Pineapple maturity classifier using image processing and fuzzy logic

Abstract

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.

Research Status and Development Trend of Key Technologies for Pineapple Harvesting Equipment: A Review

“…The indoor environment test results showed that the detection accuracy of immature and fully mature fruits was 100%, and the detection accuracy of semi-mature fruits was 86% [87]. In addition, a fuzzy logic classifier was used for classification, and the detection accuracy of semimature fruit was improved to 90% [88]. In a simulation experiment, it was found that the convolutional neural network algorithm achieved a classification accuracy of 100% for immature and fully mature pineapples, while the classification accuracy for semi-mature pineapples reached only 82% [89].…”

Fuzzy Logic-Based Size and Ripeness Classification of Banana using Image Processing Technique

“…In Arboleda et al [18], a prototype for classifying the maturity of pineapple fruits based on the color of their scales and an image processing method for extracting features was shown. The prototype system receives the photos and segments their characteristics based on the RGB color reduction.…”

Using deep learning to identify maturity and 3D distance in pineapple fields

“…Edwin et al used image processing and fuzzy theory to classify post-harvest maturity into four categories. They achieved classification accuracy of 90% for partial maturity and 95% overall accuracy 10 . Note however that all of the methods described above rely on a controllable light source under indoor conditions.…”