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| Registros recuperados : 2 | |
| 2. |  | SIRI, P.; BRUNO, C.; BALZARINI, M.; BENÍTEZ, V.; HIRIGOYEN, A.; INGARAMO, L.; POSSE, J.P.; FEDRIGO, J.K.; GONZÁLEZ BARRIOS, P. Effect of spacing and genetic material on Eucalyptus growth for solid-wood and cellulose production in Uruguay. Original article - Silviculture. FLORAM. Floresta e Ambiente, 2024, Volume 31, Issue 3, e20230050. https://doi.org/10.1590/2179-8087-FLORAM-2023-0050 -- OPEN ACCESS. Article history: Received 26 Dec 2023, Accepted 25 June 2024, Publication in this collection 12 Aug 2024, Date of issue 2024. -- Correspondence: Barrios, P.G.; Av. Garzón, 780, Montevideo, Uruguay; email:pablog@fagro.edu.uy --...| Biblioteca(s): INIA Las Brujas. |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
08/06/2022 |
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Actualizado : |
08/06/2022 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
CAL, A.; PRECIOZZI, J.; MUSÉ, PABLO |
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Afiliación : |
ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAVIER PRECIOZZI, IIE, Facultad de Ingeniería, Universidad de la Republica, Uruguay; Digital Sense, Uruguay; PABLO MUSÉ, IIE, Facultad de Ingeniería, Universidad de la Republica, Uruguay. |
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Título : |
Automatic Classification of Agricultural Summer Crops in Uruguay. [Conference paper] |
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Complemento del título : |
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels (Belgium) 12-16 July 2021. Code 176845. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
International Geoscience and Remote Sensing Symposium (IGARSS), 2021, pages 6520 - 6523. doi: http://doi.org/10.1109/IGARSS47720.2021.9555035 |
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DOI : |
10.1109/IGARSS47720.2021.9555035 |
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Idioma : |
Inglés |
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Notas : |
Publisher: Institute of Electrical and Electronics Engineers Inc. -- Sponsors: The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS). |
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Contenido : |
ABSTRACT - In this work, we present a study for the classification of summer crops on a nationwide perspective. Using both optical and radar satellite images, we implement a time-series classification algorithm using XGBoost. Two datasets with farm-level information were used: one with ground truth obtained directly from farmers' production and the other with declared crops obtained at the government level. The crops analyzed were corn, soybean, sorghum, and pastures. When trained and validated with ground truth, the classifier yields a F1-Score performance of 99% for soybean, and values higher than 80% for corn and sorghum. Predictions performed with this model on the dataset of declared crops lead to F1-Score values of 54, 97, and 50%, for corn, soybean, and sorghum, respectively. These low values for corn and sorghum indicate the presence of mislabeled data in that dataset, which in turns may suggest issues with the declarations provided by the farmers. ©2021 IEEE. |
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Palabras claves : |
Data fusion; K-means; Laser radar; Radar imaging; Satellites; Soil preservation; Sustainable agriculture; XGBoost. |
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Asunto categoría : |
A50 Investigación agraria |
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Marc : |
LEADER 01977nam a2200253 a 4500
001 1063252
005 2022-06-08
008 2021 bl uuuu u01u1 u #d
024 7 $a10.1109/IGARSS47720.2021.9555035$2DOI
100 1 $aCAL, A.
245 $aAutomatic Classification of Agricultural Summer Crops in Uruguay. [Conference paper]$h[electronic resource]
260 $aInternational Geoscience and Remote Sensing Symposium (IGARSS), 2021, pages 6520 - 6523. doi: http://doi.org/10.1109/IGARSS47720.2021.9555035$c2021
500 $aPublisher: Institute of Electrical and Electronics Engineers Inc. -- Sponsors: The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS).
520 $aABSTRACT - In this work, we present a study for the classification of summer crops on a nationwide perspective. Using both optical and radar satellite images, we implement a time-series classification algorithm using XGBoost. Two datasets with farm-level information were used: one with ground truth obtained directly from farmers' production and the other with declared crops obtained at the government level. The crops analyzed were corn, soybean, sorghum, and pastures. When trained and validated with ground truth, the classifier yields a F1-Score performance of 99% for soybean, and values higher than 80% for corn and sorghum. Predictions performed with this model on the dataset of declared crops lead to F1-Score values of 54, 97, and 50%, for corn, soybean, and sorghum, respectively. These low values for corn and sorghum indicate the presence of mislabeled data in that dataset, which in turns may suggest issues with the declarations provided by the farmers. ©2021 IEEE.
653 $aData fusion
653 $aK-means
653 $aLaser radar
653 $aRadar imaging
653 $aSatellites
653 $aSoil preservation
653 $aSustainable agriculture
653 $aXGBoost
700 1 $aPRECIOZZI, J.
700 1 $aMUSÉ, PABLO
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