• D. Vara Lakshmi
• G. Venkata Subba Rao
• K. Bhuvana Chandra
• V. Ram SatyaCharan
• R. Lalith Naga Venkata Sai Pavanu

Accurate dietary assessment is critical for maintaining health and managing various medical conditions. Traditional dietary tracking methods, however, are often time-consuming and rely on self-reported data, which can be inaccurate. This paper presents a deep learning-driven approach to food recognition that leverages advanced neural network architectures for automatic food identification and quantification. Using convolutional neural networks (CNNs) and transfer learning, we develop a model capable of recognizing a wide range of food items with high accuracy. Our model is trained on a diverse dataset to improve robustness across different food types, preparation styles, and lighting conditions. Experiments demonstrate that the proposed system achieves competitive accuracy compared to state-of-the-art methods and offers a feasible solution for real-time dietary assessment applications. The integration of this system with mobile platforms and health monitoring tools holds promise for enhancing user engagement in dietary tracking, ultimately contributing to better health outcomes. 3Accurate dietary assessment is a critical component of health management, particularly for individuals with specific nutritional requirements, such as those with chronic diseases, athletes, and the general population aiming to maintain a healthy lifestyle. Traditional methods of dietary tracking, such as food diaries and manual logging, are often prone to errors, whether through underreporting, misestimation, or simply forgetting to record meals.

Deep Learning Food Recognition Dietary Assessment Automated Nutrition Tracking Computer Vision Convolutional Neural Networks (CNNs) Food Image Classification Dietary Monitoring Health and Wellness Nutrition Analysis Machine Learning in Healthcare Food Databases AI in Healthcare

"A Survey on Deep Learning Driven Food Recognition for Accurate Dietary Assessment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.c155-c168, December-2024, Available :http://www.jetir.org/papers/JETIR2412216.pdf

"A Survey on Deep Learning Driven Food Recognition for Accurate Dietary Assessment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppc155-c168, December-2024, Available at : http://www.jetir.org/papers/JETIR2412216.pdf