Advancements in Real-Time Technology for Dietary Assessment

Dietary assessment is pivotal for exploring the impacts of diet on health and disease management and for the evaluation of dietary interventions. Yet, the challenge persists in accurately gauging energy (kcal) intake, diet quality, and the consistency of eating patterns. Conventional methods such as paper-based food records, although common, are cumbersome and lack the ability to provide immediate dietary feedback which is crucial for promoting healthy dietary behaviors. A rising trend in research and industry practice is harnessing technology like computer vision for accurate food volume and nutrient estimation to enhance this process.

This Research Topic aims to showcase cutting-edge methods for quantifying food intake behaviors in both controlled lab environments and everyday settings. Emphasizing techniques with mobile applications and remote sensing technologies, these new methods strive for real-time or nearly real-time data capture and comparison to establish gold standards for accuracy and reliability.

To invite comprehensive exploration within prescribed boundaries, this Research Topic encompasses technologies capable of instant dietary feedback. We welcome contributions addressing, but not limited to, the following themes:
- Image-based food and ingredients recognition
- Utilization of image datasets for AI-driven healthy eating patterns
- Personalized nutrition through image analysis
- Application of computer vision in food industry standards
- Cutting-edge imaging for raw and processed food assessment
- Innovative imaging technologies for dietary management in clinical settings
- Spectral imaging for food quality and safety evaluation
Submissions from diverse fields such as nutrition science, engineering, computer science, and industry are encouraged to broaden the discussion and application of these technological advancements.

Keywords: computer vision, machine learning, artificial intelligence, image processing, food image, food recognition, image-based, Nutrition AI, mobile food record, dietary assessment, just-in-time

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.