Machine learning
Hybrid Modeling Approach Enhances Accuracy of Rice Flowering Time Predictions
Accurately predicting crop traits like flowering time is crucial for modern rice breeding, especially under changing environmental conditions. Researchers at Nanjing Agricultural University have employed a novel integrative modeling approach that combines process-based rice growth models, genome-wide association studies, single nucleotide polymorphisms, and machine learning algorithms to predict rice flowering