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

Sandhu, S. S., Kaur, Prabhjyot, Singh, Jagdish, Nigam, R., Gill, K. K.
Proceedings of the National Academy of Sciences, India, Section B: biological sciences 2019 v.89 no.2 pp. 615-621
canopy, crop yield, digital images, labor, leaf area index, simulation models, sowing, wheat
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.