Skip to main content
SpringerLink
Log in

Crop Phenotyping

  • Chapter
  • First Online:
Systems Modeling

Abstract

The visible form of an organism is the result of its genotype, environment and complex interaction and is referred to as phenotype. Quick, precise and non-destructive measurement of phenotypic traits has been of key importance in the field of plant breeding and crop production. The image-based non-destructive phenotyping started in early twenty-first century, and these techniques are based on spectra, canopy temperature and visible light. Initially, such approaches were used for phenotyping the plants in a controlled environment, where the influence of the environment could not be considered for phenotypic expression. Hence, the need for the development of high-throughput phenotyping (HTPPs) was realized to get the required information. This chapter provides an overview of advanced phenotyping techniques with special focus on field phenotyping. These techniques have the ability to evaluate multiple traits of interest from mixed populations, monitoring of crop growth and development, and health, and also provide key information on various physiological processes. The range of plant phenotyping techniques starts from phenotyping the whole plant canopy to organ and tissue.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahmad S, Hasanuzzaman M (2020) Cotton production and uses. Springer Nature Singapore Pvt. Ltd., Singapore, 641 pp. https://doi.org/10.1007/978-981-15-1472-2

    Book  Google Scholar 

  • Ahmad S, Abbas G, Fatima Z, Khan RJ, Anjum MA, Ahmed M, Khan MA, Porter CH, Hoogenboom G (2017) Quantification of the impacts of climate warming and crop management on canola phenology in Punjab, Pakistan. J Agron Crop Sci 203(5):442–452. https://doi.org/10.1111/jac.12206

    Article  Google Scholar 

  • Ahmad S, Abbas G, Ahmed M, Fatima Z, Anjum MA, Rasul G, Khan MA, Hoogenboom G (2019) Climate warming and management impact on the change of phenology of the rice-wheat cropping system in Punjab, Pakistan. Field Crop Res 230:46–61. https://doi.org/10.1016/j.fcr.2018.10.008

    Article  Google Scholar 

  • Ahmed M (2020) Introduction to modern climate change. Andrew E. Dessler: Cambridge University Press, 2011, 252 pp, ISBN-10: 0521173159. Sci Total Environ 734:139397. https://doi.org/10.1016/j.scitotenv.2020.139397

  • Ahmed M, Ahmad S (2019) Carbon dioxide enrichment and crop productivity. In: Hasanuzzaman M (ed) Agronomic crops. Management practices, vol 2. Springer Singapore, Singapore, pp 31–46. https://doi.org/10.1007/978-981-32-9783-8_3

    Chapter  Google Scholar 

  • Ahmed M, Stockle CO (2016) Quantification of climate variability, adaptation and mitigation for agricultural sustainability. Springer Nature Singapore Pvt. Ltd., Singapore, 437 pp. https://doi.org/10.1007/978-3-319-32059-5

    Book  Google Scholar 

  • Ahmed M, Asif M, Hirani AH, Akram MN, Goyal A (2013) Modeling for agricultural sustainability: a review. In: Bhullar GS, Bhullar NK (eds) Agricultural sustainability progress and prospects in crop research. Elsevier, London

    Google Scholar 

  • Ahmed M, Aslam MA, Hassan FU, Asif M, Hayat R (2014) Use of APSIM to model nitrogen use efficiency of rain-fed wheat. Int J Agric Biol 16:461–470

    CAS  Google Scholar 

  • Ahmed M, Akram MN, Asim M, Aslam M, Hassan F-u, Higgins S, Stöckle CO, Hoogenboom G (2016) Calibration and validation of APSIM-wheat and CERES-wheat for spring wheat under rainfed conditions: models evaluation and application. Comput Electron Agric 123:384–401. https://doi.org/10.1016/j.compag.2016.03.015

    Article  Google Scholar 

  • Ahmed M, Stöckle CO, Nelson R, Higgins S (2017) Assessment of climate change and atmospheric CO2 impact on winter wheat in the pacific northwest using a multimodel ensemble. Front Ecol Evol 5(51). https://doi.org/10.3389/fevo.2017.00051

  • Ahmed M, Ijaz W, Ahmad S (2018) Adapting and evaluating APSIM-SoilP-wheat model for response to phosphorus under rainfed conditions of Pakistan. J Plant Nutr 41(16):2069–2084. https://doi.org/10.1080/01904167.2018.1485933

