Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data
By Elfatih M. Abdel-Rahman, Fethi B. Ahmed, Riyad Ismail. 2013
219 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/e232f10d0c990643e3fc82d8e07aa401a0cf2bfd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Iftikhar Ali
- F. Greifeneder
- J. Stamenkovic
- M. Neumann
- J. Estrada-Ávalos
- A. R. Martínez-Sifuentes
- Jorge E. Mauricio-Ruvalcaba
- C. Notarnicola
Citations in the corpus
- Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, background): “…remote sensing in combination with machine learning is not limited to crop yield estimation or mapping, but also, it can be used for the monitoring of other crop-related activities, for example: crop losses due to floods [77] or the estimation of nitrogen concentration in sugarcane leaf [78].”
- Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest (2020, methodology): “Therefore, it is necessary to perform the cross-validation k-fold [12,28,55].”