Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/32712
Nаziv: | End-to-End Deep Learning Models for Gap Identification in Maize Fields | Аutоri: | Waqar, Rana Grbović, Željana Khan, Maryam Pajević, Nina Stefanović, Dimitrije Filipović, Vladan Panić, Marko Djurić, Nemanja |
Dаtum izdаvаnjа: | јун-2024 | Kоnfеrеnciја: | CVPR 2024 | Sažetak: | 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. | URI: | https://open.uns.ac.rs/handle/123456789/32712 |
Nаlаzi sе u kоlеkciјаmа: | IBS Publikacije/Publications |
Dаtоtеkе u оvој stаvci:
Dаtоtеkа | Vеličinа | Fоrmаt | |
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M33-2024-End-to-End Deep Learning Models for Gap Identification in Maize Fields.pdf | 2.37 MB | Adobe PDF | Pоglеdајtе |
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prоvеrеnо 03.05.2024.
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prоvеrеnо 03.05.2024.
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