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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...

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Detalles Bibliográficos
Autores principales: Terentev, Anton, Dolzhenko, Viktor, Fedotov, Alexander, Eremenko, Danila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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