Skip to main content
ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #373952
Research Project: Aerial Application Technology for Sustainable Crop Production

Location: Aerial Application Technology Research

Title: Automatic classification of cotton root rot disease based on UAV remote sensing

Author
item WANG, TIANYI - Texas A&M University
item THOMASSON, JOHN - Texas A&M University
item Yang, Chenghai
item ISAKEIT, THOMAS - Texas A&M University
item NICHOLS, ROBERT - Cotton, Inc

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2020
Publication Date: 5/21/2020
Citation: Wang, T., Thomasson, J.A., Yang, C., Isakeit, T., Nichols, R.L. 2020. Automatic classification of cotton root rot disease based on UAV remote sensing. Remote Sensing. 12, 1310. https://doi.org/10.3390/rs12081310.
DOI: https://doi.org/10.3390/rs12081310

Interpretive Summary: Cotton root rot is a persistent soilborne fungal disease that is devastating to cotton in the southwestern U.S. and Mexico. Now cotton root rot can be prevented or mitigated by applying a fungicide at planting, but the fungicide should be applied only to the infested areas to minimize fungicide use and treatment cost. Previous studies have shown that remote sensing imagery from manned aircraft is an effective means of delineating infested areas within fields. In this study, an unmanned aerial vehicle (UAV) was used to collect high-resolution aerial images in three Texas fields known to be infested with the disease. Two new automated classification methods that took advantage of the high resolution inherent in UAV images were evaluated and compared with traditional classification methods. Accuracy assessment showed that the new automated methods were about 9% better than conventional classification methods in overall accuracy. The results from this study indicate that UAV-based imagery can be another useful data source for site-specific management of cotton root rot.

Technical Abstract: 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 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.