Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

By Armando A. Apan, Alex Held, Stuart R. Phinn, J. Markley. 2004

343 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/67f6f570708d227dc15e801753fdb515b4e80843

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Biswajeet Pradhan
2. Pol R. Coppin
3. Siva K. Balasundram
4. G. Vadamalai
5. K. Golhani
6. Dar A. Roberts

In Ecology

1. Dar A. Roberts
2. Biswajeet Pradhan
3. Philip E. Dennison
4. Michael Alonzo
5. Keely L. Roth

In Plant Science

1. Siva K. Balasundram

Citations in the corpus

• Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, methodology): “[41] Detected sugarcane areas affected by orange rust disease using Hyperion data and developed specific vegetation indices that are sensitive to the disease.”
• Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression (2006, neutral): “Apan et al. (2004) noted the key role of the SWIR bands in the discrimination of healthy and diseased (orange rust) sugarcane crops.”
• Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications (2007, background): “…stress (Dobrowsky et al., 2005), shade (Lateur, 2002), senescence (Mohammed et al., 2000), beetle infestation (Lawrence, 2003), sugarcane disease (Apan et al., 2004), late blight (Zhang et al., 2003), nutrient deficiency (Zhao et al., 2005), air pollution, petroleum toxicity (Campbell et al.,…”
• Applications of Remote Sensing in Precision Agriculture: A Review (2020, neutral): “EO-1 Hyperion (2000–2017) HS (30 m) 16 days Disease [88,89]”
• Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments (2006, background): “( 2004 ) found 550, 680 and 800 nm useful in discriminating between sugarcane varieties.”
• Lessons from nearly 20 years of Precision Agriculture research, development, and adoption as a guide to its appropriate application (2009, neutral): “…seem feasible, although the useofDMSV todistinguish varieties or disease is an area thatmay require considerable further work since these are applications thatmight be expected todependonhyperspectralmethods (Apan et al. 2004; Markley and Fitzpatrick 2004; Galvão et al. 2005; Almeida et al. 2006).”
• Multi-temporal wheat disease detection by multi-spectral remote sensing (2007, background): “Apan et al. (2004) discriminated sugarcane crops severely infected by orange rust diseases from non-infected crops by the use of several hyper-spectral indices.”
• View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data (2009, methodology): “This approach was used by Apan et al. (2004) and Galvão et al. (2005) to detect sugarcane disease and to discriminate sugarcane varieties with Hyperion data, respectively.”
• A review of neural networks in plant disease detection using hyperspectral data (2018, background): “[144] ead blight Bauriegel and Herppich [145] er Qin et al.”
• Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat (2007, methodology): “(2006a) Disease-Water Stress Index 4 DWSI-4 Apan et al. (2004) Green Normalized Difference Vegetation Index GNDVI Daughtry et al. (2000) Modified Chlorophyll Absorption in Reflectance Index MCARI Daughtry et al.”