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Vegetable Classification

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dc.contributor.author Rahman, Md Atikur
dc.contributor.author Hasan, Md Rakibul
dc.date.accessioned 2021-04-27T04:39:26Z
dc.date.available 2021-04-27T04:39:26Z
dc.date.issued 2021-01-27
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5646
dc.description.abstract Computer vision and example acknowledgement is an arising territory in perceiving objects in a picture. The advancements for perceiving objects in pictures have a wide reach of uses, for example, vegetable and natural product discovery frameworks, vehicle discovery, and different frameworks. Our examination work centers on the discovery of vegetable assortments into building up a proficient vegetable acknowledgement framework. The vegetables may show up the same by shading and different highlights, for instance, red tomato and red capsicum as similar tones so utilizing highlights for distinguishing vegetables may prompt bogus discovery so this exploration work proposes an intraclass vegetable acknowledgement framework utilizing profound learning. we used three types of vegetables: Cauliflower, Carrots, Tomato। Profound learning used to identify the vegetable class by removing and learning the pictures and investigated the convolution neural network (CNN). From the consequences of the assessment, intraclass vegetables perceived precisely with 95.50% and proficiently utilizing profound learning. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Vegetable processing plants en_US
dc.title Vegetable Classification en_US
dc.type Other en_US


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