Abstract Plant architectural traits are important selection criteria in plant breeding that relate to photosynthetic efficiency and crop productivity. Conventional manual measures of architectural traits for large breeding trials are labour- and time-consuming. In this study, we proposed a new method to reconstruct three-dimensional (3D) canopy architectural models for high-throughput phenotyping of canopy architectural traits using image sequences acquired by an unmanned aerial vehicle (UAV) platform. The accuracy of UAV-derived models is evaluated by comparisons with models from 3D digitizing and measured values. The results indicated that the proposed method could obtain full canopy architecture in the early growth stages and the upper parts of the canopy architecture in the later growth stages. The leaf number, plant height, individual leaf area, and vertical and horizontal distributions of the leaf area estimated from UAV-derived models were in good agreements with the reference values for maize. The derived length and maximum width of individual leaves were close to the field measurements for maize (R2 > 0.92 for both, RMSE 0.85 and RMSE

中文翻译：

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一种利用无人机图像获取冠层结构的基于场的高通量方法

摘要 植物结构性状是植物育种中的重要选择标准，与光合效率和作物生产力有关。用于大型育种试验的建筑特征的传统手动测量既费时又费力。在这项研究中，我们提出了一种新方法来重建三维 (3D) 树冠建筑模型，以使用无人机 (UAV) 平台获取的图像序列对树冠建筑特征进行高通量表型分析。通过与来自 3D 数字化和测量值的模型进行比较来评估 UAV 衍生模型的准确性。结果表明，该方法可以在生长早期获得完整的冠层结构，在生长后期可以获得冠层结构的上部。叶数、株高、单个叶面积，以及从无人机衍生模型估计的叶面积的垂直和水平分布与玉米的参考值非常吻合。单个叶子的衍生长度和最大宽度接近玉米的田间测量值（R2 > 0.92，RMSE 0.85 和 RMSE