Evaluation of GreenCrop Tracker for the Estimation of Leaf Area Index in Wheat Using Digital Photography

  • S. S. Sandhu
  • Prabhjyot Kaur
  • Jagdish Singh
  • R. Nigam
  • K. K. Gill
Research Article


The information on leaf area index (LAI) is important to agricultural scientists for in-season crop yield estimation using different crop growth simulation models. The determination of LAI using currently available instruments is costly/destructive and time consuming. A technique of LAI estimation using GreenCrop Tracker model is available, but it needs to be validated. Therefore, an investigation was conducted to validate the GreenCrop Tracker for estimation of LAI in wheat. The nadir digital photographs were captured and simultaneously LAI was measured using LAI-2000 canopy analyzer at an interval of 30 days in the field. LAI was estimated using GreenCrop Tracker from digital photographs. The LAI obtained from both methods was compared using different statistical indices. The results showed a good agreement among both the techniques having NRMSE value of 10.44, 10.84 and 17.10 along with significant R2 value of 0.79, 0.83 and 0.76 at 60, 90 and 120 days after sowing (DAS), respectively. There was under estimation of LAI at 30 and 150 DAS of wheat by GreenCrop Tracker model. The percent bias index for whole season data was 17.14 along with significant R2 (0.90) suggests that GreenCrop Tracker can be used for LAI estimation in the wheat. The GreenCrop Tracker relatively inexpensive (open source), user friendly, portable, less time and labour consuming, as compared to LAI-2000 canopy analyzer and could be used efficiently for LAI estimation in the wheat crop.


Digital photography Non-destructive leaf area index LAI-2000 Wheat GreenCrop Tracker 



The authors are thankful to the Space Applications Centre, Indian Space Research Organization, Ahmedabad, Gujarat, India for providing funds to conduct this study.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest to publish this manuscript.


  1. 1.
    Doraiswamy PC, Sinclair TR, Hollinger S, Akhmedov B, Stern A, Prueger J (2005) Application of MODIS derived parameters for regional crop yield assessment. Remote Sens Environ 97:192–202. CrossRefGoogle Scholar
  2. 2.
    Watson DJ (1947) Comparative physiological studies in the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann Bot 11:41–76CrossRefGoogle Scholar
  3. 3.
    Lang ARG (1987) Simplified estimate of leaf area index from transmittance of the sin’s beam. Agric For Meteorol 41:179–186. CrossRefGoogle Scholar
  4. 4.
    Chen JM, Black TA (1992) Defining leaf-area index for non-flat leaves. Plant Cell Environ 15:421–429. CrossRefGoogle Scholar
  5. 5.
    Myneni RB, Nemani RR, Running SW (1997) Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans Geo Remote Sens Environ 35:1380–1393. CrossRefGoogle Scholar
  6. 6.
    Fassnacht KS, Gower ST, Norman JM, McMurtrie RE (1994) A comparison of optical and direct methods for estimating foliage surface area index in forests. Agric For Meteorol 71:183–207. CrossRefGoogle Scholar
  7. 7.
    Moghaddam PA, Derafshi MH, Shayesteh M (2010) A new method in assessing sugar beet leaf nitrogen status through color image processing and artificial neural network. J Food Agric Environ 8(2):485–489Google Scholar
  8. 8.
    Gower ST, Kucharik CJ, Norman JM (1999) Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens Environ 70:29–51. CrossRefGoogle Scholar
  9. 9.
    Bréda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54:2403–2417. CrossRefPubMedGoogle Scholar
  10. 10.
    Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004) Review of methods for in situ leaf area index determination. Part I. Theories, sensors and hemispherical photography. Agric For Meteorol 121:19–35. CrossRefGoogle Scholar
  11. 11.
    Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P (2004) Review of methods for in situ leaf area index (LAI) determination: part II. Estimation of LAI, errors and sampling. Agric For Meteorol 121:37–53. CrossRefGoogle Scholar
  12. 12.
    Strachan IB, Stewart DW, Pattey E (2005) Determination of leaf area index in agricultural systems. In: Hatfield JL, Baker JM (eds) Micrometeorology in agricultural systems, agronomy monograph no. 47. ASA-CSSA-SSSA, Madison, pp 179–198. Google Scholar
  13. 13.
    Garrigues S, Shabanov N, Swanson K, Morisette J, Baret F, Myneni R (2008) Inter comparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agric For Meteorol 148:1193–1209. CrossRefGoogle Scholar
  14. 14.
    Fan X, Kawamura K, Guo W, Xuan TD, Lim J, Yuba N, Kurokawa Y, Obitsu T, Lv R, Tsumiyama Y, Yasuda T, Wang Z (2018) A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass. Comput Electron Agric 144:314–323CrossRefGoogle Scholar
  15. 15.
    White MA, Asner GP, Nemani RR, Privette JL, Running SW (2000) Measuring fractional cover and leaf area index in arid ecosystems: digital camera, radiation transmittance, and laser altimetry methods. Remote Sens Environ 74:45–57. CrossRefGoogle Scholar
  16. 16.
    Walter J, Edwards James, McDonald Glenn, Kuchel Haydn (2018) Photogrammetry for the estimation of wheat biomass and harvest index. Field Crops Res 216:165–174CrossRefGoogle Scholar
  17. 17.
    Baret F, de Solan B, Lopez-Lozano R, Ma K, Weiss M (2010) GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: theoretical considerations based on 3D architecture models and application to wheat crops. Agric For Meteorol 150:1393–1401. CrossRefGoogle Scholar
  18. 18.
    Stroppiana D, Boschetti M, Confalonieri R, Bocchi S, Brivio PA (2006) Evaluation of LAI-2000 for leaf area index monitoring in paddy rice. Field Crop Res 99:167–170. CrossRefGoogle Scholar
  19. 19.
    Keane RE, Reinhardt ED, Scott J, Gray K, Reardon J (2005) Estimating forest canopy bulk density using six indirect methods. Can J For Res 35:724–739. CrossRefGoogle Scholar
  20. 20.
    Liu J, Pattey E (2010) Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agric For Meteorol 150:1485–1490. CrossRefGoogle Scholar
  21. 21.
    Welles JM, Norman JM (1991) Instrument for indirect measurement of canopy architecture. J Agron 83:818–825. CrossRefGoogle Scholar
  22. 22.
    Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part I. A discussion of principles. J Hydrol 10:282–290. CrossRefGoogle Scholar
  23. 23.
    Jamieson PD, Porter JR, Wilson DR (1991) A test of computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crop Res 27:337–350. CrossRefGoogle Scholar
  24. 24.
    Gupta H, Sorooshian S, Yapo P (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4:135–143. CrossRefGoogle Scholar
  25. 25.
    Moriasi D, Arnold J, van Liew M, Bingner R, Harmel R, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900. CrossRefGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2018

Authors and Affiliations

  • S. S. Sandhu
    • 1
  • Prabhjyot Kaur
    • 1
  • Jagdish Singh
    • 2
  • R. Nigam
    • 3
  • K. K. Gill
    • 1
  1. 1.School of Climate Change and Agricultural MeteorologyPunjab Agricultural UniversityLudhianaIndia
  2. 2.Punjab Agricultural University Regional Research StationGurdaspurIndia
  3. 3.Space Applications Centre, Indian Space Research OrganizationAhmedabadIndia

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