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Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops
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 betwee...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271736/ https://www.ncbi.nlm.nih.gov/pubmed/34202363 http://dx.doi.org/10.3390/s21134369 |
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author | Jimenez-Sierra, David Alejandro Correa, Edgar Steven Benítez-Restrepo, Hernán Darío Calderon, Francisco Carlos Mondragon, Ivan Fernando Colorado, Julian D. |
author_facet | Jimenez-Sierra, David Alejandro Correa, Edgar Steven Benítez-Restrepo, Hernán Darío Calderon, Francisco Carlos Mondragon, Ivan Fernando Colorado, Julian D. |
author_sort | Jimenez-Sierra, David Alejandro |
collection | PubMed |
description | 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 [Formula: see text] with [Formula: see text] and [Formula: see text] g. This result increases the precision in the biomass estimation by around [Formula: see text] compared to previous works. |
format | Online Article Text |
id | pubmed-8271736 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82717362021-07-11 Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops Jimenez-Sierra, David Alejandro Correa, Edgar Steven Benítez-Restrepo, Hernán Darío Calderon, Francisco Carlos Mondragon, Ivan Fernando Colorado, Julian D. Sensors (Basel) Article 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 [Formula: see text] with [Formula: see text] and [Formula: see text] g. This result increases the precision in the biomass estimation by around [Formula: see text] compared to previous works. MDPI 2021-06-25 /pmc/articles/PMC8271736/ /pubmed/34202363 http://dx.doi.org/10.3390/s21134369 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jimenez-Sierra, David Alejandro Correa, Edgar Steven Benítez-Restrepo, Hernán Darío Calderon, Francisco Carlos Mondragon, Ivan Fernando Colorado, Julian D. Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title | Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title_full | Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title_fullStr | Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title_full_unstemmed | Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title_short | Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops |
title_sort | novel feature-extraction methods for the estimation of above-ground biomass in rice crops |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271736/ https://www.ncbi.nlm.nih.gov/pubmed/34202363 http://dx.doi.org/10.3390/s21134369 |
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