Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat

By Hamed Hamid Muhammed. 2005

102 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/1f57ac35f8cfdfd865c9d9c5713124ea41d2ee86

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. Reza Ehsani
3. Siva K. Balasundram
4. G. Vadamalai
5. K. Golhani

In Ecology

1. Biswajeet Pradhan
2. Helmi Zulhaidi Mohd Shafri
3. Reza Ehsani

In Plant Science

1. Helmi Zulhaidi Mohd Shafri
2. Reza Ehsani
3. Siva K. Balasundram
4. Shohreh Liaghat

Citations in the corpus

• A review of neural networks in plant disease detection using hyperspectral data (2018, background): “It is worth mentioning that AVIRIS hyperspectral images are useful in characterizing and estimating various fungal and bacterial diseases [135,136].”
• Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms (2014, background): “Various studies confirmed the potential of using a spectroradiometer in field conditions for disease detection (Zhang et al. 2003; Muhammed 2005; Liu, Huang, and Tao 2008; Sankaran et al. 2011; Li et al. 2012; Reynolds et al. 2012).”