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Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational impleme...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8839015/ https://www.ncbi.nlm.nih.gov/pubmed/35161504 http://dx.doi.org/10.3390/s22030757 |
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author | Terentev, Anton Dolzhenko, Viktor Fedotov, Alexander Eremenko, Danila |
author_facet | Terentev, Anton Dolzhenko, Viktor Fedotov, Alexander Eremenko, Danila |
author_sort | Terentev, Anton |
collection | PubMed |
description | The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information. |
format | Online Article Text |
id | pubmed-8839015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88390152022-02-13 Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review Terentev, Anton Dolzhenko, Viktor Fedotov, Alexander Eremenko, Danila Sensors (Basel) Review The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information. MDPI 2022-01-19 /pmc/articles/PMC8839015/ /pubmed/35161504 http://dx.doi.org/10.3390/s22030757 Text en © 2022 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 | Review Terentev, Anton Dolzhenko, Viktor Fedotov, Alexander Eremenko, Danila Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title | Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title_full | Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title_fullStr | Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title_full_unstemmed | Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title_short | Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review |
title_sort | current state of hyperspectral remote sensing for early plant disease detection: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8839015/ https://www.ncbi.nlm.nih.gov/pubmed/35161504 http://dx.doi.org/10.3390/s22030757 |
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