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An image-based food quality analysis framework driven by large language models
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AI
DOI：10.1039/D6AY00214E.png)
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Abstract
En 中文
Although computer vision approaches based on machine learning (ML) and deep learning (DL) have been applied to image-based food quality analysis; they typically require large labeled datasets; intensive training; and expert knowledge. Recent advances in large language models (LLMs) have enabled enhanced reasoning capabilities and flexible multimodal perception. In this work; an LLM-driven vision inspection framework featuring two inference routes was developed. The text-only inference (TOI) converts pre-extracted image features into structured textual prompts for LLM-based reasoning. The vision-language inference (VLI) directly processes raw food images using LLMs with built-in visual encoders; enabling end-to-end multimodal inference. A lightweight graphical user interface (GUI) was integrated to automate feature extraction and structured data generation with one-click operation. Several state-of-the-art LLMs were evaluated on food ripeness; freshness; and authenticity datasets. Using GPT-5; the TOI approach attained an accuracy of 1.000 on low-complexity datasets and maintained strong performance (0.875) on more complex tasks; while the VLI approach demonstrated consistently high accuracy (0.964–1.000) with substantially fewer training images; matching or surpassing traditional ML and DL baselines. The proposed framework relies solely on prompt-based in-context learning; eliminating task-specific fine-tuning. These results demonstrate the feasibility and practicality of efficient; fine-tuning-free; LLM-driven vision inspection for food quality analysis.
Keywords:
Large language models
Vision inspection
Food quality analysis
In-context learning
Multimodal inference