|Odvody, Gary -|
|Fernandez, Carlos -|
|Landivar, Juan -|
|Minzenmayer, Richard -|
|Nichols, Robert -|
|Thomasson, J -|
Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 22, 2013
Publication Date: April 3, 2014
Citation: Yang, C., Odvody, G.N., Fernandez, C.J., Landivar, J.A., Minzenmayer, R.R., Nichols, R.L., Thomasson, J.A. 2014. Monitoring cotton root rot progression within a growing season using airborne multispectral imagery. Journal of Cotton Science. 18:85-94. Interpretive Summary: Cotton root rot is a serious and destructive disease that affects cotton production in the southwestern United States. This study utilized airborne multispectral imagery to monitor the progression of cotton root rot in cotton fields in Edroy and San Angelo, Texas. Airborne multispectral imagery was taken of these fields 2-4 times during the 2010 growing season. Image analysis results showed that cotton root rot progressed in different patterns and at different rates over the growing season for different fields. The results from this study will be useful for understanding the progression of the disease and for developing site-specific treatment plans.
Technical Abstract: Cotton root rot, caused by the fungus Phymatotrichopsis omnivora, is a serious and destructive disease affecting cotton production in the southwestern United States. Accurate delineation of cotton root rot infections is important for cost-effective management of the disease. The objective of this study was to use airborne multispectral imagery for monitoring the progression of root rot infections within cotton fields during a growing season. A number of cotton fields near Edroy and San Angelo, Texas were selected for this study. Airborne multispectral digital imagery with blue, green, red and near-infrared bands was taken from these fields 2-4 times during the 2010 growing season. The imagery for two fields from each of the two locations was georeferenced and classified into 2-20 spectral classes using unsupervised classification techniques. The optimal number of spectral classes was determined based on the average transformed divergence for each classification and the spectral classes were then grouped into root rot-infected and non-infected zones. The infected areas within each field were determined for each imaging date and compared among the different dates. Both airborne imagery and ground observations showed that cotton root rot expanded in different patterns and at different rates over the growing season. Toward the end of the growing season, the percentage of root rot-infected areas increased to 13.2% and 26.8% in the two fields in Edroy, and to 37.8% and 50.6% in the two fields in San Angelo. The results from this study will be useful for the understanding of the progression of the disease and for the development of site-specific treatment plans for the disease.