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| Registros recuperados : 3 | |
| 1. |  | THÉNARD, V.; QUÉNON, J.; ARSENOS, G.; BAILO, G.; BAPTISTA, R.; BYRNE, T.; DE BARBIERI, I.; BRUNI, G.; FREIRE, F.; THEODORIDIS, A.; VOURAKI, S. Identifying selection strategies based on the practices and preferences of small ruminant farmers to improve the sustainability of their breeding systems. Animal. The international journal of animal biosciences, 2024, Volume 18, Issue 7, 101208. https://doi.org/10.1016/j.animal.2024.101208 --- OPEN ACCESS. Article history: Received 27 October 2023, Revised 24 May 2024, Accepted 24 May 2024, Available online 29 May 2024, Version of Record 20 June 2024. -- Corresponding author: V. Thénard, E-mail address: vincent.thenard@inrae.fr ,UMR 1388...| Biblioteca(s): INIA Las Brujas. |
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| 2. | | ASTRUC, J.M.; BRITO, L.; BAPST, B.; BRUNI, G.; BURKE, M.; CIAPPESONI, G.; CONINGTON, J.; GARCÍA-BACCINO, C.; LARROQUE, H.; LEGARRA, A.; O'BRIEN, A.; POSTA, J.; ROBERT-GRANIÉ, C.; TEISSIER, M.; UGARTE, E.; MORENO-ROMIEUX, C.; BERRY, D. Selection tools to benefit from international cooperation in small ruminants: a comprehensive work package of the SMARTER project. [conference + oral presentation + video). In: Proceedings of the 44th ICAR Annual Conference virtually held from Leeuwarden, NL, 26-30 April 2021. "Circular farming and its impact on animal genetics, animal recording of data and cattle / herd management". Editors: G. de Jong, K. de Koning, H. van den Bijgaart, M. Burke and C. Mosconi. (ICAR Technical Serie s no. 25). Corresponding Author:Jean-Michel.Astruc@idele.fr| Biblioteca(s): INIA Las Brujas. |
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| 3. | | BRITO, L.F; BERRY, D.; LARROQUE, H.; SCHENKEI, F.S.; CIAPPESONI, G.; O’BRIEN, A.; TORTEREAU, F.; UGARTE, E.; PALHIERE, I.; BAPST, B.; JAKOBSEN, J.; ANTONAKOS, G.; KOMINAKIS, A.; CLEMENT, V.; BRUNI, G.; LOYWYCK, V.; MASSENDER, E.; OLIVEIRA, H.R.; POSTA, J.; ASTRUC, J.M. Genetic evaluation systems and breeding programs in sheep and goats: an international perspective. Annual Meeting of the European Federation of Animal Science, 17., No. 26, 2020. Virtual Meeting 1-4 December, 2020. DOI: DOI: 10.3920/978-90-8686-900-8. p. 560| Biblioteca(s): INIA Tacuarembó. |
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| Registros recuperados : 3 | |
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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 actual : |
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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Circulación / Nivel : |
Internacional - -- |
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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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