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General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. eng. technol.(Online)
    • Frequency:  Bimonthly
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusEI (INSPEC, IET).
    • E-mail: ijet@vip.163.com
Editor-in-chief
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2019 Vol.11(5): 321-327 ISSN: 1793-8236
DOI: 10.7763/IJET.2019.V11.1169

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Nguyen Duc Thong, Nguyen Truong Thinh, and Huynh Thanh Cong
Abstract—Sorting and Classification 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 to find out the method of classification of mango with the best quality and accuracy. World studies of mango classification according to color, size, volume and almost done in the laboratory but not yet applied in practice. The quality assessment of mango fruit has not been resolved. Application of image processing technology, computer vision combined with artificial intelligence in the problem of mango classification or poor quality. 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. Especially, determine the density of mangoes related to its maturity and sweetness and 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 machine vision combining artificial intelligence.

Index Terms—The classification of mango, sorting of mangoes, image processing technology, artificial intelligence; computer vision, artificial neural networks.

Nguyen Duc Thong is with Dong Thap University, Vietnam (e-mail: ndthong@dthu.edu.vn).
Nguyen Truong Thinh is with Ho Chi Minh City University of Technology and Education, Vietnam (e-mail: thinhnt@hcmute.edu.vn).
Huynh Thanh Cong is with Bach Khoa Ho Chi Minh City University (e-mail: htcong@hcmut.edu.vn).

[PDF]

Cite: Nguyen Duc Thong, Nguyen Truong Thinh, and Huynh Thanh Cong, "Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence," International Journal of Engineering and Technology vol. 11, no. 5, pp. 321-327, 2019.

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