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Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery


Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, India
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, India
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, India
4 North East Space Application Centre, Barapani 793 103, India
5 ICAR Research Complex for NEH Region, Umiam 793 103, India
 

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.
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  • Comparison of Parametric and Non-Parametric Methods for Chlorophyll Estimation based on High-Resolution UAV Imagery

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Authors

Gaurav Singhal
CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, India
Babankumar Bansod
Academy of Scientific and Innovative Research, Ghaziabad 201 002, India
Lini Mathew
National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, India
Jonali Goswami
North East Space Application Centre, Barapani 793 103, India
B. U. Choudhury
ICAR Research Complex for NEH Region, Umiam 793 103, India
P. L. N. Raju
North East Space Application Centre, Barapani 793 103, India

Abstract


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.

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DOI: https://doi.org/10.18520/cs%2Fv117%2Fi11%2F1874-1879