Accurate prediction of sugarcane yield using a random forest algorithm-Reference-Cited by-同舟云学术

Accurate prediction of sugarcane yield using a random forest algorithm

Published:2016-04-19 Issue:2 Volume:36 Page:
ISSN:1774-0746
Container-title:Agronomy for Sustainable Development
language:en
Short-container-title:Agron. Sustain. Dev.

Author:

Everingham Yvette,Sexton Justin,Skocaj Danielle,Inman-Bamber Geoff

Funder

Sugar Research Australia

Publisher

Springer Science and Business Media LLC

Subject

Agronomy and Crop Science,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s13593-016-0364-z/fulltext.html

Reference36 articles.

1. Abdel-Rahman EM, Ahmed FB, Ismail R (2013) Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int J Remote Sens. doi: 10.1080/01431161.2012.713142

2. Allwinkle S, Cruickshank P (2011) Creating smarter cities: an overview. J Urban Technol. doi: 10.1080/10630732.2011.601103

3. Breiman L (2001) Random forests. Mach Learn. doi: 10.1023/A:1010933404324

4. Cao S, Manrai AK (2014) Big data in marketing & retailing. J Int Interdiscip Bus Res 1:23–42, http://www-bcf.usc.edu/~jkarayan/base%20rates.JIIBR-Volume-1.pdf#page=27

5. Caragliu A, Del Bo C, Nijkamp P (2011) Smart cities in Europe. J Urban Technol. doi: 10.1080/10630732.2011.601117

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