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Bansod, Babankumar
- Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery
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Authors
Gaurav Singhal
1,
Babankumar Bansod
2,
Lini Mathew
3,
Jonali Goswami
4,
B. U. Choudhury
5,
P. L. N. Raju
4
Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN
Source
Current Science, Vol 117, No 11 (2019), Pagination: 1874-1879Abstract
The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R2 = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R2 > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.Keywords
Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.Full Text
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