Abstract High resolution RGB imagery collected using a UAV and a handheld camera was used with structure from motion to reconstruct 3D canopies of small groups of corn plants. A methodology for the automated extraction of phenotypic characteristics of individual plants is presented based on these 3D reconstructed canopies. Such information can enhance the evaluation of crop traits and provide accurate and frequent statistics for in-season assessment of their changes with growth stage. Industries that target yield optimization and crop hybrid production can benefit greatly from this approach. The use of 3D models provides elevated information content, when compared to alternative planar methods, mainly due to the alleviation of leaf occlusions. High resolution images of corn stalks are collected and used to obtain 3D models for individual plants. Based on those extracted 3D point clouds, the calculation of phenotypic characteristics are obtained, such as the number of plants in an area, the Leaf Area Index (LAI), the individual and average plant height, the individual leaf length, the location and the angles of leaves with respect to the stem. An experimental validation using both artificial corn plants emulating real world scenarios and real corn plants in different growth stages, supports the accuracy of the proposed methodology. Our experiments conclude that phenotypic characteristics of individual plants can be extracted automatically with high accuracy based on a 3D model. The results include the individual plant segmentation and counting from a given 3D reconstructed field scene with 88.1% accuracy, the Leaf Area Index (LAI) estimation with 92.5% accuracy, the individual plant height computation with 89.2% accuracy, the leaf length extraction with 74.8% accuracy, the measurement of angles between leaves and stems, and the distance between the leaves of the same plant. We interpret the last two variables qualitatively to show that the method can show the trend of the angles to change with respect to the leaf position on the stem as the crops grow.

中文翻译：

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用于高通量表型提取的 3D 模型处理——以玉米为例

摘要 使用无人机和手持相机收集的高分辨率 RGB 图像结合运动结构重建小群玉米植物的 3D 冠层。基于这些 3D 重建冠层，提出了一种自动提取单个植物表型特征的方法。这些信息可以增强对作物性状的评估，并为季节性评估其随生长阶段的变化提供准确和频繁的统计数据。以产量优化和作物杂交生产为目标的行业可以从这种方法中受益匪浅。与替代平面方法相比，3D 模型的使用提供了更高的信息内容，这主要是由于叶遮挡的减轻。收集玉米秸秆的高分辨率图像并用于获取单个植物的 3D 模型。基于这些提取的 3D 点云，获得表型特征的计算，例如区域内的植物数量、叶面积指数 (LAI)、个体和平均植物高度、个体叶长、位置和叶子相对于茎的角度。使用模拟真实世界场景的人造玉米植物和处于不同生长阶段的真实玉米植物进行的实验验证支持所提出方法的准确性。我们的实验得出结论，可以基于 3D 模型以高精度自动提取单个植物的表型特征。结果包括来自给定 3D 重建现场场景的单个植物分割和计数，准确率为 88.1%，叶面积指数 (LAI) 估计准确率为 92.5%，单株高度计算准确率89.2%，叶长提取准确率74.8%，叶茎角度测量，同株叶间距测量。我们定性地解释了最后两个变量，以表明该方法可以显示随着作物生长，角度相对于茎上叶子位置的变化趋势。