ArticlePlant physiology2017
High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.
Article in Plant physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
What it found
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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
98 citing papers in PubMed.
- Improving multi-trait genomic prediction using synthetic traits from hyperspectral data based on co-heritability.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes.Plant phenomics (Washington, D.C.) · 2026Article
- A renewed focus on carbon uptake and use to enhance crop performance in the face of rising temperatures.Plant physiology · 2026Review
- Machine learning surrogate for the leaf PROSPECT-D model and its applications across plant species.Scientific reports · 2026Article
- Predicting Nutritional and Morphological Attributes of Fresh CommercialJournal of imaging · 2026Article
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 2026Article
- Exploring prediction mechanisms for temperature-induced variation in maximum carboxylation rate from spectral reflectance across 450-850 nm in cucumber leaves.Photosynthetica · 2026Article
- Pod photosynthesis: a new frontier for developing stress-resilient and high-yielding crops.Plant & cell physiology · 2025Review
- Hyperspectral imaging of grains uncovers the genetic architecture of nitrogen response of development in bread wheat.Molecular breeding : new strategies in plant improvement · 2025Article
- Large-scale non-destructive crown-level assessment of Ginkgo pigments via hyperspectral and machine learning techniques.Plant methods · 2025Article
- Understanding plant phenotypes in crop breeding through explainable AI.Plant biotechnology journal · 2025Review
- PhotoGEA: An R Package for Closer Fitting of Photosynthetic Gas Exchange Data With Non-Gaussian Confidence Interval Estimation.Plant, cell & environment · 2025Article
- 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
- Linking leaf hyperspectral reflectance to gene expression.Communications earth & environment · 2025Article
- Combine photosynthetic characteristics and leaf hyperspectral reflectance for early detection of water stress.Frontiers in plant science · 2025Article
- Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics.Plant methods · 2024Article
- Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves ofSensors (Basel, Switzerland) · 2024Article
- Exploring natural genetic variation in photosynthesis-related traits of barley in the field.Journal of experimental botany · 2024Article
- Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder.Frontiers in plant science · 2024Article
- Genome-wide association study of leaf photosynthesis using a high-throughput gas exchange system in rice.Photosynthesis research · 2024Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500-2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO
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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.