Uavs for vegetation monitoring: Overview and recent scientific contributions

Uavs for vegetation monitoring: Overview and recent scientific contributions

13 Pág.

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Bibliographic Details
Main Authors: Castro, Ana Isabel de, Shi, Yeyin, Maja, Joe Mari, Peña Barragán, José Manuel
Other Authors: Agencia Estatal de Investigación (España)
Format: artículo biblioteca
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021-05-29
Subjects:Disease diagnosis, Drone, RGB, Multispectral, Hyperspectral, Thermal, Machine learning,
Online Access:http://hdl.handle.net/10261/287390
http://dx.doi.org/10.13039/100005825
http://dx.doi.org/10.13039/501100011033
http://dx.doi.org/10.13039/501100000780
https://api.elsevier.com/content/abstract/scopus_id/85107883316
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spelling dig-inia-es-10261-2873902024-10-29T21:42:11Z Uavs for vegetation monitoring: Overview and recent scientific contributions Castro, Ana Isabel de Shi, Yeyin Maja, Joe Mari Peña Barragán, José Manuel Agencia Estatal de Investigación (España) European Commission National Institute of Food and Agriculture (US) de Castro, Ana I. [0000-0002-6699-2204] Shi, Yeyin [0000-0003-3964-2855] Peña, Jose M. [0000-0003-4592-3792] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] Disease diagnosis Drone RGB Multispectral Hyperspectral Thermal Machine learning 13 Pág. This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits. This research was funded by the project AGL2017-83325-C4-1R of Agencia Española de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER). The contribution of Dr. Shi and Dr. Maja were supported by the Nebraska Agricultural Experiment Station through the Hatch Act capacity funding program (Accession Number 1011130) and Project No. SC-1700543 from the USDA National Institute of Food and Agriculture, respectively. Peer reviewed 2023-01-23T14:24:10Z 2023-01-23T14:24:10Z 2021-05-29 artículo http://purl.org/coar/resource_type/c_6501 Remote Sensing 13(11): 2139 (2021) 2072-4292 http://hdl.handle.net/10261/287390 10.3390/rs13112139 http://dx.doi.org/10.13039/100005825 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000780 2-s2.0-85107883316 https://api.elsevier.com/content/abstract/scopus_id/85107883316 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83325-C4-1-R/ES/NUEVAS HERRAMIENTAS TECNOLOGICAS, AGRONOMICAS E INFORMATICAS PARA LA GESTION DE MALAS HIERBAS/ Remote Sensing Publisher's version https://doi.org/10.3390/rs13112139 Sí open Multidisciplinary Digital Publishing Institute
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
language English
topic Disease diagnosis
Drone
RGB
Multispectral
Hyperspectral
Thermal
Machine learning
Disease diagnosis
Drone
RGB
Multispectral
Hyperspectral
Thermal
Machine learning
spellingShingle Disease diagnosis
Drone
RGB
Multispectral
Hyperspectral
Thermal
Machine learning
Disease diagnosis
Drone
RGB
Multispectral
Hyperspectral
Thermal
Machine learning
Castro, Ana Isabel de
Shi, Yeyin
Maja, Joe Mari
Peña Barragán, José Manuel
Uavs for vegetation monitoring: Overview and recent scientific contributions
description 13 Pág.
author2 Agencia Estatal de Investigación (España)
author_facet Agencia Estatal de Investigación (España)
Castro, Ana Isabel de
Shi, Yeyin
Maja, Joe Mari
Peña Barragán, José Manuel
format artículo
topic_facet Disease diagnosis
Drone
RGB
Multispectral
Hyperspectral
Thermal
Machine learning
author Castro, Ana Isabel de
Shi, Yeyin
Maja, Joe Mari
Peña Barragán, José Manuel
author_sort Castro, Ana Isabel de
title Uavs for vegetation monitoring: Overview and recent scientific contributions
title_short Uavs for vegetation monitoring: Overview and recent scientific contributions
title_full Uavs for vegetation monitoring: Overview and recent scientific contributions
title_fullStr Uavs for vegetation monitoring: Overview and recent scientific contributions
title_full_unstemmed Uavs for vegetation monitoring: Overview and recent scientific contributions
title_sort uavs for vegetation monitoring: overview and recent scientific contributions
publisher Multidisciplinary Digital Publishing Institute
publishDate 2021-05-29
url http://hdl.handle.net/10261/287390
http://dx.doi.org/10.13039/100005825
http://dx.doi.org/10.13039/501100011033
http://dx.doi.org/10.13039/501100000780
https://api.elsevier.com/content/abstract/scopus_id/85107883316
work_keys_str_mv AT castroanaisabelde uavsforvegetationmonitoringoverviewandrecentscientificcontributions
AT shiyeyin uavsforvegetationmonitoringoverviewandrecentscientificcontributions
AT majajoemari uavsforvegetationmonitoringoverviewandrecentscientificcontributions
AT penabarraganjosemanuel uavsforvegetationmonitoringoverviewandrecentscientificcontributions
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