL) and Convolutional Neural Networks (CNNs) to detect food, as well as textual analysis and natural language processing (NLP) to estimate components such as nutritional content. It offers customized food suggestions according to the user's dietary preferences and constraints. Diet Engine accurately identifies and evaluates the nutritional value of food from images. The system employs a client-server architecture, using advanced deep learning techniques like YOLOv8 (You Only Look Once version 8) and Convolutional Neural Networks (CNNs) optimized for real-time object detection with 295 layers, for training and processing image requests. Our system outperforms existing algorithms, achieving an 86 % classification accuracy on food datasets. Moreover, a personalized chatbot provides diet advice, meal recommendations, and fitness suggestions. By seamlessly integrating advanced deep learning algorithms with user-centric features, this study underscores the transformative potential of Diet Engine in fostering healthier eating habits, raising nutritional awareness, and contributing to a global shift toward more informed and sustainable lifestyle choices.