Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis.

By Christoph R Mer, Mirwaes Wahabzada, A. Ballvora, F. Pinto, Micol Rossini, C. Panigada, Jan Behmann, Jens L On, Christian Thurau, Christian Bauckhage and 3 more. 2012

150 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

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Citations in the corpus

Prototypal Analysis and Prototypal Regression (2017, methodology): “Archetypal analysis has been applied in physics (Stone and Cutler, 1996; Stone, 2002; Chan et al., 2003), biology (Huggins et al., 2007; Römer et al., 2012; Thøgersen et al., 2013), psychology (Thurau and Drachen, 2011; Drachen et al., 2012, 2016; Sifa and Bauckhage, 2013), marketing (Li et al.,…”
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform (2018, methodology): “These include techniques that directly apply reflectance intensities from the whole spectrum (Mahlein et al., 2012b; Römer et al., 2012), a reconstructed sparse vector from a spectral dictionary (Roscher et al.”
A Review of Imaging Techniques for Plant Phenotyping (2014, methodology): “These appropriate mathematical treatments and algorithms include 2D correlation plots [136], partial least squares regression [137,138], principal components analysis [139], support vector machines [140], neural networks [141] and other machine learning approaches.”
Morphological, Physiological, and Genetic Responses to Salt Stress in Alfalfa: A Review (2020, background): “Imaging within the visible spectrum (~400–700 nm) can measure the morphological and color properties of plants, while hyperspectral imaging can also study the radiative properties of plant leaves to facilitate the early detection of abiotic stress [98,99].”
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping (2014, methodology): “These include the use of 2D correlation plots [66], partial least squares regression [67,68], principal components analysis [65], support vector machines (SVM) [69], neural networks [70] and other machine learning approaches for stress detection.”
Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. (2015, background): “Hyperspectral cameras detect hundreds of spectral bands with nm-level resolution between 350 and 2500 nm and are currently being developed to detect plant stress [26,34].”
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, methodology): “Simplex volume maximisation SiVM with DAR Barley (drought) Barley (drought) [44] [47] 4 days before Vegetation Indices 1.”