ArticleFrontiers in plant science2019
High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages.
Article in Frontiers in plant science, 2019. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Improving Nitrogen Use Efficiency in Wheat: Integrating Agronomic, Genomics, and Remote Sensing for Sustainable Production.Plant, cell & environment · 2026Review
- Phenotypic Variation in Water-Use Efficiency, Heat Tolerance, and Carbon Isotope Discrimination Across Canadian Spring Wheat Cultivars Under Climate Stress.Plants (Basel, Switzerland) · 2026Article
- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Pre-Harvest Agronomic Reduction inToxins · 2026Article
- A Portable High-Resolution Snapshot Multispectral Imaging Device Leveraging Spatial and Spectral Features for Non-Invasive Corn Nitrogen Treatment Classification.Sensors (Basel, Switzerland) · 2025Article
- Article
- Image-based phenomic prediction can provide valuable decision support in wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs.Sensors (Basel, Switzerland) · 2023Article
- The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations.Sensors (Basel, Switzerland) · 2023Article
- Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.Frontiers in plant science · 2023Article
- Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimatingFrontiers in plant science · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.Frontiers in plant science · 2022Article
- Resources for image-based high-throughput phenotyping in crops and data sharing challenges.Plant physiology · 2021Article
- Scaling up high-throughput phenotyping for abiotic stress selection in the field.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Identification of High Nitrogen Use Efficiency Phenotype in Rice (Frontiers in plant science · 2021Article
- Phenotyping Flowering in Canola (Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput, non-invasive phenotyping is promising for evaluating crop nitrogen (N) use efficiency (NUE) and grain yield (GY) formation under field conditions, but its application for genotypes differing in morphology and phenology is still rarely addressed. This study therefore evaluates the spectral estimation of various dry matter (DM) and N traits, related to GY and grain N uptake (Nup) in high-yielding winter wheat breeding lines. From 2015 to 2017, hyperspectral canopy measurements were acquired on 26 measurement dates during vegetative and reproductive growth, and 48 vegetation indices from the visible (VIS), red edge (RE) and near-infrared (NIR) spectrum were tested in linear regression for assessing the influence of measurement stage and index selection. For most traits including GY and grain Nup, measurements at milk ripeness were the most reliable. Coefficients of determination (
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