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@article{175537,
        author = {Nikhil B. Galfade and Prof. R. S. Deshpande and Parimal D. Ande and Yash V. Chopkar and Khushi S. Kirnapure and Rutuja R. Deulkar},
        title = {Nutrifit Vision : Smart Diet And Fitness Management System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3345-3350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175537},
        abstract = {A growing need for effective dietary management tools has arisen due to an increasing prevalence of obesity and dietrelated diseases. Here, a novel ML-based approach is introduced for food calorie detection. We leveraged large datasets with images of different types of food, along with the associated caloric value. Using machine learning algorithms, we trained models to predict food calories based on visual features. We also applied data preprocessing steps like image augmentation and normalization to improve model accuracy [10]. Model accuracy was then tested through established metrics with high accuracy within caloric estimation. Here we are proposing a Convolutional Neural Network algorithm called YOLOV8, which is mostly used for object detection, to detect the food items. The goal of this project is to develop model which recognizes the food item and estimates the calorie values in it. This system provides a platform where users can estimate the number of calories in a food item by just providing picture or videos of the food item and hence they can track the number of calories in their diet These results suggest that our machine-learning framework can help users make better dietary decisions through real-time calorie data from food images. This study adds to the growing body of literature looking at nutritional technology as a means of scaling up calorie tracking and helping people eat healthier [8]. Future research would be directed toward diversifying datasets and modeling to enhance precision and usability across a wide variety of food and culinary classes [2].},
        keywords = {},
        month = {April},
        }