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.

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In Vietnam general education curriculum 2018, a learning model called STEM education is being interested in and encouraged in teaching and learning. Choosing a STEM topic will  engage students to explore knowledge. The  topics which selected need to be linked to reality and have a direct impact on human life. In the era of industry 4.0, besides data science, machine learning, artificial intelligence, etc., the role of automation is indispensable. The previously developed STEM topics are quite diverse, including topics on physics, chemistry, biology, informatics, etc. However, STEM topics about automatic control field are rarely exploited to increase students' awareness of the role of automation in life in the era of technology revolution 4.0, and also enrich the topics of learning and experience in the contents of smart home, warning system, etc. in the general education program issued in 2018. In this paper, therefore, we propose a new STEM project in the field of smart models, which is the smart night-lamp. Furthermore, we use the 6E teaching model to design the teaching process, which emphasis on two elements of technical design and practice in STEM-oriented teaching. After proposing a new STEM topic and designing a teaching process according to the 6E process, we also conducted a pedagogical experiment to ensure the feasibility of the proposed topic in STEM education.