Apple grading method and system based on machine vision

Abstract

The invention discloses an apple grading method and system based on machine vision, and the method comprises the steps: collecting a set number of sample images, and building a sample image set; collecting a to-be-detected sample image, and carrying out image preprocessing; performing feature extraction on the sample to be detected; respectively extracting a color feature, a fruit shape feature, a fruit diameter feature and a surface defect feature of the to-be-detected sample image; establishing a classification standard, and establishing a classification model of sample characteristics and grades by utilizing a genetic algorithm and a BP neural network; and inputting the extracted color feature, fruit shape feature, fruit diameter feature and surface defect feature of the to-be-detected sample image into the classification model, and outputting the grade classification of the to-be-detected sample. The method has the advantages that clear apple surface images can be obtained, and classification errors are reduced. The BP neural network model optimized based on the genetic algorithm can accurately classify apples, and the classification efficiency of the apples is greatly improved.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a method and system for grading apples based on machine vision. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] In 2017, the world's total apple production was more than 76 million tons, and China accounted for about 57%, ranking first in the world. Among them, Shandong's annual output is as high as 9.4 million tons, ranking second in the country, and the Fuji series accounts for about 80%. On the contrary, in 2017, my country's apple export volume was less than 3% of the total output, and it was mainly oriented to some low-end markets. There is still a certain gap between the high-end apple market and European and American countries. In view of my country's current apple export status, research has found that one of the most important...

AI Technical Summary

Problems solved by technology

[0005] The inventors found that in the existing methods for grading apples by using machine vision, the grading standards are often only aimed at single factors such as fruit diameter and surface defects, and it is impossible to combine multiple factors to achieve apple grading. Such a grading method often leads to The grading of apples is inaccurate and manual picking is still required, resulting in inefficient grading

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