Rulei Chen, Hengyun Lu, Yongchun Wang, Qilin Tian, Congcong Zhou, Ahong Wang, Qi Feng, Songfu Gong, Qiang Zhao, Bin Han
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...
2024: Frontiers in Plant Science