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- Title
High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.
- Authors
Rulei Chen; Hengyun Lu; Yongchun Wang; Qilin Tian; Congcong Zhou; Ahong Wang; Qi Feng; Songfu Gong; Qiang Zhao; Bin Han
- Abstract
Introduction: Rice (Oryza sativa) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. 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.
- Subjects
GENE mapping; LOCUS (Genetics); RICE breeding; DRONE aircraft; COMPUTER vision
- Publication
Frontiers in Plant Science, 2024, p1
- ISSN
1664-462X
- Publication type
Article
- DOI
10.3389/fpls.2024.1327507