Please use this identifier to cite or link to this item: http://10.1.7.192:80/jspui/handle/123456789/11975
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dc.contributor.authorPatel, Yash B-
dc.date.accessioned2023-08-24T08:27:31Z-
dc.date.available2023-08-24T08:27:31Z-
dc.date.issued2023-06-01-
dc.identifier.urihttp://10.1.7.192:80/jspui/handle/123456789/11975-
dc.description.abstractDeep learning is a fast-growing research field today. One of its various applications is object recognition. The combination of technologies leads to the purpose of this the- sis. Due to the uneven spacing of the plants, weed recognition in vegetable plantations is more problematic than weed recognition in crops. There hasn’t been much research done on weed identification in vegetable plantations thus far. In spite of the wide vari- ety of plant species, conventional techniques of agricultural weed recognition used to be primarily concentrated on recognizing weeds straight. An image segmentation technique called semantic segmentation was used to separate the weeds from the background and recognise the weed or crop from the image. From a technological perspective the study presents a traditional weed recognition system in a vegetable crop and opens the door to recommendations on which composite you should be using to control the weed in a vegetable crop.en_US
dc.publisherInstitute of Technologyen_US
dc.relation.ispartofseries21MCEI09;-
dc.subjectComputer 2021en_US
dc.subjectProject Report 2021en_US
dc.subjectComputer Project Reporten_US
dc.subjectProject Reporten_US
dc.subject21MCEen_US
dc.subject21MCEI09en_US
dc.subjectINSen_US
dc.subjectINS 2021en_US
dc.subjectCE (INS)en_US
dc.titleWeed Recognition in Brinjal Crop using Deep learningen_US
dc.typeDissertationen_US
Appears in Collections:Dissertation, CE (INS)

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