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

Background: 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. Scope and approach: 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. Key findings and conclusions: 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.