Research on Mango Detection and Grading by Machine Vision
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.
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