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Registro Completo |
Biblioteca(s): |
Embrapa Meio Norte / UEP-Parnaíba. |
Data corrente: |
21/01/1993 |
Data da última atualização: |
21/01/1993 |
Autoria: |
JOHNSON, D. |
Afiliação: |
Universidade do Houston, Texas. |
Título: |
Zoneamento agroclimatologico do milho e do sorgo granifero no Nordestedo Brasil. |
Ano de publicação: |
1980 |
Fonte/Imprenta: |
Revista Economica do Nordeste, v.11, n.3, p.461-470, jul./set. 1980. |
Idioma: |
Português |
Conteúdo: |
O apropriado zoneamento geografico das culturas alimentares nos tropicos semi-aridos e uma estrategia de desenvolvimento que pode aumentar aprodutividade, com uma utilizacao minima de capital. O indice de Umidade Disponivel representa uma nova maneira de conceber a classificacao agroclimatica. A substituicao do milho pelo sorgo granifero, mais bem adaptado ao clima semi-arido do Nordeste do Brasil, e um exemplo de zoneamento que pode elevar a produtividade de terras marginais. |
Palavras-Chave: |
Brasil; Maize; Nordeste; Sorghum; Zoneamento agroclimatologico. |
Thesagro: |
Agricultura; Milho; Sorghum Bicolor; Sorgo; Zea Mays. |
Thesaurus Nal: |
agriculture; Brazil. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01170naa a2200265 a 4500 001 1072626 005 1993-01-21 008 1980 bl uuuu u00u1 u #d 100 1 $aJOHNSON, D. 245 $aZoneamento agroclimatologico do milho e do sorgo granifero no Nordestedo Brasil. 260 $c1980 520 $aO apropriado zoneamento geografico das culturas alimentares nos tropicos semi-aridos e uma estrategia de desenvolvimento que pode aumentar aprodutividade, com uma utilizacao minima de capital. O indice de Umidade Disponivel representa uma nova maneira de conceber a classificacao agroclimatica. A substituicao do milho pelo sorgo granifero, mais bem adaptado ao clima semi-arido do Nordeste do Brasil, e um exemplo de zoneamento que pode elevar a produtividade de terras marginais. 650 $aagriculture 650 $aBrazil 650 $aAgricultura 650 $aMilho 650 $aSorghum Bicolor 650 $aSorgo 650 $aZea Mays 653 $aBrasil 653 $aMaize 653 $aNordeste 653 $aSorghum 653 $aZoneamento agroclimatologico 773 $tRevista Economica do Nordeste$gv.11, n.3, p.461-470, jul./set. 1980.
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Embrapa Meio Norte / UEP-Parnaíba (CPAMN-UEPP) |
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Registro Completo
Biblioteca(s): |
Embrapa Algodão. |
Data corrente: |
21/08/2023 |
Data da última atualização: |
21/08/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
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. |
Título: |
Soil and satellite remote sensing variables importance using machine learning to predict cotton yield. |
Ano de publicação: |
2023 |
Fonte/Imprenta: |
Smart Agricultural Technology, v. 5, p. 1-10, 100292, 2023. |
ISSN: |
2772-3755 |
DOI: |
https://doi.org/10.1016/j.atech.2023.100292 |
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 |
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. |
Thesagro: |
Algodão; Estrutura do Solo; Gossypium Hirsutum; Sensoriamento Remoto. |
Thesaurus NAL: |
Artificial intelligence; Cotton; Remote sensing; Soil structure. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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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