|Cribben, Curtis -|
|Thomasson, J -|
|Ge, Yufeng -|
|Morgan, Cristine -|
|Nichols, Robert -|
Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: July 26, 2013
Publication Date: July 26, 2013
Citation: Cribben, C.D., Thomasson, J.A., Ge, Y., Morgan, C.L., Yang, C., Nichols, R.L. 2013. Ground-based technologies for cotton root rot control: Results from a three-year experiment. Proc. Beltwide Cotton Conf. pp. 513-521. Interpretive Summary: Accurately mapping the distribution of cotton root rot disease could facilitate more efficient and economical treatments rather than treating entire fields. In this research, plant reflectance, ground-based thermal images, surface soil moisture, temperature, and apparent electrical conductivity were measured from three fields for three years in Thrall, Sinton, and San Angelo, TX. Preliminary analysis showed that the incidence of cotton root rot appears to be correlated with electrical conductivity and aerial images of plant reflectance. These results can be used to develop strategies for site-specific treatment of this crop disease.
Technical Abstract: The overall goal of this research is to develop ground-based technologies for disease detection and mapping which can maximize the effectiveness and efficiency of CRR (cotton root rot) treatments. Accurately mapping CRR could facilitate a much more economical solution than treating entire fields. Three cotton fields around CRR-prone areas of Texas have been the sites for three years of data collection. Freshly picked cotton leaves from healthy, disease stressed, and dying or dead plants were scanned with an ASD VisNIR spectroradiometer. Within each plot, moisture, temperature, and bulk electrical conductivity of surface soil were measured with a Delta-T WET sensor or moisture only with a Theta Probe. A thermal infrared camera was used to capture leaf canopy images of healthy and disease-stressed plants. A complete soil ECa (apparent electrical conductivity) survey was conducted for each field with an EM-38 sensor. Plant status was visually inspected and recorded to form a series of disease-progression maps in each field. Leaf spectra have been evaluated with LDA (linear discriminant analysis) to relate them to classifications of infection level. Multiple linear regression was used to relate physical and chemical soil properties to the ECa values obtained from the EM-38. These data continue to be analyzed to (1) understand the spatiotemporal progression of CRR and (2) identify promising means for its detection.