Paulus Stefan
Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany.
Plant Methods. 2019 Sep 3;15:103. doi: 10.1186/s13007-019-0490-0. eCollection 2019.
Using 3D sensing for plant phenotyping has risen within the last years. This review provides an overview on 3D traits for the demands of plant phenotyping considering different measuring techniques, derived traits and use-cases of biological applications. A comparison between a high resolution 3D measuring device and an established measuring tool, the leaf meter, is shown to categorize the possible measurement accuracy. Furthermore, different measuring techniques such as laser triangulation, structure from motion, time-of-flight, terrestrial laser scanning or structured light approaches enable the assessment of plant traits such as leaf width and length, plant size, volume and development on plant and organ level. The introduced traits were shown with respect to the measured plant types, the used measuring technique and the link to their biological use case. These were trait and growth analysis for measurements over time as well as more complex investigation on water budget, drought responses and QTL (quantitative trait loci) analysis. The used processing pipelines were generalized in a 3D point cloud processing workflow showing the single processing steps to derive plant parameters on plant level, on organ level using machine learning or over time using time series measurements. Finally the next step in plant sensing, the fusion of different sensor types namely 3D and spectral measurements is introduced by an example on sugar beet. This multi-dimensional plant model is the key to model the influence of geometry on radiometric measurements and to correct it. This publication depicts the state of the art for 3D measuring of plant traits as they were used in plant phenotyping regarding how the data is acquired, how this data is processed and what kind of traits is measured at the single plant, the miniplot, the experimental field and the open field scale. Future research will focus on highly resolved point clouds on the experimental and field scale as well as on the automated trait extraction of organ traits to track organ development at these scales.
近年来，利用三维传感技术进行植物表型分析的方法逐渐兴起。本综述针对植物表型分析的需求，概述了三维特征，涵盖不同测量技术、衍生特征以及生物学应用的实例。通过比较高分辨率三维测量设备与成熟测量工具叶面积仪，对可能达到的测量精度进行了分类。此外，激光三角测量、运动恢复结构、飞行时间测量、地面激光扫描或结构光方法等不同测量技术，能够在植株和器官层面评估植物特征，如叶片宽度和长度、植株大小、体积及发育情况。文中展示了所引入的特征与被测植物类型、所用测量技术及其生物学应用实例之间的联系。这些实例包括随时间测量的特征和生长分析，以及关于水分平衡、干旱响应和数量性状位点（QTL）分析等更复杂的研究。所使用的处理流程在三维点云处理工作流程中进行了概括，展示了在植株层面、器官层面利用机器学习或通过时间序列测量推导植物参数的各个处理步骤。最后，通过甜菜的实例介绍了植物传感的下一步发展，即不同传感器类型（三维和光谱测量）的融合。这种多维度植物模型是模拟几何形状对辐射测量的影响并进行校正的关键。本出版物描述了用于植物表型分析中的植物特征三维测量的技术现状，包括数据采集方式、数据处理方法以及在单株植物、小区试验、试验田和大田尺度上所测量的特征类型。未来的研究将聚焦于试验和大田尺度上的高分辨率点云，以及器官特征的自动提取，以追踪这些尺度下的器官发育情况。
