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Registro Completo |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
09/06/2022 |
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Data da última atualização: |
23/01/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MORENO, B. M.; CRUVINEL, P. E. |
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Afiliação: |
PAULO ESTEVAO CRUVINEL, CNPDIA. |
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Título: |
Computer vision system for identifying on farming weed species. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
In: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USA, 2022. |
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Páginas: |
287 - 292 |
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ISBN: |
978-1-6654-3418-8 |
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ISSN: |
2325-6516 |
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DOI: |
10.1109/ICSC52841.2022.00054 |
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Idioma: |
Inglês |
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Conteúdo: |
In the world, agriculture has been developed by combining new technologies for aid production and profitability while keeping environmental and social responsibility. The sector is primarily responsible to supply food for people, as well as fibers and energy. To keep such results, farmers have faced the need to seek, increasingly rational use of inputs, as is the use of pesticides, plant regulators, and liquid fertilizers. This paper presents a discussion related to the design and development of a computer vision system for precision spraying in the control of weed species into agricultural crops, based on the identification of invasive plants and their quantities. Concepts such as plant segmentation using field-acquired images, leaf features and descriptors, and classification of species are analyzed and implemented in a prototype. |
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Palavras-Chave: |
Embedded platform; Plant recognition. |
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Categoria do assunto: |
-- |
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Marc: |
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001 2143889
005 2024-01-23
008 2022 bl uuuu u00u1 u #d
020 $a978-1-6654-3418-8
022 $a2325-6516
024 7 $a10.1109/ICSC52841.2022.00054$2DOI
100 1 $aMORENO, B. M.
245 $aComputer vision system for identifying on farming weed species.$h[electronic resource]
260 $aIn: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USA$c2022
300 $a287 - 292
520 $aIn the world, agriculture has been developed by combining new technologies for aid production and profitability while keeping environmental and social responsibility. The sector is primarily responsible to supply food for people, as well as fibers and energy. To keep such results, farmers have faced the need to seek, increasingly rational use of inputs, as is the use of pesticides, plant regulators, and liquid fertilizers. This paper presents a discussion related to the design and development of a computer vision system for precision spraying in the control of weed species into agricultural crops, based on the identification of invasive plants and their quantities. Concepts such as plant segmentation using field-acquired images, leaf features and descriptors, and classification of species are analyzed and implemented in a prototype.
653 $aEmbedded platform
653 $aPlant recognition
700 1 $aCRUVINEL, P. E.
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