DESIGN OF AUTOMATIC MANGO CLASSIFICATION SYSTEM BASED ON EXTERNAL FEATURES AND WEIGHT

Abstract

In this paper, the concepts of designing the automatic classification system are
described. This system classifies and evaluates based on parameters of external
characteristics of mangoes as well as their weights. External features are mentioned
such as the length, width, and height of each mango, the defect, and the color of the
mango’s exocarp. The recognition of parameters is based on vision machine and
machine learning techniques to evaluate the appropriate quality of mangoes. The
automatic mango classification system is designed for moving mangoes through
grading systems, measuring, collecting image data, weighing, and combining the
data for grading. The system classifying mangoes by color, size, and weight is
designed in modular form to be convenient for moving, assembling, and operating.
After the design, the system was developed and evaluated through an experimental
process. The system meets the capacity requirement of classification 3 tons/hour. The
results of the system classification are compared with the manual sorting and
evaluation, which shows that the classification results by the system are more
effective and efficient than the manual one. Besides, the system can also attach a
labeling module to be able to participate in the blockchain to identify and enhance the
value of local mangoes, namely Dong Thap province.