Spectral difference analysis and airborne imaging classification for citrus greening infected trees

By Xiuhua Li, Won Suk Lee, Minzan Li, Reza Ehsani, A. Mishra, Chenghai Yang, R. Mangan. 2012

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High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “Remote sensing technologies have been applied to estimate diseases [320] and monitor new outbreaks worldwide [265].”
Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images (2018, background): “It has been demonstrated that UAS platforms offer a unique opportunity to develop a variety of precision agriculture applications with the focus on assessing crop growth, vegetation and health status (Berni et al. 2009; Honkavaara et al. 2013; Li et al. 2012; López-Granados et al. 2016).”
Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms (2014, background): “This can modify the reflectance signatures of plant leaves (Liu, Huang, and Tao 2008; Sankaran et al. 2010; Li et al. 2012).”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, methodology): “Meanwhile, due to the disease, the inner cellular structure was damaged, which caused the lower reflectance in bands 7 and 8 (Li et al. 2012).”