Open AccessDesign of fuzzy inference system for apple ripeness estimation using gradient method
Author(s) -
Hamza Raja,
Chtourou Mohamed
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6524
Subject(s) - ripeness , classifier (uml) , artificial intelligence , fuzzy logic , confusion matrix , computer science , pattern recognition (psychology) , fuzzy inference system , confusion , contextual image classification , mathematics , data mining , adaptive neuro fuzzy inference system , fuzzy control system , image (mathematics) , ripening , psychology , chemistry , food science , psychoanalysis
In this study, a fuzzy classification approach based on colour features has been investigated to estimate the ripeness of apple fruits according to three maturity stages; unripe, turning‐ripe and ripe. The K nearest neighbour algorithm was applied in order to segment the fruit image into four regions namely background, green area, yellow area and red area. The last three regions represent the colour features and were subsequently given as inputs to the fuzzy classifier. Gradient method has been used for tuning the fuzzy classifier in order to obtain the best performance. Image database used for simulation has been collected and exploited for the training and testing phases using cross‐validation. Simulation results indicate that the best classifier parameters can be obtained. The efficiency of the proposed system compared with the non‐use of the gradient method has been proved by the confusion matrix and the most known classification evaluation metrics. Moreover, the trained fuzzy classifier demonstrates its outperformance in terms of accuracy and execution time compared with other existing methods.