Pre-harvest sprouting is a major physiological problem in rice caused by prolonged rainfall and high humidity during the harvest period, and it is one of the most important targets in current rice breeding programs. In this study, the effect of cold and freezing storage on the pre-harvest sprouting rate was investigated using ten rice varieties under four different treatments. The result showed storage treatments of panicle samples used for germinate evaluation had no significant influence on the pre-harvest sprouting rate. These findings may enhance the efficiency of mass screening for pre-harvest sprouting and support the development of tolerant rice varieties.

Quantitative trait locus (QTL) analysis is a powerful approach for identifying variants associated with the phenotypic variation of complex traits. However, selecting optimal methods and pre-processing steps require considerable time and effort. In this study, we demonstrated applicability and replicability of machine learning (ML) models in QTL analysis by evaluating their performance in comparison with conventional QTL analysis methods using 142 recombinant inbred lines derived from two japonica rice cultivars, Koshihikari and Baegilmi. Random forest and gradient boosting models showed the highest predictive accuracy, and consistently identified three QTLs associated with heading date: qDTH3, qDTH6, and qDTH7. Moreover, ML-based QTL analysis detected minor-effect qDTH10, where Koshihikari allele promoted heading date when combined with Koshihikari alleles of qDTH6 and qDTH7. These results demonstrate the applicability of ML models in QTL analysis on bi-parental mapping population in rice.

Citations

Citations to this article as recorded by  

• Machine Learning Method to Select Single Nucleotide Polymorphism Markers for Protein Content, Grain Filling Rate, Height, and Panicle Length in Korean Rice
  Jeong-Gu Kim, Minwoo Kim, Gyu-Hwang Park, Jinhyun Kim, Jinho Jung, Tae-Ho Lee
  Korean Journal of Breeding Science.2025; 57(4): 403.     CrossRef