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Autor: David Hodson
2022 Advanced wheat improvement course: Global wheat rust surveillance
David Hodson (2022)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE NEW TECHNOLOGY REMOTE SENSING
David Hodson (2022)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS DISEASE SURVEILLANCE REMOTE SENSING NEW TECHNOLOGY
David Hodson (2022)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT RUSTS SURVEILLANCE SYSTEMS
Wheat National Survey for DNA fingerprinting in Ethiopia
David Hodson Olaf Erenstein (2020)
This dataset include data from a national wheat survey in Ehiopia in 2016 during the main season for a DNA fingerprinting study. The data contain wheat varieties genotyped using DNA fingerprinting (DNA FP), farmers recall survey and matching of DNA FP with farmers recall.
Dataset
Gerald Blasch David Hodson Francelino Rodrigues (2023)
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.
Artículo
Very High Resolution Imagery Disease Detection Methods Early Growth Stages CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA UNMANNED AERIAL VEHICLES STEM RUST PHENOTYPING HIGH-THROUGHPUT PHENOTYPING WHEAT