Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications

By Stephanie Delalieux, Jan A. N. van Aardt, W. Keulemans, Eddie Schrevens, Pol R. Coppin. 2007

232 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Food Science

View Paper: https://www.semanticscholar.org/paper/3162484c77821df00b22cd9ddbd9477199ae2b78

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In Analytical Chemistry

In Ecology

Citations in the corpus

Current and Prospective Methods for Plant Disease Detection (2015, methodology): “Magnaporthe grisea infection of rice, Phytophthora infestans infection of tomato and Venturia inaequalis infection of apple trees have been identified and reported using hyperspectral imaging techniques [54–56].”
Spectral signatures of sugar beet leaves for the detection and differentiation of diseases (2010, background): “2002) or Venturia inaequalis on apple trees (Delalieux et al. 2007).”
Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees (2013, methodology): “Hyperspectral reflectance in the range of 350–2500 nm was used by Delalieux et al. (2007) to detect apple scab (Venturia inaequalis).”
Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards (2011, background): “Various researchers (Spinelli et al., 2006; Yang et al., 2007; Delalieux et al., 2007; Chen et al., 2008; Purcell et al., 2009) have used spectral reflectance based techniques for disease detection in plants and postharvest food products.”
Thermographic assessment of scab disease on apple leaves (2011, methodology): “Remote sensing may be used also in early warning systems; Delalieux et al. (2007, 2009) used non-imaging hyperspectroscopy for the differentiation of scab-infected and healthy apple leaves and reported that the 1460, 1940 and 2400 nm wavelengths (the first and third associated to water content) ar”
Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging (2010, background): “Within plant disease detection and severity measurement a wide range of wavelengths have been explored to study various pathosystems including 350–2500 nm (Apan et al., 2005; Delalieux et al., 2007; Liu et al., 2007; Mishra et al., 2007; Yang et al., 2007), 400–850 nm (Huang et al.”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “Nevertheless, the use of innovative hyperspectral imaging systems for early disease detection and disease severity assessment are still at the research stage [130,131].”
Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data (2011, unknown): “The applications of hyperspectral remote-sensing data are broad, including species discrimination, nutrient content assessment, stress detection and disease detection (Schmidt and Skidmore 2002, Lelong et al. 2006, Mirik et al. 2006, Delalieux et al. 2007).”
Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance (2015, background): “…and biochemical parameters (Haboudaneet al., 2002; Li et al., 2010;Thenkabail et al., 2011;Yu et al., 2013b) of vegetation and agricultural crops as well as to detect environmental stress or plant diseases (Delalieux et al., 2007; Mahlein et al., 2013; Stagakis et al., 2012; Yu et al., 2013a).”
Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex (2016, background): “…vegetation indices (Johnson et al., 1996; Zhang et al., 2002; Bravo et al., 2003; Steddom et al., 2003; Steddom et al., 2005; Graeff et al., 2006; Delalieux et al., 2007; Huang et al., 2007; Larsolle and Muhammed, 2007; Delalieux et al., 2009; Naidu et al., 2009; Wang et al., 2009; Sankarana et…”