Mango Quality Measurement System Based on Ripeness, Size, and Spot Area Using Fuzzy Inference System

Abstract

Mango (Mangifera indica) quality assessment poses challenges in traditional grading methods, primarily relying on visual inspection, which can be subjective. This research aims to develop a Fuzzy Inference System (FIS) for evaluating mango quality based on parameters such as color, size, and spots. A qualitative data collection approach was employed through literature review and expert opinions, followed by the application of fuzzy logic using MATLAB to analyze the parameters affecting quality. The results demonstrate that mangoes classified as "Good" are characterized by larger size, a high or medium distribution of red color, and small spots. Defuzzification was performed using the Centroid Method to derive a crisp output, which indicated a quality leaning towards the "Well" category with a value of approximately 0.5584. This study highlights the efficacy of fuzzy logic in transforming qualitative assessments into quantitative measures, enhancing the reliability of agricultural evaluations.