Plant Ecology

, Volume 214, Issue 12, pp 1529–1534 | Cite as

Black Spot: a platform for automated and rapid estimation of leaf area from scanned images

  • Varun Varma
  • Anand M. Osuri


Leaf area and its derivatives (e.g. specific leaf area) are widely used in ecological assessments, especially in the fields of plant–animal interactions, plant community assembly, ecosystem functioning and global change. Estimating leaf area is highly time-consuming, even when using specialized software to process scanned leaf images, because manual inputs are invariably required for scale detection and leaf surface digitisation. We introduce Black Spot Leaf Area Calculator (hereafter, Black Spot), a technique and stand-alone software package for rapid and automated leaf area assessment from images of leaves taken with standard flatbed scanners. Black Spot operates on comprehensive rule-sets for colour band ratios to carry out pixel-based classification which isolates leaf surfaces from the image background. Importantly, the software extracts information from associated image meta-data to detect image scale, thereby eliminating the need for time-consuming manual scale calibration. Black Spot’s output provides the user with estimates of leaf area as well as classified images for error checking. We tested this method and software combination on a set of 100 leaves of 51 different plant species collected from the field. Leaf area estimates generated using Black Spot and by manual processing of the images using an image editing software generated statistically identical results. Mean error rate in leaf area estimates from Black Spot relative to manual processing was −0.4 % (SD = 0.76). The key advantage of Black Spot is the ability to rapidly batch process multi-species datasets with minimal user effort and at low cost, thus making it a valuable tool for field ecologists.


Leaf area Software Pixel-based classification Batch process Functional traits Multi-species datasets 



We acknowledge Yadugiri VT, Chengappa SK, Vijay Kumar S, Siddharth Iyengar, Rutuja Dhamale, Priyanka Runwal, Atul Joshi and Harinandan PV for volunteering time to collect field samples, manually process images and test out versions of the software. We are grateful to Mahesh Sankaran, Jayashree Ratnam and Fiona Savory for several useful discussions. We are grateful to two anonymous reviewers for suggestions that have improved the quality of this manuscript. We thank the Rufford Small Grants Foundation and National Centre for Biological Sciences (NCBS) for funding fellowships, fieldwork and equipment. We acknowledge NCBS, and in particular Prasanta Baruah, for helping create the Black Spot web page and user support systems.


  1. Anyia AO, Herzog H (2004) Water-use efficiency, leaf area and leaf gas exchange of cowpeas under mid-season drought. Eur J Agron 20:327–339CrossRefGoogle Scholar
  2. Bylesjö M, Segura V, Soolanayakanahally RY, Rae AM, Trygg J et al (2008) LAMINA: a tool for rapid quantification of leaf size and shape parameters. BMC Plant Biol 8:82PubMedCrossRefGoogle Scholar
  3. Cornelissen JHC, Lavorel S, Garnier E, Diaz S, Buchmann N et al (2003) A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust J Bot 51:335–380CrossRefGoogle Scholar
  4. Corre-Hellou G, Fustec J, Crozat Y (2006) Interspecific competition for soil N and its interaction with N2 fixation, leaf expansion and crop growth in pea–barley intercrops. Plant Soil 282:195–208CrossRefGoogle Scholar
  5. Femat-Diaz A, Vargas-Vazquez D, Huerta-Manzanilla E, Rico-Garcia E, Herrera-Ruiz G (2011) Scanner image methodology (SIM) to measure dimensions of leaves for agronomical applications. Afr J Biotechnol 10:1840–1847Google Scholar
  6. Garnier E, Shipley B, Roumet C, Laurent G (2001) A standardized protocol for the determination of specific leaf area and leaf dry matter content. Funct Ecol 15:688–695CrossRefGoogle Scholar
  7. Igathinathane C, Prakash VSS, Padma U, Babu GR, Womac AR (2006) Interactive computer software development for leaf area measurement. Comput Electron Agric 51:1–16CrossRefGoogle Scholar
  8. Kattge J, Diaz S, Lavorel S, Prentice IC, Leadley P et al (2011) TRY—a global database of plant traits. Glob Change Biol 17:2905–2935CrossRefGoogle Scholar
  9. Kleiman D, Aarssen LW (2007) The leaf size/number trade-off in trees. J Ecol 95:376–382CrossRefGoogle Scholar
  10. O’Neal ME, Landis DA, Isaacs R (2002) An inexpensive, accurate method for measuring leaf area and defoliation through digital image analysis. J Econ Entomol 95:1190–1194PubMedCrossRefGoogle Scholar
  11. Rasband WS (2011) ImageJ. US National Institutes of Health, BethesdaGoogle Scholar
  12. Reich PB, Ellsworth DS, Walters MB (1998) Leaf structure (specific leaf area) modulates photosynthesis-nitrogen relations: evidence from within and across species and functional groups. Funct Ecol 12:948–958CrossRefGoogle Scholar
  13. Wilson PJ, Thompson KEN, Hodgson JG (1999) Specific leaf area and leaf dry matter content as alternative predictors of plant strategies. New Phytol 143:155–162CrossRefGoogle Scholar
  14. Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z et al (2004) The worldwide leaf economics spectrum. Nature 428:821–827PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.National Centre for Biological SciencesTata Institute of Fundamental ResearchBangaloreIndia
  2. 2.Nature Conservation FoundationMysoreIndia

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