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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
21/07/2010 |
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Data da última atualização: |
26/04/2018 |
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Tipo da produção científica: |
Comunicado Técnico/Recomendações Técnicas |
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Autoria: |
BENCHIMOL, R. L.; EL-HUSNY, J. C.; SILVEIRA FILHO, A.; BARRIGA, J. P. |
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Afiliação: |
RUTH LINDA BENCHIMOL, CPATU; JAMIL CHAAR EL HUSNY, CPATU; AUSTRELINO SILVEIRA FILHO, CPATU; JÚLIO PONTES BARRIGA, SUPERINTENDÊNCIA FEDERAL DA AGRICULTURA DO ESTADO DO PARÁ. |
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Título: |
A mela da soja no Estado do Pará nas safras de 2003 a 2005. |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Belém, PA: Embrapa Amazônia Oriental, 2005. |
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Páginas: |
4 p. |
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Série: |
(Embrapa Amazônia Oriental. Comunicado técnico, 152). |
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Idioma: |
Português |
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Notas: |
Na publicação: Ruth Linda Benchimo. |
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Palavras-Chave: |
Brasil; Mela da soja; Pará. |
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Thesagro: |
Doença de planta; Fungo; Praga; Soja. |
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Thesaurus Nal: |
Amazonia. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/18796/1/com.tec.152.pdf
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Marc: |
LEADER 00736nam a2200265 a 4500
001 1858312
005 2018-04-26
008 2005 bl uuuu u0uu1 u #d
100 1 $aBENCHIMOL, R. L.
245 $aA mela da soja no Estado do Pará nas safras de 2003 a 2005.
260 $aBelém, PA: Embrapa Amazônia Oriental$c2005
300 $a4 p.
490 $a(Embrapa Amazônia Oriental. Comunicado técnico, 152).
500 $aNa publicação: Ruth Linda Benchimo.
650 $aAmazonia
650 $aDoença de planta
650 $aFungo
650 $aPraga
650 $aSoja
653 $aBrasil
653 $aMela da soja
653 $aPará
700 1 $aEL-HUSNY, J. C.
700 1 $aSILVEIRA FILHO, A.
700 1 $aBARRIGA, J. P.
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Registro original: |
Embrapa Amazônia Oriental (CPATU) |
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Biblioteca |
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Registro Completo
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Biblioteca(s): |
Embrapa Algodão. |
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Data corrente: |
21/08/2023 |
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Data da última atualização: |
21/08/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CARNEIRO, F. M.; BRITO FILHO, A. L. de; FERREIRA, F. M.; SEBEN JUNIOR, G. de F.; BRANDÃO, Z. N.; SILVA, R. P. da; SHIRATSUCHI, L. S. |
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Afiliação: |
FRANCIELE MORLIN CARNEIRO, UTFPR; ARMANDO LOPES DE BRITO FILHO, UNESP; FRANCIELLE MORELLI FERREIRA, UNESP; GETULIO DE FREITAS SEBEN JUNIOR, UNEMAT; ZIANY NEIVA BRANDÃO, CNPA; ROUVERSON PEREIRA DA SILVA, UNESP; LUCIANO SHOZO SHIRATSUCHI, LOUISIANA STATE UNIVERSITY. |
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Título: |
Soil and satellite remote sensing variables importance using machine learning to predict cotton yield. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Smart Agricultural Technology, v. 5, p. 1-10, 100292, 2023. |
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ISSN: |
2772-3755 |
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DOI: |
https://doi.org/10.1016/j.atech.2023.100292 |
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Idioma: |
Inglês |
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Conteúdo: |
Remote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R2), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield. MenosRemote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R2), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TP... Mostrar Tudo |
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Palavras-Chave: |
Árvores de decisão; Decision trees; Imagem de satélite; Inteligência artificial; Produção sustentável; Proximal sensors; Random forest; RS; Satellite imagery; Sensores proximais; Sustainable production. |
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Thesagro: |
Algodão; Estrutura do Solo; Gossypium Hirsutum; Sensoriamento Remoto. |
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Thesaurus NAL: |
Artificial intelligence; Cotton; Remote sensing; Soil structure. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1156016/1/SOIL-SATELLITE-COTTON-ZIANY.pdf
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Marc: |
LEADER 02921naa a2200445 a 4500
001 2156016
005 2023-08-21
008 2023 bl uuuu u00u1 u #d
022 $a2772-3755
024 7 $ahttps://doi.org/10.1016/j.atech.2023.100292$2DOI
100 1 $aCARNEIRO, F. M.
245 $aSoil and satellite remote sensing variables importance using machine learning to predict cotton yield.$h[electronic resource]
260 $c2023
520 $aRemote sensing (RS) in agriculture has been widely used for mapping soil, plant, and atmosphere attributes, as well as helping in the sustainable production of the crop by providing the possibility of application at variable rates and estimating the productivity of agricultural crops. In this way, proximal sensors used by RS help producers in decision-making to increase productivity. This research aims to identify the best feature importance ranking to the Random Forest Classifier to predict cotton yield and select which one best correlates with cotton yield. This work was developed in four commercial fields on a Newellton, LA, USA farm. We evaluated the cotton in different years as 2019, 2020, and 2021. The variables evaluated were: soil parameters, topographic indices, elevation derivatives, and orbital remote sensing. The soil sensor used was: GSSI Profiler EMP400 (soil electromagnetic induction sensor) at a frequency of 15 kHz, and the RS data were collected from satellite images from Sentinel 2 (passive sensor) and active sensor from LiDAR (Light Detection and Ranging). For training (70%) and validation (30%) of dataset results, Spearman correlation was used between sensors and cotton yield data, machine learning (Random Forest Classifier and Regressor - RFC and RFR). The metric parameters were the coefficient of determination (R2), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). This study found that profiler, Sentinel-2 (blue, red, and green), TPI, LiDAR, and RTK elevation show the best correlations to predicting cotton yield.
650 $aArtificial intelligence
650 $aCotton
650 $aRemote sensing
650 $aSoil structure
650 $aAlgodão
650 $aEstrutura do Solo
650 $aGossypium Hirsutum
650 $aSensoriamento Remoto
653 $aÁrvores de decisão
653 $aDecision trees
653 $aImagem de satélite
653 $aInteligência artificial
653 $aProdução sustentável
653 $aProximal sensors
653 $aRandom forest
653 $aRS
653 $aSatellite imagery
653 $aSensores proximais
653 $aSustainable production
700 1 $aBRITO FILHO, A. L. de
700 1 $aFERREIRA, F. M.
700 1 $aSEBEN JUNIOR, G. de F.
700 1 $aBRANDÃO, Z. N.
700 1 $aSILVA, R. P. da
700 1 $aSHIRATSUCHI, L. S.
773 $tSmart Agricultural Technology$gv. 5, p. 1-10, 100292, 2023.
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Embrapa Algodão (CNPA) |
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