11 September 2020 Unmanned aerial vehicle remote sensing to delineate cotton root rot
Tianyi Wang, J. Alex Thomasson, Chenghai Yang, Thomas Isakeit, Robert L. Nichols, Ryan M. Collett, Xiongzhe Han, Cody Bagnall
Author Affiliations +
Abstract

Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only at locations where CRR exists, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing from manned aircraft is an effective means of delineating CRR-infected field areas. In 2015, an unmanned aerial vehicle was used to collect high-resolution remote-sensing images in a field known to be infected with CRR. A method was developed to produce a prescription map (PM) from these data, and in 2017, fungicide was applied based on a PM derived from the 2015 image data. The results showed that the PM reduced the fungicide applied by 88.3%, with a reduction in CRR area of 90% compared to 2015. A simple economic model suggested that it is generally better to treat an entire CRR-infested field rather than leaving it untreated, and application based on a PM becomes preferable as the size of the farm and the yield increase while the CRR-infestation level and the number of fields on the farm decrease.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2020/$28.00 © 2020 SPIE
Tianyi Wang, J. Alex Thomasson, Chenghai Yang, Thomas Isakeit, Robert L. Nichols, Ryan M. Collett, Xiongzhe Han, and Cody Bagnall "Unmanned aerial vehicle remote sensing to delineate cotton root rot," Journal of Applied Remote Sensing 14(3), 034522 (11 September 2020). https://doi.org/10.1117/1.JRS.14.034522
Received: 7 July 2020; Accepted: 31 August 2020; Published: 11 September 2020
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Cited by 8 scholarly publications.
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KEYWORDS
Unmanned aerial vehicles

Remote sensing

Cameras

Data modeling

Image resolution

Agriculture

Image classification

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