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Food Image Recognition and Nutrients Tracker

Aswan Girishkumar, Muhammed Asif S, Rohan Madhav, Suneer S, Jomy George

Abstract


The project introduces a web-based food image recognition and nutrient tracker, merging deep learning with web technologies to redefine dietary monitoring. Crafted with HTML, CSS, and Javascript, the user interface offers a seamless experience, enabling users to effortlessly capture food images, view identified items with corresponding nutrient information, and track intake within a visually appealing dashboard, complete with personalized insights and recommendations. Python orchestrates the back-end, utilizing the YOLOv8 deep learning model for real-time object detection, accurately identifying food items within images supported by a comprehensive food database offering detailed nutrient information. User profiles store dietary preferences, goals, and health conditions, enabling personalized feedback and recommendations tailored to individual needs, such as providing lower-calorie alternatives for weight management users. This convergence of technologies streamlines dietary tracking, enhancing efficiency and accuracy, while personalized insights foster healthier eating habits, potentially supporting various health goals like weight management and chronic disease prevention. Future efforts aim to expand the food database to include diverse cuisines, refine deep learning models for greater precision and optimize user experience through interface enhancements, ensuring the system's ongoing effectiveness in empowering users to manage dietary intake and promote healthier lifestyles.


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References


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