DeepTrayMeal: Automatic dietary assessment for Chinese tray meals based on deep learning

A vision-based dietary survey and assessment system for college students in China

Understanding the current dietary habits of college students is essential due to the pressing issues of unbalanced diets and insufficient nutrition. However, traditional approaches frequently depend on recollection, which can introduce unconscious bias and make them difficult to implement. Herein, we introduce a new computer vision system to evaluate the dietary habits of college students in China. A specialized food dataset comprising RGB-D images, recipes with ingredient masses, and nutrient composition was created using data collected from college canteens. First, object detection models were utilized to identify food categories and locations. Subsequently, we introduced a method for automatically estimating the food volume of nonstandard portions using position and depth information. The final nutrients were derived directly or indirectly through the database. Experimental results demonstrate the applicability of the YOLOv8 object detection model and volume estimation method to our tasks. To support the development of devices for detecting food intake and diet management, we have made our dataset publicly available. The College Canteen Food-100 dataset is publicly available at https://github.com/zichengzichengzi/CC-FOOD-100.

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.