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Registro completo
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Biblioteca (s) : |
INIA Salto Grande. |
Fecha : |
21/02/2014 |
Actualizado : |
22/02/2014 |
Autor : |
Hume, H. |
Título : |
The cultivation of citrus fruits |
Fecha de publicación : |
1949 |
Fuente / Imprenta : |
New York (Estados Unidos): Macmillan, 1949. |
Páginas : |
561 p. |
Idioma : |
Español |
Thesagro : |
ANALISIS DEL SUELO; APLICACION DE ABONOS; CONTROL DE ENFERMEDADES; CONTROL DE PLAGAS (POSTCOSECHA); DESEMPEÑO DE CULTIVOS; EMPAQUETADO; ENFERMEDADES DE LAS PLANTAS; EUA; FORTUNELLA JAPONICA; FRUTAS CITRICAS; FUMIGACION; HELADA; INJERTO; LIMA; LIMON ACIDO; MANEJO DEL CULTIVO; MERCADEO; METODOS DE RIEGO; NARANJA DULCE; PLANTAS PARA PATRON; PODA; POMELO; PONCIRUS TRIFOLIATA; PORTAINJERTOS; PROPAGACION DE PLANTAS; RENDIMIENTO DE CULTIVOS; RESISTENCIA A AGENTES DAÑINOS; RESISTENCIA A LA TEMPERATURA; RESPUESTA DE LA PLANTA; RIEGO; TECNOLOGIA POSTCOSECHA; VALOR ECONOMICO; VARIEDADES. |
Asunto categoría : |
-- |
Marc : |
LEADER 01359nam a2200505 a 4500 001 1013230 005 2014-02-22 008 1949 bl uuuu u00u1 u #d 100 1 $aHUME, H. 245 $aThe cultivation of citrus fruits 260 $aNew York (Estados Unidos): Macmillan$c1949 300 $a561 p. 650 $aANALISIS DEL SUELO 650 $aAPLICACION DE ABONOS 650 $aCONTROL DE ENFERMEDADES 650 $aCONTROL DE PLAGAS (POSTCOSECHA) 650 $aDESEMPEÑO DE CULTIVOS 650 $aEMPAQUETADO 650 $aENFERMEDADES DE LAS PLANTAS 650 $aEUA 650 $aFORTUNELLA JAPONICA 650 $aFRUTAS CITRICAS 650 $aFUMIGACION 650 $aHELADA 650 $aINJERTO 650 $aLIMA 650 $aLIMON ACIDO 650 $aMANEJO DEL CULTIVO 650 $aMERCADEO 650 $aMETODOS DE RIEGO 650 $aNARANJA DULCE 650 $aPLANTAS PARA PATRON 650 $aPODA 650 $aPOMELO 650 $aPONCIRUS TRIFOLIATA 650 $aPORTAINJERTOS 650 $aPROPAGACION DE PLANTAS 650 $aRENDIMIENTO DE CULTIVOS 650 $aRESISTENCIA A AGENTES DAÑINOS 650 $aRESISTENCIA A LA TEMPERATURA 650 $aRESPUESTA DE LA PLANTA 650 $aRIEGO 650 $aTECNOLOGIA POSTCOSECHA 650 $aVALOR ECONOMICO 650 $aVARIEDADES
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Registro original : |
INIA Salto Grande (SG) |
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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 : |
23/10/2020 |
Actualizado : |
09/04/2021 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A. |
Afiliación : |
FLORENCIA HASTINGS, School of Agronomy Universidad de la República, Montevideo, Uruguay; Directorate of Natural Resources, Ministry of Agriculture, Livestock and Fisheries, Montevideo, Uruguay; IGNACIO FUENTES, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; MARIO PÉREZ-BIDEGAIN, School of Agronomy, Universidad de la República, Montevideo, Uruguay; RAFAEL NAVAS NÚÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ÁNGELA GORGOGLIONE, School of Engineering, Universidad de la República, Montevideo, Uruguay. |
Título : |
Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper) |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52 |
ISBN : |
e-ISBN: 978-3-030-58811-3 |
DOI : |
10.1007/978-3-030-58811-3_52 |
Idioma : |
Inglés |
Notas : |
Article history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH.
20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529.
Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy |
Contenido : |
Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG. |
Palabras claves : |
Agricultural region; Google earth engine; Land-cover map; Supervised classification. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 02413nam a2200229 a 4500 001 1061424 005 2021-04-09 008 2020 bl uuuu u0uu1 u #d 024 7 $a10.1007/978-3-030-58811-3_52$2DOI 100 1 $aHASTINGS, F. 245 $aLand-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)$h[electronic resource] 260 $aIn: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52$c1007 500 $aArticle history: First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH. 20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529. Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy 520 $aLand-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG. 653 $aAgricultural region 653 $aGoogle earth engine 653 $aLand-cover map 653 $aSupervised classification 700 1 $aFUENTES, I. 700 1 $aPÉREZ-BIDEGAIN, M. 700 1 $aNAVAS, R. 700 1 $aGORGOGLIONE, A.
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