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基于机器视觉的苹果品质在线分级检测

石瑞瑶 田有文 赖兴涛 古文君

中国农业科技导报2018,Vol.20Issue(3):80-86,7.
中国农业科技导报2018,Vol.20Issue(3):80-86,7.DOI:10.13304/j.nykjdb.2017.0580

基于机器视觉的苹果品质在线分级检测

Development of Apple Intelligent On-line Inspection and Classification System Based on Machine Vision

石瑞瑶 1田有文 1赖兴涛 1古文君1

作者信息

  • 1. 沈阳农业大学信息与电气工程学院,沈阳110866
  • 折叠

摘要

Abstract

At present,the automation grading level of apple was lower.A set of apple intelligent online inspection grading system was designed to achieve an automatic,fast and accurate grading of apple quality.Taking the Hanfu apple as test object,this paper studied on apple grading by machine vision technology.The positive image of apple was divided by threshold segmentation method,and the external contour of apple was extracted by the pixel-by-pixel traversal method.The apple size feature was extracted by calculating the distance from each point to the center of gravity.At the same time,fruit shape features was extracted by calculating the ratio between apple transverse and longitudinal diameters.The support vector machine (SVM) method was used to separate the apples from both sides.Apple color characteristics were extracted by calculating the proportion of red pixels accounted for entire apple pixels,and the defective part was extracted by Fisher statistics.In this study,the hardware of the whole grading system andfunction of the software was realized.400 apple samples were graded using this system,and the results showed that the total grading accuracy rate by this system was 95%.The design of this apple intelligent online inspection and grading system based on machine vision overcomed the shortcomings of traditional grading methods,accelerated the automatic degree of quality grading,and was of important significance in the field of studying fruit quality grading.

关键词

机器视觉/苹果分级/逐像素遍历法/支持向量机/特征提取/在线检测

Key words

machine vision/apple grading/pixel-by-pixel traversal method/SVM/feature extraction/on-line detection

分类

农业科技

引用本文复制引用

石瑞瑶,田有文,赖兴涛,古文君..基于机器视觉的苹果品质在线分级检测[J].中国农业科技导报,2018,20(3):80-86,7.

基金项目

辽宁省大型仪器设备共享服务项目(LNDY201501003) (LNDY201501003)

沈阳市大型仪器设备共享服务专项(F15-166-4-00)资助. (F15-166-4-00)

中国农业科技导报

OA北大核心CSCDCSTPCD

1008-0864

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