A method and system for meat quality evaluation based on digital image processing

By using multi-angle, multi-channel imaging and illumination calibration techniques, the fascia fracture characteristics of meat products were extracted, solving the problem of identifying freeze-thaw damage in meat product texture assessment and achieving non-destructive, quantitative texture assessment results.

CN121190482BActive Publication Date: 2026-02-27SCI & TECH SUPPORT CENT SICHUAN ACAD OF AGRI SCI
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Patent Information

Application Number
CN202511734785.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-02-27
Estimated Expiration
2045-11-25

AI Technical Summary

Technical Problem

Existing methods for assessing the texture of meat products are insufficient to accurately identify the impact of freeze-thaw damage on tissue structure under non-destructive conditions. Image features cannot truly reflect the degree of tissue degradation, resulting in insufficient reliability and universality in assessing the texture of meat products.

Method used

By combining multi-angle and multi-channel imaging with illumination and polarization calibration, multi-angle/multi-channel image sequences of meat product samples are obtained. Specular and diffuse reflection are jointly unmixed and inversely rendered to extract fascia fracture features that ensure the continuity of tissue structure, correct texture consistency, generate freeze-thaw damage sensitive indicators, and input them into the texture assessment model to output texture assessment results.

Benefits of technology

It enables non-destructive, quantitative, and repeatable assessment of meat product texture, accurately identifying the impact of freeze-thaw damage on tissue structure and improving the objectivity and reliability of texture assessment.

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Abstract

The embodiment of the application provides a meat product quality evaluation method and system based on digital image processing, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a multi-angle / multi-channel image sequence of a meat product sample to obtain a calibration sequence for reflection component separation; performing joint demixing and inverse rendering correction of specular and diffuse reflection to obtain a net texture diffuse reflection image and a polarization map; extracting fascia rupture features based on the net texture diffuse reflection image and the polarization map, and correcting texture consistency based on the fascia rupture features to determine a tissue degradation score to form a freeze-thaw damage sensitive index; performing gated weighted fusion based on the freeze-thaw damage sensitive index to generate a freeze-thaw damage index vector, and inputting the freeze-thaw damage index vector into a texture evaluation model to output a texture evaluation result. The scheme of the application realizes adaptive fusion and quantitative evaluation of multi-source freeze-thaw damage features in the structure, moisture and optical layers, thereby significantly improving the accuracy and reliability of meat product quality evaluation.
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