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https://open.uns.ac.rs/handle/123456789/32712
Поље DC-а | Вредност | Језик |
---|---|---|
dc.contributor.author | Waqar, Rana | en_US |
dc.contributor.author | Grbović, Željana | en_US |
dc.contributor.author | Khan, Maryam | en_US |
dc.contributor.author | Pajević, Nina | en_US |
dc.contributor.author | Stefanović, Dimitrije | en_US |
dc.contributor.author | Filipović, Vladan | en_US |
dc.contributor.author | Panić, Marko | en_US |
dc.contributor.author | Djurić, Nemanja | en_US |
dc.date.accessioned | 2024-04-27T10:15:27Z | - |
dc.date.available | 2024-04-27T10:15:27Z | - |
dc.date.issued | 2024-06 | - |
dc.identifier.uri | https://open.uns.ac.rs/handle/123456789/32712 | - |
dc.description.abstract | We propose an approach to jointly count plants and de- tect gaps in maize fields using end-to-end deep-learning models. Unlike previous efforts that focused solely on plant detection, our methodology also integrates the task of gap identification, offering a holistic view of the state of the agricultural field. Moreover, we consider different data sources in our experiments and explore the benefits of us- ing multispectral over RGB images, which are commonly used in the industry. The findings suggest that multi-task learning on multispectral images significantly outperforms other model configurations, demonstrating the potential of the proposed approach for precision agriculture. | en_US |
dc.title | End-to-End Deep Learning Models for Gap Identification in Maize Fields | en_US |
dc.type | Conference Paper | en_US |
dc.relation.conference | CVPR 2024 | en_US |
dc.description.version | Unknown | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
crisitem.author.dept | Institut BioSense | - |
crisitem.author.dept | Institut BioSense | - |
crisitem.author.dept | Institut BioSense | - |
crisitem.author.dept | Institut BioSense | - |
crisitem.author.dept | Institut BioSense | - |
crisitem.author.orcid | 0000-0002-9214-5324 | - |
crisitem.author.orcid | 0000-0002-8643-3012 | - |
crisitem.author.orcid | 0000-0002-9085-6165 | - |
crisitem.author.orcid | 0000-0002-6625-202X | - |
crisitem.author.orcid | 0000-0002-7993-6826 | - |
crisitem.author.parentorg | Univerzitet u Novom Sadu | - |
crisitem.author.parentorg | Univerzitet u Novom Sadu | - |
crisitem.author.parentorg | Univerzitet u Novom Sadu | - |
crisitem.author.parentorg | Univerzitet u Novom Sadu | - |
crisitem.author.parentorg | Univerzitet u Novom Sadu | - |
Налази се у колекцијама: | IBS Publikacije/Publications |
Датотеке у овој ставци:
Датотека | Величина | Формат | |
---|---|---|---|
M33-2024-End-to-End Deep Learning Models for Gap Identification in Maize Fields.pdf | 2.37 MB | Adobe PDF | Погледајте |
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19
проверено 03.05.2024.
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1
проверено 03.05.2024.
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