A mutton color decision method and system based on fuzzy logic

CN121686002BActive Publication Date: 2026-07-24ANHUI AGRICULTURAL UNIVERSITY
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Patent Information

Application Number
CN202511794617.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-07-24
Estimated Expiration
2045-12-02

AI Technical Summary

Technical Problem

Existing methods for automatic grading of mutton color suffer from optical interference caused by specular reflection and subsurface scattering, which leads to distortion of color characteristics and affects the accuracy of meat quality assessment.

Method used

By using feature extraction and image segmentation based on the HSV color space, specular reflection highlight areas and subsurface scattering diffuse areas are identified. The edge interweaving complexity and effective scattering path depth are calculated to generate color distortion compensation coefficients. Combined with a fuzzy logic decision system, flesh color level is determined.

Benefits of technology

It effectively eliminates color distortion caused by optical interference, improves the reliability and accuracy of color feature data, and enhances the consistency of flesh color grading results with human evaluation.

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Abstract

The application discloses a mutton color decision method and system based on fuzzy logic, and particularly relates to the technical field of computer vision, and is used for solving the problem that the existing mutton color automatic grading method is influenced by the color feature distortion caused by specular reflection and subsurface scattering and the grading accuracy; the mutton color decision method and system are realized by the following steps: collecting a digital image of a mutton surface and performing pretreatment, extracting hue, saturation and brightness parameters based on an HSV color space as an initial color feature set, identifying a specular reflection highlight area and a subsurface scattering diffuse area, calculating edge interweaving complexity and effective scattering path depth, generating a color distortion compensation coefficient based on the parameters, applying the coefficient to linearly adjust the initial color feature, and inputting the corrected feature into a fuzzy logic decision system to output a mutton color grade result.
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Citation Information

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