ArticleJournal of experimental botany2021
Haplotype analysis from unmanned aerial vehicle imagery of rice MAGIC population for the trait dissection of biomass and plant architecture.
Article in Journal of experimental botany, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Dissecting Agronomically favorable genotypes in temperate japonica rice via haplotype analysis of a Japan-MAGIC population.The Plant journal : for cell and molecular biology · 2026Article
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning.Plant phenomics (Washington, D.C.) · 2025Article
- Selection of chromosome segment substitution lines with reduced grain chalkiness without yield penalty in rice.Breeding science · 2025Article
- Review
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.G3 (Bethesda, Md.) · 2024Article
- Phenology analysis for trait prediction using UAVs in a MAGIC rice population with different transplanting protocols.Frontiers in artificial intelligence · 2024Article
- Genome-wide association analysis identifies natural allelic variants associated with panicle architecture variation in African rice, Oryza glaberrima Steud.G3 (Bethesda, Md.) · 2023Article
- A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce.Sensors (Basel, Switzerland) · 2023Article
- AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice.The New phytologist · 2022Article
- Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review.Journal of advanced research · 2022Review
- Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice.Frontiers in plant science · 2022Article
- Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits.Plant phenomics (Washington, D.C.) · 2022Article
- Features and applications of haplotypes in crop breeding.Communications biology · 2021Review
- Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population.G3 (Bethesda, Md.) · 2021Article
- Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice.Frontiers in plant science · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unmanned aerial vehicles (UAVs) are popular tools for high-throughput phenotyping of crops in the field. However, their use for evaluation of individual lines is limited in crop breeding because research on what the UAV image data represent is still developing. Here, we investigated the connection between shoot biomass of rice plants and the vegetation fraction (VF) estimated from high-resolution orthomosaic images taken by a UAV 10 m above a field during the vegetative stage. Haplotype-based genome-wide association studies of multi-parental advanced generation inter-cross (MAGIC) lines revealed four quantitative trait loci (QTLs) for VF. VF was correlated with shoot biomass, but the haplotype effect on VF was better correlated with that on shoot biomass at these QTLs. Further genetic characterization revealed the relationships between these QTLs and plant spreading habit, final shoot biomass and panicle weight. Thus, genetic analysis using high-throughput phenotyping data derived from low-altitude, high-resolution UAV images during early stages of rice growing in the field provides insights into plant growth, architecture, final biomass, and yield.
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