Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks

By Dimitrios Moshou, C. Bravo, J. S. West, S. Wahlen, A. McCartney, H. Ramon. 2004

323 citations recorded by the source corpus.

Subfields: Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/d1efb0dc5178958b7a5f12c13a57a5ce10beeeee

How researchers see this paper

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

In Ecology

1. Andrew P. French
2. N. Harrison
3. Amy Lowe
4. Dionysis D. Bochtis
5. Sajid Hasan Apon
6. Biswajeet Pradhan
7. Simon Pearson
8. F. Nowrin

In Plant Science

1. N. Harrison
2. Andrew P. French
3. Amy Lowe
4. Dionysis D. Bochtis
5. Sajid Hasan Apon
6. Simon Pearson
7. F. Nowrin
8. Jihua Wang

Citations in the corpus

• A review of neural networks in plant disease detection using hyperspectral data (2018, methodology): “[73] Automatic detection of ‘‘yellow rust” in wheat using reflectance measurements and neural networks (2004) MLP, SOM Wheat (Triticum sp.”
• Identification and recognition of rice diseases and pests using convolutional neural networks (2018, background): “Some authors used visual spectrograph in ambient lighting conditions to detect yellow rust infestation on winter wheat [14].”
• Multi-temporal wheat disease detection by multi-spectral remote sensing (2007, background): “There are two different approaches to crop disease detection: near-range sensors (Moshou et al. 2004; Franke et al. 2005) and airborne/satellite-borne sensor systems.”
• Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, methodology): “Moshou D, Bravo C, West J, Wahlen S, McCartney A, Ramon H. Automatic detection of “yellow rust” in wheat using reflectance measurements and neural networks.”
• Machine Learning in Agriculture: A Review (2018, methodology): “The authors of [87] presented a method to detect either yellow rust infected or healthy wheat, based on ANN models and spectral reflectance features.”
• Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement (2018, background): “The early 71 success in field studies for hyperspectral image-based detection of yellow rust (Moshou et al., 2004 72 and Bravo et al., 2003) focused on the presence of yellow rust in the field, not necessarily the 73 severity.”
• Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects (2014, background): “Moshou et al. (2004) reported that the reflectance at 680, 725 and 750 nm could be used to detect yellow rust in winter wheat.”