Automatic Mango Fruit Classifier Using Image Processing Through  Pixel-Based Calculation, Correlation and Logic System

Febrey C. Lamsen

Febrey C. Lamsen Lyceum-Northwestern University Tapuac District, Dagupan City Pangasinan, Philippines

Keywords: Sorting, vision technology, image processing, mango fruit classifier

Abstract

In recent years, automatic vision-based technologies have been used in the sector of agriculture and food industry to achieve better quality of their products. The ability to identify the mango fruit based on its quality yields a greater income for the people in the industry. Manual sorting constitutes problems in achieving reliable and consistent results. An automatic mango fruit classifier based on vision technology is discussed here. The main objective of this study was to develop an automatic mango-fruit sorting machine using image processing through various methods and processes. Moreover, this study was conducted to determine the category of the mango fruit samples as a product and be sorted according to their quality. The study focused on the extraction of its physical qualities ( size, color, and spots) using a new set of algorithms, processes, methods and software and developing a new way of sorting products that could help in the agricultural industry. It aimed to simplify existing technologies on grading or classifying products and tested the accuracy of the system by conducting several experiments. The application of the vision system aims to divert the manual inspection of the mango fruit into automatic classification and sorting. To speed up the process of inspection, maintain consistency and achieve reliable results, a system that uses image processing through various methods were used. The extracted qualities of the fruits were combined using program codes on the software. The combined qualities were graded according to an established standard. Additionally, the output was represented by a Graphical – User Interface and a voice prompt. The system achieved an accuracy of 93.33 %

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