Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (3): 425-437.doi: 10.3864/j.issn.0578-1752.2022.03.001

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Advances and Perspectives of Approaches to Phenotyping Crop Root System

LI Long(),LI ChaoNan,MAO XinGuo,WANG JingYi,JING RuiLian()   

  1. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Beijing 100081
  • Received:2021-07-21 Accepted:2021-08-09 Online:2022-02-01 Published:2022-02-11
  • Contact: RuiLian JING E-mail:lilong01@caas.cn;jingruilian@caas.cn

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Abstract: Roots are the vital organs for fixing the plant shoots and absorbing soil water and nutrients. The phenotypic characteristics of roots directly affect crop productivity and adaptability. Optimizing root phenotypes is considered to be one of the important ways to achieve the second “Green Revolution”. However, the invisibility, complexity and plasticity of root system greatly restrict the efficiency of root phenotyping, which makes the root optimization process lag far behind that of aboveground organs. With the rapid development of new technologies, i.e. spectral imaging, machine learning and three-dimensional reconstruction, the approaches to phenotyping roots gradually changed from traditional sampling observation to in-situ, nondestructive and automatic detection, and the evaluation basis expanded from two-dimensional morphological indices to three-dimensional parameters, which promoted the efficiency of root phenotyping and dramatically enriched the data of root phenotype. Meanwhile, the massive data exhibited problems, such as data redundancy and low use efficiency of information resources, which put forward new requirements, i.e. standardization and shareability, for root phenotype studies. This paper summarized the principles and technical keys of main approaches to phenotyping roots, and compared systematically in terms of precision, cost and throughput. The commonly used software for quantification of root phenotype were listed out from the aspects of license, operating platform, analysis mode and so on. The important research direction in the future was put forward, that is, to develop effective approaches to phenotyping roots in the field, to establish the evaluation system for root plasticity, to strengthen the identification and utilization of root anatomical characters, to strengthen the application of molecular detection techniques in root phenotyping, and to promote standardization of root phenotyping techniques and data sharing. The aim is to provide reference for the reasonable selection and improvement of approaches to phenotyping crop root system, so as to promote crop root improvement.


Key words: crop, root phenotype, phenotyping in the laboratory, phenotyping in the field, evaluation method

Fig. 1

Approaches for phenotyping root system architecture in the lab and the field The images of clear-pot and magnetic resonance imaging are cited from RICHARD et al.[15] and VAN DUSSCHOTEN et al.[20], respectively"

Table 1

Evaluation of approaches to phenotyping root system in the field"

方法
Method		 精确度
Accuracy		 工作量
Workload		 鉴定范畴
Scope		 动态性
Dynamic		 成本
Cost		 通量
Throughput
挖掘法 Excavation +++ +++ +++ --- --- ---
土芯法 Soil core ++ +++ + -- --- ---
网袋法 Mesh bag - ++ +++ --- -- --
小篮子法 Basket +++ +++ +++ --- - ---
剖面法 Soil profile ++ +++ ++ - --- ---
微根管法 Minirhizotron - + ++ +++ ++ +
电容法 Electrical capacitance -- --- - +++ - ++
探地雷达法 Ground-penetrating radar ? --- -- +++ +++ ++
替代性状法 Proxy trait --- --- --- +++ --- +++
+++：极高;---：极低;?：精确度与待测植物根直径有关
+++: Very high; ---: Very low; ?: The accuracy depends on the root diameter of the plant to be measured

Table 2

Commonly used software for root phenotype analysis"

