Cotton root rot (CRR) is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Research has shown that CRR can be prevented or at least mitigated by applying a fungicide at planting, but the fungicide should be applied precisely to minimize the quantity of product used and the treatment cost. The CRR-infested areas within a field are consistent from year to year, so it is possible to apply the fungicide only at locations where CRR is manifest, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing (RS) from manned aircraft is an effective means of delineating CRR-infested field areas. Applying various classification methods to moderate-resolution (1.0 m/pixel) RS images has recently become the conventional way to delineate CRR-infested areas. In this research, an unmanned aerial vehicle (UAV) was used to collect high-resolution remote sensing (RS) images in three Texas fields known to be infested with CRR. Supervised, unsupervised, and combined unsupervised classification methods were evaluated for differentiating CRR from healthy zones of cotton plants. Two new automated classification methods that take advantage of the high resolution inherent in UAV RS images were also evaluated. The results indicated that the new automated methods were up to 8.89% better than conventional classification methods in overall accuracy. One of these new methods, an automated method combining k-means segmentation and morphological opening and closing, provided the best results, with overall accuracy of 88.5% and the lowest errors of omission (11.44%) and commission (16.13%) of all methods considered.