A comparative study of vision-language models for food ingredient recognition and nutrient estimation.

The accurate assessment of food composition is essential to understanding its nutritional and sensory properties. Traditional dietary assessment methods are often constrained by subjective input and low reproducibility. This study explores the use of Vision-Language Models (VLMs) for automated food composition analysis, focusing on two key tasks: food ingredient recognition and nutrient estimation. We evaluated state-of-the-art VLMs using the Nutrition5K dataset, which contains real-world food images with ingredient-level annotations. To improve model sensitivity to complex food structures, we introduce a progressive multi-view image recognition approach that enhances ingredient recognition. We also propose a prompting strategy using ingredient labels to guide nutrient estimation. Results show that while most VLMs effectively identify primary food components, challenges persist in quantifying nutrient contents, particularly for composite or visually ambiguous dishes. Our findings highlight the promise and limitations of AI-assisted food composition analysis and offer insights for future methods integrating chemical, visual, and computational perspectives.