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| 1. |  | RODRIGUES, D. C. A.; MONTES, A. C. de; BARBOSA, T. de J.; DIAS, C. B. S.; SILVA, P. C. G. da. Apicultura: a experiência de começar a criar abelhas em meio a seca severa. In: SIMPÓSIO DO BIOMA CAATINGA, 2., 2018, Petrolina. Anais... Petrolina: Embrapa Semiárido, 2019. p. 280. (Embrapa Semiárido. Documentos, 287).| Biblioteca(s): Embrapa Semiárido. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pesca e Aquicultura. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
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Data corrente: |
27/01/2024 |
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Data da última atualização: |
30/01/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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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. |
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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. |
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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. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY, 25., 2023, Amsterdam. Proceedings... Amsterdam: SPIE, 2023. |
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Série: |
(SPIE proceedings, v. 12727). |
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DOI: |
https://doi.org/10.1117/12.2680328 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Deep learning; Formoso river basin; Machine learning; Plant phenotyping; Sentinel; Tocantins. |
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Thesagro: |
Fenologia; Semente; Soja. |
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Thesaurus NAL: |
Digital images; Monitoring; Phakopsora. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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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