ArticleScientific reports2020
Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A global meta-analysis of animal manure application and soil microbial ecology based on random control treatments.PloS one · 2022Pooled it
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- Method of Characterization and Classification of the Physicochemical Quality of Polished White Rice Grains Using VIS/NIR/SWIR Techniques and Machine Learning Models for Lot Segregation and Commercialization in Storage and Processing Units.Foods (Basel, Switzerland) · 2025Article
- Visible-near infrared spectral analysis for identification of physiological and genetic features in rice.Breeding science · 2025Article
- Climate Adaptation Strategies for Maintaining Rice Grain Quality in Temperate Regions.Biology · 2025Review
- UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types.Journal of imaging · 2024Article
- Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat.Scientific reports · 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
- SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits.Plant cell reports · 2024Article
- Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis.Current research in food science · 2024Article
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Article
- Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications.Plants (Basel, Switzerland) · 2023Article
- Quantification of Photosynthetic Pigments inPlant phenomics (Washington, D.C.) · 2023Article
- Editorial: Harnessing genebanks: High-throughput phenotyping and genotyping of crop wild relatives and landraces.Frontiers in plant science · 2023Article
- Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations.Molecules (Basel, Switzerland) · 2022Article
- NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.Sensors (Basel, Switzerland) · 2022Article
- RiceFrontiers in plant science · 2022Article
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- Water Spectral Patterns Reveals Similarities and Differences in Rice Germination and Induced Degenerated Callus Development.Plants (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rice grain quality is a multifaceted quantitative trait that impacts crop value and is influenced by multiple genetic and environmental factors. Chemical, physical, and visual analyses are the standard methods for measuring grain quality. In this study, we evaluated high-throughput hyperspectral imaging for quantification of rice grain quality and classification of grain samples by genetic sub-population and production environment. Whole grain rice samples from the USDA mini-core collection grown in multiple locations were evaluated using hyperspectral imaging and compared with results from standard phenotyping. Loci associated with hyperspectral values were mapped in the mini-core with 3.2 million SNPs in a genome-wide association study (GWAS). Our results show that visible and near infra-red (Vis/NIR) spectroscopy can classify rice according to sub-population and production environment based on differences in physicochemical grain properties. The 702-900 nm range of the NIR spectrum was associated with the chalky grain trait. GWAS revealed that grain chalk and hyperspectral variation share genomic regions containing several plausible candidate genes for grain chalkiness. Hyperspectral quantification of grain chalk was validated using a segregating bi-parental mapping population. These results indicate that Vis/NIR can be used for non-destructive high throughput phenotyping of grain chalk and potentially other grain quality properties.
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