软件
Software		 使用许可
License		 运行平台
Operating platform		 分析方式
Analysis mode		 批量处理
Batch		 三维成像
3D-imaging
ArchiDART[42] 免费、开源 Free, open-source Windows, Mac, Linux 自动化 Automated 能 Yes 不能 No
DART[43] 免费、闭源 Free, closed-source Windows, Mac, Linux 手动 Manual 不能 No 不能 No
DIRT[44] 免费、开源 Free, open-source Network platform 自动化 Automated 能 Yes 不能 No
DynamicRoots[45] 免费、开源 Free, open-source Windows 半自动化 Semi-automated 不能 No 能 Yes
EZ-Root-VIS[46] 免费、闭源 Free, closed-source Windows 半自动化 Semi-automated 不能 No 不能 No
GiA Roots[47] 免费、闭源 Free, closed-source Windows, Mac, Linux 自动化 Automated 能 Yes 不能 No
GT-RootS[48] 免费、开源 Free, open-source Windows, Mac, Linux 自动化 Automated 能 Yes 不能 No
iRoCS Toolbox[49] 免费、开源 Free, open-source Windows, Linux 半自动化 Semi-automated 不能 No 能 Yes
MyROOT[50] 免费、闭源 Free, closed-source Windows, Mac, Linux 半自动化 Semi-automated 能 Yes 不能 No
RhizoVision Crown[51] 免费、开源 Free, open-source Windows 自动化 Automated 能 Yes 不能 No
RootNav[52] 免费、开源 Free, open-source Windows 半自动化 Semi-automated 能 Yes 不能 No
RooTrak[53] 免费、闭源 Free, closed-source Windows 半自动化 Semi-automated 不能 No 能 Yes
RootReader2D[54] 免费、闭源 Free, closed-source Windows 半自动化 Semi-automated 能 Yes 不能 No
RootReader3D[55] 收费、闭源 Charge, closed-source Windows 自动化 Automated 不能 No 能 Yes
saRIA[56] 免费、闭源 Free, closed-source Windows, Linux 半自动化 Semi-automated 能 Yes 不能 No
SegRoot[36] 免费、开源 Free, open-source Windows, Mac, Linux 自动化 Automated 能 Yes 不能 No
SmartRoot[57] 免费、开源 Free, open-source Windows, Mac, Linux 半自动化 Semi-automated 不能 No 不能 No
WinRhizo[58] 收费、闭源 Charge, closed-source Windows 自动化 Automated 不能 No 不能 No

Fig. 2

User interface of commonly used software for root phenotype analysis"

