海南师范大学学报（自然科学版）2024，Vol.37Issue(1)：56-64,9.DOI:10.12051/j.issn.1674-4942.2024.01.007
基于机器视觉的芒果检测与分级研究
Research on Mango Detection and Grading by Machine Vision
摘要
Abstract
In order to improve the accuracy and efficiency of mango detection and grading of Royal mango.Firstly,we take photos of mango with a calibrated industrial camera,the mango image is pre-processed with HALCON for graying and im-age segmentation.Five characteristic parameters of mango area,fruit shape index,maturity,defect area and defect number are extracted and normalized,then we take them as input vectors of GMM,MLP,SVM and KNN classifiers respectively and take the four grades of mango as output vectors of the classifier.Finally,120 training samples and 60 test samples are used to train and test the four classifiers.The results show that the average accuracy rates of the four classifiers are 92.5％,93.75％,98.75％and 98％respectively.The accuracy rates are all high and have certain practical value.关键词
芒果/机器视觉/HALCON/分类器Key words
mango/machine vision/HALCON/classifier分类
信息技术与安全科学引用本文复制引用
吴建清,苏信晨..基于机器视觉的芒果检测与分级研究[J].海南师范大学学报（自然科学版）,2024,37(1):56-64,9.基金项目
海南省高等学校科学研究项目(Hjkj2013-23) （Hjkj2013-23）