    Article  CAS  Google Scholar 

  • Ahmed M, Stöckle CO, Nelson R, Higgins S, Ahmad S, Raza MA (2019) Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci Rep 9(1):7813. https://doi.org/10.1038/s41598-019-44251-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ahmed K, Shabbir G, Ahmed M, Shah KN (2020) Phenotyping for drought resistance in bread wheat using physiological and biochemical traits. Sci Total Environ 729:139082. https://doi.org/10.1016/j.scitotenv.2020.139082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Andrade-Sanchez P, Gore M, Heun JT, Thorp KR, Carmo-Silva AE, French AN, Salvucci ME, White JW (2014) Development and evaluation of a field-based high throughput phenotyping platform. Funct Plant Biol 41:68–79

    Article  Google Scholar 

  • Burton AL, Williams M, Lynch JP, Brown KM (2012) Rootscan: software for high-throughput analysis of root anatomical traits. Plant Soil 357:189–203

    Article  CAS  Google Scholar 

  • Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R (2019) High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy 9(5):258

    Article  CAS  Google Scholar 

  • Coppens F, Wuyts N, Inzé D, Dhondt S (2017) Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Curr Opin Syst Biol 4:58–63

    Article  Google Scholar 

  • De Bei R, Cozzolino D, Sullivan W, Cynkar W, Fuentes S, Dambergs R, Pech J, Tyerman S (2011) Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Aust J Grape Wine Res 17(1):62–71

    Article  Google Scholar 

  • Deery DM, Rebetzke GJ, Jimenez-Berni JA, James RA, Condon AG, Bovill WD, Furbank RT (2016) Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Front Plant Sci 7:1808

    Article  Google Scholar 

  • Fernandez MGS, Bao Y, Tang L, Schnable PS (2017) A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol 174:2008–2022

    Article  CAS  Google Scholar 

  • Fritsche-Neto R, Borém A (2015) Phenomics how next-generation phenotyping is revolutionizing plant breeding. Springer Press, Cham

    Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16(12):635–644

    Article  CAS  Google Scholar 

  • Garbout A, Munkholm LJ, Hansen SB, Petersen BM, Munk OL, Pajor R (2012) The use of PET/CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions. Plant Soil 352:113–127

    Article  CAS  Google Scholar 

  • Großkinsky DK, Svensgaard J, Christensen S, Roitsch T (2015) Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J Exp Bot 66(18):5429–5440

    Article  Google Scholar 

  • Guo, Zhu (2014) Phenotyping of plants. Encyclopedia of analytical chemistry. Published online: http://onlinelibrary.wiley.com/doi/10.1002/9780470027318.a9934/full

  • Hoffmeister D, Waldhoff G, Korres W, Curdt C, Bareth G (2016) Crop height variability detection in a single field by multi-temporal terrestrial laser scanning. Precis Agric 17(3):296–312

    Article  Google Scholar 

  • Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866

    Article  CAS  Google Scholar 

  • Kim Y, Glenn DM, Park J, Ngugi HK, Lehman BL (2012) Characteristics of active spectral sensor for plant sensing. American Society of Agricultural and Biological Engineers 55(1):293–301

    Google Scholar 

  • McCormick RF, Truong SK, Mullet JE (2016) 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol 172:823–834

    CAS  PubMed  PubMed Central  Google Scholar 

  • Montes JM, Technow F, Dhillon B, Mauch F, Melchinger A (2011) High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res 121:268–273

    Article  Google Scholar 

  • Muller O, Keller B, Zimmermann L, Jedmowski C, Pingle V, Acebron K, Zendonadi NS, Steier A, Pieruschka R, Kraska T, Schurr U, Rascher U (2018) Field phenotyping and an example of proximal sensing of photosynthesis. In: Proceedings of the 14th International Conference on Precision Agriculture June 24–June 27, 2018, Montreal, Quebec, Canada

    Google Scholar 

  • Nandibewoor A, Hebbal SB, Hegadi R (2015) Remote monitoring of maize crop through satellite multispectral imagery. Procedia Computer Science 45:344–353

    Article  Google Scholar 

  • Paulus S (2019) Measuring crops in 3D: using geometry for plant phenotyping. Paulus Plant Methods 15:103

    Article  Google Scholar 

  • Petroua ZI, Tarantinoc C, Adamoc M, Blondac P, Petrou M (2012) Estimation of vegetation height through satellite image texture analysis. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August–01 September 2012, Melbourne, Australia

    Google Scholar 

  • Porter JR, Christensen S (2013) Deconstructing crop processes and models via identities. Plant Cell Environ 36:1919–1925

