AUG Repository

Analyzing Types of Cantaloupe Using Deep Learning

Show simple item record

dc.contributor.author Mustafa I. Abu-Tahoun
dc.date.accessioned 1/28/2021
dc.date.accessioned 1/28/2021
dc.date.accessioned 2021-01-28T09:21:19Z
dc.date.available 1/28/2021
dc.date.available 2021-01-28T09:21:19Z
dc.date.issued 2021
dc.identifier.citation International Journal of Academic Information Systems Research (IJAISR),5(1), pp. 100-104 en_US
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/2687
dc.description.abstract The name cantaloupe was derived within the eighteenth century via French cantaloup from Italian Cantalupo, that was once a apostolical seat close to Rome, once the fruit was introduced there from Hayastan.[3] it absolutely was initial mentioned in English literature in 1739.[2] The cantaloupe possibly originated in a very region from South Asia to continent.[1] it absolutely was later introduced to Europe and, around 1890, became a poster crop within the u. s. Melon derived from use in Old French as meloun throughout the thirteenth century, and from Medieval Latin melonem, a sort of pumpkin.[4] it absolutely was among the primary plants to be domesticated and cultivated.[2] The South African English name spanspek is alleged to be derived from Afrikaans Spaanse spek ('Spanish bacon'); purportedly, Sir Harry Smith, a 19th-century governor of province, Ate bacon and eggs for breakfast, whereas his Spanish-born partner Juana Marнa Diamond State los Dolores Diamond State Leуn Smith most popular cantaloupe, thus South Africans nicknamed the eponymic fruit Spanish bacon.[3][4] but, the name seems to predate the Smiths and date to 18th-century Dutch Suriname: J. van Donselaar wrote in 1770, "Spaansch-spek is that the name for the shape that grows in Dutch Guiana that, thanks to its cutis and small flesh, is a smaller amount consumed. In this paper, machine learning based approach is presented for identifying type cantaloupe with a dataset that contains 1,312 images use 788 images for training, 196 images for validation and 328 images for testing. A deep learning technique that extensively applied to image recognition was used. use 80% from image for training and 20% from image for validation. en_US
dc.language.iso en_US en_US
dc.publisher IJARW en_US
dc.subject Cantaloupe en_US
dc.subject Deep Learning en_US
dc.subject Classification en_US
dc.title Analyzing Types of Cantaloupe Using Deep Learning en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account