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Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height
Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452666/ https://www.ncbi.nlm.nih.gov/pubmed/36092445 http://dx.doi.org/10.3389/fpls.2022.938216 |
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author | Liu, Yang Feng, Haikuan Yue, Jibo Jin, Xiuliang Li, Zhenhai Yang, Guijun |
author_facet | Liu, Yang Feng, Haikuan Yue, Jibo Jin, Xiuliang Li, Zhenhai Yang, Guijun |
author_sort | Liu, Yang |
collection | PubMed |
description | Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1–5, labeled S1–S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage. |
format | Online Article Text |
id | pubmed-9452666 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94526662022-09-09 Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height Liu, Yang Feng, Haikuan Yue, Jibo Jin, Xiuliang Li, Zhenhai Yang, Guijun Front Plant Sci Plant Science Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management and the optimization of planting patterns. Many studies have confirmed that, due to canopy spectral saturation, AGB is underestimated in the multi-growth period of crops when using only optical vegetation indices. To solve this problem, this study obtains textures and crop height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images to estimate the potato AGB in three key growth periods. Textures include a grayscale co-occurrence matrix texture (GLCM) and a Gabor wavelet texture. GLCM-based textures were extracted from seven-GDS (1, 5, 10, 30, 40, 50, and 60 cm) RGB images. Gabor-based textures were obtained from magnitude images on five scales (scales 1–5, labeled S1–S5, respectively). Potato crop height was extracted based on the generated crop height model. Finally, to estimate potato AGB, we used (i) GLCM-based textures from different GDS and their combinations, (ii) Gabor-based textures from different scales and their combinations, (iii) all GLCM-based textures combined with crop height, (iv) all Gabor-based textures combined with crop height, and (v) two types of textures combined with crop height by least-squares support vector machine (LSSVM), extreme learning machine, and partial least squares regression techniques. The results show that (i) potato crop height and AGB first increase and then decrease over the growth period; (ii) GDS and scales mainly affect the correlation between GLCM- and Gabor-based textures and AGB; (iii) to estimate AGB, GLCM-based textures of GDS1 and GDS30 work best when the GDS is between 1 and 5 cm and 10 and 60 cm, respectively (however, estimating potato AGB based on Gabor-based textures gradually deteriorates as the Gabor convolution kernel scale increases); (iv) the AGB estimation based on a single-type texture is not as good as estimates based on multi-resolution GLCM-based and multiscale Gabor-based textures (with the latter being the best); (v) different forms of textures combined with crop height using the LSSVM technique improved by 22.97, 14.63, 9.74, and 8.18% (normalized root mean square error) compared with using only all GLCM-based textures, all Gabor-based textures, the former combined with crop height, and the latter combined with crop height, respectively. Therefore, different forms of texture features obtained from RGB images acquired from unmanned aerial vehicles and combined with crop height improve the accuracy of potato AGB estimates under high coverage. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9452666/ /pubmed/36092445 http://dx.doi.org/10.3389/fpls.2022.938216 Text en Copyright © 2022 Liu, Feng, Yue, Jin, Li and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Liu, Yang Feng, Haikuan Yue, Jibo Jin, Xiuliang Li, Zhenhai Yang, Guijun Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title | Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title_full | Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title_fullStr | Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title_full_unstemmed | Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title_short | Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
title_sort | estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452666/ https://www.ncbi.nlm.nih.gov/pubmed/36092445 http://dx.doi.org/10.3389/fpls.2022.938216 |
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