[1] TRACY S R, NAGEL K A, POSTMA J A, FASSBENDER H, WASSON A, WATT M. Crop improvement from phenotyping roots: Highlights reveal expanding opportunities. Trends in Plant Science, 2020, 25:105-118.
doi: 10.1016/j.tplants.2019.10.015
[2] THORUP-KRISTENSEN K, HALBERG N, NICOLAISEN M, OLESEN J E, CREWS T E, HINSINGER P, KIRKEGAARD J, PIERRET A, DRESBOLL D B. Digging deeper for agricultural resources, the value of deep rooting. Trends in Plant Science, 2020, 25:406-417.
doi: 10.1016/j.tplants.2019.12.007
[3] GREGORY P J, MCGOWAN M, BISCOE P V. Water relations of winter wheat. 2. Soil water relations. Journal of Agricultural Science, 1978, 91:103-116.
[4] VAN DER BOM F J T, WILLIAMS A, BELL M J. Root architecture for improved resource capture: Trade-offs in complex environments. Journal of Experimental Botany, 2020, 71:5752-5763.
doi: 10.1093/jxb/eraa324
[5] SUN B, GAO Y, LYNCH J P. Large crown root number improves topsoil foraging and phosphorus acquisition. Plant Physiology, 2018, 177:90-104.
doi: 10.1104/pp.18.00234
[6] LYNCH J P. Root phenotypes for improved nutrient capture: An underexploited opportunity for global agriculture. New Phytologist, 2019, 223:548-564.
doi: 10.1111/nph.2019.223.issue-2
[7] BAILEY-SERRES J, PARKER J E, AINSWORTH E A, OLDROYD G E D, SCHROEDER J I. Genetic strategies for improving crop yields. Nature, 2019, 575:109-118.
doi: 10.1038/s41586-019-1679-0
[8] LI L, PENG Z, MAO X G, WANG J Y, CHANG X P, REYNOLDS M P, JING R L. Genome-wide association study reveals genomic regions controlling root and shoot traits at late growth stages in wheat. Annals of Botany, 2019, 124:993-1006.
doi: 10.1093/aob/mcz041
[9] ATKINSON J A, POUND M P, BENNETT M J, WELLS D M. Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology, 2019, 55:1-8.
doi: 10.1016/j.copbio.2018.06.002
[10] BEKKERING C S, HUANG J, TIAN L. Image-based, organ-level plant phenotyping for wheat improvement. Agronomy-Basel, 2020, 10:1287.
[11] TAKAHASHI H, PRADAL C. Root phenotyping: Important and minimum information required for root modeling in crop plants. Breeding Science, 2021, 71:109-116.
doi: 10.1270/jsbbs.20126
[12] WASSON A P, NAGEL K A, TRACY S, WATT M. Beyond digging: Noninvasive root and rhizosphere phenotyping. Trends in Plant Science, 2020, 25:119-120.
doi: 10.1016/j.tplants.2019.10.011
[13] WALLER S, WILDER S L, SCHUELLER M J, HOUSH A B, FERRIERI R A. Quantifying plant-borne carbon assimilation by root-associating bacteria. Microorganisms, 2020, 8:700.
doi: 10.3390/microorganisms8050700
[14] LIU X L, LI R Z, CHANG X P, JING R L. Mapping QTLs for seedling root traits in a doubled haploid wheat population under different water regimes. Euphytica, 2013, 189:51-66.
doi: 10.1007/s10681-012-0690-4
[15] RICHARD C A I, HICKEY L T, FLETCHER S, JENNINGS R, CHENU K, CHRISTOPHER J T. High-throughput phenotyping of seminal root traits in wheat. Plant Methods, 2015, 11:13.
doi: 10.1186/s13007-015-0055-9
[16] RICH S M, CHRISTOPHER J, RICHARDS R, WATT M. Root phenotypes of young wheat plants grown in controlled environments show inconsistent correlation with mature root traits in the field. Journal of Experimental Botany, 2020, 71:4751-4762.
doi: 10.1093/jxb/eraa201
[17] PAEZ-GARCIA A, MOTES C M, SCHEIBLE W R, CHEN R J, BLANCAFLOR E B, MONTEROS M J. Root traits and phenotyping strategies for plant improvement. Plants, 2015, 4:334-355.
doi: 10.3390/plants4020334
[18] CORONA-LOPEZ D D J, SOMMER S, ROLFE S A, PODD F, GRIEVE B D. Electrical impedance tomography as a tool for phenotyping plant roots. Plant Methods, 2019, 15:49.
doi: 10.1186/s13007-019-0438-4
