Determination of the imaging system for seedless white grape based on image evaluation

Authors

LI Ze-ping, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
LI Xue-lian, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
ZHOU Jun, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
GUO Jun-xian, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
HUANG Hua, College of Mathematic and Physics, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
GUO Yang, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
ZHANG Liang-liang, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China

Abstract

In order to obtain the image of seedless white grape, the influence of background paper color, light source structure and lighting mode on the image was fully considered on the self-made acquisition platform. Firstly, the optimal level of each factor is determined by single factor test, then the acquisition platform is optimized by orthogonal test, and finally the optimal value level of each factor is determined by subjective and objective comprehensive evaluation image method. The results showed that the best values of the machine vision imaging system of seedless white grape were: the background paper is white, the light source structure is strip light source -60°, the lighting mode is open field mode. Under the conditions, the image quality is the best.

Publication Date

1-28-2021

First Page

161

Last Page

164

DOI

10.13652/j.issn.1003-5788.2021.01.026

Recommended Citation

Ze-ping, LI; Xue-lian, LI; Jun, ZHOU; Jun-xian, GUO; Hua, HUANG; Yang, GUO; and Liang-liang, ZHANG (2021) "Determination of the imaging system for seedless white grape based on image evaluation," Food and Machinery: Vol. 37: Iss. 1, Article 26.
DOI: 10.13652/j.issn.1003-5788.2021.01.026
Available at: https://www.ifoodmm.cn/journal/vol37/iss1/26

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