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Registro Completo |
Biblioteca(s): |
Embrapa Acre. |
Data corrente: |
08/12/2012 |
Data da última atualização: |
07/11/2023 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
PEREIRA, A. A. A.; NASCIMENTO, F. S. S. do; SIVIERO, A.; MARINHO, J. T. de S.; PEREIRA, M. M. N.; MATTAR, E. P. L.; OLIVEIRA, E. de. |
Afiliação: |
ALLANA ARYANNE A. PEREIRA, Bolsista CNPq; FRANCISCA SILVANA S. DO NASCIMENTO, UFAC; AMAURI SIVIERO, CPAF-AC; JOSE TADEU DE SOUZA MARINHO, CPAF-AC; MIRNA M. N. PEREIRA, UNINORTE; EDUARDO PACCA LUNA MATTAR, UFAC; ELIANA DE OLIVEIRA, UFAC. |
Título: |
Germinação e florescimento de cultivares locais de feijão-de-corda e feijoeiro comum em Rio Branco, Acre. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2012, Belém, PA. Anais... Brasília, DF: Sociedade Brasileira de Recursos Genéticos, 2012. |
Páginas: |
4 p. |
Descrição Física: |
1 CD-ROM. |
Idioma: |
Português |
Conteúdo: |
Este trabalho teve como objetivo quantificar os processos de germinação de sementes em campo e laboratório e o florescimento de seis variedades locais de feijão comum e de quatro variedades de feijão-de-corda no Acre. |
Palavras-Chave: |
Acre; Amazonia Occidental; Amazônia Ocidental; Caupi; Especies nativas; Frijoles; Germinación de las semillas; Inflorescencias; Variación genética; Variedade crioula; Wersten Amazon. |
Thesagro: |
Características Agronômicas; Feijão de Corda; Germinação; Inflorescência; Phaseolus Vulgaris; Semente; Variação Genética; Vigna Unguiculata. |
Thesaurus Nal: |
Agronomic traits; Beans; Cowpeas; Genetic variation; Indigenous species; Inflorescences; Seed germination. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/184050/1/24493.pdf
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Marc: |
LEADER 01774nam a2200505 a 4500 001 1941794 005 2023-11-07 008 2012 bl uuuu u00u1 u #d 100 1 $aPEREIRA, A. A. A. 245 $aGerminação e florescimento de cultivares locais de feijão-de-corda e feijoeiro comum em Rio Branco, Acre.$h[electronic resource] 260 $aIn: CONGRESSO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2012, Belém, PA. Anais... Brasília, DF: Sociedade Brasileira de Recursos Genéticos$c2012 300 $a4 p.$c1 CD-ROM. 520 $aEste trabalho teve como objetivo quantificar os processos de germinação de sementes em campo e laboratório e o florescimento de seis variedades locais de feijão comum e de quatro variedades de feijão-de-corda no Acre. 650 $aAgronomic traits 650 $aBeans 650 $aCowpeas 650 $aGenetic variation 650 $aIndigenous species 650 $aInflorescences 650 $aSeed germination 650 $aCaracterísticas Agronômicas 650 $aFeijão de Corda 650 $aGerminação 650 $aInflorescência 650 $aPhaseolus Vulgaris 650 $aSemente 650 $aVariação Genética 650 $aVigna Unguiculata 653 $aAcre 653 $aAmazonia Occidental 653 $aAmazônia Ocidental 653 $aCaupi 653 $aEspecies nativas 653 $aFrijoles 653 $aGerminación de las semillas 653 $aInflorescencias 653 $aVariación genética 653 $aVariedade crioula 653 $aWersten Amazon 700 1 $aNASCIMENTO, F. S. S. do 700 1 $aSIVIERO, A. 700 1 $aMARINHO, J. T. de S. 700 1 $aPEREIRA, M. M. N. 700 1 $aMATTAR, E. P. L. 700 1 $aOLIVEIRA, E. de
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Embrapa Acre (CPAF-AC) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Instrumentação. |
Data corrente: |
12/04/2021 |
Data da última atualização: |
16/08/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
OSCO, L. P.; NOGUEIRA, K.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVES, W. N.; JORGE, L. A. de C.; MARCATO JUNIOR, J.; SANTOS, J. A. |
Afiliação: |
LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
Título: |
Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Precision Agriculture, v. 22, n. 4,2021. |
Páginas: |
1171-1188 |
DOI: |
https://doi.org/10.1007/s11119-020-09777-5 |
Idioma: |
Inglês |
Conteúdo: |
Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation felds. MenosAccurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here pro... Mostrar Tudo |
Palavras-Chave: |
Convolutional neural network; Thematic map. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02591naa a2200265 a 4500 001 2131208 005 2022-08-16 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1007/s11119-020-09777-5$2DOI 100 1 $aOSCO, L. P. 245 $aSemantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery.$h[electronic resource] 260 $c2021 300 $a1171-1188 520 $aAccurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation felds from the remaining objects in a multispectral scene is a difcult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following fve state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3+. The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-afected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation felds. 653 $aConvolutional neural network 653 $aThematic map 700 1 $aNOGUEIRA, K. 700 1 $aRAMOS, A. P. M. 700 1 $aPINHEIRO, M. M. F. 700 1 $aFURUYA, D. E. G. 700 1 $aGONÇALVES, W. N. 700 1 $aJORGE, L. A. de C. 700 1 $aMARCATO JUNIOR, J. 700 1 $aSANTOS, J. A. 773 $tPrecision Agriculture$gv. 22, n. 4,2021.
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