A mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering

The anti-counterfeiting of agricultural products plays an important role in protecting the rights and interests of consumers and maintaining the healthy development of the food market.Traditional anti-counterfeiting technology mainly relies on anti-counterfeiting features of packaging or labeling,which has the risk of being copied and reused.Biological fingerprint anti-counterfeiting is a method of anti-counterfeiting that takes the biological fingerprint of agricultural products as the anti-counterfeiting feature.This paper aims to take the distribution of lenticels on the surface of mango as a biological fingerprint,and propose a mango biological fingerprint anti-counterfeiting method.As the mango ripens,the peel color of mango will change significantly,which will affect the accuracy of anti-counterfeiting identification.In this paper,the images of ripe mangoes are classified by Fuzzy C-means clustering,and appropriate image enhancement technology is used to highlight the features.The results show that the mango biological fingerprint anti-counterfeiting method based on Fuzzy C-means clustering has good accuracy and robustness,and effectively reduces the impact of peel color change on anti-counterfeiting identification during mango ripening.These results support that it is feasible to use the lenticels distribution of mango as a biological fingerprint.In this paper,a computer vision anti-counterfeiting method based on lenticels distribution is proposed.