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@article{204819,
        author = {Viraj Vikas Sawant and Omkar Rohit Udale and Pranav Baban Patil and Prathmesh Shankar Mhangore and Amreshwar Sunil Mahimkar},
        title = {Nutri-Track: AI Nutrition Detection and Diet Suggestion using Deep Learning Web},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4273-4282},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204819},
        abstract = {In view of the increasing prevalence of diseases caused by lifestyles, it has become crucial to consume proper nutrition in recent times. Traditional approaches to food intake monitoring require manual input, making it both cumbersome and prone to errors. This research paper proposes the development of an intelligent system, Nutri- Track, designed to automatically recognize food types from images using deep learning and computer vision, assess nutritional content, and provide personalized dietary advice. For image recognition, this approach relies on the latest models of Convolutional Neural Networks such as Res Net, VGG, Efficient Net, and Mobile Net [2][3][4][5]. Databases containing standardized foods are used to estimate the nutritional value, and the recommendation engine provides diets according to the objectives that the consumer wishes to achieve, demonstrating an accuracy rate of over 75% for food recognition and a 3-7% difference in nutritional estimation.},
        keywords = {Deep Learning, Food Recognition, Nutrition Analysis, Diet Recommendation, CNN, Computer Vision.},
        month = {June},
        }