Real-time classification of Pasaman oranges using Mamdani fuzzy inference system and ESP32 microcontroller

Abstract

Manual classification of Pasaman oranges based on visual assessment of size and color often produces inconsistent results due to human subjectivity. This study develops an automatic classification system using a Mamdani fuzzy inference system (FIS) implemented on an ESP32 microcontroller. Fruit diameter is measured using a high-frequency sound wave ranging module (HC-SR04) ultrasonic sensor, while surface color is detected using a TAOS color sensor 3200 (TCS3200) color sensor. The obtained data are processed through fuzzification, inference, and defuzzification to classify oranges into three quality grades (A, B, and C). System performance evaluation shows strong agreement between the developed system and matrix laboratory (MATLAB) simulation, with a coefficient of determination (R²) value of 0.9855, indicating reliable and consistent classification performance for automated agricultural grading applications.