Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for detecting and grading greening potatoes based on machine vision

A detection method and machine vision technology, applied in sorting and other directions, can solve the problems of unstable classification accuracy, difficult to detect greening defects, and large labor.

Inactive Publication Date: 2013-11-20
CHINA AGRI UNIV
View PDF3 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The main disadvantages of manual grading are: large amount of labor, low productivity; unstable grading accuracy: the grading standard is greatly affected by subjective; workers are in direct contact with the fruit, which affects the hygiene and safety of food and it is difficult to achieve fast, accurate, non-destructive and intelligent grading
For example, a defect in greening is evident in machine vision images with high green pixel values, however, bright whitish spots on the surface of potatoes also have high green pixel values ​​as well as high red and blue values, So greening defects are difficult to detect
There are several inspection methods, such as multivariate discriminant method, neural network, and stochastic model method, which have been used in green defect detection and classification attempts, but these algorithms have high computational complexity and are not suitable for online real-time detection grading

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for detecting and grading greening potatoes based on machine vision
  • Method for detecting and grading greening potatoes based on machine vision
  • Method for detecting and grading greening potatoes based on machine vision

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0070] Such as figure 1 Shown a kind of greening potato detection method based on machine vision, comprises the steps:

[0071] (1) Roll continuously in the image collection area, continuously collect three different surface images, and the coverage rate is 95% of the potato surface; measure the pixel values ​​of the three channels R, G and B of the complete image of the potato, and then use the G and B channels The global threshold region segmentation method of the gray value difference filters out the background and obtains a potato segmented image (see figure 2 );

[0072] (2) Calculate the potato centroid, and remove the potato outline proportionally based on the centroid,

[0073] First calculate the potato centroid, X = 1 N Σ i = 1 n x i , Y = ...

Embodiment 2

[0100] Compared with embodiment 1, the difference is only in step 6, step 6 of this embodiment is specifically:

[0101] Set the threshold β as 10, compare the green point with the threshold β, and grade the potatoes. When the green point is greater than the threshold β, which is 10, the potato is judged as a green potato; when the green point is less than 10, it is judged as a normal potato.

Embodiment 3

[0103] Compared with Embodiment 1, the only difference is that the threshold β of this embodiment is 9 or 12.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for detecting and grading greening potatoes based on machine vision. The method comprises the following steps of: (1) collecting a complete potato image and filtering a background to obtain a potato split image; (2) calculating a figure center of a potato and removing a potato outline in proportion by taking the figure center as a standard; (3) scanning a target region of the potato and calculating an H value point by point; (4) comparing the calculated H value with a threshold value alpha, wherein if the H value is equal to the threshold value alpha, a pixel point is taken as a greening point, but if not, the pixel point is taken as a normal skin point; and (5) setting the threshold value and counting the quantity of greening points, and comparing the greening points with a threshold value beta for grading the potatoes. The method disclosed by the invention is low in computation complexity and easy to realize, is applicable to rapid and real-time grading of the potatoes, is strong in objectivity and high in efficiency, and has very good application prospect.

Description

technical field [0001] The invention relates to a method for detecting and grading green potatoes, in particular to a method for detecting and grading potatoes with green skin based on an H (hue) channel gray value discrimination method. Background technique [0002] Potato is a kind of popular agricultural product with rich nutrition and both food and vegetables, which has the advantages of high yield, wide application and high economic value. Potato enters the market as a commodity with great value, and its quality is the prerequisite for whether it can gain a competitive advantage in the market competition. [0003] At present, the post-production treatment of domestic potatoes is mainly to detect the shape, size, maturity and surface defects of potatoes, and the detection is mainly carried out manually. The main disadvantages of manual grading are: large amount of labor, low productivity; unstable grading accuracy: the grading standard is greatly affected by subjective;...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): B07C5/342
Inventor 谭豫之李聪郭辉李博李伟张俊雄
Owner CHINA AGRI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com