ReviewPlant communications2021
Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.
Review in Plant communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Machine learning surrogate for the leaf PROSPECT-D model and its applications across plant species.Scientific reports · 2026Article
- Genome-wide associations of leaf spectral variation in MAGIC lines of Nicotiana attenuata.Ecology · 2026Article
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 2026Article
- Employing Spectral Features to Accelerate Sorghum Phenotyping Against Sap-Feeding Aphids.Plant direct · 2025Article
- Hyperspectral Leaf Reflectance Detects Interactive Genetic and Environmental Effects on Tree Phenotypes, Enabling Large-Scale Monitoring and Restoration Planning Under Climate Change.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
- Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust.Frontiers in plant science · 2025Article
- Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System.Plants (Basel, Switzerland) · 2024Article
- Hyperspectral Proximal Sensing for Estimating Photosynthetic Capacities at Leaf and Canopy Scales.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Phenotyping cotton leaf chlorophyll viaFrontiers in plant science · 2024Article
- Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging.Frontiers in plant science · 2024Article
- Plant Physiological Analysis to Overcome Limitations to Plant Phenotyping.Plants (Basel, Switzerland) · 2023Review
- A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.Journal of experimental botany · 2023Article
- Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Plants (Basel, Switzerland) · 2023Article
- To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance.Frontiers in plant science · 2023Article
- Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean.Plant phenomics (Washington, D.C.) · 2023Article
- Quantification of Photosynthetic Pigments inPlant phenomics (Washington, D.C.) · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Advances in field-based high-throughput photosynthetic phenotyping.Journal of experimental botany · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
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Registered trials
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