From phenome to genome: A cloud-based AI platform for integrative rice grain analysis and genetic mapping to empower grain-focused crop improvement
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Le résumé fourni par la source
Rice ( Oryza sativa L. ) is a staple crop of global importance. Accurate assessment of yield-related traits is essential for improving productivity and ensuring global food security. To address the limitations of labour-intensive and often subjective manual grain-based phenotyping, we developed the Rice Grain Phenotyping Analysis System (RGPAS), an open and cloud-based platform that integrates multiple machine learning (ML) and deep learning (DL) models to automate grain-level phenotypic analysis using smartphone-acquired images. The RGPAS platform comprises a digital rice germplasm database of 864 landraces and 176 recombinant inbred lines (RILs) cultivated worldwide, as well as a newly established World Rice Grain Dataset (WRGD) incorporating grain-level morphological and colour characteristics to enable artificial intelligence (AI)-powered analysis. The cloud-based analysis is powered by an optimised RGD-YOLO learning model for accurate grain detection under both occluded and unoccluded conditions together with an EfficientNet-RG classifier for whole-grain classification, resulting in the measurement of 18 grain-level traits such as grain length, grain width, grain number per panicle, and seed-coat colour. Using grain-level phenotypic data quantified in the 2020 and 2022 rice growing seasons, we demonstrated the accuracy, scalability, and robustness of RGPAS-derived traits, enabling reliable assessment of grain diversity and phenotypic variation suitable for genetic mapping in rice. Accordingly, we performed both genome-wide association studies (GWAS; with 864 landraces in total) and quantitative trait locus (QTL) mapping (with 176 RILs) to link the RGPAS-derived traits to specific genomic regions, successfully identifying known regulators (e.g. D61 , GE , GS3 , GW2 , GW5 , GW7 , OsSOC1 , RFT1 , and TGW6 ) and several novel loci. These results validate RGPAS as a reliable and biologically relevant tool for integrative phenotypic and genetic analysis, providing a standardised and accessible solution for breeders and researchers to characterise grains before sowing and after harvest for crop improvement.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- From phenome to genome: A cloud-based AI platform for integrative rice grain analysis and genetic mapping to empower grain-focused crop improvement
- Date Crossref
- 01/09/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Nanjing Agricultural University pays non établi dans la noticeUniversité ou école supérieure
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National Institute of Agricultural Botany Crop Science Centre (CSC) pays non établi dans la noticeStructure de recherche
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Jiangsu Academy of Agricultural Sciences pays non établi dans la noticeOrganisme public
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Center for Excellence in Molecular Plant Sciences pays non établi dans la noticeUniversité ou école supérieure
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National Center for Gene Research pays non établi dans la noticeStructure de recherche
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Institute of Crop Sciences pays non établi dans la noticeOrganisation à but non lucratif
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University of Cambridge Department of Plant Sciences pays non établi dans la noticeUniversité ou école supérieure
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College of Engineering pays non établi dans la noticeUniversité ou école supérieure
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State Key Laboratory of Plant Trait Design pays non établi dans la noticeStructure de recherche
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State Key Laboratory of Crop Gene Resources and Breeding pays non établi dans la noticeStructure de recherche
Nanjing Agricultural University, Crop Science Centre (CSC) — National Institute of Agricultural Botany et Jiangsu Academy of Agricultural Sciences, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.