Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble-Reference-Cited by-同舟云学术

Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble

Published:2017-05-25 Issue:16 Volume:38 Page:4631-4644
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Fernandes Jeferson Lobato¹,Ebecken Nelson Francisco Favilla²^ORCID,Esquerdo Júlio César Dalla Mora³

Affiliation:

1. Center of Technology, Federal University of Rio de Janeiro, Civil Engineering Postgraduate Program - COPPE/UFRJ, Rio de Janeiro/RJ, Brazil

2. Center of Technology, Federal University of Rio de Janeiro - COPPE/UFRJ, Rio de Janeiro/RJ, Brazil

3. Embrapa Agricultural Informatics, Campinas/SP, Brazil

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2017.1325531

Reference45 articles.

1. ASTER and Landsat ETM+ images applied to sugarcane yield forecast

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3. Bagging predictors

4. Conab. Levantamentos sobre o acompanhamento da safra brasileira de cana-de-açúcar. Accessed October 10 2014. http://www.conab.gov.br/conteudos.php?a=1253&t=2.

5. A biophysical model of Sugarcane growth

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