Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects

被引:120
作者
Jin, Shichao [1 ,2 ,3 ,7 ]
Sun, Xiliang [2 ,3 ]
Wu, Fangfang [2 ,3 ]
Su, Yanjun [2 ,3 ]
Li, Yumei [4 ]
Song, Shiling [2 ,3 ]
Xu, Kexin [2 ,3 ]
Ma, Qin [5 ]
Baret, Frederic [1 ,6 ,7 ]
Jiang, Dong [1 ]
Ding, Yanfeng [1 ,7 ]
Guo, Qinghua [2 ,3 ]
机构
[1] Nanjing Agr Univ, Plant Phen Res Ctr, 1 Weigang, Nanjing 210095, Peoples R China
[2] Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[4] Chinese Acad Sci, Inst Zool, Key Lab Anim Ecol & Conservat Biol, Beijing 100101, Peoples R China
[5] Mississippi State Univ, Dept Forestry, Mississippi State, MS 39759 USA
[6] INRA, Unite Mixte Rech 1114, Environm Mediterraneen & Modelisat AgroHydrosyst, F-84914 Avignon, France
[7] Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, Nanjing 210095, Peoples R China
关键词
Lidar; Traits; Phenomics; Breeding; Management; Multi-omits; LEAF-AREA INDEX; TERRESTRIAL LASER SCANNER; WAVE-FORM LIDAR; ESTIMATING ABOVEGROUND BIOMASS; INDIVIDUAL TREE CROWNS; DENSIFICATION FILTERING ALGORITHM; ESTIMATING CANOPY STRUCTURE; FIELD PHENOTYPING PLATFORM; MARKER-FREE REGISTRATION; DIFFERENT GROWTH-STAGES;
D O I
10.1016/j.isprsjprs.2020.11.006
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field environments. Light detection and ranging (lidar) provides a powerful new tool for 3D phenotyping with the rapid development of facilities and algorithms. Numerous efforts have been devoted to studying static and dynamic changes of structural and functional phenotypes using lidar in agriculture. These progresses also improve 3D plant modeling across different spatial-temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices and affords new insights into breeding and management. Beyond agriculture phenotyping, lidar shows great potential in forestry, horticultural, and grass phenotyping. Although lidar has resulted in remarkable improvements in plant phenotyping and modeling, the synthetization of lidar-based phenotyping for breeding and management has not been fully explored. We identify three main challenges in lidar-based phenotyping development: 1) developing low cost, high spatial-temporal, and hyperspectral lidar facilities, 2) moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and 3) embracing open source and big data.
引用
收藏
页码:202 / 223
页数:22
相关论文
共 407 条
  • [1] Non-intersecting leaf insertion algorithm for tree structure models
    Akerblom, Markku
    Raumonen, Pasi
    Casella, Eric
    Disney, Mathias I.
    Danson, F. Mark
    Gaulton, Rachel
    Schofield, Lucy A.
    Kaasalainen, Mikko
    [J]. INTERFACE FOCUS, 2018, 8 (02)
  • [2] Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning
    Anderson, Kyle E.
    Glenn, Nancy F.
    Spaete, Lucas P.
    Shinneman, Douglas J.
    Pilliod, David S.
    Arkle, Robert S.
    McIlroy, Susan K.
    Derryberry, DeWayne R.
    [J]. ECOLOGICAL INDICATORS, 2018, 84 : 793 - 802
  • [3] [Anonymous], 2010, WORKSH DEF SOLV REAL
  • [4] Deep learning
    LeCun, Yann
    Bengio, Yoshua
    Hinton, Geoffrey
    [J]. NATURE, 2015, 521 (7553) : 436 - 444
  • [5] [Anonymous], 2006, S GEOMETRY PROCESSIN
  • [6] Leaf area index estimation in vineyards using a ground-based LiDAR scanner
    Arno, Jaume
    Escola, Alexandre
    Valles, Josep M.
    Llorens, Jordi
    Sanz, Ricardo
    Masip, Joan
    Palacin, Jordi
    Rosell-Polo, Joan R.
    [J]. PRECISION AGRICULTURE, 2013, 14 (03) : 290 - 306
  • [7] Estimation of canopy cover in dense mixed-species forests using airborne lidar data
    Arumae, Tauri
    Lang, Mait
    [J]. EUROPEAN JOURNAL OF REMOTE SENSING, 2018, 51 (01): : 132 - 141
  • [8] LEAST-SQUARES FITTING OF 2 3-D POINT SETS
    ARUN, KS
    HUANG, TS
    BLOSTEIN, SD
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (05) : 699 - 700
  • [9] FOREST CONSERVATION Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation
    Asner, G. P.
    Martin, R. E.
    Knapp, D. E.
    Tupayachi, R.
    Anderson, C. B.
    Sinca, F.
    Vaughn, N. R.
    Llactayo, W.
    [J]. SCIENCE, 2017, 355 (6323) : 385 - 388
  • [10] Ayrey E, 2019, BIORXIV