Deep Learning Approaches for Nutritional Profiling of Food Plates

Accurate nutritional profiling is a cornerstone of personalized healthcare and chronic disease prevention, yet manual dietary tracking remains burdensome and prone to error. This review examines the application of deep learning to automate nutritional assessment through image-based analysis of food plates. We discuss the progression of computational frameworks used to identify food items, estimate portion sizes, and quantify macro- and micronutrient content. Specifically, we analyze the utility of convolutional neural networks (CNNs), such as ResNet and EfficientNet, alongside vision transformers for capturing global context, and generative adversarial networks (GANs) for synthetic data generation. Performance evaluations across benchmark datasets, including Food-101 and Nutrition5k, demonstrate substantial improvements in classification accuracy and caloric estimation. However, we identify persistent technical and systemic barriers: dataset bias that limits applicability across diverse culinary cultures, poor generalization under variable imaging conditions, and the computational latency of deploying heavy models on mobile devices. To address these issues, we propose that future research must pivot toward multimodal integration, combining visual data with textual or sensor-based inputs, and the development of lightweight architectures suitable for edge computing. This article synthesizes current capabilities and limitations, outlining the trajectory required to translate computer vision advancements into reliable, real-world public health tools.