Indian Journal of Plant Physiology

, Volume 23, Issue 2, pp 369–375 | Cite as

Non-invasive quantification of tomato (Solanum lycopersicum L.) plant biomass through digital imaging using phenomics platform

  • R. H. LaxmanEmail author
  • P. Hemamalini
  • R. M. Bhatt
  • A. T. Sadashiva
Original Article


Phenotyping approaches, using high throughput imaging techniques, are being adopted over the traditional methodologies which are manpower intensive, time consuming and low throughput. However, the effectiveness of high throughput plant phenotyping through imaging in plant phenomics facility essentially requires establishing relationship between plant areas quantified through imaging and the actual biomass. The present study was conducted with an aim to standardise the methodology for digital quantification of tomato biomass using plant phenomics facility. A strong linear relationship was observed between the actual tomato plant fresh mass, digital biomass and projected shoot area. The correlations between plant fresh mass, plant digital biomass and projected shoot area were highly significant at 30, 45 and 60 days after transplanting, but at 75 days no correlation was observed. Hence, the present study clearly demonstrated that the growth of tomato plants could be monitored through digital imaging using either projected shoot area or digital biomass till 60 days after transplanting across genotypes for high throughput phenotyping.


Plant phenomics Digital biomass Projected shoot area Digital imaging Image analysis 



This work is a part of the National Innovations in Climate Resilient Agriculture (NICRA) project funded by Indian Council of Agricultural Research, New Delhi. The authors are grateful to the Director, ICAR-Indian Institute of Horticultural Research, Bengaluru for all the support. We would also like to acknowledge Mr. Sridhar, C. for the technical assistance.

Supplementary material

40502_2018_374_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (DOCX 48 kb)


  1. Bumgarner, N. R., Miller, W. S., & Kleinhenz, M. D. (2012). Digital image analysis to supplement direct measures of lettuce biomass. HortTechnology, 24(4), 547–555.Google Scholar
  2. Edwards, D., Batley, J., & Snowdon, R. J. (2013). Accessing complex crop genomes with next-generation sequencing. Theoretical and Applied Genetics, 126, 1–11.CrossRefPubMedGoogle Scholar
  3. Furbank, R. T., & Tester, M. (2011). Phenomics—Technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16, 635–644.CrossRefPubMedGoogle Scholar
  4. Gerszberg, A., & Huatuszko-Konka, K. (2017). Tomato tolerance to abiotic stress: A review of most often engineered target sequences. Plant Growth Regulation, 83, 175–198.CrossRefGoogle Scholar
  5. Golzarian, M. R., Frick, R. A., Rajendran, K., Berger, B., Roy, S., Tester, M., & Lun, D.S. (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods, 7, 2.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Hairmansis, A., Berger, B., Tester, M., & Roy, S. J. (2014). Image based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice, 7, 16.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Harris, B., Sadras, V., & Tester, M. (2010). A water-centered framework to assess the effects of salinity on the growth and yield of wheat and barley. Plant and Soil, 336, 377–389.CrossRefGoogle Scholar
  8. Honsdorf, N., March, T. J., Berger, B., Tester, M., & Pillen, K. (2014). High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS ONE, 9(5), e97047.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Klukas, C., Chen, D., & Pape, J. M. (2014). Integrated analysis platform: An open-source information system for high-throughput plant phenotyping. Plant Physiology, 165, 506–518.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Petrozza, A., Ssysnirllo, A., & Summerer, S. (2014). Physiological responses to Megafol treatments in tomato plants under drought stress: A phenomic and molecular approach. Scientia Horticulturae, 174, 185–192.CrossRefGoogle Scholar
  11. Rajendran, K., Tester, M., & Roy, S. J. (2009). Quantifying the three main components of salinity tolerance in cereals. Plant Cell and Environment, 32, 237–249.CrossRefGoogle Scholar
  12. Rao, N. K. S., & Laxman, R. H. (2013). Phenotyping horticultural crops for abiotic stress tolerance. In H. P. Singh, N. K. S. Rao, & K. S. Shivashankara (Eds.), Climate resilient horticulture: Adaptation and mitigation strategies (pp. 147–157). New Delhi: Springer.CrossRefGoogle Scholar
  13. Tackenberg, O. (2007). A new method for non-destructive measurement of biomass, growth rates, vertical biomass distribution and dry matter content based on digital image analysis. Annals of Botany, 99, 777–783.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Indian Society for Plant Physiology 2018

Authors and Affiliations

  • R. H. Laxman
    • 1
    Email author
  • P. Hemamalini
    • 1
  • R. M. Bhatt
    • 1
  • A. T. Sadashiva
    • 2
  1. 1.Division of Plant Physiology and BiochemistryICAR-Indian Institute of Horticultural ResearchBangaloreIndia
  2. 2.Division of Vegetable CropsICAR-Indian Institute of Horticultural ResearchBangaloreIndia

Personalised recommendations