Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

Abstract

One of the major goals in all wheat (Triticum aestivum L.) breeding programs is to develop disease-resistant varieties. Unmanned Aerial Vehicles (UAVs) equipped with remote sensors can provide spectral measurements that can be used to assess foliage disease severity. Measurement of disease severity trait during the genotype selection process has always been challenging as it takes a substantial amount of time, cost, and labor to phenotype many breeding lines. This study investigates the potential use of low-cost UAV, equipped with digital cameras as a field phenotyping tool for foliage disease severity in awheat breeding program. A field experiment was conducted in 2017 and 2018 at Castroville, Texas. The experiment site has favorable weather conditions for developing wheat leaf rust (Puccinia triticina f. sp.tritici). Red, Green, and Blue band (RGB) images were acquired by flying rotary-wing UAV. Images were then processed to develop orthomosaics and three vegetation indices were calculated. The obtained image dataset was further processed to generate plot-level data. Visual notes on field response and leaf rust severity were taken to calculate the coefficient of infection (CI). A significant variation in vegetation indices was found among the wheat genotypes in both years. Normalized Difference Index (NDI), Green Index (GI), and Green Leaf Index (GLI) were linearly related to CI with R2 values ranging from 0.72 to 0.79 (p < 0.05) in 2017 and 0.63 to 0.68 (p < 0.05) in 2018. Ground-based Normalized Difference Vegetation Indices (NDVI) also showed a significant relationship with CI in both years (R2 = 0.86, p < 0.05 in 2017 and R2 = 0.83, p < 0.05 in 2018). The results showed that UAVs imaging and automated data extraction can facilitate the acquisition of high throughput phenotyping data for disease severity ratings.