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Classification of Fruits Using Deep Learning

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dc.contributor.author Alkahlout, Mohammed A.
dc.contributor.author Abu-Naser, Samy S.
dc.contributor.author Alsaqqa, Azmi H.
dc.contributor.author Abu-Jamie, Tanseem N.
dc.date.accessioned 2022-01-03T08:08:35Z
dc.date.available 2022-01-03T08:08:35Z
dc.date.issued 2022
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/2830
dc.description.abstract Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether a certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. en_US
dc.language.iso en_US en_US
dc.subject Fruit Classification en_US
dc.subject Deep Learning en_US
dc.subject Classification en_US
dc.subject Detection en_US
dc.title Classification of Fruits Using Deep Learning en_US
dc.type Article en_US


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