|Schepers, Aaron - UNIV OF NE/STUDENT|
|Ferguson, R - UNIV OF NE/CLAY CENTER NE|
Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: June 25, 1999
Publication Date: June 25, 1999
Citation: Shanahan, J.F., Tringe, J.M., Schepers, A.R. Ferguson, R. B., Francis, D.D., Schepers. J. S. 1999. Normalizing strategies to detect corn N stress and grain yield from imagery. American Society of Agronomy Abstracts. p. 247. Technical Abstract: The goal of this research is to compare different normalizing strategies for remote sensing data as a means of predicting nitrogen stress and final grain yield for corn. The work was conducted near Shelton, NE during the 1997-98 seasons. Treatments consisted of a combination of 4 corn hybrids, differing in canopy architecture, and 5 N rates. Remotely sensed data for the entire plot area were collected at different crop growth stages using four-band multi-spectral (blue, green, red, and near-infrared) system from aircraft. Images (0.5-m spatial resolution) were geo-referenced and converted into various vegetation indices, including NDVI. Grain yield for each plot was also determined at maturity. Vegetation indices collected during mid grain fill were most highly correlated with grain yield, and normalizing absolute grain yields to the treatment average further improved this relationship.