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Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth
Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119880/ https://www.ncbi.nlm.nih.gov/pubmed/33995432 http://dx.doi.org/10.3389/fpls.2021.626516 |
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author | Huang, Yiqi Li, Jie Yang, Rui Wang, Fukuan Li, Yanzhou Zhang, Shuo Wan, Fanghao Qiao, Xi Qian, Wanqiang |
author_facet | Huang, Yiqi Li, Jie Yang, Rui Wang, Fukuan Li, Yanzhou Zhang, Shuo Wan, Fanghao Qiao, Xi Qian, Wanqiang |
author_sort | Huang, Yiqi |
collection | PubMed |
description | Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450–998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild. |
format | Online Article Text |
id | pubmed-8119880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81198802021-05-15 Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth Huang, Yiqi Li, Jie Yang, Rui Wang, Fukuan Li, Yanzhou Zhang, Shuo Wan, Fanghao Qiao, Xi Qian, Wanqiang Front Plant Sci Plant Science Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450–998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild. Frontiers Media S.A. 2021-04-30 /pmc/articles/PMC8119880/ /pubmed/33995432 http://dx.doi.org/10.3389/fpls.2021.626516 Text en Copyright © 2021 Huang, Li, Yang, Wang, Li, Zhang, Wan, Qiao and Qian. 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 Huang, Yiqi Li, Jie Yang, Rui Wang, Fukuan Li, Yanzhou Zhang, Shuo Wan, Fanghao Qiao, Xi Qian, Wanqiang Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title | Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title_full | Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title_fullStr | Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title_full_unstemmed | Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title_short | Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth |
title_sort | hyperspectral imaging for identification of an invasive plant mikania micrantha kunth |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119880/ https://www.ncbi.nlm.nih.gov/pubmed/33995432 http://dx.doi.org/10.3389/fpls.2021.626516 |
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