Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses

By Minghua Zhang, X. Liu, M. O’Neill. 2002

82 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Ecology

View Paper: https://www.semanticscholar.org/paper/2ca7b2c45e317187169cc6f19edf831fc9c363a8

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. Anne-Katrin Mahlein
2. J. D. Leeuw
3. Iris van Duren
4. C. Joshi
5. J. Lamichhane

In Atmospheric Science

1. J. D. Leeuw
2. Iris van Duren
3. C. Joshi

In Ecology

1. Heinz-Wilhelm Dehne
2. J. D. Leeuw
3. Anne-Katrin Mahlein
4. Erich-Christian Oerke
5. Iris van Duren
6. Ulrike Steiner
7. C. Joshi
8. J. Lamichhane

Citations in the corpus

• Spectral signatures of sugar beet leaves for the detection and differentiation of diseases (2010, result): “Other researchers successfully used spectral data to detect Phytophthora infestans on tomato (Zhang et al. 2002) or Venturia inaequalis on apple trees (Delalieux et al. 2007).”
• Remote sensing and GIS applications for mapping and spatial modelling of invasive species (2004, methodology): “Using principal component and cluster analyses Zhang et al. (2002) used spectral ratio analysis based on principle component analysis and clustered analysis.”