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 | Acesso ao texto completo restrito à biblioteca da Embrapa Algodão. Para informações adicionais entre em contato com cnpa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
22/08/2023 |
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Data da última atualização: |
23/08/2023 |
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Autoria: |
CARNEIRO, F. M.; BRITO FILHO, A. L. de; MARTINS, M. de S.; BRANDÃO, Z. N.; SHIRATSUCHI, L. S. |
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Afiliação: |
FRANCIELE MORLIN CARNEIRO, UTFPR; ARMANDO OPES DE BRITO FILHO, UNESP; MURILO DE SANTANA MARTINS, LOUISIANA STATE UNIVERSITY; ZIANY NEIVA BRANDÃO, CNPA; LUCIANO SHOZO SHIRATSUCHI, LOUISIANA STATE UNIVERSITY. |
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Título: |
Machine learning approach for corn nitrogen recommendation. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SILVA-MATOS, R. R. S. da; DOIHARA, I. P.; LINHARES, S. C. (org.). Medio ambiente: Agricultura, desarrollo y sustentabilidad 2. Ponta Grossa, PR: Atena, 2023. c. 3. |
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Páginas: |
p. 21-28. |
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ISBN: |
978-65-258-1530-5 |
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DOI: |
https://doi.org/10.22533/at.ed.3052302083 |
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Idioma: |
Inglês |
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Conteúdo: |
Nitrogen (N) fertilizer recommendation tools are vital to precise agricultural management. The objectives of this research were to determine how many variables and remote sensor dataare needed to prescribe N fertilizer in corn (Zea mays L.), PFP (partial factor productivity), and yield integrating remote sensing and soil sensor technologies. The variables of this work were NIR, Red, Red-Edge wavelengths, plant height, canopy temperature, LAI (leaf area index), and apparent soil electrical conductivity (ECa). Random Forest Classifier was used to select the best input to estimate N rates, PFP, and corn yield. A confusion matrix was used to identify the accuracy of the Random Forest Classifier to detect the best inputs to estimate for which input we evaluated in this work. According to Random Forest, the best inputs to estimate the N rate and PFP were Red-Edge, Red, and NIR wavelengths, plant height, and canopy temperature. For estimate corn yield were: NIR wavelengths, N rates, plant height, Red-Edge, and canopy temperature. |
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Palavras-Chave: |
Active sensor; Estimativa de produtividade; Linguagem de máquina; Machine learning; Maize; Random Forest; Sensor ativo; Yield estimate. |
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Thesagro: |
Desenvolvimento Sustentável; Meio Ambiente; Milho; Sensoriamento Remoto. |
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Thesaurus Nal: |
Environment; Remote sensing; Sustainable development. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02265naa a2200385 a 4500
001 2156056
005 2023-08-23
008 2023 bl uuuu u00u1 u #d
020 $a978-65-258-1530-5
024 7 $ahttps://doi.org/10.22533/at.ed.3052302083$2DOI
100 1 $aCARNEIRO, F. M.
245 $aMachine learning approach for corn nitrogen recommendation.$h[electronic resource]
260 $c2023
300 $ap. 21-28.
520 $aNitrogen (N) fertilizer recommendation tools are vital to precise agricultural management. The objectives of this research were to determine how many variables and remote sensor dataare needed to prescribe N fertilizer in corn (Zea mays L.), PFP (partial factor productivity), and yield integrating remote sensing and soil sensor technologies. The variables of this work were NIR, Red, Red-Edge wavelengths, plant height, canopy temperature, LAI (leaf area index), and apparent soil electrical conductivity (ECa). Random Forest Classifier was used to select the best input to estimate N rates, PFP, and corn yield. A confusion matrix was used to identify the accuracy of the Random Forest Classifier to detect the best inputs to estimate for which input we evaluated in this work. According to Random Forest, the best inputs to estimate the N rate and PFP were Red-Edge, Red, and NIR wavelengths, plant height, and canopy temperature. For estimate corn yield were: NIR wavelengths, N rates, plant height, Red-Edge, and canopy temperature.
650 $aEnvironment
650 $aRemote sensing
650 $aSustainable development
650 $aDesenvolvimento Sustentável
650 $aMeio Ambiente
650 $aMilho
650 $aSensoriamento Remoto
653 $aActive sensor
653 $aEstimativa de produtividade
653 $aLinguagem de máquina
653 $aMachine learning
653 $aMaize
653 $aRandom Forest
653 $aSensor ativo
653 $aYield estimate
700 1 $aBRITO FILHO, A. L. de
700 1 $aMARTINS, M. de S.
700 1 $aBRANDÃO, Z. N.
700 1 $aSHIRATSUCHI, L. S.
773 $tIn: SILVA-MATOS, R. R. S. da; DOIHARA, I. P.; LINHARES, S. C. (org.). Medio ambiente: Agricultura, desarrollo y sustentabilidad 2. Ponta Grossa, PR: Atena, 2023. c. 3.
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