In-line grading system for mango fruits using GLCM feature extraction and soft-computing techniques. (26th December 2019)

• Record Type: Journal Article
• Authors: Olaniyi, Ebenezer O.; Oyedotun, Oyebade K.; Ogunlade, Clement A.; Khashman, Adnan
• Is Part Of: International journal of applied pattern recognition. Volume 6:Number 1(2019)
• Page Start: 58
• Page End: 75
• Publication Date: 2019-12-26
• Subjects: mature mango -- unripe mango -- food quality control -- radial basis function -- RBF -- neural network; Pattern perception -- Periodicals; Pattern recognition systems -- Periodicals; 006.405
• Languages: English
• ISSNs: 2049-887X

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.