ArticleScientific reports2022
A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Crop legacies as genetic targets for sustainable farming systems.Nature genetics · 2026Review
- Beyond Data: Artificial intelligence, knowledge graphs, and the next revolution in wheat breeding.Plant communications · 2026Review
- Engineering vascular potassium transport increases yield and drought resilience of cassava.Nature plants · 2025Article
- Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field.Journal of experimental botany · 2025Article
- Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes: The Genetic Latent Factor Approach.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- Improved bivariate analysis of canola survivability against blackleg disease.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.GigaScience · 2025Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.Plant phenomics (Washington, D.C.) · 2024Article
- Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.Nature communications · 2023Article
- Satellite imagery for high-throughput phenotyping in breeding plots.Frontiers in plant science · 2023Article
- Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.Frontiers in plant science · 2023Article
- Frost Damage Index: The Antipode of Growing Degree Days.Plant phenomics (Washington, D.C.) · 2023Article
- A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library.Frontiers in plant science · 2023Article
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Authors and funding
8 authors.
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Abstract
High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.
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