AUTHOR=Chen Rulei , Lu Hengyun , Wang Yongchun , Tian Qilin , Zhou Congcong , Wang Ahong , Feng Qi , Gong Songfu , Zhao Qiang , Han Bin 

TITLE=High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits

JOURNAL=Frontiers in Plant Science

VOLUME=15

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1327507

DOI=10.3389/fpls.2024.1327507

ISSN=1664-462X

ABSTRACT=<sec><title>Introduction</title><p>Rice (<italic>Oryza sativa</italic>) 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.</p></sec><sec><title>Method</title><p>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.</p></sec><sec><title>Results</title><p>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.</p></sec><sec><title>Discussion</title><p>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.</p></sec>