UAS-derived vegetation indices detect wheat leaf rust infection and its influence on grain yield and canopy temperature

Authors: RAMAN, RUHL - Texas A&M University; NEELY, HALY - Washington State University; RAJAN, NITHYA - Texas A&M University; BHANDARI, MAHENDRA - Texas A&M Agrilife; SIEGFRIED, JEFFREY - Kansas State University; IBRAHIM, AMIR - Texas A&M University; Adams, Curtis; HARDIN, ROBERT - Texas A&M University

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/7/2025
Publication Date: 4/27/2025
Citation: Raman, R., Neely, H., Rajan, N., Bhandari, M., Siegfried, J., Ibrahim, A., Adams, C.B., Hardin, R. 2025. UAS-derived vegetation indices detect wheat leaf rust infection and its influence on grain yield and canopy temperature. Crop Science. 65(3). Article e70062. https://doi.org/10.1002/csc2.70062.
DOI: https://doi.org/10.1002/csc2.70062

Interpretive Summary: Leaf rusts are a class of plant diseases that reduce wheat yields around the world. To breed rust-resitant wheat varieties, breeders rate prospective varieties for rust and select the best. In contrast to traditional manual rust rating methods that are labor- and time-intensive, unmanned aerial systems (UAS) have the potential to scan many varieties quickly. To gain insight on this issue, a field experiment was conducted at two sites (College Station and Castroville, TX) in two seasons to assess the performance of three spectral vegetation indices in the detection of leaf rust progression. Differences among wheat varieties in rust severity were detectable using standard vegetation indices. However, the vegetation indices could not distinguish effects of rust from other factors affecting wheat growth and yield. Thus, although UAS-derived vegetation indices showed potential in detecting leaf rust severity, interpretation of the results may be complicated if multiple factors affect winter wheat simultaneously.Leaf rusts are a class of plant diseases that reduce wheat yields around the world. To breed rust-resitant wheat varieties, breeders rate prospective varieties for rust and select the best. In contrast to traditional manual rust rating methods that are labor- and time-intensive, unmanned aerial systems (UAS) have the potential to scan many varieties quickly. To gain insight on this issue, a field experiment was conducted at two sites (College Station and Castroville, TX) in two seasons to assess the performance of three spectral vegetation indices in the detection of leaf rust progression. Differences among wheat varieties in rust severity were detectable using standard vegetation indices. However, the vegetation indices could not distinguish effects of rust from other factors affecting wheat growth and yield. Thus, although UAS-derived vegetation indices showed potential in detecting leaf rust severity, interpretation of the results may be complicated if multiple factors affect winter wheat simultaneously.Leaf rusts are a class of plant diseases that reduce wheat yields around the world. To breed rust-resitant wheat varieties, breeders rate prospective varieties for rust and select the best. In contrast to traditional manual rust rating methods that are labor- and time-intensive, unmanned aerial systems (UAS) have the potential to scan many varieties quickly. To gain insight on this issue, a field experiment was conducted at two sites (College Station and Castroville, TX) in two seasons to assess the performance of three spectral vegetation indices in the detection of leaf rust progression. Differences among wheat varieties in rust severity were detectable using standard vegetation indices. However, the vegetation indices could not distinguish effects of rust from other factors affecting wheat growth and yield. Thus, although UAS-derived vegetation indices showed potential in detecting leaf rust severity, interpretation of the results may be complicated if multiple factors affect winter wheat simultaneously.

Technical Abstract: Leaf rust is a major biotic factor affecting wheat yield globally. However, the visual scoring technique to assess fungal disease in breeding programs requires significant expert manual labor and time. Unmanned aerial systems have the potential to scan large acreage in a short time for disease screening. An experiment was conducted at College Station and Castroville, TX, in 2018-2019 and 2019-2020 to assess the performance of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (GCI) in detecting leaf rust infection. Other measurements included proximal canopy temperature, grain yield, and visual screening for infection type and severity. A significant positive relationship (p < 0.001; R2 = 0.42-0.62) of grain yield with all three vegetation indices (VIs) was observed in mid-April 2019 at College Station. At College Station, the highest leaf rust severity coincided with the senescence stage in mid-April 2020. No relationship between the VIs and grain yield was observed. In mid-April 2020, when the leaf rust infection was high, the VIs showed a significant negative relationship (p < 0.05; R2 = 0.27) with grain yield at Castroville. All three VIs showed a significant linear negative relationshipwith canopy temperature at College Station (p < 0.05; R2 = 0.30-0.34) and Castroville (p < 0.001; R2 = 0.52-0.54) in mid-April 2020. At high leaf rust severity, the repeatability of GCI was less than NDVI and NDRE at both locations in 2019 and 2020. These results may differ if multiple factors affect winter wheat simultaneously.