Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances

Nutrition plays a vital role in maintaining human health. Traditional methods used for assessing food composition & nutritional content often require destructive sample preparation, which can be time-consuming and costly. Therefore, computer vision-based approaches have emerged as promising alternatives that enable rapid and non-destructive analysis of various nutritional parameters in foods. In this review, we summarized computer vision applications in meat processing, grains, fruits and vegetables, and seafood. We reviewed recent advancements in computer vision and deep learning-based algorithms employed for food recognition and nutrient estimation. Various existing food recognition and nutrient estimation datasets are also reviewed. Conventional methods offer some limitations, while vision-based technologies provide quick and non-destructive analysis of food composition & nutritional content. Computer vision and deep neural network architectures provide remarkable accuracy for food nutrient measurement. In conclusion, deep learning-based models are paving the way for a promising future in nutritional and health optimization research. In the future, vision-based technologies are expected to transform food classification and detection by enabling more rapid, affordable, and accurate nutritional analyses. Therefore, computer vision is developing into a useful tool for fast and precise evaluation of food nutrients without enabling samples to be damaged.