Title: In-line grading system for mango fruits using GLCM feature extraction and soft-computing techniques

Authors: Ebenezer O. Olaniyi; Oyebade K. Oyedotun; Clement A. Ogunlade; Adnan Khashman

Addresses: Quantum Systems Research Center, Department of Electrical and Electronics Engineering, Adeleke University, Ede, Osun State, Nigeria; European Centre for Research and Academic Affairs (ECRAA), Lefkosa, Mersin 10, Turkey ' Interdiscipilary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg; European Centre for Research and Academic Affairs (ECRAA), Lefkosa, Mersin 10, Turkey ' Department of Agricultural Engineering, Adeleke University, Ede, Osun State, Nigeria ' European Centre for Research and Academic Affairs (ECRAA), Lefkosa, Mersin 10, Turkey

Abstract: In the fruit production industries and supermarkets, mature (ripe) fruits are demanded for consumption by the consumers and also for production in fruit processing industries. Therefore, there is an urgent need for an in-line grading system in such industry to aid the grading of mango fruit; in order to enhance the use of ripe and mature mangoes for production. Also, such in-line grading systems will speed up the production in these industries since machines are faster which gives a better and standard result as compared with human operators. In this work, we have implemented an in-line grading system using GLCM feature extraction and soft computing techniques. Two models have been implemented to classify the mango fruits into mature (ripe) and immature (unripe) fruits. These models are the feed-forward network trained with back-propagation neural network and the radial basis function network. These models are compared with each other and also with the result of other proposed systems using the same database to ascertain the best result required in such industry.

Keywords: mature mango; unripe mango; food quality control; radial basis function; RBF; neural network.

DOI: 10.1504/IJAPR.2019.104294

International Journal of Applied Pattern Recognition, 2019 Vol.6 No.1, pp.58 - 75

Received: 21 Nov 2018
Accepted: 03 Jun 2019
Published online: 02 Jan 2020 *

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