A Review of High-Throughput Crop Phenotyping: Progress and Application for Vietnam

Received: 06-08-2021

Accepted: 09-12-2021

DOI:

Views

0

Downloads

0

Issue: Vol. 20 No. 1 (2022)

Section:

TỔNG QUAN

How to Cite:

Duc, N., Tuan, P., Anh, N., Muoi, N., Huan, P., Hai, V., … Liet, V. (2024). A Review of High-Throughput Crop Phenotyping: Progress and Application for Vietnam. Vietnam Journal of Agricultural Sciences, 20(1), 98–112. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/941

A Review of High-Throughput Crop Phenotyping: Progress and Application for Vietnam

Nguyen Trung Duc (*) 1 , Pham Quang Tuan 1 , Nguyen Thi Nguyet Anh 1 , Nguyen Van Muoi 1 , Phung Danh Huan 1 , Vu Hai 2 , Tran Van Quang 3 , Vu Thi Xuan Binh 4 , Vu Van Liet 3

  • 1 Viện Nghiên cứu và Phát triển cây trồng, Học viện Nông nghiệp Việt Nam
  • 2 Viện Nghiên cứu quốc tế về Thông tin đa phương tiện, Truyền thông và Ứng dụng (MICA), Đại học Bách Khoa Hà Nội
  • 3 Khoa Nông học, Học viện Nông nghiệp Việt Nam
  • 4 Ban Khoa học và Công nghệ, Học viện Nông nghiệp Việt Nam

    • Keywords

    HTP, phenotyping, G×E×M, big data, machine learning

    Abstract


    Double increase in food production to feed 10 billion people sustainably by 2050 is a global challenge, which requires novel breeding methods with high-throughput and accuracy. The development of computer science, image sensors, machine learning, and artificial intelligence provided scientists with new methods for quantitative evaluation of plant phenotypes in the interaction between genotype and environment. It has generated a new area for quantitative analysis of phenotypes: high-throughput crop phenotyping (HTP) combining multidimensional from cellular, tissue, organ, individual to population level. Vietnam is a developing country, agriculture still plays a vital role in economic activity and is strongly influenced by climate change. Therefore, the application of achievements from HTP technology will contribute to shorten the time of evaluation and breeding cycle and develop new resilience varieties highly adaptable to climate change. This study highlighted the history, development and challenges of HTP and its potential application for Vietnam.

    References

    Al-Tam F., Adam H., Anjos A.d., Lorieux M., Larmande P., Ghesquière A., Jouannic S. & Shahbazkia H. R. (2013). P-TRAP: a Panicle Trait Phenotyping tool. BMC Plant Biology.13(1): 122.

    Araus J.L. & Cairns J.E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science.19(1): 52-61.

    Araus J.L., Kefauver S.C., Zaman-Allah M., Olsen M.S. & Cairns J.E. (2018). Translating high-throughput phenotyping into genetic gain. Trends in Plant Science.23(5): 451-466.

    Behjati S. & Tarpey P.S. (2013). What is next generation sequencing? Archives of Disease in Childhood: Education & Practice.98(6): 236-238.

    Beres B.L., Hatfield J.L., Kirkegaard J.A., Eigenbrode S.D., Pan W.L., Lollato R.P., Hunt J.R., Strydhorst S., Porker K., Lyon D., Ransom J. & Wiersma J. (2020). Toward a Better Understanding of Genotype × Environment × Management Interactions - A Global Wheat Initiative Agronomic Research Strategy. Frontiers in Plant Science.11(828).

    Burton A.L., Williams M., Lynch J.P. & Brown K.M. (2012). RootScan: Software for high-throughput analysis of root anatomical traits. Plant and soil.357(1): 189-203.

    Carroll A.A., Clarke J., Fahlgren N., Gehan M.A., Lawrence-Dill C.J. & Lorence A. (2019). NAPPN: Who We Are, Where We Are Going, and Why You Should Join Us! The Plant Phenome Journal.2(1).

    Cobb J.N., Declerck G., Greenberg A., Clark R. & Mccouch S. (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics.126(4): 867-887.

