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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
08/02/2022 |
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Data da última atualização: |
11/03/2022 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
KUCHLER, P. C.; SIMÕES, M.; BEGUE, A.; FERRAZ, R. P. D. |
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Afiliação: |
PATRICK CALVANO KUCHLER, UERJ; MARGARETH GONCALVES SIMOES, CNPS; AGNÈS BEGUE, CIRAD; RODRIGO PECANHA DEMONTE FERRAZ, CNPS. |
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Título: |
Big earth observation data and machine learning for mapping crop-livestock integrated system in Brazil. |
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Ano de publicação: |
2021 |
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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. |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Machine learning; MODIS time series. |
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Thesagro: |
Agricultura Sustentável. |
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Thesaurus Nal: |
Sustainable agricultural intensification. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/231050/1/Big-earth-observation-data-and-machine-learning-2021.pdf
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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.
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Registro original: |
Embrapa Solos (CNPS) |
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Origem |
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Registro |
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URL |
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Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
30/11/1999 |
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Data da última atualização: |
17/02/2022 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
QUEIROZ, M. A. de; DIAS, R. de C. S.; SOUZA, F de F.; FERREIRA, M. A. J. F.; ASSIS, J. G. de A.; BORGES, R. M. E.; ROMAO, R. L.; RAMOS, S. R. R.; COSTA, M. S. V.; MOURA, M. da C. C. L. |
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Afiliação: |
MANOEL ABILIO DE QUEIROZ, CPATSA; RITA DE CASSIA SOUZA DIAS, CPATSA; FLAVIO DE FRANÇA SOUZA; MARIA ALDETE JUSTINIANO DA FONSECA, CPATSA; JOSÉ GERALDO DE AQUINO ASSIS; RITA MERCIA ESTIGARRIBIA B FAUSTINO; ROBERTO LISBOA ROMÃO; SEMÍRAMIS RABELO RAMALHO RAMOS; MÁRCIO SIMON VIANA COSTA; MARIA DA CRUZ CHAVES LIMA MOURA. |
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Título: |
Recursos genéticos e melhoramento de melância no Nordeste brasileiro. |
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Ano de publicação: |
1999 |
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Fonte/Imprenta: |
In: QUEIROZ, M. A. de; GOEDERT, C. O.; RAMOS, S. R. R. (Ed.). Recursos genéticos e melhoramento de plantas para o Nordeste brasileiro. Petrolina: Embrapa Semi-Árido; Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 1999. |
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Idioma: |
Português |
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Conteúdo: |
Importância da cultura da melancia; Cultivares existentes; recursos genéticos; Objetivos do programa de melhoramento de melancia. |
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Palavras-Chave: |
Genetical resources; Nordeste; Northeast; Watermelon. |
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Thesagro: |
Citrullus Lanatus; Melancia; Melhoramento; Recurso Genético. |
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Thesaurus NAL: |
Genetic improvement; plant breeding; watermelons. |
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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/item/107454/1/Abilio.pdf
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Marc: |
LEADER 01319naa a2200361 a 4500
001 1133697
005 2022-02-17
008 1999 bl uuuu u00u1 u #d
100 1 $aQUEIROZ, M. A. de
245 $aRecursos genéticos e melhoramento de melância no Nordeste brasileiro.$h[electronic resource]
260 $c1999
520 $aImportância da cultura da melancia; Cultivares existentes; recursos genéticos; Objetivos do programa de melhoramento de melancia.
650 $aGenetic improvement
650 $aplant breeding
650 $awatermelons
650 $aCitrullus Lanatus
650 $aMelancia
650 $aMelhoramento
650 $aRecurso Genético
653 $aGenetical resources
653 $aNordeste
653 $aNortheast
653 $aWatermelon
700 1 $aDIAS, R. de C. S.
700 1 $aSOUZA, F de F.
700 1 $aFERREIRA, M. A. J. F.
700 1 $aASSIS, J. G. de A.
700 1 $aBORGES, R. M. E.
700 1 $aROMAO, R. L.
700 1 $aRAMOS, S. R. R.
700 1 $aCOSTA, M. S. V.
700 1 $aMOURA, M. da C. C. L.
773 $tIn: QUEIROZ, M. A. de; GOEDERT, C. O.; RAMOS, S. R. R. (Ed.). Recursos genéticos e melhoramento de plantas para o Nordeste brasileiro. Petrolina: Embrapa Semi-Árido; Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 1999.
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Embrapa Semiárido (CPATSA) |
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| Registros recuperados : 1 | |
| 1. |  | 1250047, CEPALINDEX; RESUMENES DE DOCUMENTOS CEPAL/ILPES, Comision Economica para America Latina. Centro Latino Americano de Documentacion Economica y Social, Santiago-Chile
Biblioteca(s): Catálogo Coletivo de Periódicos Embrapa; Embrapa Acre; Embrapa Amazônia Oriental; Embrapa Meio-Norte; Embrapa Soja. |   |
| Registros recuperados : 1 | |
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