Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max)

By Cliff H. Koger, Lori Mann Bruce, David R. Shaw, Krishna N. Reddy. 2003

106 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/974f5b4f256b7747891e35c3b6abbd8a5f55a1a4

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. Jingfeng Huang
2. George Alan Blackburn

In Ecology

1. Jingfeng Huang
2. George Alan Blackburn

In Plant Science

1. Jingfeng Huang
2. George Alan Blackburn

Citations in the corpus

• Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation (2007, methodology): “A classification accuracy of at least 87% was found, regardless of mother wavelet selection, with three particular wavelets (Daubechies 3 and 5 and Coiflet 5) producing 100% accuracy (Koger et al. 2003).”
• Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data (2011, methodology): “Koger et al. (2003) demonstrated that the weed pitted morning glory could be detected in soybean crops with high levels of accuracy using wavelet analysis of hyperspectral data, and such methods were robust with respect to variations in the cover of crop residue (Huang et al. 2001).”