Plant height, a key agronomic trait, affects crop structure, photosynthesis, and thus the final yield and seed quality. The combination of digital cameras on unmanned aerial vehicles (UAVs) and use of structure from motion have enabled high-throughput crop canopy height estimation. However, the focus of prior research has mainly been on plot-level height prediction, neglecting precise estimations for individual plants. This study aims to explore the potential of UAV RGB images with mask region-based convolutional neural network (Mask-RCNN) for high-throughput phenotyping of individual-level height (IH) in oilseed rape at different growth stages. Field-measured height (FH) of nine sampling plants in each subplot of the 150 subplots was obtained by manual measurement after the UAV flight. An instance segmentation model for oilseed rape with data augmentation based on the Mask-RCNN model was developed. The IHs were then used to obtain plot-level height based on individual-level height (PHIH). The results show that Mask-RCNN performed better than the conventional Otsu method with the F1 score increased by 60.8% and 26.6% under high and low weed pressure, respectively. The trained model with data augmentation achieved accurate crop height estimation based on overexposed and underexposed UAV images, indicating the model’s applicability in practical scenarios. The PHIH can be predicted with the determination coefficient (r²) of 0.992, root mean square error (RMSE) of 4.03 cm, relative root mean square error (rRMSE) of 7.68%, which outperformed the results in the reported studies, especially in the late bolting stage. The IHs of the whole growth stages of oilseed can be predicted by this method with an r² of 0.983, RMSE of 2.60 cm, and rRMSE of 7.14%. Furthermore, this method enabled a comprehensive Genome-wide association study (GWAS) in a 293-accession genetic population. The GWAS identified 200 and 65 statistically significant single nucleotide polymorphisms (SNPs), which were tightly associated with 28 and 11 candidate genes, at the late bolting and flowering stages, respectively. These findings demonstrated that the proposed method is promising for accurate estimations of IHs in oilseed rape as well as exploring the variations within the subplot, thus providing great potential for high-throughput plant phenotyping in crop breeding.

株高是一个关键的农艺性状，影响作物结构、光合作用，从而影响最终的产量和种子质量。无人机（UAV）上的数码相机与运动结构的结合使高通量作物冠层高度估计成为可能。然而，先前的研究重点主要集中在地块级高度预测上，忽视了对单株植物的精确估计。本研究旨在探索基于掩模区域的卷积神经网络（Mask-RCNN）的无人机 RGB 图像在不同生长阶段油菜个体高度（IH）高通量表型分析中的潜力。 150个小区中每个小区中9个样地的实测高度（FH）是无人机飞行后通过人工测量获得的。开发了基于 Mask-RCNN 模型的数据增强油菜实例分割模型。然后使用 IH 根据个体水平高度 (PHIH) 获得地块水平高度。结果表明，Mask-RCNN 的表现优于传统的 Otsu 方法，在高杂草压力和低杂草压力下，F1 分数分别提高了 60.8% 和 26.6%。经过数据增强的训练模型根据曝光过度和曝光不足的无人机图像实现了准确的作物高度估计，表明该模型在实际场景中的适用性。预测PHIH的判定系数（r ² ）为0.992，均方根误差（RMSE）为4.03 cm，相对均方根误差（rRMSE）为7.68%，优于实验结果。已报道的研究，特别是在抽苔后期。该方法可以预测油籽全生育期的IH，r ² 为0.983，RMSE为2.60 cm，rRMSE为7.14%。 此外，该方法还能够在 293 个基因群体中进行全面的全基因组关联研究 (GWAS)。 GWAS 在抽苔后期和开花阶段分别鉴定了 200 个和 65 个具有统计显着性的单核苷酸多态性 (SNP)，它们分别与 28 个和 11 个候选基因紧密相关。这些发现表明，所提出的方法有望准确估计油菜中的 IH 以及探索子小区内的变化，从而为作物育种中的高通量植物表型分析提供巨大潜力。