ArticleJournal of experimental botany2020
High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response.
Article in Journal of experimental botany, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
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- Methodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.Plants (Basel, Switzerland) · 2026Article
- Visible-near infrared spectral analysis for identification of physiological and genetic features in rice.Breeding science · 2025Article
- Nitrate improves plant growth by promoting nitrogen assimilation and photosynthetic performance in citrus seedlings.BMC plant biology · 2025Article
- Evo-Inspired Engineering of Radical Phenotypes and Emergent Traits.Integrative and comparative biology · 2025Review
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Targeted integrating hyperspectral and metabolomic data with spectral indices and metabolite content models for efficient salt-tolerant phenotype discrimination inPlant phenomics (Washington, D.C.) · 2025Article
- Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.Plants (Basel, Switzerland) · 2024Article
- Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content inInternational journal of molecular sciences · 2024Article
- Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform.Plant methods · 2023Article
- Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat.Plants (Basel, Switzerland) · 2023Article
- An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse.Plants (Basel, Switzerland) · 2023Article
- Climate change challenges, plant science solutions.The Plant cell · 2023Article
- Deep learning-empowered crop breeding: intelligent, efficient and promising.Frontiers in plant science · 2023Article
- Pitfalls and potential of high-throughput plant phenotyping platforms.Frontiers in plant science · 2023Article
- The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.Plant phenomics (Washington, D.C.) · 2023Article
- Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel.BMC plant biology · 2022Article
- Capturing crop adaptation to abiotic stress using image-based technologies.Open biology · 2022Review
- Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.The New phytologist · 2022Article
- Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants.Plant phenomics (Washington, D.C.) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.
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