[19] TERAMOTO S, TAKAYASU S, KITOMI Y, ARAI-SANOH Y, TANABATA T, UGA Y. High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography. Plant Methods, 2020, 16:66.
doi: 10.1186/s13007-020-00612-6
[20] VAN DUSSCHOTEN D, METZNER R, KOCHS J, POSTMA J A, PFLUGFELDER D, BUEHLER J, SCHURR U, JAHNKE S. Quantitative 3D analysis of plant roots growing in soil using magnetic resonance imaging. Plant Physiology, 2016, 170:1176-1188.
doi: 10.1104/pp.15.01388
[21] ROGERS E D, MONAENKOVA D, MIJAR M, NORI A, GOLDMAN D I, BENFEY P N. X-ray computed tomography reveals the response of root system architecture to soil texture. Plant Physiology, 2016, 171:2028-2040.
doi: 10.1104/pp.16.00397
[22] BAO Y, AGGARWAL P, ROBBINS N E, STURROCK C J, THOMPSON M C, TAN H Q, THAM C, DUAN L, RODRIGUEZ P L, VERNOUX T, MOONEY S J, BENNETT M J, DINNENY J R. Plant roots use a patterning mechanism to position lateral root branches toward available water. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111:9319-9324.
[23] PFLUGFELDER D, METZNER R, VAN DUSSCHOTEN D, REICHEL R, JAHNKE S, KOLLER R. Non-invasive imaging of plant roots in different soils using magnetic resonance imaging (MRI). Plant Methods, 2017, 13:102.
doi: 10.1186/s13007-017-0252-9
[24] METZNER R, EGGERT A, VAN DUSSCHOTEN D, PFLUGFELDER D, GERTH S, SCHURR U, UHLMANN N, JAHNKE S. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: Potential and challenges for root trait quantification. Plant Methods, 2015, 11:17.
doi: 10.1186/s13007-015-0060-z
[25] JAHNKE S, MENZEL M I, VAN DUSSCHOTEN D, ROEB G W, BUEHLER J, MINWUYELET S, BLUEMLER P, TEMPERTON V M, HOMBACH T, STREUN M, BEER S, KHODAVERDI M, ZIEMONS K, COENEN H H, SCHURR U. Combined MRI-PET dissects dynamic changes in plant structures and functions. The Plant Journal, 2009, 59:634-644.
doi: 10.1111/tpj.2009.59.issue-4
[26] OYIGA B C, PALCZAK J, WOJCIECHOWSKI T, LYNCH J P, NAZ A A, LEON J, BALLVORA A. Genetic components of root architecture and anatomy adjustments to water-deficit stress in spring barley. Plant, Cell & Environment, 2020, 43:692-711.
[27] ZHENG Z, HEY S, JUBERY T, LIU H, YANG Y, COFFEY L, MIAO C, SIGMON B, SCHNABLE J C, HOCHHOLDINGER F, GANAPATHYSUBRAMANIAN B, SCHNABLE P S. Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant Physiology, 2020, 182:977-991.
doi: 10.1104/pp.19.00752
[28] WASSON A P, REBETZKE G J, KIRKEGAARD J A, CHRISTOPHER J, RICHARDS R A, WATT M. Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding. Journal of Experimental Botany, 2014, 65:6231-6249.
doi: 10.1093/jxb/eru250
[29] KITOMI Y, HANZAWA E, KUYA N, INOUE H, HARA N, KAWAI S, KANNO N, ENDO M, SUGIMOTO K, YAMAZAKI T, SAKAMOTO S, SENTOKU N, WU J, KANNO H, MITSUDA N, TORIYAMA K, SATO T, UGA Y. Root angle modifications by the DRO1 homolog improve rice yields in saline paddy fields. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117:21242-21250.
[30] VOSS-FELS K P, ROBINSON H, MUDGE S R, RICHARD C, NEWMAN S, WITTKOP B, STAHL A, FRIEDT W, FRISCH M, GABUR I, MILLER-COOPER A, CAMPBELL B C, KELLY A, FOX G, CHRISTOPHER J, CHRISTOPHER M, CHENU K, FRANCKOWIAK J, MACE E S, BORRELL A K, EAGLES H, JORDAN D R, BOTELLA J R, HAMMER G, GODWIN I D, TREVASKIS B, SNOWDON R J, HICKEY L T. VERNALIZATION1 modulates root system architecture in wheat and barley. Molecular Plant, 2018, 11:226-229.
doi: 10.1016/j.molp.2017.10.005
[31] LOU Q, CHEN L, MEI H, WEI H, FENG F, WANG P, XIA H, LI T, LUO L. Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice. Journal of Experimental Botany, 2015, 66:4749-4757.
doi: 10.1093/jxb/erv246
[32] UGA Y, SUGIMOTO K, OGAWA S, RANE J, ISHITANI M, HARA N, KITOMI Y, INUKAI Y, ONO K, KANNO N, INOUE H, TAKEHISA H, MOTOYAMA R, NAGAMURA Y, WU J, MATSUMOTO T, TAKAI T, OKUNO K, YANO M. Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nature Genetics, 2013, 45:1097-1102.
