| |
|
|
| Registros recuperados : 192 | |
| 161. |  | SANTOS, R. T. dos S. e; TORRES, L. H. P. S.; BIASOTO, A. C. T.; FREITAS, S. T. de; MELO, N. F. de; SILVA, F. L. H. da. Optimization of pectinolytic hydrolysis in Caatinga passion fruit wine must with commercial pectinase, according to the central composite rotatable design approach. Research, Society and Development, v. 11, n. 5, e14511528088, 2022.| Biblioteca(s): Embrapa Semiárido. |
|    |
| 162. | | VILVERT, J. C.; FREITAS, S. T. de; SANTOS, L. F. dos; CARDOSO, A. D.; LOPES, P. R. C.; AMARANTE, A. V. T. do. Portable VIS-NIR spectrometer for non-destructive determination of quality traits in apples and pears. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 27., 2022, Florianópolis. Valorização da ciência brasileira para a produção de frutas: anais. Florianópolis: SBF: Epagri: UDESC: UFSC: Embrapa, 2022. p. 419-421| Biblioteca(s): Embrapa Semiárido. |
|  |
| 164. | | MIRANDA, I. B.; SANTOS, I. E. dos A.; TURCO, S. H. N.; FREITAS, S. T. de; FAUSTINO, A. C.; LINS, A. C. de S. S. Precooling of table grapes on a commercial scale as function of packaging. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 25, n. 8, p. 566-572, 2021.| Biblioteca(s): Embrapa Semiárido. |
| |
| 168. | | VARJÃO, L. L.; FERREIRA, M. A. R.; MOURA, N. R.; SANTOS, T. B.; ALBUQUERQUE, J. G. de; LOPES, P. R. C.; FREITAS, S. T. de. Uso de embalagem a vácuo para remoção da adstringência de caquis rama forte. In: JORNADA DE INTEGRAÇÃO DA PÓS-GRADUAÇÃO DA EMBRAPA SEMIÁRIDO, 2., 2017, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2017. p. 131-136. (Embrapa Semiárido. Documento, 280)| Biblioteca(s): Embrapa Semiárido. |
| |
| 169. | | SOARES FILHO, A. de A.; LINO, J. de O. S.; CUNHA, J. G. da; SANTANA, E. A.; OLIVEIRA, V. R. de; FREITAS, S. T. de. Uso de hexanal para aumentar a vida pós-colheita do umbu. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 26., 2019, Juazeiro, BA/Petrolina, PE. Fruticultura de precisão: desafios e oportunidades - anais. Petrolina: Embrapa Semiárido: UNIVASF: SBF, 2019. p. 2777-2780.| Biblioteca(s): Embrapa Semiárido. |
| |
| 170. | | MENDONÇA, S. de L. R. de; VIEIRA, D. M.; MOREIRA, R. T.; BIASOTO, A. C. T.; FREITAS, S. T. de; RYBKA, A. C. P. Uva-passa: uma revisão de literatura. In: CONGRESSO INTERNACIONAL DE GASTRONOMIA E CIÊNCIA DE ALIMENTOS, 2., 2016, Fortaleza. Gastronomia: da tradição à inovação - anais. Fortaleza: UFC, 2016. p. 1171-1172.| Biblioteca(s): Embrapa Semiárido. |
| |
| 172. | | VILVERT, J. C.; FREITAS, S. T. de; FERREIRA, M. A. R.; COSTA, C. dos S. R.; LEITE, R. H. de L.; SANTOS, F. J. G. dos; AROUCHA, E. M. M. Preservation of quality and bioactive compounds in mangoes using chitosan-graphene-oxide-based biodegradable packaging. Horticulturae, v. 9, 1145, 2023.| Biblioteca(s): Embrapa Semiárido. |
| |
| 173. | | ANDRADE, M. E. A. de; SILVA, B. O. S. da; RIBEIRO, T. da S.; SANTOS, L. F. dos; LIMA, A. M. N.; OLIVEIRA, F. F. de; FREITAS, S. T. de. Fruit traits at harvest and after storage related to the incidence of postharvest physiological disorders in Keitt mangoes. Acta Scientific Nutritional Health, v. 6, n. 1, p. 48-63, 2022.| Biblioteca(s): Embrapa Semiárido. |
| |
