Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R² = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.

油菜 ( Brassica napus )L.) 是一种重要的含油经济作物。有效识别油菜开花日期对于产量估算和病害控制具有重要意义。传统的油菜花开花现场测量耗时、劳动密集且主观性很强。在这项研究中，获得了来自无人机 (UAV) 的油菜开花的红、绿和蓝 (RGB) 图像，共有 17 张可用的正射镶嵌图像，涵盖了 299 个油菜品种的整个开花期。采用五种不同的机器学习方法来识别和提取每个地块中的开花区域。结果表明，基于决策树的分割模型（DTSM）提取开花区域的准确率高于朴素贝叶斯、K近邻（KNN）、²  = 0.97 和均方根误差 (RMSE) = 0.051 像素/像素。开花面积比例及其动态数据显示，品种间每个开花日期的时间和持续时间存在差异。基于k-means聚类分析将所有品种分为四个聚类。4个簇的8个表型参数存在显着差异，特别是在最大开花率时间和进入早中期开花日期的时间。本研究结果可为基于开花动态的油菜育种提供依据。