ArticleG3 (Bethesda, Md.)2023
Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.
Article in G3 (Bethesda, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Remote sensing data and machine learning models estimate sorghum grain yield in a plant breeding program.Plant phenomics (Washington, D.C.) · 2026Article
- Comparative analysis of genomic prediction approaches for multiple time-resolved traits in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems.The Plant cell · 2025Review
- Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change.Plants (Basel, Switzerland) · 2025Review
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning.Frontiers in plant science · 2025Article
- Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.The Plant journal : for cell and molecular biology · 2024Article
- Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.The plant genome · 2024Article
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- Using drone-retrieved multispectral data for phenomic selection in potato breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Current challenges and future of agricultural genomes to phenomes in the USA.Genome biology · 2024Article
- Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.Frontiers in plant science · 2024Article
- Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder.Frontiers in plant science · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.Scientific reports · 2022Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.
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