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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
05/06/2018 |
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Actualizado : |
27/01/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
BERGER, A.; ETTLIN , G.; QUINCKE, CH.; RODRÍGUEZ-BOCCAB, P. |
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Afiliación : |
ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUILLERMO ETTLIN, Instituto de Computación, Facultad de Ingeniería, Universidad de la República.; CHRISTOPHER QUINCKE, Instituto de Computación, Facultad de Ingeniería, Universidad de la República.; PABLO RODRÍGUEZ-BOCCAB, Instituto de Computación, Facultad de Ingeniería, Universidad de la República. |
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Título : |
Predicting the Normalized Diference Vegetation Index (NDVI) by training a crop growth model with historical data. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Computers and Electronics in Agriculture, Volume 161, June 2019, Pages 305-311, 2019.Doi: https://doi.org/10.1016/j.compag.2018.04.028 |
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DOI : |
10.1016/j.compag.2018.04.028 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 29 December 2017/ Revised 17 April 2018/ Accepted 29 April 2018/ Available online 10 May 2018. |
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Contenido : |
ABSRACT:
Normalized Difference Vegetation Index (NDVI) is an important remote measurement in agriculture because it has a high correlation with crop growth and yield result. In this paper, we present a methodology to predict the NDVI by training a crop growth model with historical data. Although we use a very simple soybean growth model, the methodology could be extended to other crops and more complex models. The training process is an optimization problem, that is solved using the spectral projected gradient method. The quality of the prediction
is measured by computing the Root-Mean-Square Error (RMSE) between predicted and true values, obtaining an error lower than 9%, which improves the results obtained by simple forecast techniques used as baseline estimators. |
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Palabras claves : |
CROP GROWTH; CROP GROWTH MODEL; ÍNDICE DE VEGETACIÓN; NORMALIZED DIFFERENCE VEGETATION INDEX; PREDICTIVE ANALYSIS; REMOTE MEASUREMENT; ROOT MEAN SQUARE ERRORS; SOYBEAN CROP; SPECTRAL PROJECTED GRADIENT METHOD; TRAINING PROCESS. |
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Thesagro : |
CULTIVOS; GLICINE MAX; MEJORAMIENTO DE CULTIVOS; SOJA. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02044naa a2200349 a 4500
001 1058666
005 2021-01-27
008 2019 bl uuuu u00u1 u #d
024 7 $a10.1016/j.compag.2018.04.028$2DOI
100 1 $aBERGER, A.
245 $aPredicting the Normalized Diference Vegetation Index (NDVI) by training a crop growth model with historical data.$h[electronic resource]
260 $c2019
500 $aArticle history: Received 29 December 2017/ Revised 17 April 2018/ Accepted 29 April 2018/ Available online 10 May 2018.
520 $aABSRACT: Normalized Difference Vegetation Index (NDVI) is an important remote measurement in agriculture because it has a high correlation with crop growth and yield result. In this paper, we present a methodology to predict the NDVI by training a crop growth model with historical data. Although we use a very simple soybean growth model, the methodology could be extended to other crops and more complex models. The training process is an optimization problem, that is solved using the spectral projected gradient method. The quality of the prediction is measured by computing the Root-Mean-Square Error (RMSE) between predicted and true values, obtaining an error lower than 9%, which improves the results obtained by simple forecast techniques used as baseline estimators.
650 $aCULTIVOS
650 $aGLICINE MAX
650 $aMEJORAMIENTO DE CULTIVOS
650 $aSOJA
653 $aCROP GROWTH
653 $aCROP GROWTH MODEL
653 $aÍNDICE DE VEGETACIÓN
653 $aNORMALIZED DIFFERENCE VEGETATION INDEX
653 $aPREDICTIVE ANALYSIS
653 $aREMOTE MEASUREMENT
653 $aROOT MEAN SQUARE ERRORS
653 $aSOYBEAN CROP
653 $aSPECTRAL PROJECTED GRADIENT METHOD
653 $aTRAINING PROCESS
700 1 $aETTLIN , G.
700 1 $aQUINCKE, CH.