    Crowell S., Falcão A.X., Shah A., Wilson Z., Greenberg A.J. & Mccouch S.R. (2014). High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama. Plant Physiology.165(2): 479-495.

    Crowell S., Korniliev P., Falcão A., Ismail A., Gregorio G., Mezey J. & Mccouch S. (2016). Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters. Nature Communications.7(1): 10527.

    Danilevicz M.F., Bayer P.E., Nestor B.J., Bennamoun M. & Edwards D. (2021). Resources for image-based high-throughput phenotyping in crops and data sharing challenges. Plant Physiology.10.1093/plphys/kiab301.

    Deery D.M., Rebetzke G.J., Jimenez-Berni J.A., James R.A., Condon A.G., Bovill W.D., Hutchinson P., Scarrow J., Davy R. & Furbank R.T. (2016). Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography. Frontiers in Plant Science.7(1808).

    Enders T.A., St. Dennis S., Oakland J., Callen S.T., Gehan M.A., Miller N.D., Spalding E.P., Springer N.M. & Hirsch C.D. (2019). Classifying cold-stress responses of inbred maize seedlings using RGB imaging. Plant Direct.3(1): e00104.

    Falk K.G., Jubery T.Z., Mirnezami S.V., Parmley K.A., Sarkar S., Singh A., Ganapathysubramanian B. & Singh A. K. (2020a). Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods.16(1): 5.

    Falk K.G., Jubery T.Z., O’rourke J.A., Singh A., Sarkar S., Ganapathysubramanian B. & Singh A.K. (2020b). Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters. Plant Phenomics.2020: 1925495.

    Fiorani F. & Schurr U. (2013). Future Scenarios for Plant Phenotyping. Annual Review of Plant Biology.64(1): 267-291.

    Furbank R.T. & Tester M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science.16(12): 635-644.

    Gehan M.A., Fahlgren N., Abbasi A., Berry J.C., Callen S.T., Chavez L., Doust A.N., Feldman M.J., Gilbert K.B., Hodge J.G., Hoyer J.S., Lin A., Liu S., Lizárraga C., Lorence A., Miller M., Platon E., Tessman M. & Sax T. (2017). PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ.5: e4088.

    Hartmann A., Czauderna T., Hoffmann R., Stein N. & Schreiber F. (2011). HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics.12(1): 148.

    Hickey L.T., Hafeez N.A., Robinson H., Jackson S.A., Leal-Bertioli S.C.M., Tester M., Gao C., Godwin I.D., Hayes B.J. & Wulff B.B.H. (2019). Breeding crops to feed 10 billion. Nature Biotechnology.37(7): 744-754.

    Johannsen W. (1911). The genotype conception of heredity. International Journal of Epidemiology.43(4): 989-1000.

    Klukas C., Chen D. & Pape J.M. (2014). Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping. Plant Physiol.165(2): 506-518.

    Knecht A.C., Campbell M.T., Caprez A., Swanson D.R. & Walia H. (2016). Image Harvest: an open-source platform for high-throughput plant image processing and analysis. Journal of Experimental Botany.67(11): 3587-3599.

    Lobet G., Pagès L. & Draye X. (2011). A Novel Image-Analysis Toolbox Enabling Quantitative Analysis of Root System Architecture Plant Physiology.157(1): 29-39.

    Lynch J.P. (2019). Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytologist.223(2): 548-564.

    Makanza R., Zaman-Allah M., Cairns J. E., Eyre J., Burgueño J., Pacheco Á., Diepenbrock C., Magorokosho C., Tarekegne A., Olsen M. & Prasanna B. M. (2018). High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging. Plant Methods.14(1): 49.

    Miller N.D., Haase N. J., Lee J., Kaeppler S.M., De Leon N. & Spalding E.P. (2017). A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images. The Plant Journal.89(1): 169-178.

    Nguyen T.D. (2020). High-throughput phenotyping of rice genotypes for nitrogen use efficiency. ICAR-Indian Agricultural Research Institute, New Delhi.Master Thesis: T-10424.

    Pautasso M. (2013). Ten simple rules for writing a literature review. PLOS Computational Biology.9(7): e1003149.

    Pound M.P., French A.P., Murchie E.H. & Pridmore T.P. (2014). Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images Plant Physiology.166(4): 1688-1698.

