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Registro Completo |
Biblioteca(s): |
Embrapa Hortaliças. |
Data corrente: |
08/11/1995 |
Data da última atualização: |
08/11/1995 |
Autoria: |
KITAJIMA, E. W.; POZZER, L. |
Afiliação: |
UnB. |
Título: |
Doencas causadas por virus em batata-doce, beterraba, cara, gengibre e inhame. |
Ano de publicação: |
1995 |
Fonte/Imprenta: |
Informe Agropecuario, Belo Horizonte, v.17, n.182, p.39-41, 1995. |
Idioma: |
Português |
Palavras-Chave: |
Beet; Cara; CMV; Dioscorea spp; Disease; DMV; SPCFV; SPFMV; Sweet potato; Yam; Yam Green Vein Banding Virus; Yam Mosaic; Zingiber officinali. |
Thesagro: |
Batata Doce; Beta Vulgaris; Beterraba; Colocasia Esculenta; Doença; Gengibre; Inhame; Ipomoea Batatas; Taro; Vírus. |
Thesaurus Nal: |
Cucumber mosaic virus; Dasheen mosaic virus; Potyvirus; Sweet potato chlorotic fleck virus; Sweet potato feathery mottle virus; Zingiber. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01212naa a2200469 a 4500 001 1754064 005 1995-11-08 008 1995 bl uuuu u00u1 u #d 100 1 $aKITAJIMA, E. W. 245 $aDoencas causadas por virus em batata-doce, beterraba, cara, gengibre e inhame. 260 $c1995 650 $aCucumber mosaic virus 650 $aDasheen mosaic virus 650 $aPotyvirus 650 $aSweet potato chlorotic fleck virus 650 $aSweet potato feathery mottle virus 650 $aZingiber 650 $aBatata Doce 650 $aBeta Vulgaris 650 $aBeterraba 650 $aColocasia Esculenta 650 $aDoença 650 $aGengibre 650 $aInhame 650 $aIpomoea Batatas 650 $aTaro 650 $aVírus 653 $aBeet 653 $aCara 653 $aCMV 653 $aDioscorea spp 653 $aDisease 653 $aDMV 653 $aSPCFV 653 $aSPFMV 653 $aSweet potato 653 $aYam 653 $aYam Green Vein Banding Virus 653 $aYam Mosaic 653 $aZingiber officinali 700 1 $aPOZZER, L. 773 $tInforme Agropecuario, Belo Horizonte$gv.17, n.182, p.39-41, 1995.
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Registro Completo
Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
Data corrente: |
27/01/2024 |
Data da última atualização: |
30/01/2024 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
CARNEIRO, B. M.; CARVALHO JÚNIOR, O. A. de; CARVALHO, O. L. F. de; ALBUQUERQUE, A. O. de; CASTRO FILHO, H. C. de; RODRIGUES, V. S.; LIMA, A. M.; ANTONY, D. S.; EVANGELISTA, B. A.; OLIVEIRA, M. C. de; PINTO, C. B. |
Afiliação: |
BRUNO MACHADO CARNEIRO, INSTITUTO FEDERAL DO TOCANTINS; OSMAR ABÍLIO DE CARVALHO JÚNIOR, UNB; OSMAR LUIZ FERREIRA DE CARVALHO, UNB; ANESMAR OLINO DE ALBUQUERQUE, UNB; HUGO CRISÓSTOMO DE CASTRO FILHO, UNB; VIVIANE SOARES RODRIGUES, UNB; ALINE MARCIMIANO LIMA, UNB; DORA SILVA ANTONY, UNB; BALBINO ANTONIO EVANGELISTA, CNPASA; MARLEY CAMILO DE OLIVEIRA, ADAPEC; CLEOVAN BARBOSA PINTO, ADAPEC. |
Título: |
Feasibility analysis of using Sentinel-1 images to phenologically differentiate the areas of soybean seed and sub-irrigated bean planting in the period of sanitary void in the tropical floodplains of the Formoso River basin, Tocantins, Brazil. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
In: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 25., 2023, Amsterdam. Proceedings... Amsterdam: SPIE, 2023. |
Série: |
(SPIE proceedings, v. 12727). |
DOI: |
https://doi.org/10.1117/12.2680328 |
Idioma: |
Inglês |
Conteúdo: |
Food production is one of the significant challenges for the world's population. Countries like Brazil, with a vast territorial dimension and good availability of resources, stand out in the production of grains, especially soy. Soy cultivation requires care and management to ensure phytosanitary production and reduce the risk of diseases such as Asian Soybean Rust (ASR) caused by the fungus Phakopsora pachyrhizi. In Brazil, soy cultivation occurs in the spring/summer (September/March), with greater solar energy and rainfall in the country. Brazil has established a fallow period to reduce the risk of ASR, which prohibits planting outside the agricultural calendar. However, there is the possibility of authorizing planting in the floodplains of the tropical plains of the Formoso River basin, Tocantins, Brazil. The government of the State of Tocantins created the State Program for the Control of ASR, authorizing the planting of soybeans during the dry season (April to September) through registration and monitoring of areas. However, other plantings, such as beans, with a shorter cycle and less water demand, also occur. This study aims to monitor the soybean crop development phases considering data collected in the field by the Agricultural Defense Agency (ADAPEC) and digital processing using deep-learning techniques of Sentinel-1 image time series. The phenological differences of cultivation farms enabled agricultural mapping and the fight against ASR. The digital processing steps of the Sentinel-1 time series dataset (10 m resolution) consisted of image pre- processing using Sentinel Application Platform (SNAP); time series filtering using Savitzky-Golay; evaluation of deep learning methods (Long Short-Term Memory - LSTM, Bidirectional LSTM - Bi-LSTM, Gated Recurrent Unit - GRU, and Bidirectional GRU - Bi-GRU); and accuracy