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Classification of Mango Using Deep Learning

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dc.contributor.author Abu Mettleq, Alaa Soliman
dc.contributor.author Dheir, Ibtesam M.
dc.contributor.author Elsharif, Abeer A.
dc.date.accessioned 2020-02-14T15:10:48Z
dc.date.available 2020-02-14T15:10:48Z
dc.date.issued 2020
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/562
dc.description.abstract In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven Mango classification applications when used in classification automation it enables people to know the type of mango properly. The trained model achieved an accuracy of 100% on test set, demonstrating the feasibility of this approach. en_US
dc.language.iso en_US en_US
dc.subject Mango en_US
dc.subject Deep Learning en_US
dc.subject Artificial Intelligence en_US
dc.title Classification of Mango Using Deep Learning en_US
dc.type Article en_US


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