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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
06/06/2018 |
Tipo de producción científica : |
Documentos |
Autor : |
VIÑOLES, C.; CUADRO, P.; CABRERA, J.; FERNÁNDEZ, J.; MOREIRA, E.; RODRÍGUEZ, H.; FERREIRA, E.; CUADRO, R.; DE BARBIERI, I.; FRUGONI, J.C.; SOARES DE LIMA, J.M.; MONTOSSI, F. |
Afiliación : |
CAROLINA VIÑOLES GIL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ANDRES CUADRO BRAS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JHON DARWIN CABRERA GUEDES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EDGAR DANIEL FERREIRA FABILA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JULIO CESAR FRUGONI SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Entore precoz: una alternativa para aumentar la productividad de la cría. |
Fecha de publicación : |
2011 |
Fuente / Imprenta : |
ln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011. |
Páginas : |
p. 17-18 |
Serie : |
(INIA Serie Actividades de Difusión ; 657) |
Idioma : |
Español |
Palabras claves : |
ENTORE; REPRODUCCIÓN. |
Thesagro : |
VACA. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10050/1/SAD657p17-18.pdf
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Marc : |
LEADER 01047naa a2200301 a 4500 001 1027335 005 2018-06-06 008 2011 bl uuuu u00u1 u #d 100 1 $aVIÑOLES, C. 245 $aEntore precoz$buna alternativa para aumentar la productividad de la cría. 260 $c2011 300 $ap. 17-18 490 $a(INIA Serie Actividades de Difusión ; 657) 650 $aVACA 653 $aENTORE 653 $aREPRODUCCIÓN 700 1 $aCUADRO, P. 700 1 $aCABRERA, J. 700 1 $aFERNÁNDEZ, J. 700 1 $aMOREIRA, E. 700 1 $aRODRÍGUEZ, H. 700 1 $aFERREIRA, E. 700 1 $aCUADRO, R. 700 1 $aDE BARBIERI, I. 700 1 $aFRUGONI, J.C. 700 1 $aSOARES DE LIMA, J.M. 700 1 $aMONTOSSI, F. 773 $tln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011.
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INIA Tacuarembó (TBO) |
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| Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
12/10/2020 |
Actualizado : |
09/04/2021 |
Tipo de producción científica : |
Artículos Indexados |
Autor : |
CAL, A.; TISCORNIA, G. |
Afiliación : |
ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUADALUPE TISCORNIA TOSAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Unsupervised Methodology to In-Season Mapping of Summer Crops in Uruguay with Modis EVI's Temporal Series and Machine Learning. (Conference-paper) |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, March 2020, Article number 9165614, Pages 183-188. Doi: https://doi.org/10.1109/LAGIRS48042.2020.9165614 |
ISBN : |
e-ISBN: 978-1-7281-4350-7 |
Idioma : |
Inglés |
Notas : |
Artilce history: Date of Conference: 22-26 March 2020. Date Added to IEEE Xplore: 12 August 2020. Published in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). INSPEC Accession Number: 19872572. Publisher: IEEE. Conference Location: Santiago, Chile, Chile. |
Contenido : |
ABSTRACT. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of l,667,400 hectares for the 2014-15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops. |
Palabras claves : |
CROP MAPPING; ELBOW METHOD; EVI; K-MEANS; SMOOTHING SPLINE; TIME-SERIES; UNSUPERVISED. |
Asunto categoría : |
P40 Meteorología y climatología |
Marc : |
LEADER 02424nam a2200217 a 4500 001 1061411 005 2021-04-09 008 2020 bl uuuu u01u1 u #d 100 1 $aCAL, A. 245 $aUnsupervised Methodology to In-Season Mapping of Summer Crops in Uruguay with Modis EVI's Temporal Series and Machine Learning. (Conference-paper)$h[electronic resource] 260 $aIEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020 - Proceedings, March 2020, Article number 9165614, Pages 183-188. Doi: https://doi.org/10.1109/LAGIRS48042.2020.9165614$c2020 500 $aArtilce history: Date of Conference: 22-26 March 2020. Date Added to IEEE Xplore: 12 August 2020. Published in: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). INSPEC Accession Number: 19872572. Publisher: IEEE. Conference Location: Santiago, Chile, Chile. 520 $aABSTRACT. This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of l,667,400 hectares for the 2014-15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops. 653 $aCROP MAPPING 653 $aELBOW METHOD 653 $aEVI 653 $aK-MEANS 653 $aSMOOTHING SPLINE 653 $aTIME-SERIES 653 $aUNSUPERVISED 700 1 $aTISCORNIA, G.
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