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http://10.1.7.192:80/jspui/handle/123456789/11975
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DC Field | Value | Language |
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dc.contributor.author | Patel, Yash B | - |
dc.date.accessioned | 2023-08-24T08:27:31Z | - |
dc.date.available | 2023-08-24T08:27:31Z | - |
dc.date.issued | 2023-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/11975 | - |
dc.description.abstract | Deep 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.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 21MCEI09; | - |
dc.subject | Computer 2021 | en_US |
dc.subject | Project Report 2021 | en_US |
dc.subject | Computer Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 21MCE | en_US |
dc.subject | 21MCEI09 | en_US |
dc.subject | INS | en_US |
dc.subject | INS 2021 | en_US |
dc.subject | CE (INS) | en_US |
dc.title | Weed Recognition in Brinjal Crop using Deep learning | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, CE (INS) |
Files in This Item:
File | Description | Size | Format | |
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21MCEI09.pdf | 21MCEI09 | 3.5 MB | Adobe PDF | ![]() View/Open |
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