DeRiBa-Fuz: Detection of Ripening Banana Using Fuzzy Logic with RGB and GLCM Extraction

The natural ripening banana, is a vital matter for the banana trading associations. This exploration analyzes a detection of the ripening banana, which is called DeRiBa-Fuz. The DeRiBa-Fuz endeavors to identify the ripening bananas in the 7-ripening criteria. The DeRiBa-Fuz is proposed by using the Fuzzy logic algorithm and classified by the RGB and gray level co-occurrence matrix (GLCM) extraction classifiers. We train and test with 73% the training and 27% testing data from the 940 number of banana dataset, respectively. We exercise 254 data testing and the 686 data training of bananas, respectively. The upshots of the DeRiBa-Fuz are probed with the disclosed classifier models which report the DeRiBa-Fuz 100% detection accuracy for the full green (category 1/unripe), the green to yellow (category 2/unripe), the numerous green (category 3/unripe), the numerous yellow (category 4/ripe), the full yellow (category 6/overripe), and the yellow speckled-brown (category 7/overripe), unless the deep yellow and green (category 5/ripe) which has 96.55% detection accuracy compared other approaches.