林业科学 ›› 2020, Vol. 56 ›› Issue (6): 113-126.doi: 10.11707/j.1001-7488.20200612
边黎明1,张慧春2,*
收稿日期:
2019-05-15
出版日期:
2020-06-25
发布日期:
2020-07-17
通讯作者:
张慧春
基金资助:
Liming Bian1,Huichun Zhang2,*
Received:
2019-05-15
Online:
2020-06-25
Published:
2020-07-17
Contact:
Huichun Zhang
摘要:
缺乏有效的表型采集与分析能力已成为林木育种研究领域的瓶颈,其关键难点是生成准确的表型数据,以便正确解释获得的结果。在精确林业中面临的核心挑战是实现自动化、大范围、快速实时的表型性状分析。长久以来,林木遗传育种和精确林业监测要花费大量的人力收集常规表型数据,传统的表型研究方法具有效率低、维度低、通量低、精度低、劳动量大、主观性强等缺点,无法满足挖掘"基因型-表型-环境型"内在关联、揭示特定生物性状形成机制的科研需求。因此,亟需在林业上发展并应用非破坏式、自动化、高通量、高精度的表型监测技术。现代表型技术使用搭载多种类型成像传感器的系统,自动收集林木形态结构和生理生化等大量表型数据,实现对大批量林木个体的生长监测。另外,无损测量的特点使对同一林木个体进行连续监测得以实现,从而获取林木生长相关的表型性状,如在胁迫研究中,表型技术能明晰林木对胁迫的响应模式及其对胁迫的抗性。利用新型传感器技术对遗传测定群体进行准确、高通量、无损式、快速高效的表型信息采集,对于加快林木遗传改良进程、实施精确林业战略、挖掘优良种质、提高森林质量和抗逆能力至关重要。本文回顾林木表型技术的发展,介绍了基于个体和基于林分(群体)的林木表型技术的应用领域和研究内容。详细分析可见光相机、荧光成像仪、近红外成像仪、高光谱成像仪、热红外成像仪和激光雷达扫描仪等各成像传感器的测量参数、频谱范围、成像原理、优缺点,以及在林木表型信息采集上的应用现状等。林木表型技术的研究趋势为:1)构建新型采集平台获取林分和个体的关键表型性状以提高精度及通量;2)利用环境监测技术,分析林木在温度、湿度、水分、光照等非生物胁迫下的表型反应,以进行抗逆良种选育;3)利用生物胁迫下的表型变化分析推动精确林业中的病虫害监测、分类、识别和防治等;4)利用高通量表型技术与全基因组选择、数量性状位点和全基因组关联分析相结合以鉴定基因的功能,提高选择的准确性。表型技术的应用将实现快速实时、高质量、高精度、高通量的采集林木数据,从而提高育种效率,优化精确林业实践,加速林业信息化的发展进程。
中图分类号:
边黎明,张慧春. 表型技术在林木育种和精确林业上的应用[J]. 林业科学, 2020, 56(6): 113-126.
Liming Bian,Huichun Zhang. Application of Phenotyping Techniques in Forest Tree Breeding and Precision Forestry[J]. Scientia Silvae Sinicae, 2020, 56(6): 113-126.
表1
用于林木表型信息采集的成像技术"
成像技术 Imaging technology | 频谱范围 Spectral range/nm | 测量的表型参数 Measured phenotyping parameters | 应用优势 Application advantages | 缺点 Disadvantages |
可见光成像 Visible (VIS) | 400~700 | 尺寸、颜色、拓扑形态、几何结构、叶面积、冠幅、冠形、投影面积、茎干生物量、果实数量和分布、开花时间 Size, color, topological morphology, geometric structure, leaf area, canopy, crown shape, projection area, stem biomass, fruit number and distribution, flowering time | 成本低、具有颜色信息 Low cost, with color information | 对环境光照敏感 Sensitive to ambient light |
荧光成像 Fluorescence(FLUO) | 400~500 | 代谢状态的信息、叶绿素监测、光合作用相关参数 Information on metabolic status, chlorophyll monitoring, photosynthetic parameters | 能监控叶片健康状况和光合状况 Monitor leaf health and photosynthetic status | 视场小、要求强光源 The field of view is small and requires a strong light source |
近红外成像Near infrared (NIR) | 700~2 500 | 含水率、叶面积指数、叶绿素含量 Water content, leaf area index, chlorophyll content | 冠层机构信息获取的效率高 The efficiency of acquiring canopy information is high | 成本高、价格贵、色彩还原差 High cost, poor color reduction |
热红外成像Thermal infrared (TIR) | 700~1 000 000 | 冠层或叶片温度、气孔导度、元素缺失等内部信息、受病虫害侵染情况 Canopy or leaf temperature, stomatal conductance, element loss and other internal information, infection by diseases and insect pests | 检测指标多样、能进行健康和水分胁迫响应监控 Monitor health and water stress responses with various detection indexes | 对周围环境条件敏感、需频繁校正 Sensitive to ambient conditions and need frequent calibration |
高光谱成像 Hyperspectral(HS) | 550~1 750 | 叶面及冠层水分状况、植被健康状况、生物量、覆盖密度 Leaf and canopy moisture status, vegetation health status, biomass, cover density | 分辨率较高、测量时对植物无损害、无污染、测量速度快 High resolution, no damage to plants, no pollution, fast measurement speed | 图像数据大、计算量大、对周围环境条件敏感、存在背景干扰 Large image data, large computation, sensitive to the surrounding environment and background interference |
雷达 LiDAR | 200~1 620 | 叶倾角分布、冠层结构、地上部分生物量、位置 Leaf angle distribution, canopy structure, aboveground biomass, location | 能进行三维形态获取 3D shape acquisition | 价格昂贵、难以处理闭合和阴影的情况 High cost, difficult to handle closure and shading |
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