Aerial Remote Sensing Survey of Fusarium Wilt of Cotton in New Mexico and Texas

Thursday, January 4, 2018: 2:15 PM
Salon D (Marriott Rivercenter Hotel)
Chenghai Yang , USDA-ARS
Jason Woodward , Department of Plant and Soil Science, Texas Tech University
Robert L. Nichols , Cotton Incorporated
Fusarium wilt of cotton, caused by the fungus Fusarium oxysporum f. sp. vasinfectum (FOV), is a widespread cotton disease, but the more virulent FOV race 4 (FOV4) has recently been identified in the New Mexico-Texas border area near El Paso, Texas. A preliminary aerial remote sensing survey was conducted on September 20, 2017 for mapping the distribution and severity of this emerging disease in the area. An aircraft equipped with multispectral, hyperspectral and thermal imaging systems was flown at approximately 5000 ft above ground level from three apparently infested areas: a 3800-ac area to the northwest of El Paso, Texas and a 39,000-ac area and a 2500-ac area to the southeast of El Paso. The multispectral imaging system consisted of a RGB camera and a near-infrared (NIR) camera with a pixel array of 7360 x 4912. Over 600 pairs of RGB and NIR images were acquired from the three areas, and the images for each area were mosaicked using Pix4D software. Hyperspectral and multispectral imagery was also acquired simultaneously. The mosaicked RGB and color-infrared images were examined visually to determine the characteristic signatures of FOV4-infested fields that had been identified by ground observations and pathogen sampling. The hyperspectral and thermal imagery was also evaluated. Due to the potential decline in the value of the infested land, no specific field or land information will be identified in this paper. This preliminary aerial survey has provided some useful information on FOV4 infestations in the New Mexico-Texas border area, but other coexisting stresses such as other FOV races, nematode infestation and soil-related problems can complicate the identification. Therefore, additional research is needed to evaluate multispectral imagery as well as hyperspectral and thermal imagery for monitoring the progression of the disease over the season and for distinguishing it from other stresses.