水稻根系三维建模及可视化方法研究进展

吴盼盼; 唐子宗; 杨乐; 彭军; 张欢欢; 施俊林

doi:10.19303/j.issn.1008-0384.2021.08.015

水稻根系三维建模及可视化方法研究进展

doi: 10.19303/j.issn.1008-0384.2021.08.015

吴盼盼^1,,
唐子宗¹,
杨乐^{1, 2, ,},
彭军¹,
张欢欢¹,
施俊林¹

1.
江西农业大学计算机与信息工程学院，江西　南昌　330045
2.
江西省高等学校农业信息技术重点实验室，江西　南昌　330045

基金项目: 国家自然科学基金项目（61862032）；江西省自然科学基金项目（20202BABL202034）；江西省研究生创新专项资金项目（YC2021-S347）

详细信息

作者简介:
吴盼盼（1996−），女，硕士研究生，研究方向：农业信息技术（E-mail：1376068702@qq.com）

通讯作者:
杨乐（1979−），男，副教授，研究方向：深度学习在农业领域的应用研究（E-mail：jxnzhyangle@163.com）

中图分类号: S 511
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-01
- 修回日期: 2021-07-23
- 网络出版日期: 2021-08-10
- 刊出日期: 2021-08-28

Visualization of Rice Root System by 3D Modeling: A Review

1.
College of Computer Information and Engineering, Jiangxi Agricultural University, Nanchang, Jiangxi　330045, China
2.
Key Laboratory of Information Technology in Agriculture for Colleges and Universities in Jiangxi Province, Jiangxi Agriculture University, Nanchang, Jiangxi　330045, China

摘要

摘要: 根系是水稻获取养分的主要器官，水稻根系三维建模及可视化有助于进一步了解其根系的形态、结构和功能。随着计算机视觉和非侵入性技术的不断发展，根系形态和功能研究已进入数字化和可视化的阶段。近年来许多研究者分别从制作出土根系手绘图、计算机断层扫描（CT）等非侵入性技术、数学建模以及仿真模拟等方面推进水稻根系三维建模及可视化的研究。根系数据的获取是三维建模的有效前提，根据是否破坏根系原有生长环境，根系数据探测被分为破坏性探测和原位探测两类，本文对比分析了两种探测方式的方法和特点。从人工观察测量、机器视觉、光学仪器或断层扫描的三维数字化等方面对水稻根系的三维建模进行了阐述，总结了水稻根系三维建模及可视化的研究进展，并对当下主流三维重构技术进行分类和对比，总结了不同根系三维重构方法在重建效果、成本、操作水平等方面的优劣势。此外，由于根系生长在复杂多变的土壤环境中，不同时期根系的生长发育受土壤紧实度，水分、养分分布等因素的影响而存在差异，且受限于土壤的不透明和不稳定性，更多水稻根系的三维建模研究主要停留在根系基本指标与非环境因素（如土层深度、时间）的统计拟合及单环境因子对水稻根系生理生态的影响上，而根系与多环境因子动态交互方面的研究较少。在高度非结构化的根系数据处理困难的情况下，探究水稻根系与环境的动态转化过程及根系生长与多环境因子的定量关系模型将成为未来根系三维建模研究的重要方向，为构建更具真实意义的可视化模型提供基础。
- 水稻根系 /
- 探测方法 /
- 三维重构 /
- 环境-根系模型
Abstract: As an organ that extracts water and nutrients from the soil, the root system is vital for a rice plant. Establishing a 3D model to visualize the system structure can materially help the studies on the morphology and functional traits of the roots. Recent advancements in the computerized and non-invasive technologies make the information digitization for scientific research increasingly accessible and significant progresses possible. For instance, utilizing hand drawings and computer tomography (CT), mathematical models were built to vividly simulate the configuration of unearthed root system. Since data acquisition that proceeds model building is essential for an accurate and reliable representation, this article compares and analyzes the principles and characteristics of two classes of detection methods for information collection on the root systems. These methods can be either destructive or in-situ in applications depending upon whether or not the original growth environment was interrupted or destroyed. The 3D modeling and visualization of rice root system is explained in this article from the aspects of manual observation and measurement, machinery vision, 3D digitization by optical instruments, and tomography, etc. The mainstream reconstruction technologies are classified, compared, and analyzed with respect to the pros and cons on the resulting effect as well as the cost and ease of operation. Since environmental conditions are ever-changing, the development of a root system is invariably complex and varied. The affecting factors include the firmness, moisture content, and nutrients distribution of the soil a plant grows on. In addition, the non-transparency and instability of soil has so far hindered the related studies and confined to the fundamental and non-environmental elements, such as, depth of layer and time, for statistical analysis. Consequently, few reports dealt with the dynamic interactions among the multi-environmental factors that effect on the root development are available. Evidently, in the foreseeable future, the newly developed modeling and visualization technologies would usher in innovative applications and deep understanding in the field of study.
- Rice root system /
- detection method /
- 3D reconstruction /
- root system-environment model

