RESUMEN
Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.
Asunto(s)
Basidiomycota , Imágenes Satelitales , Dispositivos Aéreos No Tripulados , Etiopía , Hojas de la Planta , Triticum , Grano Comestible , Progresión de la EnfermedadRESUMEN
Stem rust of wheat (Triticum spp.), caused by Puccinia graminis f. sp. tritici (Pgt), is one of the most impactful wheat diseases because of its threat to global wheat production. While disease mitigation has primarily been achieved through the deployment of resistant wheat varieties, emerging new virulent races continue to pose risks to the crop. For example, races such as Ug99 (TTKSK), TKTTF, and TTRTF have caused epidemics in different wheat growing regions of the world in recent years. A continual search for new and effective sources of resistance is therefore necessary to safeguard wheat production. This study assessed a breeding panel from the Ethiopian Institute of Agricultural Research (EIAR) wheat breeding program for seedling and field plant resistance to TTRTF and reports genomic regions conferring resistance to TTRTF. Trait correlations (r) were medium to strong (range = .38-.71) and heritabilities were moderate (.32-.56). Association analysis for resistance to TTRTF resulted in detection of 20 markers in 11 chromosomes; the marker S1B_175439851 was associated with resistance at both seedling and adult plant stages. Models with two to four QTL combinations reduced seedling and field disease severity by 12-48 and 9-17%, respectively. Genomic prediction for TTRTF resistance resulted in low to moderately-high predictions (mean correlations of .25-.47). Identification of resistant lines and QTL in the EIAR population is expected to assist in selection toward improved resistance to TTRTF. Specifically, the application of genomic selection (GS) in identifying resistant lines in future related breeding populations will further assist breeding efforts against this new stem rust pathogen race.