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Article

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

1
Department of Electronics and Computer Science, Pontificia Universidad Javeriana Cali, Cali 760031, Colombia
2
School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110311, Colombia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Fernando José Aguilar
Sensors 2021, 21(13), 4369; https://doi.org/10.3390/s21134369
Received: 14 May 2021 / Revised: 17 June 2021 / Accepted: 23 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Emerging Robots and Sensing Technologies in Geosciences)
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works. View Full-Text
Keywords: data-fusion; feature-extraction; multispectral imagery; crop biomass; phenotyping data-fusion; feature-extraction; multispectral imagery; crop biomass; phenotyping
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MDPI and ACS Style

Jimenez-Sierra, D.A.; Correa, E.S.; Benítez-Restrepo, H.D.; Calderon, F.C.; Mondragon, I.F.; Colorado, J.D. Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops. Sensors 2021, 21, 4369. https://doi.org/10.3390/s21134369

AMA Style

Jimenez-Sierra DA, Correa ES, Benítez-Restrepo HD, Calderon FC, Mondragon IF, Colorado JD. Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops. Sensors. 2021; 21(13):4369. https://doi.org/10.3390/s21134369

Chicago/Turabian Style

Jimenez-Sierra, David Alejandro, Edgar Steven Correa, Hernán Darío Benítez-Restrepo, Francisco Carlos Calderon, Ivan Fernando Mondragon, and Julian D. Colorado. 2021. "Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops" Sensors 21, no. 13: 4369. https://doi.org/10.3390/s21134369

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