Early Disease Detection in Wheat Fields using Spectral Reflectance

By C. Bravo, Dimitrios Moshou, J. West, A. McCartney, H. Ramon. 2003

267 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/6890088406d0dd149f903d0330aa23d63cf14dfe

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In Analytical Chemistry

In Plant Science

Citations in the corpus

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, methodology): “Using QDA, the overall accuracy reached 92% with 4 wavebands [26].”
High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, background): “Pioneering work was done by [77] by mounting a spectrograph (460–900 nm) on spray-boom height to Puccinia striiformis (yellow rust) in wheat and showed that four selected wavebands could distinguish disease at a level of 4 to 5% infection with 96% accuracy.”
Spectral signatures of sugar beet leaves for the detection and differentiation of diseases (2010, methodology): “Using a quadratic discriminating model based on reflectance, Bravo et al. (2003) classified yellow rust infestation on winter wheat with a reliability of 96%.”
Advanced methods of plant disease detection. A review (2014, unknown): “Field-based studies examined yellow rust disease on wheat plants (Bravo et al. 2003) and tried to distinguish among wheat diseases and abiotic stress conditions (Moshou et al. 2004).”
Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex (2016, methodology): “…non-destructive methods for disease detection based on reflectance data and spectral 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…”
Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects (2014, methodology): “It was found that yellow rust could be etected using NDVI (Normalized Difference Vegetation Index) or RI (Photochemical Reflectance Index) (Bravo et al., 2003; Huang t al., 2007). In detection of aphid infestation, Mirik et al. (2007) proosed an index called AI (Aphid Index) to indicate the abundanc”
Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement (2018, background): “Yellow rust (Puccinia . striiformis) is a foliar disease that 39 is common in cool climates, and is one of the most devastating diseases of wheat worldwide, reducing 40 crop yields by up to 7 tonne ha-1 in severe epidemics (Ma et al., 2001; Bravo et al., 2003).”
Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves (2008, methodology): “…sensor technology and remote sensing techniques may provide for an objective, time- and cost-effective method of recording and/or mapping disease intensity for large crop areas (Bravo et al. 2003; Moshou et al. 2005; Qin and Zhang 2005; Sharp et al. 1985; Steddom et al. 2005; Zhang et al. 2003).”
Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging (2010, background): “This is illustrated by a search of the literature that brings up over 200 publications in the past 10 years that deal with the use of hyperspectral imaging in agriculture, although only relatively few of these have focused on plant pathology (Bravo et al., 2003; Apan et al., 2005; Mishra et al., 200”