Harumanis Mango Classification and Grading System Based on Geometric Shape Extraction for Quality Assessment

Abstract

In agricultural research, a fruit's look, which affects its market value, is the first and most crucial sensory evaluation. According to external criteria like shape, size, ripeness index, and flaws or internal criteria like sweetness, flavor, and nutrients, fruits can be categorized and rated. Due to the grading task's importance and difficulty in post-harvest management, there is a high standard for fruit quality that must constantly be met. Growing interest in various categorization and grading algorithms aims to use non-contact and novel measurements to lessen the subjectivity of visual examination. In this paper, the proposed system that uses fruit shape and size as feature parameters for evaluating fruit quality has been implemented. A morphological analysis, median filter, and multilevel threshold-based image processing technique were created to assess the geometric shape of the mango image, including its length, width, and area. These factors are required to compute the standard shape of the Harumanis mango's similarity index and divide its size into four mass grades: small (S), medium (M), large (L), and extra-large (XL). The test results show the suggested technique had an accuracy rate of 98% and 96% for grading the size based on mass estimation and was very successful for quality classification based on measurement of the similarity index for a standard shape.