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| 25. |  | RECH, F.; FAORO, I.; SINSKI, I.; OLIVEIRA, P. R. D. de; RITSCHEL, P. S. Identificação de acessos de pereira por meio de marcadores moleculares SSR. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 20., 2008, Vitória, ES. Anais... Vitória: Incaper, 2008. Não Paginado.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 26. | | RECH, F. R.; RITSCHEL, P. S.; OLIVEIRA, P. R. D. de; SINSKI, I.; FAORO, I. Identificação de acessos de pereira por meio de marcadores moleculares SSR. In: SIMPÓSIO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2008, Brasília, DF. Anais... Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2008. p. 227. Resumo.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 27. | | RECH, F. R.; SINSKI, I.; RITSCHEL, P. S.; OLIVEIRA, P. R. D. de; FAORO, I. Identificação de acessos de pereira por meio de Marcadores Moleculares SSR. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA UVA E VINHO, 6.; ENCONTRO DE PÓS-GRADUANDOS DA EMBRAPA UVA E VINHO, 2., 2008, Bento Gonçalves. Resumos... Bento Gonçalves: Embrapa Uva e Vinho, 2008. p. 42. Resumo.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 28. | | OLIVEIRA, P. R. D. de; RITSCHEL, P. S.; LEITE, G. B.; NICKEL, O.; DEGENHARDT, J. Com a genética vamos superar o desafio da produção de pêra? Jornal da Fruta, Lages, v. 14, n. 173, p. 2, 2006.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 32. | | GIRARDI, C. L.; OLIVEIRA, P. R. D. de; FIALHO, F. B.; FIORAVANÇO, J. C.; MONTIPÓ, S. Qualidade de frutos de diferentes cultivares e clones de macieira. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 20., 2008, Vitória, ES. Anais... Vitória: Incaper, 2008. Não paginado.| Biblioteca(s): Embrapa Uva e Vinho. |
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| 34. | | SILVEIRA, F.; ANDOLFATO, W.; OLIVEIRA, P. R. D. de; FIORAVANÇO, J. C. Avaliação de cultivares de pereira no município de Vacaria na safra 2013-2014. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA UVA E VINHO, 12., ENCONTRO DE PÓS-GRADUANDOS DA EMBRAPA UVA E VINHO, 8., 2014, Bento Gonçalves. Resumos... Bento Gonçalves: Embrapa Uva e Vinho, 2014. p. 26| Biblioteca(s): Embrapa Uva e Vinho. |
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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 |
|
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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