Das Choudhury Sruti, Maturu Srikanth, Samal Ashok, Stoerger Vincent, Awada Tala
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States.
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.
Front Plant Sci. 2020 Dec 9;11:521431. doi: 10.3389/fpls.2020.521431. eCollection 2020.
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
基于高通量图像的植物表型分析有助于在相对较短的时间内以非侵入性方式提取大量植物的形态和生物物理特征。它通过将植物视为单个对象（整体表型）或其组成部分，即叶子和茎（组成部分表型），来促进高级表型的计算。由于自遮挡和叶序（即叶子围绕茎的排列）的变化，植物的结构复杂性会随着时间增加。从植物的二维（2D）单视图图像计算表型，尤其是在存在自遮挡叶子的高级营养阶段，面临的核心挑战之一是2D图像中捕获的信息不完整，因此计算出的表型不准确。我们引入了一种新颖的算法，使用植物的体素网格重建（3DPhenoMV）从多视图图像计算三维（3D）植物表型。本文还提出了一种新颖的方法，使用体素重叠一致性检查和点云聚类技术，从植物的3D体素网格中可靠地检测和分离出单个叶子和茎。为了评估所提出算法的性能，我们引入了内布拉斯加大学林肯分校3D植物表型数据集（UNL - 3DPPD）。还提出了基于3D图像的植物表型的通用分类法，以促进3D植物表型研究。使用计算机视觉算法计算了这些表型的一个子集，并讨论了它们在植物科学背景下的意义。本文的核心贡献包括：（a）一种使用来自多个2D视图的图像对处于高级营养阶段的玉米植物进行3D体素网格重建的算法；（b）基于3D图像的植物表型的通用分类法和一个公共基准数据集，即UNL - 3DPPD，以促进基于3D图像的植物表型研究的发展；（c）新颖的体素重叠一致性检查和点云聚类技术，用于检测和分离玉米植物的单个叶子和茎，以计算组成部分表型。详细的实验分析证明了所提出方法的有效性，还展示了3D表型在解释受遗传和环境相互作用调控的植物形态特征方面的潜力。
