Evaluation of GreenCrop Tracker for the Estimation of Leaf Area Index in Wheat Using Digital Photography
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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.
KeywordsDigital 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.
- 4.Chen JM, Black TA (1992) Defining leaf-area index for non-flat leaves. Plant Cell Environ 15:421–429. https://doi.org/10.1111/j.1365-3040.1992.tb00992.x CrossRefGoogle Scholar
- 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
- 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. https://doi.org/10.1016/j.agrformet.2008.02.014 CrossRefGoogle Scholar
- 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
- 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. https://doi.org/10.1016/j.agrformet.2010.04.011 CrossRefGoogle Scholar
- 21.Welles JM, Norman JM (1991) Instrument for indirect measurement of canopy architecture. J Agron 83:818–825. https://doi.org/10.2134/agronj1991.00021962008300050009x CrossRefGoogle Scholar
- 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. https://doi.org/10.1061/(asce)1084-0699(1999)4:2(135) CrossRefGoogle Scholar