Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks

By Francisca López-Granados, José Manuel Peñá-Barragán, Montserrat Jurado-Expósito, Mario Francisco-Fernández, Ricardo Cao, Amparo Alonso-Betanzos, Oscar Fontenla-Romero. 2008

58 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/5daa18576d3a7024f7b8872f115b168cd6712e9b

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. U. Shapira
2. Arnon Karnieli
3. Ittai Herrmann
4. David J. Bonfil

In Ecology

1. Arnon Karnieli
2. Ittai Herrmann
3. David J. Bonfil
4. U. Shapira
5. S. Kinast

In Plant Science

1. Arnon Karnieli
2. Ittai Herrmann
3. David J. Bonfil

Citations in the corpus

• Ground-level hyperspectral imagery for detecting weeds in wheat fields (2013, background): “Lopez-Granados et al. (2008) classified the ground-level spectral reflectance of wheat, four grass weeds and soil, and concluded that one sampling date per growth season, when phenological distinction is maximal, can provide high quality classification.”
• Field spectroscopy for weed detection in wheat and chickpea fields (2013, background): “Spectral data obtained at ground level by Lopez-Granados et al. (2008) resulted in high classification, but no relation to an optimal time for herbicide application was mentioned.”
• Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images (2017, result): “Similar experiments have been conducted in field conditions (Brown et al. 1994; Hadoux et al. 2012; Lopez-Granados et al. 2008; Shapira et al. 2013).”