An image-based food quality analysis framework driven by large language models

Xiao-Yue Yin; HL Wu; Luo-Yuan Han; Ye He; X G Wang; T H Wang; Ru-Qin Yu

doi:10.1039/d6ay00214e

January 1, 2026Analytical Methods

An image-based food quality analysis framework driven by large language models

View at Publisher

Ask AI

Bookmark

Share

Key Points

To develop a framework for food quality analysis leveraging large language models and visual inspection.
Created a GUI for one-click automation of food quality assessment.
Implemented two inference paths: text-only and vision-language.
Models evaluated on tasks for ripeness, freshness, and authenticity.
Framework automates the inspection process for food quality.
Demonstrated effectiveness in assessing ripeness and authenticity.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

An image-based framework improves food quality assessment for ripeness and authenticity, suggesting automation benefits.

Abstract

LLM-based visual inspection framework with two paths: text-only inference and vision-language inference. A lightweight GUI enables one-click automation. Models are evaluated on ripeness, freshness, and authenticity tasks.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Yin et al. (2026) studied this question.

synapsesocial.com/papers/69f154f9879cb923c494553b https://doi.org/10.1039/d6ay00214e

View at Publisher

Ask AI

Bookmark

Share

An image-based food quality analysis framework driven by large language models

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

An image-based food quality analysis framework driven by large language models

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading