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Apple grading method and system based on machine vision

A technology of machine vision and grading method, applied in neural learning methods, instruments, genetic models, etc., can solve the problems of low grading efficiency and inaccurate apple grading, and achieve improved grading rate, high grading accuracy and good grading effect Effect

Pending Publication Date: 2019-09-24
UNIV OF JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

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  • Apple grading method and system based on machine vision
  • Apple grading method and system based on machine vision
  • Apple grading method and system based on machine vision

Examples

Experimental program
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Effect test

Embodiment 1

[0056] In one or more implementations, a method for grading apples based on machine vision is disclosed, such as figure 1 shown, including the following steps:

[0057] (1) Collect a set number of sample images and construct a sample image set;

[0058] (2) Collect the image of the sample to be tested, and perform image preprocessing;

[0059] (3) feature extraction of the sample to be tested; extract the color feature, fruit shape feature, fruit diameter feature and surface defect feature of the sample image to be tested respectively;

[0060] (4) Formulate grading standards, use genetic algorithm and BP neural network to establish the classification model of sample features and grades; use the sample image set to train the classification model;

[0061] (5) Input the color feature, fruit shape feature, fruit diameter feature and surface defect feature of the sample image to be tested into the post-training classification model, and output the grade classification of the sa...

Embodiment 2

[0100] In one or more implementations, a machine vision-based apple grading system is disclosed, comprising:

[0101] A module for collecting a set number of sample images and constructing a sample image set;

[0102] A module for collecting images of samples to be tested and performing image preprocessing;

[0103] It is used for feature extraction of the sample to be tested; a module for separately extracting the color features, fruit shape features, fruit diameter features and surface defect features of the sample image to be tested;

[0104] A module for establishing a classification model of sample characteristics and grades using genetic algorithms and BP neural networks;

[0105] A module for inputting the extracted color features, fruit shape features, fruit diameter features and surface defect features of the image of the sample to be tested into the classification model, and outputting the grade classification of the sample to be tested.

Embodiment 3

[0107] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program realizes the apple grading method based on machine vision in the first embodiment. For the sake of brevity, details are not repeated here.

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06N3/12G06T7/00
CPCG06N3/084G06N3/126G06T7/0002G06F18/214
Inventor 申涛赵钦君毕淑慧徐元聂茂勇
Owner UNIV OF JINAN
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