Using Random Forest Algorithm to Grading Mango's Quality Based on External Features Extracted from Captured Images

Abstract

The grading of mango is still a manual process in agriculture. Nowadays, mangoes are classified based on human experience, which makes the grade not uniform for agricultural product export establishments. Therefore, the automated grading of mango is very important to solve these problems. In this study, a random forest algorithm is proposed for an automated mango grading system based on quality attributes such as density, surface defect, and weight. The internal features including dimensions and surface defects are extracted via the captured image. These features are combined with the weight to estimate density. This study uses 732 mangoes that are collected from several local farms. The experiment of the grading system has high accuracy with 98.3%. Instead of using Non-Destructive Testing (NDT) equipment, this grading method can be used to apply to evaluate the quality of other tropical fruits.