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dc.date.accessioned 2022-08-18T19:08:32Z
dc.date.available 2022-08-18T19:08:32Z
dc.date.issued 2021
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/140705
dc.description.abstract An accurate estimation of soybean yield while the plants are still in the field is highly necessary for industry applications and decision-making policies related to planning. Remote sensing is a powerful tool, due to its spatiotemporal coverage, for developing empirical models to predict and evaluate crop yields at regional and national scales. en
dc.format.extent 163-163 es
dc.language en es
dc.subject NDVI es
dc.subject Soybean es
dc.subject Regression model es
dc.subject Córdoba es
dc.subject Argentina es
dc.title Estimating soybean yield using time series of anomalies in vegetation indices from MODIS en
dc.type Objeto de conferencia es
sedici.identifier.uri http://50jaiio.sadio.org.ar/pdfs/cai/CAI-23.pdf es
sedici.identifier.issn 2525-0949 es
sedici.creator.person Nolasco, Miguel es
sedici.creator.person Ovando, Gustav es
sedici.creator.person Sayago, Silvina es
sedici.creator.person Magario, Ivana es
sedici.creator.person Bocco, Mónica es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Resumen es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2021-10
sedici.relation.event XIII Congreso de AgroInformática (CAI 2021) - JAIIO 50 (Modalidad virtual) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)