Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat

By Jingcheng Zhang, Lin Yuan, Ruiliang Pu, R. Loraamm, Guijun Yang, Jihua Wang. 2014

127 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/7ad3f4de5de6b223fbb5dacc24d25bc3a0d3fd91

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1. Jinbao Jiang
2. Ruyan He
3. Hui Li
4. Xiaojun Qiao

Citations in the corpus

• Advanced methods of plant disease detection. A review (2014, unknown): “…RS: Fiji leaf gall sugarcane disease (Purcell et al. 2009), wheat powdery mildew and take-all disease (Graeff et al. 2006), curl mite (Stilwell et al. 2013), sugarcane yellow leaf virus (Grisham et al. 2010), grapevine viruses (Naidu et al. 2009), and winter wheat yellow rust (Zhang et al. 2014).”
• Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress (2018, background): “Flowchart of the selection of wavelet coefficient feature (modified from Zhang et al. 2014).”