ArticlePlant methods2022
Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Early screening of salt-tolerant and high-yielding rice varieties via cost-effective UAV data.Plant phenomics (Washington, D.C.) · 2026Article
- Genomic Loci for Sclerotinia Stem Rot Resistance and Chlorophyll Stability inPlant-environment interactions (Hoboken, N.J.) · 2025Article
- Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Co-Location of QTL for Vigor and Resistance to Three Diseases inInternational journal of molecular sciences · 2025Article
- Genotypic variation in winter wheat for fusarium foot rot and its biocontrol using Clonostachys rosea.G3 (Bethesda, Md.) · 2024Article
- Broad-spectrum resistance to fungal foliar diseases in wheat: recent efforts and achievements.Frontiers in plant science · 2024Review
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms.Plant methods · 2023Article
- A deep learning model for rapid classification of tea coal disease.Plant methods · 2023Article
- To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?Plant phenomics (Washington, D.C.) · 2023Article
- An integrated feature selection approach to high water stress yield prediction.Frontiers in plant science · 2023Article
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5 authors.
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Abstract
backgroundHigh-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial.
resultsThe results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r
conclusionsThe proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.
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