|
|
Registro Completo |
Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
08/02/2022 |
Data da última atualização: |
11/03/2022 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
KUCHLER, P. C.; SIMÕES, M.; BEGUE, A.; FERRAZ, R. P. D. |
Afiliação: |
PATRICK CALVANO KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; AGNÈS BEGUE, CIRAD; RODRIGO PECANHA DEMONTE FERRAZ, CNPS. |
Título: |
Big earth observation data and machine learning for mapping crop-livestock integrated system in Brazil. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. WCCLF 2021 proceedings. Brasília, DF: Embrapa, 2021. p. 904-909. WCCLF 2021. Evento online. |
Idioma: |
Inglês |
Conteúdo: |
The adoption of crop-livestock (iCL) integrated systems has been pointed out as an important strategy for increasing production based on sustainable intensification of land use in Brazil. Mapping and monitoring the iCL areas would allow us to know the expansion rates and the adoption level of the integrated system, being an important instrument for public policy management. However, due to the time-space variability from integrated production systems, developing methods based on remote sensing remains a major challenge. In this sense, this work discusses the application of Big Data and machine learning concepts in Earth Observation Data as a strategy to compose a methodology for monitoring the iCL in Brazil. We tested the capacity of the Random Forest (RF) classifier applied to MODIS time series to iCL detection in the Mato Grosso State, Brazil. For this, we evaluated the classification accuracy for the years between 2012 and 2019, totaling 3,864 images processed. The overall accuracy founded was between 0.77 and 0.89 and an fscore average of 0.85 was found for the iCL class. The generated maps showed a trajectory of sustainable intensification, with the expansion of the iCL area from 1,100,000 ha in 2012/2013 to 2,597,000 ha in 2018/2019, an increase of 135%. The results indicate that the use of the RF classification technique with MODIS times series has great potential to compose an iCL monitoring methodology, requiring parallel and cloud computing applied to advanced algorithms. MenosThe adoption of crop-livestock (iCL) integrated systems has been pointed out as an important strategy for increasing production based on sustainable intensification of land use in Brazil. Mapping and monitoring the iCL areas would allow us to know the expansion rates and the adoption level of the integrated system, being an important instrument for public policy management. However, due to the time-space variability from integrated production systems, developing methods based on remote sensing remains a major challenge. In this sense, this work discusses the application of Big Data and machine learning concepts in Earth Observation Data as a strategy to compose a methodology for monitoring the iCL in Brazil. We tested the capacity of the Random Forest (RF) classifier applied to MODIS time series to iCL detection in the Mato Grosso State, Brazil. For this, we evaluated the classification accuracy for the years between 2012 and 2019, totaling 3,864 images processed. The overall accuracy founded was between 0.77 and 0.89 and an fscore average of 0.85 was found for the iCL class. The generated maps showed a trajectory of sustainable intensification, with the expansion of the iCL area from 1,100,000 ha in 2012/2013 to 2,597,000 ha in 2018/2019, an increase of 135%. The results indicate that the use of the RF classification technique with MODIS times series has great potential to compose an iCL monitoring methodology, requiring parallel and cloud computing applied to advanced algo... Mostrar Tudo |
Palavras-Chave: |
Machine learning; MODIS time series. |
Thesagro: |
Agricultura Sustentável. |
Thesaurus Nal: |
Sustainable agricultural intensification. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/231050/1/Big-earth-observation-data-and-machine-learning-2021.pdf
|
Marc: |
