ArticleFrontiers in plant science2024
High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multi-scale spatial-temporal remote sensing fusion for phenology identification in rice germplasm resources.Plant phenomics (Washington, D.C.) · 2026Article
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Basketball detection based on YOLOv8.PloS one · 2025Article
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10 authors.
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Abstract
Introduction: Rice ( Method: Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Results: Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Discussion: Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.
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