Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps

Clicks: 262
ID: 113118
2006
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
The objective of this research was to detect plant stress caused by disease infestation and to discriminate this type of stress from nutrient deficiency stress in field conditions using spectral reflectance information. Yellow Rust infected winter wheat plants were compared to nutrient stressed and healthy plants. In-field hyperspectral reflectance images were taken with an imaging spectrograph. A normalisation method based on reflectance and light intensity adjustments was applied. For achieving high performance stress identification, Self-Organising Maps (SOMs) and Quadratic Discriminant Analysis (QDA) were introduced. Winter wheat infected with Yellow Rust was successfully recognised from nutrient stressed and healthy plants. Overall performance using five wavebands was more than 99%.
Reference Key
moshou2006precisionsimultaneous Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors D. Moshou;C. Bravo;S. Wahlen;J. West;A. McCartney;J. De Baerdemaeker;H. Ramon;D. Moshou;C. Bravo;S. Wahlen;J. West;A. McCartney;J. De Baerdemaeker;H. Ramon;
Journal precision agriculture
Year 2006
DOI
doi:10.1007/s11119-006-9002-0
URL
https://dx.doi.org/10.1007/s11119-006-9002-0
Keywords
agriculture physics soil science & conservation remote sensing/photogrammetry statistics for engineering computer science chemistry and earth sciences atmospheric sciences

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.