HTML全文

图 1 根系节间单位模式

Figure 1. Unit pattern of root internodes

下载: 全尺寸图片幻灯片

图 2 水稻根系构成

Figure 2. Schematic root system of rice plant

下载: 全尺寸图片幻灯片

图 3 基于形态参数构建的水稻根系生长30、60 d生长模拟可视化图

Figure 3. Visualization of root growth simulation for 30 and 60 d based on morphological parameters

下载: 全尺寸图片幻灯片

表 1 不同方法在根系数据探测的优缺点

Table 1. Pros and cons of detection methods for data acquisition on rice root system

根系探测方式 Detecting methods	方法名称 Method name	优点 Advantages	缺点 Disadvantages
破坏性探测 Destructive detection	挖掘法 Excavation method^{[15−16 ]}	所获数据真实 Data obtained authentic	难以保证根系的完整性，末梢数据精准度有待考量 Ensure difficultly the integrity of roots, low accuracy of terminal data
	保护挖掘清洗法 Protect the excavation cleaning method ^[17]	一定程度上保证根系的完整性和有序性 Ensure the integrity and order of root system to a certain extent	细节还原度不高，末梢数据精准度有待考量 Low detail reduction, low accuracy of terminal data
	染色扫描图像分析法 Staining scanning image analysis^[18]	可获得细微处的拓扑结构信息 Subtle topology information can be obtained	有设备要求，操作过程繁琐 Equipment requirements, cumbersome operation process
原位探测 In-situ detection	土壤留置法 Soil retention method^[19−20]	可获得连续生长的根系数据 Root data of continuous growth can be obtained	扫描范围有限，仅能获取管壁周围的局部信息 Limited scanning range, only obtained local information around the pipe
	特殊培养环境法 Special culture environment method^[21−24]	根系的生长发育全过程透明可见 The whole process of root growth and development is transparent and visible	根脱离了自然土壤，生长发育可能存在较大差异 The root is separated from the natural soil, and there may be great differences in growth and development
	穿透射线成像法 Penetrating ray imaging ^[25−28]	数据精准，操作方便，效率较高 Accurate data, convenient operation, high efficiency	对设备要求高，成本昂贵 High requirements for equipment and high cost
	作物图像解析法 Crop image analysis^[12−14]	高效、自动和准确性高 High efficiency, automation and accuracy	照片获取过程费时费力，数据量大 Time-consuming and laborious photo acquisition process, large amount of data

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表 2 常见的植物根系三维重建方法的比较

Table 2. Comparison of commonly available 3D reconstruction methods on root system of plants

三维重构方法 3D reconstruction methods	重建效果 Reconstruction effect	材料成本 Material cost	优点 Advantages	缺点 Disadvantages
L-系统 L-System^{[19, 21, 36−38]}	缺乏根系细节描述 Lack of root detail description	低 Low	逼真地描述根系生长过程，可用于模拟根-根、根-环境的相互作用 Describe the root growth process realistically, can be used to simulated the interaction of root-root , root-environment	文法规则与实际有一定偏差 A certain degree of deviation deviation between grammar rules and reality
CT、雷达 CT^{[25, 28]}、Radar ^[48]	较精确 More-precision	高 High	不受外界光照影响，重建精度较高 Not affected by outside light, higher reconstruction accuracy	成本昂贵，数据量大 High cost, large amount of data
结构光 Structural light^{[8, 44]}	较精确 More-precision	高 High	技术成熟，深度图像信息丰富 Technical maturity, rich in depth image information	易受光照影响，识别距离有限 Susceptible to light, limited recognition distance
多视觉图像 Multi-visual images^{[22, 40]}	精度高 High-precision	低 Low	能描述根部细节特征，有真实的色彩纹理 Describe the root detail characteristics, with a real color texture	易受环境影响，结构复杂的根数据获取困难 Susceptible to environment, difficulty in obtaining complex root data
双目立体视觉 Binocular stereo vision^[49−50]	精度一般 Middle-precision	适中 Middle	重建效果稳定 Stable reconstruction effect	相机严格标定，点云数据匹配困难，须根系细节处理效果一般 Camera calibrated strictly, point cloud data matched difficultly, fibrous root system detail processing effect is general

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