Pineapple maturity classifier using image processing and fuzzy logic - Edwin R. Arboleda; Christian Louis T. de Jesus; Leahlyne Mae S. Tia (IAES International Journal of Artificial Intelligence (IJ-AI), 2021)
This paper describes the development of a prototype using an image processing system for extracting features and fuzzy logic for classifying the maturity of pineapple fruits depending on the colors of its scales. The standards that the system used are from Philippine National Standards for fresh fruits-pineapple for the 'queen' variant. The prototype automatically classified the maturity of queen pineapple variant grown in Munting Ilog, Silang, Cavite, Philippines. Data gathered are from the images loaded into the system using a camera unit under a controlled environment. The images loaded consist of the three faces of the pineapple sample, each with 120-degree coverage to capture the whole 360-degree view of the scale. The images then are sent to the system of the prototype where the features of the images are segmented based on the RGB color reduction. By using the fuzzy logic classifier, the obtained experimental results showed 100% accuracy for both the unripe and overripe maturity and 90% accuracy for the under-ripe and ripe maturity classification. The results obtained show that the developed image processing algorithm and the fuzzy-logic-based classifier could be used as an accurate and effective tool in classifying the maturity of pineapples.