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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
Important wheat diseases in E. Africa and strategies to enhance durable resistance
sridhar bhavani (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS DISEASE RESISTANCE WHEAT BLASTS (OF PLANTS) ADULT PLANT RESISTANCE
Breeding for durable rust resistance
sridhar bhavani (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING RUSTS DISEASE RESISTANCE WHEAT
E. African spring wheat breeding pipeline and Network (CIMMYT-KALRO)
sridhar bhavani (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT BREEDING RESEARCH NETWORKS
JULIO HUERTA_ESPINO Ravi Singh (2017, [Artículo])
Near Isogenic Lines Virulence Spectrum Yellow Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA VIRULENCE PUCCINIA STRIIFORMIS WHEAT RESISTANCE GENES
Gerald Blasch David Hodson Francelino Rodrigues (2023, [Artículo])
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.
Very High Resolution Imagery Disease Detection Methods Early Growth Stages CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA UNMANNED AERIAL VEHICLES STEM RUST PHENOTYPING HIGH-THROUGHPUT PHENOTYPING WHEAT
Harbans Bariana Lakshmi Kant Naeela Qureshi Urmil Bansal (2022, [Artículo])
Kompetitive Allele Specific PCR Stripe Rust Yr Genes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC MARKERS MARKER-ASSISTED SELECTION RUSTS WHEAT GENES
Satellite imagery for high-throughput phenotyping in breeding plots
Francisco Pinto Mainassara Zaman-Allah Matthew Paul Reynolds Urs Schulthess (2023, [Artículo])
Optimized Soil Adjusted Vegetation Index CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HIGH-THROUGHPUT PHENOTYPING SATELLITES WHEAT MAIZE BREEDING NORMALIZED DIFFERENCE VEGETATION INDEX
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023, [Artículo])
Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION