Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

By N. Jain, S. S. Ray, J. Singh, S. Panigrahy. Precision Agriculture, 2007

110 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/b537ce7d24ac3fee4c8f4ac28072e6a85e18e298

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Elfatih M. Abdel-Rahman
2. Fethi B. Ahmed
3. Yangyang Fan
4. Lei Zhou
5. Osama Elsherbiny
6. Onisimo Mutanga
7. Riyad Ismail
8. Arnon Karnieli

In Ecology

1. Arnon Karnieli
2. Ittai Herrmann
3. David J. Bonfil
4. Elfatih M. Abdel-Rahman
5. Fethi B. Ahmed
6. Yafit Cohen
7. Onisimo Mutanga
8. Riyad Ismail

In Plant Science

1. Elfatih M. Abdel-Rahman
2. Fethi B. Ahmed
3. Yafit Cohen
4. Onisimo Mutanga
5. Riyad Ismail
6. Victor Alchanatis
7. Arnon Karnieli
8. Ittai Herrmann

Citations in the corpus

• Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets (2015, methodology): “Previous studies indicate that the SMLR model has been extensively used in modelling the relationship between remotely sensed data and different tree-structural attributes with reasonable accuracy rates (Jones and Ricciardi 2005; Jain et al. 2007).”
• Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data (2013, methodology): “SML regression has been widely employed to relate remotely sensed data to vegetation variables (Bortolot and Wynne 2003; Kumar et al. 2003; Zhao et al. 2003; Mutanga, Skidmore, and Prins 2004; Jain et al. 2007).”
• SWIR-based spectral indices for assessing nitrogen content in potato fields (2010, background): “Email: karnieli@bgu.ac.il International Journal of Remote Sensing Vol. 31, No. 19, 10 September 2010, 5127–5143 fundamental for optimizing yields (Bonfil et al. 2004, Reyniers et al. 2006, Jain et al. 2007, Feng et al. 2008, Zhu et al. 2008).”
• Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data (2021, background): “Primarily, the VI that combined one or more bands provides spectral responses to changes in water content and can reduce additive and multiplicative errors associated with ambient environmental conditions [15].”