    Reynolds D., Baret F., Welcker C., Bostrom A., Ball J., Cellini F., Lorence A., Chawade A., Khafif M., Noshita K., Mueller-Linow M., Zhou J. & Tardieu F. (2019). What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Science.282: 14-22.

    Rosero A., Granda L., Pérez J.L., Rosero D., Burgos-Paz W., Martínez R., Morelo J., Pastrana I., Burbano E. & Morales A. (2019). Morphometric and colourimetric tools to dissect morphological diversity: an application in sweet potato [Ipomoea batatas(L.) Lam.]. Genetic Resources and Crop Evolution.66(6): 1257-1278.

    Schork N.J. (1997). Genetics of Complex Disease. American Journal of Respiratory and Critical Care Medicine.156(4): S103-S109.

    Seethepalli A., Guo H., Liu X., Griffiths M., Almtarfi H., Li Z., Liu S., Zare A., Fritschi F. B., Blancaflor E.B., Ma X.F. & York L.M. (2020). RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping. Plant Phenomics.p. 3074916.

    Shi Y., Thomasson J.A., Murray S.C., Pugh N.A., Rooney W.L., Shafian S., Rajan N., Rouze G., Morgan C.L.S., Neely H.L., Rana A., Bagavathiannan M.V., Henrickson J., Bowden E., Valasek J., Olsenholler J., Bishop M. P., Sheridan R., Putman E.B., Popescu S., Burks T., Cope D., Ibrahim A., Mccutchen B.F., Baltensperger D.D., Avant R.V., Jr., Vidrine M. & Yang C. (2016). Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research. PLOS ONE.11(7): e0159781.

    Tanabata T., Shibaya T., Hori K., Ebana K. & Yano M. (2012). SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis Plant Physiology.160(4): 1871-1880.

    Tanger P., Klassen S., Mojica J.P., Lovell J.T., Moyers B.T., Baraoidan M., Naredo M.E.B., Mcnally K.L., Poland J., Bush D.R., Leung H., Leach J.E. & Mckay J.K. (2017). Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports.7: 42839.

    Tardieu F., Cabrera-Bosquet L., Pridmore T. & Bennett M. (2017). Plant Phenomics, From Sensors to Knowledge. Current Biology.27(15): R770-R783.

    Tuberosa R. (2012). Phenotyping for drought tolerance of crops in the genomics era. Frontiers in Physiology.3(347).

    Vadez V., Kholová J., Hummel G., Zhokhavets U., Gupta S.K. & Hash C.T. (2015). LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. Journal of Experimental Botany.66(18): 5581-5593.

    Varshney R.K., Bohra A., Roorkiwal M., Barmukh R., Cowling W.A., Chitikineni A., Lam H.M., Hickey L.T., Croser J.S., Bayer P.E., Edwards D., Crossa J., Weckwerth W., Millar H., Kumar A., Bevan M.W. & Siddique K.H.M. (2021). Fast-forward breeding for a food-secure world. Trends in Genetics. https://doi.org/10.1016/j.tig.2021.08.002.

    Walter A., Liebisch F. & Hund A. (2015). Plant phenotyping: from bean weighing to image analysis. Plant Methods.11(1): 14.

    Whan A.P., Smith A.B., Cavanagh C.R., Ral J.P.F., Shaw L.M., Howitt C.A. & Bischof L. (2014). GrainScan: a low cost, fast method for grain size and colour measurements. Plant Methods.10(1): 23.

    Yang W., Feng H., Zhang X., Zhang J., Doonan J.H., Batchelor W.D., Xiong L. & Yan J. (2020). Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant.13(2): 187-214.

    Yang W., Guo Z., Huang C., Duan L., Chen G., Jiang N., Fang W., Feng H., Xie W., Lian X., Wang G., Luo Q., Zhang Q., Liu Q. & Xiong L. (2014). Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature Communications.5: 5087.

    Zhao C., Zhang Y., Du J., Guo X., Wen W., Gu S., Wang J. & Fan J. (2019). Crop Phenomics: Current Status and Perspectives. Frontiers in Plant Science.10(714).