ArticleThe plant genome2025
Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.
Article in The plant genome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrating genomic and phenomic data improves prediction of key traits in potato breeding.Horticulture research · 2026Article
- Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes.Plant phenomics (Washington, D.C.) · 2026Article
- Harnessing genomics approaches for heat stress resilience in wheat: from discovery to deployment.Journal of experimental botany · 2026Review
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.The plant genome · 2025Article
- Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.The plant genome · 2025Article
- Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions. The most comprehensive parametric model tested, which included SNPs, HSI, and environmental covariates data, consistently achieved the best results, closely followed by machine learning (ML) approaches when considering the same omics data. For example, the most comprehensive model (M9), under the forward prediction cross-validation scheme, predicted the GY of the 2023 growing season using data from 2021 and 2022 for a correlation between predicted and observed values of 0.53. This model demonstrated superior performance compared to less complex models, emphasizing the advantage of integrating numerous data sources and their interactive effects. Furthermore, when comparing the top 25% of the predicted lines versus the corresponding observed lines with the highest GY, the M9 model returned a coincide index (CI) of 55% (i.e., in both sets, 55% of the top 25% values were common), whereas for the highest performing ML model (gradient boosting regression), the CI was of 46%. This study highlights the potential of multi-data source approaches to accelerate the selection of heat-tolerant wheat genotypes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.