Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE–YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE–YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.

Successfully breeding high-yield, lodging-resistant hybrid rice varieties is critical for ensuring food security. Two-line hybrid rice system plays an essential role in rice breeding, and 1892S, an important two-line sterile line, has contributed significantly to the development of over 100 hybrid rice varieties with superior agronomic traits, including lodging resistance. Despite its importance, a comprehensive understanding of the genomic basis underlying these traits in 1892S has been lacking due to the limitations of short-read sequencing technologies. To address this gap, we utilized advanced telomere-to-telomere (T2T) genome assembly techniques to generate a high-quality, gap-free genome of 1892S—the final genome comprises 12 complete chromosomes with 40,560 protein-coding genes. Comparative genomic analysis identified multiple known lodging resistance genes, including SD1, Sdt97, SBI, OsFBA2, APO1, and OsTB1, with unique allelic variations that may enhance resistance. The pan-genome analysis identified 2347 strain-specific genes in 1892S, further supporting its unique genetic advantages. This study represents the complete T2T genome assembly of a two-line sterile line and provides novel insights into the genetic foundation of lodging resistance in hybrid rice. This study highlights the genetic potential of 1892S in hybrid rice breeding and provides a model for the genomic analysis of other two-line sterile lines, offering valuable insights for improving in hybrid rice, including traits lodging resistance, yield stability, and adaptability, which are crucial for global food security.