Investigation of Winter Wheat Leaf Rust Disease and Soil Background Effects on Vegetation Indices Estimated Using Proximal and Unmanned Aerial System (UAS) Based Remote Sensing Data

Abstract

Remote sensing tools such as unmanned aerial systems (UAS) can screen thousands of genotypes in a short time, thus accelerating the selection process for crop improvement. An experiment was conducted at College Station and Castroville, TX in 2018-19 and 2019-20 to investigate the potential of UAS for effectively detecting wheat leaf rust disease. The performance of three UAS-derived vegetation indices (VIs), the normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (GCI) were compared for their ability to detect leaf rust and predict grain yield. On 12 April 2019 at College Station, when leaf rust infection was high, significant positive relationships (p < .001; R^2 = 0.42 to 0.62) between grain yield and the VIs were observed. However, on 13 April 2020, when leaf rust infection was high, the VIs showed significant negative relationships with grain yield (p < .05; R^2 = 0.27) at Castroville. Compared to NDVI and NDRE, the repeatability of genotypes for GCI was more influenced by fungicide application in 2018-19 and with an increase in leaf rust severity in 2019-20. It suggests that GCI is a more reliable indicator of leaf rust infection. Although these VIs showed potential in detecting leaf rust infection, several factors affect plant performance simultaneously in actual field conditions. This triggers the need to develop novel indices for detecting diseases that could be applied across diverse environments. In 2019-20, 2020-21, and 2021-22, a spectroradiometer was used to collect canopy reflectance in the wavelength range 350-2500 nm. Based on the stepwise discriminant analysis, the wavebands 511-520, 521-530, 681-690, and 691-700 nm were found effective in discriminating leaf rust severity. Compared to the other VIs, normalized difference leaf rust index 2 (NDLRI2), developed using wavebands 511-520 and 691-700 nm was found to be the most reliable index for effectively detecting leaf rust severity across the winter wheat genotypes in field conditions. The performance of the normalized pigment chlorophyll ratio index (NPCI) was similar to NDLRI2 but was less effective in some environments and showed higher CV in 2020 and 2021. Although UAS and spectroradiometer proved their potential in winter wheat leaf rust detection, exposed soil due to low vegetation cover or in open canopy crops influences above-ground phenotyping. An experiment was conducted at College Station, TX in 2020 and 2021 to investigate the potential of six UAS-derived and proximally sensed VIs in suppressing soil background brightness. However, the UAS-derived VIs were minimally impacted by soil background variations. The proximally sensed VIs were influenced by soil background brightness at an estimated mean canopy cover of ~29-35%. Among all the VIs, PVI was least influenced by canopy cover or soil background variations. The findings of this study encourage the use of UAS but limit the use of proximally sensed VIs investigated in this study for large-scale research.