A steak grading method based on decision tree induction learning

Abstract

The invention discloses a steak grade division method based on decision tree induction learning, which mainly comprises the following steps: A. establishing a steak grade division model, uniformly collecting a cross-sectional image of beef tenderloin, preprocessing the image, and eliminating image background; B, calculate that edge distribution of the steak image, carrying out level set evolutionon the steak image to obtain an image gradient, thereby extract the edge of the image and carrying out muscle and fat segmentation; C. establishing pixel gray geometric moments of steak marbling, andextracting the pixel gray moments as marbling image features; D. Establish the correlation function of marbling feature as the test attribute to construct the decision tree, carry on the inductive learning, classify the steak automatically, finish the steak grade division. The method has good stability and robustness, can achieve large-scale, standardized, batch, muscle and fat segmentation accurate, objective evaluation results, evaluation speed, high accuracy, so as to ensure the efficient and continuous supply of goods.

Description

technical field [0001] The invention relates to a steak grading method based on decision tree inductive learning, which belongs to the fields of food production, image recognition and mathematics. Background technique [0002] As a signature dish of western food, steak is favored by many consumers. As steak becomes more and more popular, consumers pay more attention to the grade of steak. At present, most steaks are graded by marbling, which is difficult to judge only by human senses. Due to the subjective factors of graders and environmental factors, the efficiency is low and the speed of classification is slow. However, using computer vision for non-destructive testing, due to the messy distribution of muscle and fat in steak, the efficiency of target extraction is low, and the classification is inaccurate, which may easily cause losses to the enterprise. Contents of the invention [0003] In order to solve the above problems, the object of the present invention is to p...

AI Technical Summary

Problems solved by technology

At present, most steaks are graded by marbling, which is difficult to judge only by human senses. Due to the subjective factors of graders and environmental factors, the efficiency is low and the speed of classification is slow.

However, using computer vision for non-destructive testing, due to the messy distribution of muscle and fat in steak, the efficiency of target extraction is low, and the classification is not accurate, which may easily cause losses to the enterprise.

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