LEADER 02262nam a2200193 a 4500 001 2139789 005 2022-03-11 008 2021 bl uuuu u00u1 u #d 100 1 $aKUCHLER, P. C. 245 $aBig earth observation data and machine learning for mapping crop-livestock integrated system in Brazil.$h[electronic resource] 260 $aIn: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. WCCLF 2021 proceedings. Brasília, DF: Embrapa, 2021. p. 904-909. WCCLF 2021. Evento online.$c2021 520 $aThe adoption of crop-livestock (iCL) integrated systems has been pointed out as an important strategy for increasing production based on sustainable intensification of land use in Brazil. Mapping and monitoring the iCL areas would allow us to know the expansion rates and the adoption level of the integrated system, being an important instrument for public policy management. However, due to the time-space variability from integrated production systems, developing methods based on remote sensing remains a major challenge. In this sense, this work discusses the application of Big Data and machine learning concepts in Earth Observation Data as a strategy to compose a methodology for monitoring the iCL in Brazil. We tested the capacity of the Random Forest (RF) classifier applied to MODIS time series to iCL detection in the Mato Grosso State, Brazil. For this, we evaluated the classification accuracy for the years between 2012 and 2019, totaling 3,864 images processed. The overall accuracy founded was between 0.77 and 0.89 and an fscore average of 0.85 was found for the iCL class. The generated maps showed a trajectory of sustainable intensification, with the expansion of the iCL area from 1,100,000 ha in 2012/2013 to 2,597,000 ha in 2018/2019, an increase of 135%. The results indicate that the use of the RF classification technique with MODIS times series has great potential to compose an iCL monitoring methodology, requiring parallel and cloud computing applied to advanced algorithms. 650 $aSustainable agricultural intensification 650 $aAgricultura Sustentável 653 $aMachine learning 653 $aMODIS time series 700 1 $aSIMÕES, M. 700 1 $aBEGUE, A. 700 1 $aFERRAZ, R. P. D.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Solos (CNPS) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registro Completo
Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
17/01/2018 |
Data da última atualização: |
17/01/2018 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
ANTHERO, A. G. da S.; BACH, F.; AMARAL, B. do; JORGE, R. M. M.; MARQUETTI, I.; CLETO, P. B.; LIMA, J. M. de; HELM, C. V.; PERALTA-ZAMORA, P. G.; JORGE, L. M. |
Afiliação: |
Ana Gabriela da Silva Anthero; Fabiane Bach; Bianca do Amaral; Regina Maria Matos Jorge; Izabele Marquetti; Paulo Berbeka Cleto; Jardel Machado de Lima; CRISTIANE VIEIRA HELM, CNPF; P. G. PERALTA-ZAMORA; Luiz Mario Jorge. |
Título: |
Production and characterization of oat malt, and Principal Component Analysis (PCA). |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
In: SIMPÓSIO LATINO AMERICANO DE CIÊNCIA DE ALIMENTOS , 12., 2017, Campinas. Ciência de alimentos e seu impacto no mundo em transformação. Campinas: UNICAMP, 2017. |
Idioma: |
Português |
Palavras-Chave: |
Atividade antioxidante. |
Thesagro: |
Aveia; Germinação; Malte. |
Thesaurus NAL: |
Antioxidant activity; Germination; Malting; Oats. |
Categoria do assunto: |
Q Alimentos e Nutrição Humana |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/171216/1/2017-CrisH-SLACA-Production.pdf
|
Marc: |
LEADER 00961nam a2200301 a 4500 001 2085606 005 2018-01-17 008 2017 bl uuuu u00u1 u #d 100 1 $aANTHERO, A. G. da S. 245 $aProduction and characterization of oat malt, and Principal Component Analysis (PCA).$h[electronic resource] 260 $aIn: SIMPÓSIO LATINO AMERICANO DE CIÊNCIA DE ALIMENTOS , 12., 2017, Campinas. Ciência de alimentos e seu impacto no mundo em transformação. Campinas: UNICAMP$c2017 650 $aAntioxidant activity 650 $aGermination 650 $aMalting 650 $aOats 650 $aAveia 650 $aGerminação 650 $aMalte 653 $aAtividade antioxidante 700 1 $aBACH, F. 700 1 $aAMARAL, B. do 700 1 $aJORGE, R. M. M. 700 1 $aMARQUETTI, I. 700 1 $aCLETO, P. B. 700 1 $aLIMA, J. M. de 700 1 $aHELM, C. V. 700 1 $aPERALTA-ZAMORA, P. G. 700 1 $aJORGE, L. M.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Florestas (CNPF) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|