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Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review
Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285134/ https://www.ncbi.nlm.nih.gov/pubmed/32429394 http://dx.doi.org/10.3390/s20102836 |
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author | Liang, Hao Xing, Linyin Lin, Jianhui |
author_facet | Liang, Hao Xing, Linyin Lin, Jianhui |
author_sort | Liang, Hao |
collection | PubMed |
description | Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities. |
format | Online Article Text |
id | pubmed-7285134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72851342020-06-15 Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review Liang, Hao Xing, Linyin Lin, Jianhui Sensors (Basel) Review Attention to the natural environment is equivalent to observing the space in which we live. Plant roots, which are important organs of plants, require our close attention. The method of detecting root system without damaging plants has gradually become mainstream. At the same time, machine learning has been achieving good results in recent years; it has helped develop many tools to help us detect the underground environment of plants. Therefore, this article will introduce some existing content related to root detection technology and machine detection algorithms for root detection, proving that machine learning root detection technology has good recognition capabilities. MDPI 2020-05-16 /pmc/articles/PMC7285134/ /pubmed/32429394 http://dx.doi.org/10.3390/s20102836 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Liang, Hao Xing, Linyin Lin, Jianhui Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title | Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title_full | Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title_fullStr | Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title_full_unstemmed | Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title_short | Application and Algorithm of Ground-Penetrating Radar for Plant Root Detection: A Review |
title_sort | application and algorithm of ground-penetrating radar for plant root detection: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285134/ https://www.ncbi.nlm.nih.gov/pubmed/32429394 http://dx.doi.org/10.3390/s20102836 |
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