Development of spectral indices for detecting and identifying plant diseases

By Anne-Katrin Mahlein, Till Rumpf, Pascal Welke, Heinz-Wilhelm Dehne, Lutz Plümer, Ulrike Steiner, Erich-Christian Oerke. 2013

595 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/d353d4b04d5bce84ed3ccdf5ce59e3ecf00217fa

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In Analytical Chemistry

In Ecology

In Media Technology

Citations in the corpus

HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging (2015, background): “index CLSI ð Þ 1⁄4 ρ698−ρ750 ρ698þρ750−ρ734; [13]) was calculated from the ρ, τ and ρ + τ data, respectively.”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “However, each disease may affect the leaf reﬂectance spectrum in a speciﬁc way [153].”
A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy (2016, unknown): “…spectral indices designed for plant disease identi ﬁ cation based on this cleaned image is much more reliable to detect these diseases at early infection stages, especially when these indices are based on wavelengths strongly affected by BRDF effects, e.g., the SBRI (Mahlein et al., 2013).”
Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks (2019, background): “However, they require precise instruments and bulky sensors [2], [3], which lead to high cost and low efficiency.”
Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging (2018, background): “Pigment concentration in combination with spatial distribution mapping is particularly successful approach for prediction of various plant diseases that affect the leaves directly [20,65].”
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification (2016, methodology): “Last layer was altered and the output of the softmax layer was parameterized to the requirements of presented study.”
Advanced methods of plant disease detection. A review (2014, unknown): “2012); (2) assessment and derivation of spectral vegetation indices (SVIs), general or specifically introduced, which are sensitive to disease presence (Mirik et al. 2006; Huang et al. 2007; Naidu et al. 2009; Reynolds et al. 2012; Mahlein et al. 2013; Stilwell et al. 2013); (3) data mining algorith”
Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “, NDVI), the development of disease specific spectral disease indices (SDI) can increase the accuracy of disease detection and differentiation under real world field conditions [249,250].”