Abstract—For each type of mango, there are different colors,
weights, sizes, shapes and densities. Currently, classification
based on the above features is being carried out mainly by
manuals due to farmers' awareness of low accuracy, high costs,
health effects and high costs, costly economically inferior. This
study was conducted on three main commercial mango species
of Vietnam as Cat Chu, Cat Hoa Loc and Statue of green skin to
find out the method of classification of mango with the best
quality and accuracy. Research on mango classification based
on the color and volume being conducted does not meet the
quality of commercial mangoes and the accuracy is not high.
Therefore, a method of mango classification is most effective. In
this study, we have proposed and implemented methods, using
algorithms to analyze the content combining statistical methods
based on image processing techniques to identify commercial
mangoes in Vietnam. The main content of this study is to
develop an efficient algorithm to design mango classification
system with high quality and accuracy. The goal of the study is
to create a system that can classify mangoes in terms of color,
volume, size, shape and fruit density. The classification system
using image processing incorporates artificial intelligence
including the use of CCD cameras, C language programming,
computer vision and artificial neural networks. The system uses
the captured mango image, processing the split layer to
determine the mass, volume and defect on the mango fruit
surface. Determine the percentage of mango defects to
determine the quality of mangoes for export and domestic or
recycled mangoes. This article is about the development of an
automatic mango classification system to control and evaluate
mango quality before packaging and exporting to the market. It
is in the research, design and fabrication of mango classification
model and the completion of an automatic mango classification
system using image processing technology combining artificial
Index Terms—Fruit classification, mango sorting, image processing, artificial intelligence, computer vision.
Nguyen Truong Thinh is with the Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam (e-mail: email@example.com).
Nguyen Duc Thong is with Dong Thap University, Vietnam (e-mail: firstname.lastname@example.org).
Huynh Thanh Cong is with Vietnam National University, Ho Chi Minh City, Vietnam (e-mail: email@example.com).
Cite: Nguyen Truong Thinh, Nguyen Duc Thong, and Huynh Thanh Cong, "Sorting and Classification of Mangoes based on Artificial Intelligence," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 374-380, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).