analysis. However, the classification has some erroneous portions that can be improved by increasing the number of classes and samples in future works. MenosFood production is one of the significant challenges for the world's population. Countries like Brazil, with a vast territorial dimension and good availability of resources, stand out in the production of grains, especially soy. Soy cultivation requires care and management to ensure phytosanitary production and reduce the risk of diseases such as Asian Soybean Rust (ASR) caused by the fungus Phakopsora pachyrhizi. In Brazil, soy cultivation occurs in the spring/summer (September/March), with greater solar energy and rainfall in the country. Brazil has established a fallow period to reduce the risk of ASR, which prohibits planting outside the agricultural calendar. However, there is the possibility of authorizing planting in the floodplains of the tropical plains of the Formoso River basin, Tocantins, Brazil. The government of the State of Tocantins created the State Program for the Control of ASR, authorizing the planting of soybeans during the dry season (April to September) through registration and monitoring of areas. However, other plantings, such as beans, with a shorter cycle and less water demand, also occur. This study aims to monitor the soybean crop development phases considering data collected in the field by the Agricultural Defense Agency (ADAPEC) and digital processing using deep-learning techniques of Sentinel-1 image time series. The phenological differences of cultivation farms enabled agricultural mapping and the fight against ASR. The digital processing st... Mostrar Tudo |
Palavras-Chave: |
Deep learning; Formoso river basin; Machine learning; Plant phenotyping; Sentinel; Tocantins. |
Thesagro: |
Fenologia; Semente; Soja. |
Thesaurus NAL: |
Digital images; Monitoring; Phakopsora. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 03387nam a2200397 a 4500 001 2161406 005 2024-01-30 008 2023 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1117/12.2680328$2DOI 100 1 $aCARNEIRO, B. M. 245 $aFeasibility analysis of using Sentinel-1 images to phenologically differentiate the areas of soybean seed and sub-irrigated bean planting in the period of sanitary void in the tropical floodplains of the Formoso River basin, Tocantins, Brazil.$h[electronic resource] 260 $aIn: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 25., 2023, Amsterdam. Proceedings... Amsterdam: SPIE$c2023 490 $a(SPIE proceedings, v. 12727). 520 $aFood production is one of the significant challenges for the world's population. Countries like Brazil, with a vast territorial dimension and good availability of resources, stand out in the production of grains, especially soy. Soy cultivation requires care and management to ensure phytosanitary production and reduce the risk of diseases such as Asian Soybean Rust (ASR) caused by the fungus Phakopsora pachyrhizi. In Brazil, soy cultivation occurs in the spring/summer (September/March), with greater solar energy and rainfall in the country. Brazil has established a fallow period to reduce the risk of ASR, which prohibits planting outside the agricultural calendar. However, there is the possibility of authorizing planting in the floodplains of the tropical plains of the Formoso River basin, Tocantins, Brazil. The government of the State of Tocantins created the State Program for the Control of ASR, authorizing the planting of soybeans during the dry season (April to September) through registration and monitoring of areas. However, other plantings, such as beans, with a shorter cycle and less water demand, also occur. This study aims to monitor the soybean crop development phases considering data collected in the field by the Agricultural Defense Agency (ADAPEC) and digital processing using deep-learning techniques of Sentinel-1 image time series. The phenological differences of cultivation farms enabled agricultural mapping and the fight against ASR. The digital processing steps of the Sentinel-1 time series dataset (10 m resolution) consisted of image pre- processing using Sentinel Application Platform (SNAP); time series filtering using Savitzky-Golay; evaluation of deep learning methods (Long Short-Term Memory - LSTM, Bidirectional LSTM - Bi-LSTM, Gated Recurrent Unit - GRU, and Bidirectional GRU - Bi-GRU); and accuracy analysis. However, the classification has some erroneous portions that can be improved by increasing the number of classes and samples in future works. 650 $aDigital images 650 $aMonitoring 650 $aPhakopsora 650 $aFenologia 650 $aSemente 650 $aSoja 653 $aDeep learning 653 $aFormoso river basin 653 $aMachine learning 653 $aPlant phenotyping 653 $aSentinel 653 $aTocantins 700 1 $aCARVALHO JÚNIOR, O. A. de 700 1 $aCARVALHO, O. L. F. de 700 1 $aALBUQUERQUE, A. O. de 700 1 $aCASTRO FILHO, H. C. de 700 1 $aRODRIGUES, V. S. 700 1 $aLIMA, A. M. 700 1 $aANTONY, D. S. 700 1 $aEVANGELISTA, B. A. 700 1 $aOLIVEIRA, M. C. de 700 1 $aPINTO, C. B.
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