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| 1. |  | OGASAWARA, M.; OTTO, M.; MATTOS, E. J. de; ROCHA, H. R. da; FIGUEIREDO, R. de O.; FERRAZ, S. Effects of climate change on water yield and water quality of forested watersheds in Southeastern Brazil. Pesquisa Florestal Brasileira, v. 39, e201902043, p. 649, 2019. Special issue. Abstracts of the XXV IUFRO World Congress, 2019. p. 649| Biblioteca(s): Embrapa Meio Ambiente. |
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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
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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: |
CRUVINEL, P. E. |
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Afiliação: |
PAULO ESTEVAO CRUVINEL, CNPDIA. |
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Título: |
Advanced digital platform for agricultural risk management. |
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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: |
299 - 306 |
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ISBN: |
978-1-6654-3418-8 |
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ISSN: |
2325-6516 |
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DOI: |
10.1109/ICSC52841.2022.00056 |
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Idioma: |
Inglês |
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Conteúdo: |
—This paper presents a robust system for agricultural risk management based on semantic knowledge and decisionmaking processes at farm scale. Thus, in order to anticipate, avoid and react to shocks arising from negative externalities that may occur during a production process, the method has been structured using algorithms that enable not only an efficient agricultural management, but also a rational use of inputs in the crop management phase. The processing services have been implemented on a cloud computing infrastructure, being specific for geospatialized agricultural applications, i. e., involving large amounts of data and analytics. For the validation case studies were considered based on corn crop cycles (Zea Mays. L.) under rainfed conditions, two of them in the normal harvest period and the third in the off-season period, using the concept related to Research-on-Farm |
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Palavras-Chave: |
Agricultural risk; Big data; Intelligent systems; Semantic computation. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01542nam a2200205 a 4500
001 2143888
005 2024-01-23
008 2022 bl uuuu u00u1 u #d
020 $a978-1-6654-3418-8
022 $a2325-6516
024 7 $a10.1109/ICSC52841.2022.00056$2DOI
100 1 $aCRUVINEL, P. E.
245 $aAdvanced digital platform for agricultural risk management.$h[electronic resource]
260 $aIn: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USA$c2022
300 $a299 - 306
520 $a—This paper presents a robust system for agricultural risk management based on semantic knowledge and decisionmaking processes at farm scale. Thus, in order to anticipate, avoid and react to shocks arising from negative externalities that may occur during a production process, the method has been structured using algorithms that enable not only an efficient agricultural management, but also a rational use of inputs in the crop management phase. The processing services have been implemented on a cloud computing infrastructure, being specific for geospatialized agricultural applications, i. e., involving large amounts of data and analytics. For the validation case studies were considered based on corn crop cycles (Zea Mays. L.) under rainfed conditions, two of them in the normal harvest period and the third in the off-season period, using the concept related to Research-on-Farm
653 $aAgricultural risk
653 $aBig data
653 $aIntelligent systems
653 $aSemantic computation
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Embrapa Instrumentação (CNPDIA) |
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