ArticlePlant phenomics (Washington, D.C.)2024
Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Early detection and genetic insights into salt tolerance in rice by linking physiology and spectral reflectance during seed germination.Plant phenomics (Washington, D.C.) · 2026Article
- Unlocking almond breeding for nutritional composition with hyperspectral imaging.Plant phenomics (Washington, D.C.) · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Domain-adapted modeling of wheat protein content across grain and flour morphological scales.Current research in food science · 2026Article
- Integrated hyperspectral-RGB modeling for the estimation of wheat protein and dough rheological properties using machine and deep transfer learning.Current research in food science · 2026Article
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Authors and funding
10 authors.
Funding
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Abstract
Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF
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