| 174. | | PADILHA, C. V. da S.; BIASOTO, A. C. T.; BRAGARD, L. F. dos S.; OLIVEIRA, J. B. de; OLIVEIRA, W. P. de; CARVALHO, E. S. S. de; FREITAS, S. T. de; PEREIRA, G. E. Description of sensory profile of tropical wines of guard issued in the Valley of São Francisco, Northeast Brazil. In: WORLD CONGRESS OF VINE AND WINE, 37., 2014, Mendonza, Argentina. Abstracts... Mendonza: International Organisation of Vine and Wine, 2014. 4 p.| Biblioteca(s): Embrapa Semiárido; Embrapa Uva e Vinho. |
| |
| 175. | | CAVALCANTE, I. H. L.; SILVA, G. J. N. e; CAVACINI, J. A.; AMARIZ, R. A. e; FREITAS, S. T. de; SOUSA, K, A. O. de; SILVA, M. A. da; CUNHA, J. G. da. Metconazole on inhibition of gibberellin biosynthesis and flowering management in mango. Erwerbs-Obstbau, v. 62, n. 1, p. 89-95, 2020.| Biblioteca(s): Embrapa Semiárido. |
| |
| 176. | | OLIVEIRA, V. R.; SANTOS, M. F.; MORAIS, A. A. DE; LIMA, M. F.; BOITEUX, L. S.; COSTA, N. D.; YURI, J. E.; FREITAS, S. T. de; COSTA-LIMA, T. C. da; ARAGAO, F. A. S. de; BUSO, G. S. C. BRS Anton: híbrido de melão amarelo tipo exportação. Brasília, DF: Embrapa Hortaliças, 2017. Np. 1 Folder.| Biblioteca(s): Embrapa Semiárido. |
| |
| 177. | | OLIVEIRA, V. R.; SANTOS, M. F.; MORAIS, A. A. DE; LIMA, M. F.; BOITEUX, L. S.; COSTA, N. D.; YURI, J. E.; FREITAS, S. T. de; LIMA, T. C. da C.; ARAGAO, F. A. S. de; BUSO, G. S. C. BRS Anton: híbrido de melão amarelo tipo exportação. Brasília/DF: Embrapa Hortaliças, 2017 1 folder| Biblioteca(s): Embrapa Agroindústria Tropical. |
| |
| 178. | | CARVALHO, E. S. S. de; NUNES, G. da S.; BARROS, A. P. A.; LIMA, M. A. C. de; BARROS, E. S. de; FREITAS, S. T. de; BIASOTO, A. C. T.; DRUZIAN, J. I. La etapa de maduración de la uva influye en el contenido fenólico, el color y la capacidad antioxidante de los vinos tintos tropicales Touriga Nacional? In: CONGRESO LATINOAMERICANO DE VITICULTURA Y ENOLOGIA, 16., 2019, Ica, Peru. Desierto: pasión vitivinícola en Latinoamérica. Ica: Asociacion Peruana de Enologos: Organización Internacional de la Viña y del Vino, 2019. p. 152-155.| Biblioteca(s): Embrapa Semiárido. |
| |
| 179. | | MOURA, N. R.; FERREIRA, M. A. R.; PASSOS, T. O.; LIMA, A. S.; CASTRO, C. D. P. da C.; RYBKA, A. C. P.; OLIVEIRA, V. R. de; FREITAS, S. T. de. Qualidade físico-química e características biométricas de frutos de umbuzeiro do Banco de Germoplasma da Embrapa Semiárido. In: JORNADA DE INICIAÇÃO CIENTÍFICA DA EMBRAPA SEMIÁRIDO, 12., 2017, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2017. p. 271-277. (Embrapa Semiárido. Documentos, 279).| Biblioteca(s): Embrapa Semiárido. |
| |
| 180. | | VILVERT, J. C.; FREITAS, S. T. de; FERREIRA, M. A. R.; LEITE, R. H. de L.; SANTOS, F. K. G. dos; COSTA, C. dos S. R.; AROUCHA, E. M. M. Chitosan and graphene oxide-based biodegradable bags: an eco-friendly and effective packaging alternative to maintain postharvest quality of Palmer mango. LWT - Food Science and Tecnology, v. 154, 112741, 2022.| Biblioteca(s): Embrapa Semiárido. |
| |
| Registros recuperados : 192 | |
|
|
|
 | 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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Instrumentação (CNPDIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