doi: 10.1038/ng.2725
[33] BATES G H. A device for the observation of root growth in the soil. Nature, 1937, 139:966-967.
[34] MAJDI H. Root sampling methods - applications and limitations of the minirhizotron technique. Plant and Soil, 1996, 185:255-258.
doi: 10.1007/BF02257530
[35] SVANE S F, DAM E B, CARSTENSEN J M, THORUP- KRISTENSEN K. A multispectral camera system for automated minirhizotron image analysis. Plant and Soil, 2019, 441:657-672.
doi: 10.1007/s11104-019-04132-8
[36] WANG T, ROSTAMZA M, SONG Z, WANG L, MCNICKLE G, IYER-PASCUZZI A S, QIU Z, JIN J. Segroot: A high throughput segmentation method for root image analysis. Computers and Electronics in Agriculture, 2019, 162:845-854.
doi: 10.1016/j.compag.2019.05.017
[37] ALANI A M, LANTINI L. Recent advances in tree root mapping and assessment using non-destructive testing methods: A focus on ground penetrating radar. Surveys in Geophysics, 2020, 41:605-646.
doi: 10.1007/s10712-019-09548-6
[38] STREDA T, HABERLE J, KLIMESOVA J, KLIMEK-KOPYRA A, STREDOVA H, BODNER G, CHLOUPEK O. Field phenotyping of plant roots by electrical capacitance-a standardized methodological protocol for application in plant breeding: A review. International Agrophysics, 2020, 34:173-184.
doi: 10.31545/intagr/117622
[39] WASSON A P, RICHARDS R A, CHATRATH R, MISRA S C, PRASAD S V S, REBETZKE G J, KIRKEGAARD J A, CHRISTOPHER J, WATT M. Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. Journal of Experimental Botany, 2012, 63:3485-3498.
doi: 10.1093/jxb/ers111
[40] LI X, INGVORDSEN C H, WEISS M, REBETZKE G J, CONDON A G, JAMES R A, RICHARDS R A. Deeper roots associated with cooler canopies, higher normalized difference vegetation index, and greater yield in three wheat populations grown on stored soil water. Journal of Experimental Botany, 2019, 70:4963-4974.
doi: 10.1093/jxb/erz232
[41] JIN X, ZARCO-TEJADA P J, SCHMIDHALTER U, REYNOLDS M P, HAWKESFORD M J, VARSHNEY R K, YANG T, NIE C, LI Z, MING B, XIAO Y, XIE Y, LI S. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine, 2021, 9:200-231.
doi: 10.1109/MGRS.6245518
[42] DELORY B M, LI M, TOPP C N, LOBET G. Archidart v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems version 1; referees: 2 approved, 1 approved with reservations. F1000Research, 2018, 7:22.
doi: 10.12688/f1000research
[43] LE BOT J, SERRA V, FABRE J, DRAYE X, ADAMOWICZ S, PAGES L. Dart: A software to analyse root system architecture and development from captured images. Plant and Soil, 2010, 326:261-273.
doi: 10.1007/s11104-009-0005-2
[44] DAS A, SCHNEIDER H, BURRIDGE J, ASCANIO A K M, WOJCIECHOWSKI T, TOPP C N, LYNCH J P, WEITZ J S, BUCKSCH A. Digital imaging of root traits (DIRT): A high- throughput computing and collaboration platform for field-based root phenomics. Plant Methods, 2015, 11:51.
doi: 10.1186/s13007-015-0093-3
[45] SYMONOVA O, TOPP C N, EDELSBRUNNER H. Dynamicroots: A software platform for the reconstruction and analysis of growing plant roots. PLoS ONE, 2015, 10:e0127657.
doi: 10.1371/journal.pone.0127657
[46] SHAHZAD Z, KELLERMEIER F, ARMSTRONG E M, ROGERS S, LOBET G, AMTMANN A, HILLS A. EZ-root-VIS: A software pipeline for the rapid analysis and visual reconstruction of root system architecture. Plant Physiology, 2018, 177:1368-1381.
doi: 10.1104/pp.18.00217
[47] GALKOVSKYI T, MILEYKO Y, BUCKSCH A, MOORE B, SYMONOVA O, PRICE C A, TOPP C N, IYER-PASCUZZI A S, ZUREK P R, FANG S, HARER J, BENFEY P N, WEITZ J S. GiA roots: Software for the high throughput analysis of plant root system architecture. BMC Plant Biology, 2012, 12:116.
