Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height (PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle (UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading (IH) and full heading (FH), and panicle initiation (PI), and growth period after transplanting (GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model (DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest (RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th (R2 = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features (CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI (R2 = 0.834, RMSE = 4.344 d), IH (R2 = 0.877, RMSE = 2.721 d), and FH (R2 = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.
