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Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV
It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees (Camellia sinensis L.) have been proven to show great differences in taste and quality between cultivars. We hypo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978401/ https://www.ncbi.nlm.nih.gov/pubmed/29868272 http://dx.doi.org/10.7717/peerj.4858 |
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author | Tu, Yexin Bian, Meng Wan, Yinkang Fei, Teng |
author_facet | Tu, Yexin Bian, Meng Wan, Yinkang Fei, Teng |
author_sort | Tu, Yexin |
collection | PubMed |
description | It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees (Camellia sinensis L.) have been proven to show great differences in taste and quality between cultivars. We hypothesize that hyperspectral remote sensing would make it possibly to classify cultivars of plants and even to estimate their taste-related biochemical components. In this study, hyperspectral data of the canopies of tea trees were collected by hyperspectral camera mounted on an unmanned aerial vehicle (UAV). Tea cultivars were classified according to the spectral characteristics of the tea canopies. Furthermore, two major components influencing the taste of tea, tea polyphenols (TP) and amino acids (AA), were predicted. The results showed that the overall accuracy of tea cultivar classification achieved by support vector machine is higher than 95% with proper spectral pre-processing method. The best results to predict the TP and AA were achieved by partial least squares regression with standard normal variant normalized spectra, and the ratio of TP to AA—which is one proven index for tea taste—achieved the highest accuracy (R(CV) = 0.66, RMSE(CV) = 13.27) followed by AA (R(CV) = 0.62, RMSE(CV) = 1.16) and TP (R(CV) = 0.58, RMSE(CV) = 10.01). The results indicated that classification of tea cultivars using the hyperspectral remote sensing from UAV was successful, and there is a potential to map the taste-related chemical components in tea plantations from UAV platform; however, further exploration is needed to increase the accuracy. |
format | Online Article Text |
id | pubmed-5978401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59784012018-06-04 Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV Tu, Yexin Bian, Meng Wan, Yinkang Fei, Teng PeerJ Biogeochemistry It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees (Camellia sinensis L.) have been proven to show great differences in taste and quality between cultivars. We hypothesize that hyperspectral remote sensing would make it possibly to classify cultivars of plants and even to estimate their taste-related biochemical components. In this study, hyperspectral data of the canopies of tea trees were collected by hyperspectral camera mounted on an unmanned aerial vehicle (UAV). Tea cultivars were classified according to the spectral characteristics of the tea canopies. Furthermore, two major components influencing the taste of tea, tea polyphenols (TP) and amino acids (AA), were predicted. The results showed that the overall accuracy of tea cultivar classification achieved by support vector machine is higher than 95% with proper spectral pre-processing method. The best results to predict the TP and AA were achieved by partial least squares regression with standard normal variant normalized spectra, and the ratio of TP to AA—which is one proven index for tea taste—achieved the highest accuracy (R(CV) = 0.66, RMSE(CV) = 13.27) followed by AA (R(CV) = 0.62, RMSE(CV) = 1.16) and TP (R(CV) = 0.58, RMSE(CV) = 10.01). The results indicated that classification of tea cultivars using the hyperspectral remote sensing from UAV was successful, and there is a potential to map the taste-related chemical components in tea plantations from UAV platform; however, further exploration is needed to increase the accuracy. PeerJ Inc. 2018-05-28 /pmc/articles/PMC5978401/ /pubmed/29868272 http://dx.doi.org/10.7717/peerj.4858 Text en © 2018 Tu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Biogeochemistry Tu, Yexin Bian, Meng Wan, Yinkang Fei, Teng Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title | Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title_full | Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title_fullStr | Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title_full_unstemmed | Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title_short | Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV |
title_sort | tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by uav |
topic | Biogeochemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978401/ https://www.ncbi.nlm.nih.gov/pubmed/29868272 http://dx.doi.org/10.7717/peerj.4858 |
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