doi: 10.1186/1471-2229-12-116
[48] BORIANNE P, SUBSOL G, FALLAVIER F, DARDOU A, AUDEBERT A. GT-RootS: An integrated software for automated root system measurement from high-throughput phenotyping platform images. Computers and Electronics in Agriculture, 2018, 150:328-342.
doi: 10.1016/j.compag.2018.05.003
[49] SCHMIDT T, PASTERNAK T, LIU K, BLEIN T, AUBRY-HIVET D, DOVZHENKO A, DUERR J, TEALE W, DITENGOU F A, BURKHARDT H, RONNEBERGER O, PALME K. The iRoCS Toolbox-3D analysis of the plant root apical meristem at cellular resolution. The Plant Journal, 2014, 77:806-814.
doi: 10.1111/tpj.12429
[50] GONZALEZ A, SEVILLANO X, BETEGON-PUTZE I, BLASCO- ESCAMEZ D, FERRER M, CANO-DELGADO A I. MYROOT 2.0: An automatic tool for high throughput and accurate primary root length measurement. Computers and Electronics in Agriculture, 2020, 168:105125.
doi: 10.1016/j.compag.2019.105125
[51] SEETHEPALLI A, GUO H, LIU X, GRIFFITHS M, ALMTARFI H, LI Z, LIU S, ZARE A, FRITSCHI F B, BLANCAFLOR E B, MA X, YORK L M. RhizoVision Crown: An integrated hardware and software platform for root crown phenotyping. Plant Phenomics, 2020, 2020:3074916.
[52] POUND M P, FRENCH A P, ATKINSON J A, WELLS D M, BENNETT M J, PRIDMORE T. RootNav: Navigating images of complex root architectures. Plant Physiology, 2013, 162:1802-1814.
doi: 10.1104/pp.113.221531
[53] MAIRHOFER S, ZAPPALA S, TRACY S R, STURROCK C, BENNETT M, MOONEY S J, PRIDMORE T. RooTrak: Automated recovery of three-dimensional plant root architecture in soil from X-ray microcomputed tomography images using visual tracking. Plant Physiology, 2012, 158:561-569.
doi: 10.1104/pp.111.186221
[54] CLARK R T, FAMOSO A N, ZHAO K, SHAFF J E, CRAFT E J, BUSTAMANTE C D, MCCOUCH S R, ANESHANSLEY D J, KOCHIAN L V. High-throughput two-dimensional root system phenotyping platform facilitates genetic analysis of root growth and development. Plant, Cell & Environment, 2013, 36:454-466.
[55] CLARK R T, MACCURDY R B, JUNG J K, SHAFF J E, MCCOUCH S R, ANESHANSLEY D J, KOCHIAN L V. Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiology, 2011, 156:455-465.
doi: 10.1104/pp.110.169102
[56] NARISETTI N, HENKE M, SEILER C, SHI R, JUNKER A, ALTMANN T, GLADILIN E. Semi-automated root image analysis (saRIA). Scientific Reports, 2019, 9:19674.
doi: 10.1038/s41598-019-55876-3
[57] LOBET G, PAGES L, DRAYE X. A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiology, 2011, 157:29-39.
doi: 10.1104/pp.111.179895
[58] PANG W, CROW W T, LUC J E, MCSORLEY R, GIBLIN-DAVIS R M, KENWORTHY K E, KRUSE J K. Comparison of water displacement and WINRHIZO software for plant root parameter assessment. Plant Disease, 2011, 95:1308-1310.
doi: 10.1094/PDIS-01-11-0026
[59] BEYER S, DABA S, TYAGI P, BOCKELMAN H, BROWN- GUEDIRA G, MOHAMMADI M, IWGSC . Loci and candidate genes controlling root traits in wheat seedlings - a wheat root GWAS. Functional & Integrative Genomics, 2019, 19:91-107.
[60] FERNANDO K M C, EHOCHE O G, ATKINSON J A, SPARKES D L. Root system architecture and nitrogen uptake efficiency of wheat species. Journal of Agricultural Sciences, 2021, 16:37-53.
[61] VESCIO R, ABENAVOLI M R, SORGONA A. Single and combined abiotic stress in maize root morphology. Plants-Basel, 2021, 10:5.
[62] YASRAB R, ATKINSON J A, WELLS D M, FRENCH A P, PRIDMORE T P, POUND M P. RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. Gigascience, 2019, 8: giz123.
doi: 10.1093/gigascience/giz123
[63] GUICHARD M, ALLAIN J M, BIANCHI M W, FRACHISSE J M. Root hair sizer: An algorithm for high throughput recovery of different root hair and root developmental parameters. Plant Methods, 2019, 15:104.
