Deep Learning Applications and Challenges in Food Image Recognition and Nutrient Calculation

Food image recognition and nutrient calculation are technologies with vast application prospects. This paper summarizes the technological frontiers and challenges, delving into model training, nutrient calculation, applications of various methods and algorithms, and future research directions. Data augmentation techniques introduce random transformations, enhancing data diversity and improving model robustness. Data sets are divided into training, validation, and test sets, effectively monitoring model performance and generalization ability. Batch normalization techniques enhance convergence speed and model performance. The selection of suitable optimization algorithms such as SGD, Adam, or RMSprop is crucial for the training process. Learning rate scheduling dynamically adjusts learning rates, achieving a balanced optimization process. After recognizing the types of food images, nutrient components of each food, including calories, protein, carbohydrates, and fats, are calculated based on a food database. This includes estimating food weight and extracting nutrient information from the database. The establishment of a food database is crucial for accuracy and reliability and requires continuous updating and maintenance. Deep learning models, especially CNNs, excel in image feature extraction. They identify features such as the shape, texture, and color of food, supporting nutrient calculation. Traditional image processing methods are used for food weight estimation and image enhancement, improving recognition accuracy. Specialized algorithms and tools, such as image segmentation-based methods, color analysis techniques, and deep learning regression models, are used for different types of food image recognition and nutrient calculation. Future research directions include algorithm improvement, enhancing user experience, developing practical applications, and addressing privacy concerns. Challenges to be addressed include diverse food recognition and complex environmental conditions. Enhancing user experience involves developing user-friendly applications and tools to increase user satisfaction. Simultaneously, attention to privacy issues ensures the security and compliance of user data. The field of food image recognition and nutrient calculation has extensive research prospects. By improving algorithms, enhancing user experience, developing practical applications, and addressing privacy issues, better dietary management tools will be provided to users, promoting health management and dietary monitoring.