Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps
By Dimitrios Moshou, C. Bravo, R. Oberti, J. S. West, L. Bodria, A. McCartney, H. Ramon. 2005
218 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Ecology, Plant Science
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Citations in the corpus
- A survey of data fusion in smart city applications (2019, neutral): “[127], [128] 3,4 1 2,4 2,4,5 1 1 Agriculture Plant Disease Classification [87] 4 2,3 1,3 1,8 5 3 Customer Profiling [129] 4 4 1,4 5 5 3 Consumer Awareness [107] 4 4 1 9 5 2 Blockchain and Supply Chain [130] 3,4 4 2,4 1,5,8 5 3 Supply Chain Management [77] 3 2,3 2,3 5,8 4 3 Tourist Behavior Analysis ”
- An In-field Automatic Wheat Disease Diagnosis System (2017, background): “In this context, various spectroscopic and imaging techniques have been studied for identifying crop disease symptoms (Bravo et al., 2004; Moshou et al., 2005; Chaerle et al., 2007; Belasque Jr et al., 2008; Qin et al., 2009).”
- Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement (2018, methodology): “Adopting a data fusion approach of hyperspectral data 291 (450 – 900 nm) and fluorescence data (550 – 690 nm), Moshou et al. (2005), reported a high accuracy 292 (94.5”
- 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).”