doi: 10.1186/s13007-019-0483-z
[64] PAULUS S. Measuring crops in 3D: Using geometry for plant phenotyping. Plant Methods, 2019, 15:103.
doi: 10.1186/s13007-019-0490-0
[65] UGA Y, ASSARANURAK I, KITOMI Y, LARSON B G, CRAFT E J, SHAFF J E, MCCOUCH S R, KOCHIAN L V. Genomic regions responsible for seminal and crown root lengths identified by 2D & 3D root system image analysis. BMC Genomics, 2018, 19:273.
doi: 10.1186/s12864-018-4639-4
[66] POSTMA J A, KUPPE C, OWEN M R, MELLOR N, GRIFFITHS M, BENNETT M J, LYNCH J P, WATT M. OPENSIMROOT: Widening the scope and application of root architectural models. New Phytologist, 2017, 215:1274-1286.
doi: 10.1111/nph.2017.215.issue-3
[67] LOBET G, POUND M P, DIENER J, PRADAL C, DRAYE X, GODIN C, JAVAUX M, LEITNER D, MEUNIER F, NACRY P, PRIDMORE T P, SCHNEPF A. Root system markup language: Toward a unified root architecture description language. Plant Physiology, 2015, 167:617-627.
doi: 10.1104/pp.114.253625
[68] LOBET G, DRAYE X, PÉRILLEUX C. An online database for plant image analysis software tools. Plant Methods, 2013, 9:38.
doi: 10.1186/1746-4811-9-38
[69] 刘旭, 李立会, 黎裕, 方沩. 作物种质资源研究回顾与发展趋势. 农学学报, 2018, 8:1-6.
LIU X, LI L H, LI Y, FANG W. Crop germplasm resources: Advances and trends. Journal of Agriculture, 2018, 8:1-6. (in Chinese)
[70] LI C N, LI L, REYNOLDS M P, WANG J Y, CHANG X P, MAO X G, JING R L. Recognizing the hidden half in wheat: Root system attributes associated with drought tolerance. Journal of Experimental Botany, 2021, 72:5117-5133.
doi: 10.1093/jxb/erab124
[71] PATURKAR A, SEN GUPTA G, BAILEY D. Making use of 3D models for plant physiognomic analysis: A review. Remote Sensing, 2021, 13:2232.
doi: 10.3390/rs13112232
[72] GAO K, CHEN F, YUAN L, ZHANG F, MI G. A comprehensive analysis of root morphological changes and nitrogen allocation in maize in response to low nitrogen stress. Plant, Cell & Environment, 2015, 38:740-750.
[73] VYSOTSKAYA L, AKHIYAROVA G, FEOKTISTOVA A, AKHTYAMOVA Z, KOROBOVA A, IVANOV I, DODD I, KULUEV B, KUDOYAROVA G. Effects of phosphate shortage on root growth and hormone content of barley depend on capacity of the roots to accumulate ABA. Plants-Basel, 2020, 9:1722.
[74] SCHNEIDER H M, LYNCH J P. Should root plasticity be a crop breeding target? Frontiers in Plant Science, 2020, 11:546.
doi: 10.3389/fpls.2020.00546
[75] SIDDIQUI M N, LEON J, NAZ A A, BALLVORA A. Genetics and genomics of root system variation in adaptation to drought stress in cereal crops. Journal of Experimental Botany, 2021, 72:1007-1019.
doi: 10.1093/jxb/eraa487
[76] WANG C, LI X, CARAGEA D, BHEEMANAHALLIA R, JAGADISH S V K. Root anatomy based on root cross-section image analysis with deep learning. Computers and Electronics in Agriculture, 2020, 175:105549.
doi: 10.1016/j.compag.2020.105549
[77] 武晶, 黎裕. 基于作物种质资源的优异等位基因挖掘: 进展与展望. 植物遗传资源学报, 2019, 20:1380-1390.
WU J, LI Y. Mining superior alleles in crop germplasm resources: Advances and perspectives. Journal of Plant Genetic Resources, 2019, 20:1380-1390. (in Chinese)
[78] DEJA-MUYLLE A, PARIZOT B, MOTTE H, BEECKMAN T. Exploiting natural variation in root system architecture via genome- wide association studies. Journal of Experimental Botany, 2020, 71:2379-2389.
doi: 10.1093/jxb/eraa029
[79] 刘旭. 四十年改革开放几代人梦想成真——记中国作物种质资源40年发展巨变. 中国种业, 2019, 1:1-6.
LIU X. Forty years of reform and opening-up and several generations' dreams come true-Recording the great changes in the development of crop germplasm resources in China in the past 40 years. China Seed Industry, 2019, 1:1-6. (in Chinese)
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