ArticleRemote sensing of environment2019
High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity.
Article in Remote sensing of environment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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58 citing papers in PubMed, 203 citations in OpenAlex.
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Hyperspectral phenotyping and GWAS identify novel QTLs for soybean photosynthetic rate.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Machine learning surrogate for the leaf PROSPECT-D model and its applications across plant species.Scientific reports · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 2026Article
- Smarter stomata: emergent technologies unlocking yield potential in a changing climate.AoB PLANTS · 2025Review
- A guide to understanding and measuring photosynthetic induction: considerations and recommendations.The New phytologist · 2025Review
- PhotoGEA: An R Package for Closer Fitting of Photosynthetic Gas Exchange Data With Non-Gaussian Confidence Interval Estimation.Plant, cell & environment · 2025Article
- A Handheld Multispectral Device for Assessing Leaf Nitrogen Concentrations in Maize.Sensors (Basel, Switzerland) · 2025Article
- High-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits.Plant phenomics (Washington, D.C.) · 2025Article
- Boosting leaf trait estimation from reflectance spectra by elucidating the transferability of PLSR models.Plant phenomics (Washington, D.C.) · 2025Article
- Linking leaf hyperspectral reflectance to gene expression.Communications earth & environment · 2025Article
- Remote sensing inversion of nitrogen content in silage maize plants based on feature selection.Frontiers in plant science · 2025Article
- Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery.Plant methods · 2024Article
- Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis.Foods (Basel, Switzerland) · 2024Article
- Exploring natural genetic variation in photosynthesis-related traits of barley in the field.Journal of experimental botany · 2024Article
- Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2024Article
- A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging.Scientific reports · 2024Article
- Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation.Frontiers in plant science · 2024Article
- Tools for Measuring Photosynthesis at Different Scales.Methods in molecular biology (Clifton, N.J.) · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (
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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 OpenQuestion graph.