    PubMed  Google Scholar 

  • Pratap A, Gupta S, Nair RM, Gupta SK, Schafleitner R, Basu PS, Singh CM, Prajapati U, Gupta AK, Nayyar H, Mishra AK, Baek K-H (2019) Using plant phenomics to exploit the gains of genomics. Agronomy 9:126

    Article  Google Scholar 

  • Punshon T, Ricachenevsky F, Hindt M, Socha A, Zuber H (2013) Methodological approaches for using synchrotron X-ray fluorescence (SXRF) imaging as a tool in ionomics: examples from Arabidipsis thaliana. Metallomics 5:1133

    Article  CAS  Google Scholar 

  • Romano G, Zia S, Spreer W, Sanchez C, Cairns J, Araus JL, Müller J (2011) Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput Electron Agric 79:67–74

    Article  Google Scholar 

  • Sankaran S, Khot LR, Espinoza CZ, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Pavek MJ (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur J Agron 70:112–123

    Article  Google Scholar 

  • Schork NJ (1997) Genetics of complex disease: approaches, problem, and solutions. Am J Respir Crit Care Med 156:S103–S109

    Article  CAS  Google Scholar 

  • Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S, Rajan N, Rouze G, Morgan CLS, Neely HL, Rana A, Bagavathiannan M, Henrickson J, Bowden E, Valasek J, Olsenholler J, Bishop MP, Sheridan R, Putman EB, Popescu S, Burks T, Cope D, Ibrahim A, McCutchen BF, Baltensperger DD, Avant RV Jr, Vidrine M, Yang C (2016) Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS One 11(7):e0159781

    Article  Google Scholar 

  • Siddiqui ZS, Umar M, Kwon TR, Park SC (2019) Phenotyping through infrared thermography in stress environment. In: Gul B et al (eds) Sabkha ecosystems, tasks for vegetation science. Springer Nature Switzerland AG, Cham

    Google Scholar 

  • Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21:110

    Article  CAS  Google Scholar 

  • Sirault XR, James RA, Furbank RT (2009) A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Funct Plant Biol 36(11):970–977

    Article  CAS  Google Scholar 

  • Su W, Zhang M, Bian D, Liu Z, Huang J, Wang W, Wu J, Guo H (2019) Phenotyping of corn plants using unmanned aerial vehicle (UAV) images. Remote Sens 11:2021

    Article  Google Scholar 

  • Thompson AL, Thorp KR, Conley M, Andrade-Sanchez P, Heun JT, Dyer JM, White JW (2018) Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping. Front Plant Sci 9:507

    Article  Google Scholar 

  • Thoren D, Schmidhalter U (2009) Nitrogen status and biomass determination of oil seed rape by laser-induced chlorophyll fluorescence. Eur J Agron 30:238–242

    Article  CAS  Google Scholar 

  • Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:347

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wang LH, Hu S (2012) Photoacoustic tomography: in vivo imaging from organelles to organs. Science 335:1458–1462

    Article  CAS  Google Scholar 

  • Wang Y, Wen W, Wu S, Wang C, Yu Z, Guo X, Zhao C (2019) Maize plant phenotyping: comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sens 11:63

    Article  Google Scholar 

  • Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, Xu B, Yang X, Zhu D, Zhang X, Zhang R, Feng H, Zhao X, Li Z, Li H, Yang H (2017) Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci 8:1111

    Article  Google Scholar 

  • Zhao C, Zhang Y, DuJ GX, En W, Gu S, Wang J, Fan J (2019) Crop phenomics: current status and perspectives. Front Plant Sci 10:714

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Central Cotton Research Institute, Multan, Pakistan

    Muhammad Tariq

  2. Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden

    Mukhtar Ahmed

  3. Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

  4. Department of Botany, University of Agriculture, Faisalabad, Pakistan

    Pakeeza Iqbal

  5. Department of Agronomy, Bahauddin Zakariya University, Multan, Pakistan

    Zartash Fatima & Shakeel Ahmad

Authors
  1. Muhammad Tariq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukhtar Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pakeeza Iqbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zartash Fatima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shakeel Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shakeel Ahmad .

Editor information

Editors and Affiliations

  1. Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden, Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tariq, M., Ahmed, M., Iqbal, P., Fatima, Z., Ahmad, S. (2020). Crop Phenotyping. In: Ahmed, M. (eds) Systems Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-15-4728-7_2

Download citation

Publish with us

Policies and ethics

Search

Navigation