700 1 $aRODRÍGUEZ-BOCCAB, P.
773 $tComputers and Electronics in Agriculture, Volume 161, June 2019, Pages 305-311, 2019.Doi: https://doi.org/10.1016/j.compag.2018.04.028
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Registro original : |
INIA La Estanzuela (LE) |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
04/10/2019 |
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Actualizado : |
05/11/2019 |
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Autor : |
ZOLIN, C.A.; RODRIGUES, R. DE A.R. (Ed.). |
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Afiliación : |
CORNÉLIO ALBERTO ZOLIN, Brazilian Agricultural Res. Corporation, Embrapa Agrosilvopastoral, Mato Grosso, Brazil.; RENATO DE A.R. RODRIGUES, Brazilian Agricultural Res. Corporation, Embrapa Soils, Rio de Janeiro, Brazil. |
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Título : |
Impact of climate change on water resources in agriculture. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Boca Raton, FL (USA): CRC Press, 2016, 221 p. |
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ISBN : |
978-1-4987-0617-9 |
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Idioma : |
Inglés |
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Contenido : |
Contents
1. The role of the Agriculture on teh UNFCCC negotiation process
2. Policies and initiatives related to water and climate change in agriculture: case studies from Brazil and Africa.
3. Global Research Alliance on agricultural greenhouse gases
4. Porposal for the construction of a greenhouse gas emissions monitoring system for teh ABC Plan - sectoral plan for mitigation and adaptation to climate change forteh consolitation of a low carbon agriculture economy.
5. Agrosilvopastoral systems in Brazil: An agricultural productive strategy based on green economy concepts.
6. Soil conservation and carbon sequestration: agroforestry systems as a promising alternative.
7. Forests, land use change, and water.
8. Uncertainty is the key challenge for agricultural water resources management under climate change in the BRICS
9. Trading off agriculture with nature's other benefits, spatially
10. Can investment in river basins sustain global development of food and energy systems? |
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Palabras claves : |
AGROFORESTACIÓN; CONSERVACIÓN DEL SUELO; EMISIONES DE GASES DE EFECTO INVERNADERO; GASES DE EFECTO INVERNADERO (GEI); SECUESTRO DEL CARBONO. |
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Thesagro : |
AGRICULTURA; CAMBIO CLIMÁTICO; RECURSOS HIDRICOS. |
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Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
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Marc : |
LEADER 01707nam a2200229 a 4500
001 1060279
005 2019-11-05
008 2016 bl uuuu u00u1 u #d
020 $a978-1-4987-0617-9
100 1 $aZOLIN, C.A.
245 $aImpact of climate change on water resources in agriculture.$h[electronic resource]
260 $aBoca Raton, FL (USA): CRC Press, 2016, 221 p.$c2016
520 $aContents 1. The role of the Agriculture on teh UNFCCC negotiation process 2. Policies and initiatives related to water and climate change in agriculture: case studies from Brazil and Africa. 3. Global Research Alliance on agricultural greenhouse gases 4. Porposal for the construction of a greenhouse gas emissions monitoring system for teh ABC Plan - sectoral plan for mitigation and adaptation to climate change forteh consolitation of a low carbon agriculture economy. 5. Agrosilvopastoral systems in Brazil: An agricultural productive strategy based on green economy concepts. 6. Soil conservation and carbon sequestration: agroforestry systems as a promising alternative. 7. Forests, land use change, and water. 8. Uncertainty is the key challenge for agricultural water resources management under climate change in the BRICS 9. Trading off agriculture with nature's other benefits, spatially 10. Can investment in river basins sustain global development of food and energy systems?
650 $aAGRICULTURA
650 $aCAMBIO CLIMÁTICO
650 $aRECURSOS HIDRICOS
653 $aAGROFORESTACIÓN
653 $aCONSERVACIÓN DEL SUELO
653 $aEMISIONES DE GASES DE EFECTO INVERNADERO
653 $aGASES DE EFECTO INVERNADERO (GEI)
653 $aSECUESTRO DEL CARBONO
700 1 $aRODRIGUES, R. DE A.R.
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