光谱学与光谱分析, 2020, 40 (8): 2352, 网络出版: 2020-12-02  

基于移动Kinect的低成本植物三维结构表型分析

Kinect Sensor Moving for Low-Cost Mobile Phenotyping of 3D Plant Structures
作者单位
北京大学地球与空间科学学院, 遥感与地理信息系统研究所, 北京 100871
摘要
表型分析对于理解植物基因型与环境之间的关系非常重要, 开发高效且成本低的相关技术是精准农业等领域的一项典型需求。 其中, 代表性的RGB-D设备Kinect已用于植物表型分析, 但其应用潜力尚未被充分挖掘。 本文首先梳理比较了Kinect表征三维结构的三种原理方式, 即点云基于深度图像(DI)生成, 通过运动恢复结构(SfM)从彩色图像获得, 以及合并DI和SfM点云生成融合数据(MD), 并以FARO X330激光扫描仪获取的基准数据评估三种方式的性能。 以植物玉簪为例的分析结果表明, 对叶面积的估算DI点云的准确度最高, 对叶片圆形度和偏心率的反演MD点云表现最佳, 对叶倾角的反演SfM点云的性能最好。 三种方式的结果差异源于它们表征不同结构的表现不同, 对于叶面积的反演, SfM表征叶片相对不完整, 而MD重建叶片的边缘存在不平滑的现象, 导致两者精度不足; 对于表征叶片的几何特征, 通过合并DI和SfM数据生成的MD点云实现了信息增强的效果, 使得其表现优于DI和SfM点云; 叶倾角对深度测量的准确性更敏感, 由于Kinect测量深度过程中通常存在误差, 导致DI和MD点云反演精度偏低, 而SfM点云仅通过彩色图像生成, 因此其表现出反演叶倾角的最佳性能。 性能比较与原因分析表明, 三种方式对不同的结构特征有不同的适用空间, 它们的集成有助于提升Kinect用于植物表型分析的整体性能, 由此形成一种基于Kinect的移动表型高效分析技术; 此外, 提出的叶片几何描绘(LGD)模型可较好拟合叶片轮廓, 有助于恢复部分被遮挡叶片的几何形态。 提出了一种基于Kinect的低成本但高效的移动型三维植物结构表型分析技术, 这对于促进作物监控、农业增产等有基础技术意义。
Abstract
Phenotyping is important for understanding of the relationships between plant genotypes and environment. Developing efficient and low-cost phenotyping technologies is a typical demand in many fields such asprecision agriculture. As a representative RGB-D device, Kinect has been used for plant phenotyping, but its technical potential has not been fully explored. To address this gap, this study compared the three mainstream principles of Kinect characterizing three-dimensional structures, i.e., point clouds generated from depth images (DI), from the color images using the method of Structure from Motion (SfM), and from the data by merging the DI- and SfM-derived data (MD). The performance of the three methods was evaluated based on the reference data, which was measured by a FAROX330 laser scanner. The results after the analyses in the case of Hosta plantaginea showed that DI made the most accurate estimationsin terms of leaf areas, MD out performed DI and SfM when regarding the predictions of leaf circularities and eccentricities, and SfM had the best performance on the retrievals of leaf inclinations. The difference between the results of the three methods stems from their distinctive performance for different structures. For leaf area estimation,SfMcan characterize plant leaves in a relatively incomplete way, while the edges of the MD-recon structed leaves are not smooth, resulting in the lowness of accuracy for these two methods. For the geometric characteristics of leaves, MD point clouds generated by merging the related DI and SfM data can achieve the effect of information enhancement, making its performance better than DI and SFM point clouds. The leaf inclination angle is more sensitive to the accuracy of depth measurement. Due to Kinect depth measurement often with the errors, the accuracies of the DI and MD point cloud-based leaf inclination retrievals may be low. TheSfM point cloudsare only generated from the color images, and so this method canpresent the best performance on retrieval of leaf inclination angles. Performance comparison sindicated that the three methods have their advantages for different structural features.Their integration can help to improve the overall performance of Kinect for plant phenotyping and,eventually, forma new Kinect-based mobile phenotyping technique. In addition, the proposed leaf geometry delineation (LGD) model proved todraw the contours of leaves and restore the geometries of those partially occluded leaves. Overall, this study developed a novel Kinect-based low-cost but efficient mobile three-dimensional plant structure phenotyping technique, which is of implications for promoting crop monitoring and increasing agricultural production.

孟祥爽, 林沂. 基于移动Kinect的低成本植物三维结构表型分析[J]. 光谱学与光谱分析, 2020, 40(8): 2352. MENG Xiang-shuang, LIN Yi. Kinect Sensor Moving for Low-Cost Mobile Phenotyping of 3D Plant Structures[J]. Spectroscopy and Spectral Analysis, 2020, 40(8): 2352.

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